Internet of things (IoT) is a network of smart things. This indicates the ability of these physical things to transfer information with other physical things. The characteristics of these networks, such as topology dynamicity and energy constraint, challenges the routing problem in these networks. Previous routing methods could not achieve the required performance in this type of network. Therefore, developers of this network designed and developed specific methods in order to satisfy the requirements of these networks. One of the routing methods is utilization of multi-path protocols which send data to its destination using routes with separate links. One of such protocols is AOMDV routing protocol. In this paper, this method is improved using gray system theory which chooses the best paths used for separate routes to send packets. To do this, AOMDV packet format is altered and some fields are added to it so that energy criteria, link expiration time, and signal to noise ratio can also be considered while selecting the best route. The proposed method named RMPGST-IoT is introduced which chooses the routes with highest rank for concurrent transmission of data, using a specific routine based on the gray system theory. In order to evaluate and report the results, the proposed RMPGST-IoT method is compared to the ERGID and ADRM-IoT approaches with regard to throughput, packet receiving rate, packet loss rate, average remaining energy, and network lifetime. The results demonstrate the superior performance of the proposed RMPGST-IoT compared to the ERGID and ADRM-IoT approaches.

Nowadays, people live amidst the smart home domain, business opportunities in the industrial smart city and health care, though, along with concerns about security. Security is central for IoT systems to protect sensitive data and infrastructure, whilst security issues become increasingly expensive, in particular in Industrial Internet of Things (IIoT) domains. Nonetheless, there are some key challenges for dealing with those security issues in IoT domains: Applications operate in distributed environments such as Blockchain, varied smart objects are used, and sensors are limited in what comes to machine resources. In this way, traditional security does not fit in IoT systems. In this vein, the issue of cyber security has become paramount to the Internet of Things (IoT) and Industrial Internet of Things (IIoT) in mitigating cyber security risk for organizations and end users. New cyber security technologies / applications present improvements for IoT security management. Nevertheless, there is a gap on the effectiveness of IoT cyber risk solutions. This review article discusses the, trends around opportunities and threats in cyber security for IIoT.

A smart city is a futuristic approach that evolves with new technologies. It can start as a simple community and evolve into future cities. In most developing countries, where the population growth is faster, and where the organization and the urbanization are slower, there is a serious need for a framework for progressive change towards sustainable cities. This article discusses the level of intelligence of smart city components and proposes indicators as well as a methodology for measuring intelligence based on the objectives to be achieved as well as a certain number of technical requirements and some cognitive processes implemented.

The article describes the application of the Smart City concept and the economic opportunities it creates, infrastructure and services, and opportunities to improve governance. The main features of the Smart City concept, development directions and evolution, standards and solutions, factors and obstacles to its implementation have been analyzed by the author. The experience of different countries in the application of digital technologies is discussed. The article provides the scope and structure of the "smart" market, application stages and scenarios. International experience in this field was widely analyzed and examples were given, comparisons were made and suggestions were made. The article talks about smart cities, the construction of which has already begun in Azerbaijan. The application of the smart city concept in Azerbaijan has been studied. It is stressed that the spread of digital technologies for the construction of a smart city in Azerbaijan is a prerequisite. The Network Readiness Index (NRI) identifies the indicators that are holding back Azerbaijan in the ranking for 2021. In our study, based on the growth dynamics for 2017-2020, the forecast was calculated by analyzing the trend in the ICT development index in Azerbaijan for 2022-2025.

As the embedded device and internet of things (IoTs) concept prevalent in today's world, there is an increasing demand for the security and performance requirements on deploying these devices to private and public sectors. The crucial part of it is to protect privacy, confidentiality and integrity, meanwhile, maintain an adequate level of performance during transmission, storage and access of critical information. While the conventional cryptography methods, such as the Advanced Encryption Standard (AES), SHA$-$2 hashing method and RSA and Diffie Hellman for message authentication and identification, work well on systems which have reasonable processing power and memory capabilities. These do not scale well into a world with embedded systems and sensor networks, in conjunction with their nature of smaller size and lower cost. Thus, in the context of resource constrained device, lightweight cryptography methods are proposed to overcome many of the problems of conventional cryptography possessed. This includes constraints related to physical size, processing requirement, memory limitation, energy drain and production cost. This paper provides a survey of the architectures that are defined as replacements for conventional ciphers within an IoTs space and discuss some trends in the design of future lightweight algorithms. The performance metrics are carefully chosen to reflect and assess the suitability for embedded devices. The aim of this research is to identify the various performance metrics applied on different types of symmetric block ciphers especially lightweight ciphers and compare the results on versatile platforms with stream ciphers and public key methods. The comparative analysis on efficient LW cipher will be tested against other similar block ciphers on both MacBook Pro with Intel core and resource constrained device Raspberry Pi with ARM processor.

Based on 2010, 2013 and 2015 CGSS data, the impact of Internet use and social capital on the income gap among farmers in the past five years is assessed at three time points using the OLS method and a quantile regression method. The study finds that (1) the income gap among farmers increases continuously in the five-year period, while Internet use plays a positive impact on farmers’ income growth in all five quartiles; the coefficient differences are all significantly negative, indicating that Internet use plays a positive role in alleviating the income gap between high-income and low-income farmer subgroups, and (2) social capital plays a positive role in moderating the income gap among farmers and that Internet use by farmers expands the boundary of social capital, which in turn increases the income level of and alleviates the income gap among farmers.

Abstract: Intensity of natural and man-made disasters is increasing day by day. Disaster is one of the major threats that affects the sustainable development of tourist attractions. Big data and Internet of Things(IoT) will greatly improve the disaster management. Based on the Big Data and IoT, a tourism attraction disaster management system is designed, divided into several stages namely pre-disaster early warning prevention, disaster mitigation, recovery and reconstruction after disaster and updating disaster planning. Then, the system flow is analysed, as well as the system structure is constructed. Additional, system function and its operation flow are introduced, including disaster warning, disaster relief, disaster assessment, real-time monitoring and supporting disaster planning functions. Finally, an application case is introduced. Research intends to improve tourism area disasters management.

The present era of the Internet of Things (IoT) having intelligent functionalities in solving problems pertaining to realtime mission-critical systems has brought an immense revolution in diverse fields including healthcare and navigation systems. However, to the best of our knowledge, the potential of IoT has not been fully exploited yet in the field of the energy sector. We argue that there is an immense need to shift the traditional mission-critical electric power system architecture to IoT-based fully orchestrated architecture in order to increase efficiency, as billions of investment is reserved for the energy sector globally. Since network orchestration deals with auomating the interaction between multiple components involved to execute a particular service, therefore, scheduling the relevant processes within strict deadlines becomes the core pillar of the architecture. The mission-critical systems with urgent task execution often suffer from issues of missing task deadlines. In this study, we present a novel IoT task orchestration architecture for efficient energy management of a nanogrid system that focuses on minimizing the use of nonrenewable energy resources and maximizing the use of renewable energy resources. Moreover, major components of IoT task orchestration such as task mapping and task scheduling are also enhanced using NLP and PSO optimization modules. The proposed task scheduling algorithm incorporates the optimized surplus time, and efficiently executes the energy management-related tasks contemplating to their types. The study utilizes sensors to obtain data from physical IoT devices, including photovoltaic (PV), Energy Storage System (ESS), and diesel generator (DG). The performance of the proposed model is evaluated using data set of nanogrid houses. The outcomes revealed that IoT-task orchestration has played a pivotal role in efficient energy management for nanogrid mission-critical system. Furthermore, the comparison with state-of-the-art scheduling algorithms showed that the task starvation rate is reduced to 16% and 12% when compared with RR and FEF algorithms, respectively.

Continuous and emerging advances in Information and Communication Technology (ICT) have enabled IoT-to-Cloud applications to be induced by data pipelines coupled with Edge Intelligence-based architectures. Advanced vehicular networks greatly benefit from these architectures due to the implicit functionalities that are focused on realizing the Internet-of-Vehicle (IoV) vision. However, IoV is susceptible to attacks, where adversaries can easily exploit existing vulnerabilities. Several attacks may succeed due to inadequate or weaker authentication techniques. Hence, there is a timely need for hardening the authentication process through cutting-edge access control mechanisms. This paper proposes a Blockchain-based Multi-Factor authentication model that uses an embedded Digital Signature (MFBC_eDS) for vehicular clouds and Cloud-enabled IoV. Our proposed MFBC_eDS model consists of a scheme that integrates the Security Assertion Mark-up Language (SAML) to the Single Sign-On (SSO) capabilities for a connected Edge-to Cloud ecosystem. MFBC_eDS draws an essential comparison with the baseline authentication scheme suggested by Karla and Sood. Based on the foundations of Karla and Sood’s scheme, an embedded Probabilistic Polynomial-Time Algorithm (ePPTA) and an additional Hash function for the Pi generated during Karla and Sood’s authentication are proposed and discussed. The preliminary analysis of the proposition shows that the approach is more suitable to counter major adversarial attacks in an IoV-centered environment based on Dolev-Yao adversarial model while satisfying aspects of the CIA triad.

Internet of things (IoT) is a network of smart things. This indicates the ability of these physical things to transfer information with other physical things. The characteristics of these networks, such as topology dynamicity and energy constraint, challenges the routing problem in these networks. Previous routing methods could not achieve the required performance in this type of network. Therefore, developers of this network designed and developed specific methods in order to satisfy the requirements of these networks. One of the routing methods is utilization of multipath protocols which send data to its destination using routes with separate links. One of such protocols is RPL routing protocol. In this paper, this method is improved using composite metrics which chooses the best paths used for separate routes to send packets. We propose Energy and Load aware RPL (ELaM-IoT) protocol, which is an enhancement of RPL protocol. It uses a composite metric, calculated based on remaining energy, hop count, Link Expiration Time (LET), load and battery depletion index (BDI) for the route selection. In order to evaluate and report the results, the proposed ELaM-IoT method is compared to the ERGID and ADRM-IoT approaches with regard to average remaining energy, and network lifetime. The results demonstrate the superior performance of the proposed ELaM-IoT compared to the ERGID and ADRM-IoT approaches.

Many research work has demonstrated that taken the Combined Cooling Heating and Power system (CCHP) as the core equipment, the integrated energy system (IES) can bring obvious benefit to energy efficiency, CO2 emission reduction and operation economy in urban areas. Compared with isolated IES, integrated energy micro-grid (IEMG) which is formed by connecting multiple regions IES together, through distribution and thermal network, can further improve the reliability, flexibility, cleanliness and economy of regional energy supply. Based on the existing IES model, this paper describes the basic structure of IEMG and built a IEMG planning model. The planning based on the mixed integer linear programming, and economically construction planning scheme are calculated by using known electricity, heating and cooling loads information and the given multiple equipment selection schemes. At last, the model is validated by a case study. The results show that the application of IEMG can effectively improve the economy of regional energy supply.

Surveillance along the Kenya-Somalia border has been a big challenge that has continuously puzzled the security personnel, due to insurgency of armed militia Al-Shabaab from Somalia , the Kenyan government proposed construction of a barrier wall. This project developed a low cost wireless sensor network surveillance system to be deployed along the Kenya-Somalia border. The research study utilized two PIR sensor for detecting human intrusion, one motion is detected the sensor transmit the data via an Xbee module. Arduino microcontroller was used to process the data collected by the sensor before transmission. The system developed has two units, the Transmitter unit and a User Graphic interface running on Tuna Term software that displays the received data. During testing, the prototype system detected human intrusion, using the Arduino serial monitor the results were displayed before being package for transmission.

Thanks to rapid advances in the Internet of Things (IoT) and Edge-Fog-Cloud Computing technologies, Smart Public Safety (SPS) system has become feasible by integrating heterogeneous computing devices and different types of networks to collaboratively provide seamless public safety services. While SPS facilitates convenient exchanges of surveillance data streams among device owners and third-party applications, the existing monolithic service oriented architecture (SOA) architecture is unable to provide scalable and extensible services in a large-scale heterogeneous network environment. Moreover, traditional security solutions rely on centralized trusted third-party authority, which not only can be a performance bottleneck or the single point of failure, but it also incurs privacy concerns on improperly use of private information. Inspired by blockchain and microservices technologies, this paper proposed a BLockchain-ENabled Decentralized Smart Public Safety (BlendSPS) system. Leveraging hybrid blockchain fabrics, a microservices based security mechanism is implemented to enable decentralized security architecture, and it supports immutability, auditability and traceability for secured data sharing and operations among participants of the SPS system. An extensive experimental study verified the feasibility of the proposed Blend-SPS that possesses security and privacy proprieties with limited overhead on IoT based edge networks.

Excessive online behaviors refer to harmful or disproportionate use of digital network applications. Such behaviors are likely to be associated with escapist motives. Our aim was to analyze whether escapism predicts excessive gambling, excessive gaming, and excessive internet use over time. A longitudinal sample of Finnish residents aged 18–75 years (N = 1,022, 51.27% male) was surveyed at three time points in 6-month intervals: April 2021 (Time 1), October–November 2021 (Time 2), April–May 2022 (Time 3). Of the original Time 1 respondents, 66.80% took part in the surveys at both Time 2 and Time 3. All surveys included measures for excessive gambling (Problem Gambling Severity Index), excessive gaming (Internet Gaming Disorder Test), and excessive internet use (Compulsive Internet Use Scale). Three escapism-specific questions were used to construct a dedicated escapism variable. Socio-demographic variables, alcohol consumption, and psychological distress were used as controls. The study was conducted with multilevel regression analyses using hybrid models. Our research showed that escapism had strong within-person effects on excessive gambling, B = 0.18, p = .003; excessive gaming, B = 0.50, p < .001; and excessive internet use, B = 0.77, p < .001 over time. The between-person effect of escapism was demonstrated on excessive gaming B = 0.91, p <.001; and excessive internet use B = 0.61, p = .036. Adverse societal events and uncertain times can manifest in excessive online behaviors motivated by escapism, highlighting a need to focus prevention efforts on healthy coping methods.

Environmental studies, metabolic research, and state of the art neurobiology point towards the reduced amount of natural day and sunlight exposure of the developing child’s organism as the consequence of increasingly long hours spent indoors online as the single unifying source of a whole set of health risks identified worldwide, as is made clear in this review of the current literature. Over exposure to digital environments, from abuse to addiction, now concerns even the youngest (ages 0 to 2), and triggers, as argued on the basis of clear examples herein, a chain of interdependent negative and potentially long-term metabolic changes. This leads to a deregulation of the serotonin and dopamine neurotransmitter pathways in the developing brain, currently associated with online activity abuse and/or internet addiction, and akin to that found in severe substance abuse syndromes. A general functional working model is proposed under the light of evidence brought to the forefront in this review.

A Smart Home is characterized by the presence of a huge number of small, low power devices, along with more classical devices. According to the Internet of Things (IoT) paradigm, all of them are expected to be always connected to the Internet in order to provide enhanced services. In this scenario, an attacker can undermine both the network security and the user’s security/privacy. Traditional security measures are not sufficient, because they are too difficult to setup and are either too weak to effectively protect the user or too limiting for the new services effectiveness. The paper suggests to dynamically adapt the security level of the smart home network according to the user perceived risk level what we have called network sentiment analysis. The security level is not fixed, established by a central system (usually by the Internet Service Provider) but can be changed with the users cooperation. The security of the smart home network is improved by a distributed firewalling and Intrusion Detection Systems both to the smart home side as to the Internet Service Provider side. These two parts must cooperate and integrate their actions for reacting dynamically to new and ongoing threats. Moreover, the level of network sentiment detected can be propagate to nearby home networks (e.g. the smart home networks of the apartments inside a building) to increase/decrease their level of security, thus creating a true in-line Intrusion Prevention System (IPS). The paper also presents a test bed for Smart Home to detect and counteract to different attacks against the IoT devices,,Wi-Fi and Ethernet connections .

This paper investigates the effect of the Internet on tax revenue instability, notably through the international trade channel. It has used a sample of 142 countries over the period 1995-2017, and relied primarily on the two-step system Generalized Methods of Moments (GMM) estimators (but also incidentally on the Error Component Two-Stage Least Squares estimator). Tax revenue instability is primarily measured by the instability of non-resource tax revenue, but also by the instability of total tax revenue (for robustness check). The findings indicate that the Internet exerts a negative effect on tax revenue instability. Interestingly, this effect genuinely translates through the international trade channel, regardless of the measure of tax revenue instability considered. Countries enjoy a higher negative effect of the Internet on tax revenue instability as they enjoy a greater participation in international trade. These findings, therefore, add to the potential benefits of the Internet adoption (e.g., strengthening countries' participation in international trade, enhance their tax revenue performance and promote tax reform, including in developing countries) by showing that it could also help to stabilize tax revenue, particularly through the degree of countries' participation in international trade.

The skyrocketing growth in the number of Internet of Things (IoT) devices will certainly pose a huge traffic demand for fifth-generation (5G) wireless networks and beyond. In-band full-duplex (IBFD), which is theoretically expected to double the spectral efficiency of a half-duplex (HD) wireless channel and to connect more devices, has been considered as a promising technology to accelerate the development of IoT. To exploit the full potential of IBFD, the key challenge is how to handle network interference (including self-interference, co-channel interference and multiuser interference) more effectively. In this paper, we propose a simple yet efficient user grouping method, where a base station (BS) serves strong downlink users and weak uplink users and vice versa in different frequency bands, mitigating severe network interference. We aim to maximize a minimum rate among all users subject to bandwidth and power constraints, which is formulated as a highly nonconvex optimization problem. By leveraging inner approximation framework, we develop a very efficient iterative algorithm to solve this problem, which guarantees at least a local optimal solution. Numerical results are provided to show not only the benefit of using full-duplex raido at BS, but also the advantage of the proposed user grouping method.

This report is on online banking and information systems in online banking. It throws some light on the introduction and historical background of online banking. Next, in this term report online banking information is explained in detailed. We collected a few articles, related to internet banking, on which other researchers have worked. Then further we have brief them into a table where we have mentioned their problems and limitations. Next, we discussed them in detail, giving an overview of each article. Lastly, recommendations for the online banking sectors are also mentioned.

It is important to increase the quality of health and medicine. A wearable system for continuous monitoring of the patient is important to overcome this issue. Thus a patient with Arrhythmia due to its low cost and success in saving the life of the patient was the right option for the care partner. In addition, the device will provide a consumer with a smart smartphone application with accurate pulse beat and body temperature data in real time. MAX 30100 and LM35 are primarily used for the detection of human heart and temperature. The performance of these sensors is generated by an arrhythmia algorithm in the esp32 segment.

This paper is an attempt to forecast the potential application of Digital Twin in an industrial environment by adopting scenario analysis as part of technology monitoring. In recent years Digital Twin has become one of the most emerging topics in both the tech industry and academia. This field is the latest addition to industry 4.0. It has paved a great growth opportunity in the fields of Healthcare, manufacturing, and smart city areas. The Digital twin can be defined as mirroring the physical entities into a virtual environment where the status, health, and control of these entities can be monitored and controlled. It is a field where Artificial intelligence and the Internet of Things play a major role in the handshake between the two environments. In this paper we have done a thorough analysis to find out the possible future outcome of this technology in the manufacturing industry from the perspective of our fictious company. These scenarios expose various challenges and opportunities our organization may face in our future endeavour.

Abstract: Objective: Internet gaming disorder (IGD) is an emerging psychiatric disorder that has received attention over the past decade. Few studies have attempted to describe this disorder in the Saudi population. This study aimed to examine the prevalence of IGD and associated factors. A cross-sectional study was conducted using translated Arabic and a validated questionnaire targeting both genders in Saudi Arabia. Methodology: A cross-sectional study using a validated questionnaire (IGD-20) and targeting Arabic-speaking children, youth, and transitional age including both genders. A snowball approach was used to sample our population using an electronic survey. Logistic regression was used to examine factors associated with IGD diagnosis. The study was guided by the STROBE statement. Results: Among 419 individuals who participated in the study, 171 were classified as non-IGD, 167 were at risk for IGD (RIGD), and 72 were IGD. There is no significant association between IGD diagnosis and gender, nationality, residence, and family income. Time playing per week was significantly associated with IGD diagnosis (X2=49.256, p<0.01). There is a significant association between IGD-20 groups and categorical age groups (X2=10.096, p<0.01). Among our sample, the percentages of males (54.2%) and females (45.8%) who met the criteria for IGD were comparable. Conclusion: IGD and RIGD prevalence was significantly high in both age groups. Both males and females were affected similarly.

This manuscript presented an autonomous home farm for predicting metrological characteristics that can not only automate the process of growing crops but also, due to a neural network, significantly increase the productivity of the farm. The developed farm monitors and manages the following indicators: illumination, soil PH, air temperature, earth temperature, air humidity, CO2 concentration, and soil moisture. The presented farm can also be considered as a device for testing various weather conditions to determine the optimal temperature characteristics for different crops. This farm as a result is completely autonomous grows tomatoes at home.

From professors overwhelmed by anxiety-driven e-mails from students, through faculty and administrative staff wasting valued time on e-mail minutia, misuse of electronic mail in the academy has become ubiquitous. After a brief overview of the unique features of e-mail communication, this study provides insight and guidelines to plan new educational activities on healthy and productive utilization of e-mail in the academy of the digital era. The overall aim is to prioritize scholarly deep work by focusing on teaching and research work, freeing working time wasted on unproductive use of e-mail.

A Tangible User Interface (TUI) is a new interaction option that uses nontraditional input and output elements. A tangible interface thus allows the manipulation of physical objects using digital information. The exploration and manipulation of physical objects is a factor to be considered in learning in children, especially those with some kind of disability such as hearing, who maximize the use of other senses such as vision and touch. In a tangible interface, three elements are related - physical, digital and social. The potential of IoT for children is growing. This technology IoT integrated with TUI, can help for that parents or teachers can monitoring activities of the child. Also to identify behavior patterns in the child with hearing impairment. This article shows four case studies, where had been designed different products of Internet of Things Tangible applied a several contexts and with products of low cost.

Over the past few years, we have experienced great technological advancements in the information and communication field, which has significantly contributed to reshaping the Intelligent Transportation System (ITS) concept. Evolving from the platform of a collection of sensors aiming to collect data, the data exchanged paradigm among vehicles is shifted from the local network to the cloud. With the introduction of cloud and edge computing along with ubiquitous 5G mobile network, it is expected to see the role of Artificial Intelligence (AI) in data processing and smart decision imminent. So as to fully understand the future automobile scenario in this verge of industrial revolution 4.0, it is necessary first of all to get a clear understanding of the cutting-edge technologies that going to take place in the automotive ecosystem so that the cyber-physical impact on transportation system can be measured. CIoV, which is abbreviated from Cognitive Internet of Vehicle, is one of the recently proposed architectures of the technological evolution in transportation, and it has amassed great attention. It introduces cloud-based artificial intelligence and machine learning into transportation system. What are the future expectations of CIoV? To fully contemplate this architecture’s future potentials, and milestones set to achieve, it is crucial to understand all the technologies that leaned into it. Also, the security issues to meet the security requirements of its practical implementation. Aiming to that, this paper presents the evolution of CIoV along with the layer abstractions to outline the distinctive functional parts of the proposed architecture. It also gives an investigation of the prime security and privacy issues associated with technological evolution to take measures.

The Internet has become an essential tool for adolescents. It is part of their social integration within peers and supports their identity construction. The Internet can also become a source of addiction; especially when used as a coping strategy towards unpleasant life situations. The feeling of loneliness is an emotion present during the adolescence. However, when in excess, it can lead to suicidal ideations. We questioned ourselves on the impact of an excessive use of the Internet by adolescents, with their feeling of loneliness and the risks of suicidal ideations. We attempted to find an answer to this question by performing a literature review. We found one result matching our search criteria’s, which is itself a literature review. We noted the absence of studies with regards to the interaction between the feeling of loneliness, the addiction to the Internet, and the risk of suicidal ideations amongst adolescents. We established a theoretical model which could be used as a lead for future research. We insist on the importance that studies are made in this domain, in order to enable us to establish efficient preventive measure on the risks of suicidal ideations amongst adolescents.

The city of Lisbon, has any other capital of a European country, has a large number of issues while managing the waste and recycling containers spread throughout the city. This document presents the results of a study promoted by the Lisbon City Council for trialing LPWAN technology on the waste management vertical under the Lisbon Smart City initiative. Current waste management is done using GSM sensors, and the aim is to use LPWAN to reduce the costs, improve range and reduce provisioning times when changing the communications provider. After an initial study, LoRa was selected as the LPWAN of choice for the trials. The study is composed of multiple use cases at different distances, types of recycling waste containers, placements (underground and surface) and different kinds of waste level measurement LoRa sensors, deployed in order to assess the impact of the different use cases on the LoRa sensor usage. The results shown that the underground waste containers present the most difficult challenge, where the container itself imposes attenuation levels of 26dB on the link budget. The results promoted the deployment of a city wide LoRa network available to all departments inside the Lisbon City Council, and considering the network capacity the network, the network is also available to citizens to be used freely.

Early diagnosis and treatment have always been highly desired in the fight against cancer, and detection of circulating tumor DNA (ctDNA) has recently been touted as highly promising for early cancer screening. Consequently, the detection of ctDNA in liquid biopsy gains much attention in the field of tumor diagnosis and treatment, which has also attracted research interest from the industry. However, traditional gene detection technology is difficult to achieve low cost, real-time and portable measurement of ctDNA. Electroanalytical biosensors have many unique advantages such as high sensitivity, high specificity, low cost and good portability. Therefore, this review aims to discuss the latest development of biosensors for minimal-invasive, rapid, and real-time ctDNA detection. Various ctDNA sensors are reviewed with respect to their choices of receptor probes, detection strategies and figures of merit. Aiming at the portable, real-time and non-destructive characteristics of biosensors, we analyze their development in the Internet of Things, point-of-care testing, big data and big health.

Bike sharing systems are a key element of a smart city as they have the potential for reducing pollutant emissions and traffic congestion thus substantially improving citizens’ quality of life. In these systems, bicycles are made available for shared use to individuals on a very short-term basis. They are rented in a station and returned in any other station with free docks. However, to achieve a satisfactory user experience, all the stations in the system must be neither overloaded nor empty. The occupancy level of the stations can be constantly monitored through IoT-based services. The goal of this work is to analyze occupancy level data acquired from real systems to discover situations of dock overload in multiple stations which could lead to service disruption. The proposed methodology relies on a pattern mining approach. A new pattern type, called Occupancy Monitoring Pattern (OMPs), is proposed to characterize situations of dock overload in multiple stations. Since stations are geo-referenced and their occupancy levels are periodically monitored, OMPs can be filtered and evaluated by considering also the spatial and temporal correlation of the acquired measurements. The results achieved on real Smart City data highlight the potential of these techniques in supporting domain experts in maintenance activities, such as periodic re-balancing of the occupancy levels of the stations, as well as in improving the user experience, such as suggesting alternative stations in the neighborhood.

Using Internet of Things (IoT) applications has been a growing trend in the last few years. They have been deployed in several areas of life including secure and sensitive sectors like military and health. In these sectors, sensory data is the main factor in any decision-making process. This introduces the need to ensure the integrity of data. Secure techniques are needed to detect any data injection attempt before catastrophic effects happen. Sensors have limited computational and power resources. This limitation creates a challenge to design a security mechanism that is both secure and energy-efficient. This work presents a Randomized Watermarking Filtering Scheme (RWFS) for IoT applications that provides en-route filtering to get rid of any injected data at an early stage of the communication. Filtering injected data is based on a watermark that is generated from the original data and embedded directly in random places throughout the packet's payload. The scheme uses homomorphic encryption techniques to conceal the report's measurement from any adversary. The advantage of homomorphic encryption is that it allows the data to be aggregated and, thus, decreases the packet's size. The results of our proposed scheme proved that it improves the security and energy consumption of the system as it mitigates some of the limitations in the existing works.

A software defined radio (SDR) is a communication system that makes use of components that can be configured through software, in contrast to traditional systems where these components are variable through hardware, these radio devices are much more versatile, this article describes the factors that must be considered when implementing a communication system based on Software Defined Radios (SDR), in order to reduce the attenuation factors and thus obtain the maximum distance for a transmission of data effectively in the UHF band. The calculations made for the first Fresnel zone and for the design of the Ground Plane type antennas used in the transmission/reception stages of the x40 bladeRF platforms are also presented. The tests were carried out at the facilities of the Huarangal Nuclear Center of the Peruvian Institute of Nuclear Energy, obtaining favorable results that allow ratifying the versatility and performance of the SDRs.

We aimed to assess the prevalence, content structure and psychological comorbidity of PIU in Russian adolescents. In addition, the design of our research provided an opportunity to compare demographic and psychological patterns of different forms of PIU: generalised (PIUgen) and specific – problematic video game use (PUgame) as well as problematic social media use (PUsocial). Methods: This is a one-stage cross-sectional observational study of school sampling in three major Siberian cities. A total of 4514 schoolchildren aged 12-18 (mean age 14.52±1.52 years) were surveyed. Chen Internet Addiction Scale, Game Addiction Scale for Adolescents”, and The Social Media Disorder Scale were used to identify PIU and its types. Results: The prevalence of PIUgen among adolescents in Central Siberia was 7.2%; the prevalence of PUgame was 10.4%; the prevalence of PUsocial was 8.0%. The results of structural equation modelling, as well as the correlation analysis data, suggest two possible patterns of psychosocial problems with PIU – the first one characteristic of both PIUgen and PUsocial, the second one, significantly different, – of PUgame. Conclusions: Urban adolescents in Central Siberia do not differ significantly from their Asian and European peers. Our findings support the concept of rejecting the term “generalised PIU” as a single psychological construct.

This study aims to establish a link between disturbances in the night sleep habitus, quality of sleep, and daytime sleepiness in adolescents with Internet addiction and different types of content consumed. Methods: This is a cross-sectional observational study of a school sample in three large cities in Central Siberia. 4,615 schoolchildren of 12–18 years old were examined. The Russian-language versions of the Chen Internet Addiction Scale, the Game Addiction Scale for Adolescents, and the Social Media Disorder Scale were used to identify Internet addiction. Questions from the Pittsburgh Sleep Quality Index questionnaire were used to assess nighttime sleep. Daytime sleepiness was assessed using the Pediatric Daytime Sleepiness Scale questionnaire. Results: Adolescents with Internet addiction go to bed and wake up late; they are characterized by a decrease in the duration of nighttime sleep, an increase in sleep onset latency, and frequent nighttime awakenings, as well as more pronounced daytime sleepiness. Among the sleep parameters studied, the indicators of daytime sleepiness and night awakening scales have the highest effect size in Internet-addicted adolescents, regardless of the media consumed. Conclusion: Internet-addicted adolescents are characterized by significant disturbances in the quality of nighttime sleep and excessive daytime sleepiness, which requires appropriate psychological correction.

A brain-computer interface for controlling elements commonly used at home is presented in this paper. It includes the electroencephalography device needed to acquire signals associated to the brain activity, the algorithms for artefact reduction and event classification, and the communication protocol.

Machine fault diagnosis systems need to collect and transmit dynamic monitoring signals, like vibration and current signals, at high-speed. However, industrial wireless sensor networks (IWSNs) and Industrial Internet of Things (IIoT) are generally based on low-speed wireless protocols, such as ZigBee and IEEE802.15.4. To address this tension when implementing machine fault diagnosis applications in IIoT, this paper proposes a novel IWSN with on-sensor data processing. On-sensor wavelet transforms using four popular mother wavelets are explored for fault feature extraction, while an on-sensor support vector machine classifier is investigated for fault diagnosis. The effectiveness of the presented approach is evaluated by a set of experiments using motor bearing vibration data. The experimental results show that compared with raw data transmission, the proposed on-sensor fault diagnosis method can reduce the payload transmission data by 99.95%, and reduce the node energy consumption by about 10%, while the fault diagnosis accuracy of the proposed approach reaches 98%.

Considering the great growth of IoT networks and the need for highly reliable networks and also considering the manufacturing divides of IoT equipment which are highly limited by their memory, processing power and battery; we need a highly efficient routing for guaranteeing our network's high life span. So, this paper has suggested an efficient energy routing method based on the overlapping clustering method which is inspired from the Grey theory. The Overlap clustering method means that some Things collect data that must be sent to two or more Fog nodes for processing. In the suggested method the best node is selected as the cluster head based on factors such as remaining energy, distance, link expiration time, signal power for receiving data from things by the Fog nodes. In the next step the Fog node's data are sent in a hierarchical method using a symmetrical tree of processed data to the server. Thus, the main issue here is making using a proper routing method for data sending to the Cloud that doesn't just focus on energy, but also considers other factors such as delay and network life span. The simulation results show that the HR-IoT reduces the average end to end delay more than 17.2% and 23.1%, decreases the response time more than 20.1% and 25.78% and increase packet delivery rate more than 23.1% and 28.78% and lifetime more than 25.1% and 28.78% compared to EECRP and ERGID approaches.

Subjective cognitive decline (SCD) would correspond to a preclinical phase of Alzheimer's disease. The aim of this study was to find associations between lifestyle individual factors compatible scores with cognitive impairment (CI) in SCD people. Methods: This is a case-control study to detect SCD, CI and potential associated factors in 497 patients over 50 years in Community Pharmacies. Three screening tests detected possible CI and patients with at least one test compatible with CI were referred to Primary Care to be evaluated. Results: In self-complaint patients statistically significant with depressive feelings were found (86.8%) with benzodiazepines consumers (83.9%) and female patients (81.2%). Thirty percent of our sample obtained scores compatible with CI. Being older than 70 years old increased the odds of obtaining scores compatible with CI. High level education, reading and internet use were able to reduce the odds of positive scores compatible with CI (37%-91%, 7%-18% and 67%-86%), whereas, one extra hour television/day increased the risk in 8%-30%. Reading was able to nullify the effect of both internet and TV consumption. Conclusion: Not just the age but also modifiable lifestyle factors are acting in favour of a cognitive decline.

The Internet of Things (IoT) technology and devices constitute an exciting field in computer science that is rapidly emerging worldwide. IoT devices function by connecting real-world objects to the internet, resulting in a higher number of interconnected devices than ever witnessed in history. Through internet connectivity, these devices can be utilized in various ways, such as monitoring and tracking. Their prevalence is increasing exponentially, coinciding with advancements in wireless networking technologies. The internet’s enhanced connectivity has played a vital role in fostering the proliferation of IoT devices. Presently, almost any everyday object can be network-connected. The demand for automation and efficiency has also been a contributing factor to the advancements in this technology. This paper aims to review the emergence of IoT devices, analyze their common applications, and explore the future prospects in this promising field of computer science. The examined applications encompass healthcare, agriculture, and smart cities. Although IoT technology exhibits similar deployment trends, this paper will explore different fields to discern the subtle nuances that exist among them. IoT technology can be applied to nearly any domain, and each use case has unique requirements. To comprehend the future of IoT, it is essential to comprehend the driving forces behind its advancements in various industries. By gaining a better understanding of the emergence of IoT devices, readers will develop insights into the factors that have propelled their growth and the conditions that led to technological advancements. Moreover, a comprehensive understanding of the prevalent methodologies will enable readers to distinguish between current practices and future methods. Given the rapid rate at which IoT technology is advancing, this paper aims to provide researchers with an understanding of the factors that have brought us to this point and the ongoing efforts to shape the future of IoT.

We applied evolutionary game theory to extend a resource constrained security game model for confidentiality attacks in an Advanced Metering Infrastructure (AMI), which is a component of IoT-enabled Smart Grids. The AMI is modelled as a tree structure where each node aggregates the information of its children before encrypting it and passing it on to its parent. As a part of the model, we developed a discretization scheme for solving the replicator equations. The aim of this work is to explore the space of possible behaviours of attackers and to develop a framework where the AMI nodes adaptively select the most profitable strategies. Using this model, we simulated the evolution of a population of attackers and defenders on various cases resembling the real life implementation of AMI. We discuss in depth how to enhance security in AMI using evolutionary game theory either by a priori analysis or as a tool to run dynamic and adaptive infrastructure defence.

The radio operation in wireless sensor networks (WSN) in the Internet of Things (IoT) applications are the most common source for power consumption. However, recognizing and controlling the factors affecting radio operation can be valuable for managing the node power consumption. ContikiMAC is a low-power Radio Duty-Cycle protocol in Contiki OS used in WakeUp mode, which is a clear channel assessment (CCA) to check radio status periodically. The time spent to check the radio is of utmost importance for monitoring power consumption. It can lead to false WakeUp or idle listening in Radio Duty-Cycles and ContikiMAC. This paper presents a detailed analysis of radio WakeUp time factors of ContikiMAC. Then, we propose lightweight CCA (LW-CCA) as an extension to ContikiMAC to reduce the percentage of Radio Duty-Cycles in false WakeUps and idle listenings by using dynamic received signal strength indicators (RSSI) status check time. The simulation results in the Cooja simulator show that LW-CCA reduces about 8% energy consumption in nodes while maintaining up to 99% of the packet delivery rate (PDR).

Chronic non-communicable diseases (NCDs) are major public health problems and a significant financial burden on public health systems. By 2030, mortality due to NCDs, such as cardiovascular diseases, cancer, respiratory diseases, and diabetes, is predicted to increase in Brazil. A peculiar aspect of NCDs involves their long-term and integrated care management. This paper proposes a chronic disease management platform based on artificial intelligence to deliver digital health services everywhere. The proposed platform is anchored and built with healthcare 4.0 technologies, such as wearable devices, the internet of medical things, and artificial intelligence cloud-based solutions that allow the deployment of a smart healthcare system. In addition, the paper presents the feasibility of the platform in a diabetes prediction study case. For the study case, an initial dataset was established with bio-impedance, oxygen concentration, pulse rate, skin impedance, and skin temperature attributes. A baseline was implemented with ten regression models to assess the prediction performance of the mean squared error, root mean squared error, and r-squared score to compare predictive findings with capillary blood glucose measurements. Results evidence that the decision tree regressor and three ensemble methods (bagging decision tree regressor, random forest regressor, and AdaBoost regressor) yielded improvement over the other models. Moreover, a comparison among those models revealed that the decision tree regressor outperforms them and presents promissory outcomes.

Advances in communication technologies have made the interaction of small devices, such as smartphones, wearables, and sensors, scattered on the Internet, bringing a whole new set of complex applications with ever greater task processing needs. These Internet of Things (IoT) devices run on batteries with strict energy restrictions. They tend to offload task processing to remote servers, usually to Cloud Computing (CC) in datacenters geographically located away from the IoT device. In such a context, this work proposes a dynamic cost model to minimize energy consumption and task processing time for IoT scenarios in Mobile Edge Computing environments. Our approach allows for a detailed cost model, with an algorithm called TEMS that considers energy, time consumed during processing, the cost of data transmission, and energy in idle devices. The task scheduling chooses among Cloud or Mobile Edge Computing (MEC) server or local IoT devices to better execution time with lower cost. The simulated environment evaluation saved up to 51.6% energy consumption and improved task completion time up to 86.6%.

The integration of Artificial Intelligence (AI) with the Internet of Things (IoT) has propelled technological innovation across various industries. This systematic literature review explores the current state and future trajectories of AI in IoT, with a particular focus on emerging trends in intelligent data analysis and privacy protection. The proliferation of IoT devices, marked by voluminous data generation, has reshaped data processing methods, providing actionable insights for informed decision-making. While previous reviews have offered valuable insights, they often fall short of comprehensively addressing the multifaceted dimensions within the AI-driven IoT landscape. This review aims to bridge this gap by systematically examining existing literature and acknowledging the limitations of past studies. To achieve this aim, the study uses a meticulous approach guided by established methodologies. The chosen methodology ensures the rigor and validity of the review, aligning with PRISMA guidelines for systematic reviews. This systematic literature review serves as a comprehensive guide for researchers, practitioners, and policymakers, offering insights into the current landscape and paving the way for future research directions. The identified trends and challenges provide a valuable resource for navigating the evolving domain of AI in IoT, fostering a balanced, secure, and sustainable advancement in this dynamic field.

Fall prediction using machine learning has become one of the most fruitful and socially relevant applications of computer vision in gerontological research. Since its inception in the early 2000s, this subfield has proliferated into a robust body of research underpinned by various machine learning algorithms (including neural networks, support vector machines, and decision trees) as well as statistical modeling approaches (Markov chains, Gaussian mixture models, and hidden Markov models). Furthermore, some advancements have been translated into commercial and clinical practice, with companies in various stages of development capitalizing on the aging population to develop new commercially available products. Yet despite the marvel of modern machine learning-enabled fall prediction, little research has been conducted to shed light on the security and privacy concerns that such systems pose for older adults. The present study employs an interdisciplinary lens in examining privacy issues associated with machine learning fall prediction and exploring the implications of these models in elderly care and the Internet of Medical Things (IoMT). Ultimately, a justice-informed set of best practices rooted in social geroscience is suggested to help fall prediction researchers and companies continue to advance the field while preserving elderly privacy and autonomy.

Many academics are critical of the current publishing system, but it is difficult to create a better alternative. This review relates to the Sciences and Social Sciences, and discusses the primary purpose of academic journals as providing a seal of approval for perceived quality, impact, significance, and importance. The key issues considered include the role of anonymous refereeing, continuous rather than discrete frequency of publications, avoidance of time wasting, and seeking adventure. Here we give recommendations about the organization of journal articles, the roles of associate editors and referees, measuring the time frame for refereeing submitted articles in days and weeks rather than months and years, encouraging open access internet publishing, emphasizing the continuity of publishing online, academic publishing as a continuous dynamic process, and how to improve research after publication. Citations and functions thereof, such as the journal impact factor and h-index, are the benchmark for evaluating the importance and impact of academic journals and published articles. Even in the very top journals, a high proportion of published articles are never cited, not even by the authors themselves. Top journal publications do not guarantee that published articles will make significant contributions, or that they will ever be highly cited. The COVID-19 world should encourage academics worldwide not only to rethink academic teaching, but also to re-evaluate key issues associated with academic journal publishing in the future.

The use of technologies and the Internet poses problems and risks related to digital security. This article presents the results of a study on the evaluation of the digital competence of future teachers in the DigCompEdu European framework. 317 undergraduate students from Spain and Portugal answered a questionnaire with 59 items, validated by experts, in order to assess the level and predominant competence profile in initial training (including knowledge, uses and interactions and attitudinal patterns). The results show that 47% of the participants belong to the profile of teachers at medium digital risk, evidencing habitual practices that involve risks such as sharing information and digital content inappropriately, not using strong passwords, and ignoring concepts such as identity, digital “footprint” and digital reputation. The average valuations of each item in the seven categories show that future teachers have an average competence in the area of digital security. They have good attitudes toward security but less knowledge and fewer skills and practices related to the safe and responsible use of the Internet. Future lines of work are proposed, aimed at responding to the demand for a better prepared and more digitally competent citizenry. The demand for education in security, privacy and digital identity is becoming increasingly important, and these elements form an essential part of initial training.

The Internet use has become an integral part of daily life, adolescents are especially at a higher risk to develop problematic Internet use (PIU). Although one of the most well-known comorbid conditions of PIU is sleep disturbance, little is known about the sex disparity in this association. This school-based survey in students of grades 7-9 was conducted to estimate the prevalence of PIU and sleep disturbance among Chinese adolescents, to test the association between PIU and sleep disturbance, and to investigate the role of the child’s sex in this association. A two-stage stratified cluster sampling method was used to recruit participants, and a two-level logistic regression models were fitted. The mean Internet addiction test scores was 37.2 (SD: 13.2), and 15.5% (736) met the criteria for PIU. After adjusting for control variables, problematic Internet users were at a higher risk of sleep disturbance (adjusted odds ratio=2.41, 95% CI=2.07-3.19). Sex-stratified analyses also demonstrated that association was greater in girls than boys. In this respect, paying more attention to the sleep patterns of adolescents who report excessive Internet use is recommended, and this early identification may be of practical importance for schools, parents, and adolescents themselves.

Air pollution contributes to a critical environmental problem in various towns and cities. With the emergence of the smart cities concept, appropriate methods to curb associated with exposure to pollutants must have been a portion of appropriate urban development policy. This study presents a technologically driven air quality solution in smart cities to advertise energy-efficient and cleaner sequestration in these areas. It aims to address the issue of how to integrate the data-based strategies and artificial intelligence into efficient public sector pollution management in smart cities as a core part of the smart city definition. Exploratory research has been used in 152 smart cities, and environmental experts contributed to this study. It further addresses the technical criteria for implementing such a framework that the public administration uses to prepare the renovation of public buildings, minimize energy use and costs, and link these smart police stations to monitor air pollution as a part of integrated cities. Such a digital transition in resource management will increase public governance's energy performance, a higher standard of operation, and a healthier environment.

Microbes are the most prevalent and widely-distributed life form on planet Earth. They are also cognitive organisms with memory and superior problem-solving skills that use quantum phase transitions during their remarkably-effective gathering and dissemination of information. This narrative review explores the biological ramifications of human holobionts physically embodying the human microbiome and connecting to the Internet of Microbes (IOM) beyond our body. Within the review, we consider: 1) the ways in which our microbial co-partners exerted control over our ancestors and continue to influence our current human generation, 2) how they mold virtually every key aspect of our life and even remain with our body in death and 3) how inward-looking contemplative tools such as meditation and embodied cognition provide an ideal opportunity to connect to the microbial information super highway and tap the full range of human holobiont capacities.

The performance evaluation of the Internet of Things (IoT) based supply chain is challenging due to the involvement of multiple decision makers, the multi-dimensional nature of the evaluation process, and the existence of uncertainty and imprecision in the decision making process. To ensure effective decisions are made, this paper presents a fuzzy multicriteria analysis model for evaluating the performance of IoT based supply chain. The inherent uncertainty and imprecision of the performance evaluation process is adequately handled by using intuitionistic fuzzy numbers. A new algorithm is developed for determining the overall performance index for each alternative across all criteria. The development of the fuzzy multicriteria group decision making model provides organizations with the ability to effectively evaluate the performance of their IoT based supply chains for improving their competitiveness. An example is presented for demonstrating the applicability of the model for dealing with real world IoT-based performance evaluation problems.

The Internet of Medical Things (IoMT) is playing a pivotal role in the healthcare sector by allowing faster and more informed hospital care, personalized treatment, and medical solutions. A very effective and trustworthy solution for resource-constrained medical devices is provided by Physical Unclonable Functions (PUF) - based identity and authentication systems, however they are not yet entirely reliable. This paper proposes VXorPUF, a Vedic Principles - Based Hybrid XOR Arbiter PUF. Modeling attacks were performed on the proposed architecture and an accuracy of 49.80 % was achieved. Uniqueness, Reliability and Randomness were the figures of merit used to evaluate PUF.

The mHealth is a term that is used for mobile health supported by smart devices such as mobile phones, tablets, and wearable smart devices, etc. The smart devices strengthen the efficiency and effectiveness of interaction with patients, physicians, and specialists. Patients nowadays would like to be intimately involved in their diagnosis as well as to make more informed decisions concerning their care. It has begun to measure the success of the quality of treatment. This was a reason that patients trust mHealth to provide them with consistency in their communications with the physicians. Most wireless strategies do not measure up to this standard so that patient engagement ultimately ended up decreasing. The blockchain can boost mHealth through storing and sharing electronic data securely and transparently. It can enhance the accessibility of patient information in real-time. The Internet of Things (IoT) provides a unique identification number to every connected device such as mobile devices, medical devices, and wearable devices. This framework uses the blockchain and IoT technologies together to provide quick help to the patients, monitor remotely, reduce the cost and unnecessarily hospitalization physically and find the real diagnosis. In order to increase patient involvement, mHealth framework with blockchain and IoT technologies has built with the key objective of providing patients with full information on their treatment and diagnosis.

Presently power control and management play a vigorous role in information technology and power management. Instead of non-renewable power manufacturing, renewable power manufacturing is preferred by every organization for controlling resource consumption, price reduction and efficient power management. Smart grid efficiently satisfies these requirements with the integration of machine learning algorithms. Machine learning algorithms are used in a smart grid for power requirement prediction, power distribution, failure identification etc. The proposed Random Forest-based smart grid system classifies the power grid into different zones like high and low power utilization. The power zones are divided into number of sub-zones and map to random forest branches. The sub-zone and branch mapping process used to identify the quantity of power utilized and the non-utilized in a zone. The non-utilized power quantity and location of power availabilities are identified and distributed the required quantity of power to the requester in a minimal response time and price. The priority power scheduling algorithm collect request from consumer and send the request to producer based on priority. The producer analysed the requester existing power utilization quantity and availability of power for scheduling the power distribution to the requester based on priority. The proposed Random Forest based sustainability and price optimization technique in smart grid experimental results are compared to existing machine learning techniques like SVM, KNN and NB. The proposed random forest-based identification technique identifies the exact location of the power availability, which takes minimal processing time and quick responses to the requestor. Additionally, the smart meter based smart grid technique identifies the faults in short time duration than the conventional energy management technique is also proven in the experimental results.

Supermedia streams transfer video, audio, haptic and other sensory data. Real -time transfering of supermedia streams over the Internet is quite challenging. This paper outlines the proposed protocols for transferring supermedia streams over the Internet. Moreover, it describes the Quality of Service (QoS) requirements for supermedia applications that a network has to fulfill. Extensive simulations and experiments for the performance evaluation of transport protocols for real time transferring HEVC streams with supermedia data are carried out. Complements, differences and relevancies between simulation and real world experiments are discussed. The metrics that are measured for the performance evaluation are delay, jitter, throughput, efficiency, packet loss and one proposed by the authors, packet arrival deviation. The simulation tests reveal which protocols could be used for the transfer of real-time supermedia data with a HEVC video stream.

Development of agriculture in Russia and Belarus is based on the practical implementation of "smart" systems in agriculture based on the use of modern wireless, intelligent technologies and Internet of Things. This review presents research articles (mainly, in Russian) published in the period of 2013 – 2022 on the use of cloud technologies and Internet of Things for the development of agriculture in Russia and Belarus. An analysis of the use of cloud technologies and Internet of Things in the modern world is given on the basis of research articles and reviews published in English in the period of 2017 – 2022. The main directions of digitalization of modern agriculture are listed. The uses of cloud technologies and Internet of Things in agriculture are described along with promising directions for further research and applications.

Internet of Things (IoT) technologies can greatly benefit from machine learning techniques and Artificial Neural Networks for data mining and vice versa. In the agricultural field, this convergence could result in the development of smart farming systems suitable for use as decision support systems by peasant farmers. This work presents the design of a smart farming system for crop production, which is based on low-cost IoT sensors and popular data storage services and data analytics services on the Cloud. Moreover, a new data mining method exploiting climate data along with crop production data is proposed for the prediction of production volume from heterogeneous data sources. This method was initially validated using traditional machine learning techniques and open historical data of the northeast region of the state of Puebla, Mexico, which were collected from data sources from the National Water Commission and the Agri-food Information Service of the Mexican Government.

In the age of next-generation computer, the role of the cloud, the internet and smart devices will become stronger. These days we all know the word smart well. This word is often used in our daily lives. The Internet of Things (IoT) will generate a variety of information from a variety of resources. It can store big data in the cloud. Fog computing acts as a signal between cloud and IoT. Fog extensions in this framework apply to material under IoT. IoT devices are called Fog nodes, which can be accessed anywhere within the network range. A blockchain is a novel way of recording in a secure sequence. Creating a new framework in the development of Internet of Things is one of the critical problems of wireless communication where solving such a problem can lead to continued growth in the use and popularity of IoT. Proposed research creates a framework for providing a framework for middleware on the internet of smart devices network for the internet of things using blockchains technology. Our great offering connects new research that integrates blockchains into the Internet of Things and provides secure Internet connection for smart devices. Blockchain (BC) Internet of Things (IoT) is a new technology that works with low-level, distributed, public and real-time leaders to maintain transactions between IoT sites. A blockchain is a series of blocks, each block being linked to its previous blocks. All blocks have cryptographic hash code, previous block hash, and its data. Transactions in BC are the basic components used to transfer data between IoT nodes. IoT nodes are a variety of portable but smart devices with embedded sensors, actuators, systems and the ability to communicate with other IoT nodes. The role of BC in IoT is to provide a process for processing secure data records using IoT nodes. BC is a protected technology that can be used publicly and openly. IoT requires this type of technology to allow secure communication between IoT nodes in different environments. Events in BC can be tracked and monitored by anyone who is certified to communicate within IoT.

Seniors safety is a compelling need, which necessitates 24/7 real-time monitoring and timely dangerous action recognition. Being able to mirror characteristics of physical objects (PO) to corresponding logical objects (LO) and seamlessly monitor their footprints thus detect anomaly parameters, Digital Twins (DT) has been considered a practical way to provide virtual health services for seniors safety. Meanwhile, widely adopted Internet of Medical Things (IoMT) consisting of wearable sensors and non-contact optical cameras for self and remote health data monitoring also raises concerns on information security and privacy violation. Therefore, security of POs, LOs and reliable data sharing among healthcare professionals are challenging as constructing trust and privacy-preserving virtual health services. Thanks to characteristics of decentralization, traceability and unalterability, Blockchain is promising to enhance security and privacy properties in many areas like data analysis, finance and healthcare. This paper envisions a lightweight authentication framework (LAF) to enable secure and privacy-preserving virtual healthcare services. Leveraging Non-Fungible Token (NFT) technology to tokenize LOs and data streams on blockchain, anyone can certify the authenticity of a digital LO along with its synchronized data between PO without relying on a third-party agency. In addition, the NFT-based tokenization not only allows owners fully control their IoMT devices and data, but it also enables verifiable ownership and traceable transferability during data sharing process. Moreover, NFT only contains references to encrypted raw data that are saved on off-chain storage like local files or distributed databases, such a hybrid storage strategy ensures privacy-preservation for sensitive information. A proof-of-concept prototype is implemented and tests are conducted on a case study of seniors safety. The experimental evaluation shows the feasibility and effectiveness of the proposed LAF solution.

This venture targets giving a correspondence framework that can improve the legal framework execution and cooperation and furthermore make it simple to lead execution audits. This examination depicts restricted admittance to the right legitimate data just as admittance to courts and court administrations as the essential factors that limit admittance to equity. The basic target fundamental for accomplishing the point incorporates planning an online easy to understand data framework that would improve correspondence and association between legal professionals like legal advisors and disputants. The undertaking uses a subjective methodology that zeroed in on the substance investigation of essential information. Key members were court laborers like attorneys and agents, just as court clients like disputants. Pre-plan discoveries uncovered that current legitimate data frameworks – basically court sites – neglect to recognize their crowd by treating all court clients similarly comparative with the correspondence of lawful data. Likewise, the issue of admittance to equity includes the significant expense of equity also, restricted legal data that elevates admittance to equity. Post-plan discoveries from the online overview directed after the turn of events and execution of the IT curio uncovered a powerful and productive legitimate data framework. Exploration discoveries supported the advancement of an IT antiquity that records for huge shortcomings in current legal data frameworks, for example, their failure to pass on fitting legitimate data intelligently and productively. In outline, the investigation prescribes the reception of saw control to work fair and square of certainty of court clients and the differentiation between court clients to convey the right data to the right crowd.

Background:As cyber-physical systems (CPS) continue to grow, it is progressively crucial to address the challenges of data protection using artificial intelligence (AI). Objective:The goal of this research is to provide an up-to-date overview of the security and privacy issues of CPS that incorporate AI techniques. Method:To achieve this, the author conducted a systematic literature review, focusing on 35 relevant articles. Results:The data collected from these studies was then categorized into three main areas: 1) different security and privacy issues, 2) application areas and vulnerabilities, and 3) the AI techniques used to address security and privacy concerns. The literature review highlights that intrusion detection and cyberattacks are the most commonly studied areas in CPS, while Machine Learnning (ML)-based attacks and vessel trajectory are less explored. The review identifies various CPS applications such as water treatment, energy, healthcare, and transportation that address security and privacy concerns. However, a relatively small proportion of studies focused on the manufacturing domain. The review also notes that while supervised machine learning algorithms under the classification category are commonly used to address data protection issues, there are comparatively fewer studies that have implemented automation processes using robots and deep learning. Limitations:The articles related to blockchain-based research were not included in this review to focus solely on AI techniques. Conclusion:The results of this analysis indicate that there is a significant need for innovative AI/ML techniques to protect intelligent systems and networks from ML-based security threats.

Advanced state-of-the-art technologies, mainly computational intelligence including Machine/Deep Learning and Fuzzy Computing, through applied research, can provide added value to modern science and, in general, to entrepreneurship and the economy. Artificial intelligence is the field of computer science that attempts to model concepts such as learning, adaptability and perception to synthesize intelligent behavior in solving complex problems, utilizing elements of adaptation to the environment and philosophical reasoning. About the science of civil engineering and, in general, the construction industry, which is one of the most important in economic entrepreneurship, both in terms of the size of the workforce employed and the amount of capital invested, the penetration of artificial intelligence is possible to change industry business models, eliminate costly mistakes, reduce Jobsite injuries and generally make large-scale engineering projects more efficient. The purpose of the paper is to present the recent research on artificial intelligence methods (machine-deep learning, computer vision, natural language processing, fuzzy systems, robotics, etc.) and the corresponding related technologies related to it (extensive data analysis, blockchain, cloud computing, internet of things, augmented reality), in the fields of application of civil engineering science, including structural engineering, geotechnical engineering, hydraulics and water resources management, marine and coastal technology, transport and transportation infrastructure, planning and technical project management, critical infrastructure security and disaster mitigation..

Computers have evolved over the years and as the evolution continues, we have been ushered into an era where high-speed internet has made it possible for devices in our homes, hospital, energy and industry to communicate with each other. This era is what is known as the Internet of Things (IoT). IoT has several benefits in the health, energy, transportation and agriculture sectors of a country’s economy. These enormous benefits coupled with the computational constraint of IoT devices which makes it difficult to deploy enhanced security protocols on them make IoT devices a target of cyber-attacks. One approach that has been used in traditional computing over the years to fight cyber-attacks is Intrusion Detection System (IDS). However, it is practically impossible to deploy IDS meant for traditional computers in IoT environments because of the computational constraint of these devices. In this regard, this study proposes a lightweight IDS for IoT devices using an incremental ensemble learning technique. We used Gaussian Naive Bayes and Hoeffding tree to build our incremental ensemble model. The model was then evaluated on the TON IoT dataset. Our proposed model was compared with other state-of-the-art methods proposed and evaluated using the same dataset. The experimental results show that the proposed model achieved an average accuracy of 99.98\%. We also evaluated the memory consumption of our model which showed that our model achieved a lightweight model status of 650.11KB as the highest memory consumption and 122.38KB as the lowest memory consumption.

The new coronavirus SARS Cov2 disease from 2019. (COVID-19), started as a cluster of unexplained pneumonia cases in Wuhan in December 2019, has spread globally and caused a serious public health threat. People were scared due to the COVID-19 cases that were rapidly increasing all over the world and the quick changes in how people lived. The COVID-19 pandemic has affected various aspects of life, one of which is the increased use of the internet, especially social media platforms. Past research has clearly linked a pandemic with signs of stress, depression, anxiety, and suicide thoughts, as well as with excessive internet use. The findings of research conducted around the world indicate that the higher the level of stress associated with the COVID-19 pandemic in an individual, the greater the tendency to develop an addiction to the internet. The aim of this paper was to provide a brief overview of the available scientific findings on the impact of the COVID-19 pandemic on mental health and internet addiction. Methods: A sweep through available literature was performed using the database Medline via the PubMed interface for articles written in English, using keywords and MeSH terms "Internet addiction", "mental health" and "COVID-19". Results: With insight into the scientific literature on COVID-19, mental health, and internet addiction, we have concluded that during the COVID-19 pandemic, time spent on the internet increased. Conclusion: Additionally, due to reduced social activities, above mentioned consequently led to internet addiction and thus to psychological distress, increased loneliness and depression.

Specific climate adaptation and resilience measures can be efficiently designed and implemented at the regional and local level. Climate and environmental databases are of critical importance to achieving sustainability goals (SDGs) and for the efficient planning and implementation of suitable mitigation measures: Available databases can serve municipalities as a vital starting points to determine requirements, prioritize resources and allocate investments under consideration of commonly tight budget restrictions. High-quality geo, climate and environmental data are now available – data from remote sensing, i.e. Copernicus services will be of crucial importance. Forward-looking approaches exist to using such data to derive forecasts for urban planning process optimization for municipal administrations. On municipal level, however, the existing data have so far only been used to a limited extent, since there are no practical tools for urban planning that can be used to merge and meaningfully combine remote sensing data with local data and to further process and apply in municipal planning processes. Therefore, our project CoKLIMAx aims at the development of new digital products, advanced urban services and procedures, such as the development of practice-oriented technical tools that acquire various remote sensing and in-situ data sets for validation and further processing.

Since smart cities aim at becoming self-monitoring and self-response systems, their deployment relies on close resource monitoring through large-scale urban sensing. The subsequent gathering of massive amounts of data makes essential the development of event filtering mechanisms that enable the selection of what is relevant and trustworthy. Due to the rise of mobile event producers, location information has become a valuable filtering criterion as it not only offers extra information on the event described but also enhances trust on the producer. Implementing mechanisms that validate the quality of location information becomes then imperative. The lack of such strategies in cloud architectures compels the adoption of new communication schemes for IoT-based urban services. To serve the demand for location verification in urban event-based systems (DEBS), we have designed three different fog architectures that combine proximity and cloud communication. Moreover, we have successfully assessed their performance using network simulations with realistic urban traces.

The need for new technologies into healthcare services has been stressed. However, little is known about the effectiveness of digital interventions integrated in psychosomatic rehabilitation processes. Data from 724 patients from psychosomatic rehabilitation clinics were analyzed for effectiveness of digital trainings examined by a change in symptoms related to depression, anxiety, stress and loneliness from pre- to post-rehabilitation. Rehabilitation satisfaction was examined in association with reaching rehabilitation goals and satisfaction with communication. Mixed repeated measures analysis of covariances, analysis of covariances, and hierarchical stepwise regression analyses were performed. Results indicated a superior effectiveness for the intervention group receiving all offered digital treatments in addition to the regular face-to-face rehabilitation program with regard to symptoms of depression, F(2,674)=3.93, p<.05, ηp2=.01), and anxiety, F(2,678)=3.68, p<.05, ηp2=.01), post-rehabilitation with large effect sizes for both depression (d=1.28) and anxiety (d=1.08). In addition, rehabilitation satisfaction was positively associated with reaching rehabilitation goals and perceived communication with healthcare workers. Digital interventions appeared effective in supporting mental health of psychosomatic rehabilitation patients post-rehabilitation. This finding supports the inclusion of multidisciplinary and interdisciplinary digital and face-to-face treatment programs and call for more implementations of new technologies in a context of complexity to improve health and healthcare service.

The wide adoption of the recently coined fog and edge computing paradigms alongside conventional cloud computing creates a novel scenario, known as the cloud continuum, where services may benefit from the overall set of resources to optimize their execution. To operate successfully, such a cloud continuum scenario demands for novel management strategies, enabling a coordinated and efficient management of the entire set of resources, from the edge up to the cloud, designed in particular to address key edge characteristics, such as mobility, heterogeneity and volatility. The design of such a management framework poses many research challenges and has already promoted many initiatives worldwide at different levels. In this paper we present the results of one of these experiences driven by an EU H2020 project, focusing on the lessons learnt from a real deployment of the proposed management solution in three different industrial scenarios. We think that such a description may help understand the benefits brought in by a holistic cloud continuum management and also may help other initiatives in their design and development processes.

Global demand for sleep-tracking wearables, or consumer sleep technologies (CSTs), is steadily increasing. CST marketing campaigns often feature a scientific component, but the scientific relevancy and monetary value of CST features within the sleep research community remains unquantified. Sleep medicine experts were recruited through social media and nonprobability sampling techniques to complete a survey identifying sleep metrics and device features that are most desirable to the scientific community. A hypothetical purchase task (HPT) estimated economic valuation for devices with different features by price. Forty-six (N=46) respondents with an average of 10±6 years’ experience conducting research in real-world settings completed the online survey. Total sleep time was ranked as the most important measure of sleep followed by objective sleep quality while sleep architecture/depth and diagnostic information were ranked as least important. Experts preferred wrist-worn devices that could reliably determine sleep episodes as short as 20 minutes. Economic value was greater for hypothetical devices with longer battery life. These data set a precedent to determine how scientific relevance of a product impacts the potential market value of a CST device. This is the first known attempt to establish consensus opinion or economic valuation for scientifically-desirable CST features and metrics using expert elicitation.

Deep Learning (DL) and Machine Learning (ML) are widely used in many fields, but rarely used in Frequency Estimation (FE) and Slope Estimation (SE) of signals. Frequency and slope estimation for Frequency-Modulated (FM) and single-tone sinusoidal signals are essential in various applications, such as wireless communications, sonar, and radar measurements. In this work, artificial neural network (ANN) and convolutional neural network (CNN) are used in frequency and slope estimation for FM signals under Additive White Gaussian Noise (AWGN) and Additive Symmetric alpha Stable Noise (SαSN). SαS distributions are impulsive noise disturbances found in many communication environments like marine systems; their distribution lacks a closed-form Probability Density Function (PDF), except for specific cases, and infinite second-order statistic, hence Geometric SNR (GSNR) is used in this work to determine the impulsiveness of noise in a mixture of Gaussian and SαS noise processes. ANN is a machine learning classifier, designed with few layers for reducing FE and SE complexity while getting higher accuracy as compared with classical techniques. CNN is a deep learning classifier, designed with many layers for FE and SE, and proved to be more accurate than ANN when dealing with big data and finding optimal features. Simulation results show that SαS noise can be much more harmful for FE and SE of FM signals than Gaussian noise. DL and ML can significantly reduce FE complexity, memory cost, and power consumption, which is important in many systems such as some Internet of Things (IoT) sensor applications. After training DCNN for frequency and slope estimation of LFM signals, the performance of DCNN (in terms of accuracy) can give acceptable results at very low signal-to-noise ratios where TFD fails, giving more than 20dB difference in the GSNR working range.

Packet re-transmissions are a common technique to improve link reliability in low-power wireless networks. However, since packet re-transmissions increase the end-device energy consumption and the network load, a maximum number of re-transmissions per packet is typically set, also considering the duty-cycle limitations imposed by radio-frequency regulations. Moreover, the number of re-transmissions per packet is typically set to a constant value, meaning that all packet re-transmissions are treated the same regardless of actual channel conditions (i.e., multi-path propagation or internal/external interference effects). Taking that into account, in this paper we propose and evaluate the concept of re-transmission shaping, a mechanism that manages packet re-transmissions to maximize link reliability, while minimizing energy consumption and meeting radio-frequency regulation constraints. The proposed re-transmission shaping mechanism operates by keeping track of unused packet re-transmissions and allocating additional retransmission when the instantaneous link quality decreases due to channel impairments. To evaluate the re-transmission shaping mechanism we use trace-based simulations using a IEEE 802.15.4g SUN data-set and two widely used metrics, the PDR (Packet Delivery Ratio) and the RNP (Required Number of Packets). The obtained results show that re-transmission shaping is a useful mechanism to improve link reliability of low-power wireless communications, as it can increase PDR from 77.9% to 99.2% while sustaining a RNP of 2.35 re-transmissions per packet, when compared to using a single re-transmission per packet.

In this paper, we propose a lightweight physical layer aided authentication and key agreement (PL-AKA) protocol in the internet of things (IoT). Conventional evolved packet system AKA (EPS-AKA) used in long-term evolution (LTE) systems may suffer from congestions in core networks by the large signaling overhead as the number of IoT devices increases. Thus, in order to alleviate the overhead, we consider a cross-layer authentication by integrating physical layer approaches to cryptography-based schemes. To demonstrate the feasibility of the PL-AKA, universal software radio peripheral (USRP) based tests are conducted as well as numerical simulations. The proposed scheme shows a significant reduction in signaling overhead compared to the conventional EPS-AKA in both simulation and experiment. Therefore, the proposed lightweight PL-AKA has the potential for practical and efficient implementation of large-scale IoT networks.

Dietary supplements are commonly used among athletes. The Australian Institute of Sport (AIS) groups supplements into 4 categories, being group D considered by the World Anti-Doping Agency (WADA) as prohibited supplements. The online availability of four doping substances: oxandrolone, dehydroepiandrosterone (DHEA), androstenedione and Tribulus terrestris, purchased from Spain, Puerto Rico, Canada, USA, Ukraine and Russia was evaluated. The characteristics of the websites, the countries the webs sold to, the pharmaceutical forms offered and the recommendations for its use were analyzed by using a computer tool designed ad hoc. There were significant differences between countries in the number of webpages that sold the products (Chi-square test, p<0.05). Oxandrolone was available for purchase mainly in Spain (27.12%) and Ukraine (26.58%), coming from websites dedicated to sports (77.26%). For DHEA, most of the pages were located in Canada (23.34%) and Russia (21.44%). Tribulus terrestris was the compound with the highest number of web pages. In the case of androstenedione, none of the pages for its sale requested prescription. Products such as androstenedione or DHEA are claimed to enhance sports performance or for sports use without providing details. The results showed that a limited number of Internet sites request prescriptions. Most of the doping substances are purchased from the country where they are requested. Athletes should be encouraged to consult health professionals about which supplements are suitable for their type of training and sports objectives.

Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents a comprehensive analysis of deep-learning (DL) approach for frequency estimation of single-tones. It is shown that DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques. The study is comprehensive, filling gaps of existing works, where it analyzes error under different signal-to-noise ratios, numbers of nodes, and numbers of input samples; also, under missing SNR information. It is found that DL-based FE is not significantly affected by SNR bias or number of nodes. DL-based approach can work properly using minimal number of input nodes N at which classical methods fail. It is possible for DL to use as little as two layers with two or three nodes each, with complexity of O{N} versus O{Nlog2 (N)} for DFT-based FE, noting that less N is required for DL. Hence, DL can significantly reduce FE complexity, memory, cost, and power consumption, making DL-based FE attractive for resource-limited systems like some IoT sensor applications. Also, reduced complexity opens the door for hardware-efficient implementation using short word-length (SWL) or time-efficient software-defined radio (SDR) communications.

Despite the fact that COVID-19 is no longer a global pandemic due to development and integration of different technologies for the diagnosis and treatment of the disease. Technological advancement in the field of molecular biology, electronics, computer science, artificial intelligence, Internet of Things, nanotechnology etc. has led to the development of molecular approaches and computer aided diagnosis for the detection of COVID-19. This study provides a holistic approach on COVID-19 detection based on (1) molecular diagnosis which include RT-PCR, antigen-antibody and CRISPR-based biosensors and (2) computer aided detection based on AI-driven models which include Deep Learning and Transfer learning approach. The review also provide comparison between these 2 emerging technologies and open research issues for the development of smart-IoMT-enable platform for the detection of COVID-19.

To ensure the security of spatial-temporal Top-k query in two-tiered wireless sensor networks, many schemes have been proposed in the literature in the past decade. However, most of them only consider the scenario where sensor nodes are static, and cannot achieve the security goal for spatial-temporal Top-k query in mobile sensor networks, because the mobility of the sensor nodes will affect the spatial-temporal relationships of the sensory data items generated by the sensor nodes. Although we have proposed some schemes for two-tiered mobile wireless sensor networks (TMWSNs) in our previous work, there is still large room to improve their performances. In this paper, we proposed a novel scheme named STQ-TMWSN for secure fine-grained spatial-temporal Top-k query in TMWSNs based on the virtual-grid construction and the size-order encryption binding. Theoretic analysis shows that STQ-TMWSN can achieve low computation complexity and high security performance. Simulation results indicate that STQ-TMWSN brings much lower communication cost than the state-of-the-art schemes on securing Top-k query in TMWSNs.

In developing countries today, population growth and the penetration of higher standard of living appliances in homes has resulted in a rapidly increasing residential load. In South Africa, the recent rolling blackouts and electricity price increase only highlighted this reality calling for sustainable measures to reduce the overall consumption and peak load. The dawn of the smart grid concept, embedded systems and ICTs have paved the way to novel HEMS design. In this regard, the Internet of Things (IoT), an enabler for smart and efficient energy management systems is seeing increasing attention for optimizing HEMS design and mitigate its deployment cost constraints. In this work, we propose an IoT platform for residential energy management applications focusing on interoperability, low-cost, technology availability and scalability. We focus on the backend complexities of IoT Home Area Networks (HAN) using the OCF IoTivity-Lite middleware. To augment the quality, servicing and reduce cost and complexities, this work leverages open-source Cloud technologies from Back4App as BaaS to provide consumer and Utilities with a data communication platform within an experimental study illustrating time and space agnostic “mind-changing” energy feedback, Demand Response Management (DRM) and appliance operation control via a HEM App via an Android smartphone.

Transportation, environmental conditions, quality of human life within smart cities, and system infrastructure have all needed practical and dependable smart solutions as urbanization has accelerated in recent years. In addition, the emerging Internet of Things (IoT) provides access to a plethora of cutting-edge, all-encompassing apps for smart cities, all of which contribute significantly to lowering energy consumption and other negative environmental impacts. For smart cities to meet the challenge of using less energy, the authors of this research article suggest planning and implementing an integrated power and heat architecture that puts renewable energy infrastructure and energy-storage infrastructure at the top of the list. To address these issues, we describe a smart proposed NEOSRD architecture that uses a distributed smart area domain to optimize renewable demand energy in a smart city across a wide area network. The energy requirements of desalination procedures are negligible when compared to the total local energy consumption and transportation, a feat accomplished by the proposed NEOSRD system. Here, the computational model shows how the established system is a valuable response to our problems and a cost-effective strategy for creating smarter structural elements that cut down on overall smart cities' energy costs.

This paper examines dynamic identity, as it pertains to the IoT; and explores the practical implementation of a mitigation to some of the key weaknesses of a conventional dynamic identity model. This paper explores human-centric and machine-based observer approaches for confirming device identity, permitting automated identity confirmation for deployed systems. It also assesses the advantages of dynamic identity in the context of identity revocation permitting secure change of ownership for IoT devices. The paper explores use-cases for human and machine-based observation for authentication of device identity when devices join a C2 network, and considers the relative merits for these two approaches for different types of system.

The Internet of Things (IoT) is experiencing widespread adoption across industry sectors ranging from supply chain management to smart cities, buildings, and health monitoring. However, most software architectures for IoT deployment rely on centralized cloud computing infrastructures to provide storage and computing power, as cloud providers have high economic incentives to organize their infrastructure into clusters. Despite these incentives, there has been a recent shift from centralized to decentralized architecture that harnesses the potential of edge devices, reduces network latency, and lowers infrastructure cost to support IoT applications. This shift has resulted in new edge computing architectures, but many still rely on centralized solutions for managing applications. A truly decentralized approach would offer interesting properties required for IoT use cases. To address these concerns, we introduce a decentralized architecture tailored for large scale deployments of peer-to-peer IoT sensor networks and capable of run-time application migration. The solution combines a blockchain consensus algorithm and verifiable random functions to ensure scalability, fault tolerance, transparency, and no single point of failure. We build on our previously presented theoretical simulations with many protocol improvements and an implementation tested in a use case related to monitoring a Slovenian cultural heritage building located in Bled, Slovenia.

To live in the information society means to be surrounded by billions of electronic devices full of sensors that constantly acquire data. This enormous amount of data must be processed and classified. A solution commonly adopted is to send these data to server farms to be remotely elaborated. The drawback is a huge battery drain due to high amount of information that must be exchanged. To compensate this problem data must be processed locally, near the sensor itself. But this solution requires huge computational capabilities. While microprocessors, even mobile ones, nowadays have enough computational power, their performance are severely limited by the Memory Wall problem. Memories are too slow, so microprocessors cannot fetch enough data from them, greatly limiting their performance. A solution is the Processing-In-Memory (PIM) approach. New memories are designed that are able to elaborate data inside them eliminating the Memory Wall problem. In this work we present an example of such system, using as a case of study the Bitmap Indexing algorithm. Such algorithm is used to classify data coming from many sources in parallel. We propose an hardware accelerator designed around the Processing-In-Memory approach, that is capable of implementing this algorithm and that can also be reconfigured to do other tasks or to work as standard memory. The architecture has been synthesized using CMOS technology. The results that we have obtained highlights that, not only it is possible to process and classify huge amount of data locally, but also that it is possible to obtain this result with a very low power consumption.

The Industrial Internet of Things (IIoT) paradigm is a key research area derived from the Internet of Things (IoT). The emergence of IIoT has enabled a revolution in manufacturing and production, through the employment of various embedded sensing devices connected with each other by an IoT network, along with a collection of enabling technologies such as artificial intelligence (AI) and edge/fog computing. One of the unrivaled characteristics of IIoT is the inter-connectivity provided to industries; however, this characteristic might open the door for cyber-criminals to launch various attacks. In fact, one of the major challenges hindering the prevalent adoption of the IIoT paradigm is IoT security. Inevitably, an increasing number of research proposals have been introduced over the last decade to overcome these security concerns. To obtain an overview of this research area, conducting a literature survey of the published research is necessary, eliciting the various security requirements and their considerations. This paper provides a literature survey of IIoT security, focused on the period from 2017 to 2023. We identify IIoT security threats and classify them into three categories, based on the IIoT layer they exploit to launch these attacks. Additionally, we characterize the security requirements that these attacks violate. Finally, we highlight how emerging technologies, such as AI and edge/fog computing, can be adopted to address security concerns and enhance IIoT security.

The Internet of Vehicles (IoV) is an emerging field that has attracted significant attention due to its potential to transform transportation and mobility. IoV enables the development of new services and applications that can improve transportation systems' efficiency, safety, and sustainability. However, ensuring secure and trustworthy communication among the different components of an IoV system is a critical challenge that needs to be addressed. This paper proposes a blockchain-enabled communication framework for secure and trustworthy IoV applications. The proposed framework leverages the security and decentralization features of blockchain technology to provide secure communication channels for different components of the IoV system, including vehicles, infrastructure, and service providers. The proposed framework provides an identity management system to authenticate and authorize users and devices, thereby preventing unauthorized access and data breaches. To evaluate the effectiveness of the proposed framework, we conducted experiments using a real-world IoV scenario, and the results demonstrate that the proposed framework can provide secure and trustworthy communication for IoV applications. The proposed blockchain-enabled communication framework offers a promising approach to address the security and trust challenges in IoV communication systems.

Building a widely distributed hotspot network is a very tedious task due to its complexity. Providing security, fully distributed network services, and cost-conscious impact are the major challenges behind this goal. To overcome these issues, we have presented a novel distributed hotspot network architecture with five layers that can provide large-scale hotspot coverage as an assimilated result. Our contributions to this new architecture highlight important aspects. First, scalability can be increased by including many Internet-of-Things (IoT) devices with sensors and Wi-Fi and/or LoraWAN connectivity modules. Second, hotspot owners can rent out their hotspots to create a distributed hotspot network in which the hotspots can act as an ordinary data gateway, a full-fledged hotspot miner, and a lightweight hotspot miner to earn crypto tokens as rewards for certain activities. Third, the advantages of Wi-Fi and LoraWAN can be seamlessly leveraged to achieve optimal coverage, higher network security, and suitable data transmission rate for transferring sensor data from IoT devices to remote application servers and users. Fourth, blockchain is used to enhance the decentralized behavior of the architecture presented here by providing immutability and independence from a centralized regulator and making the network architecture more reliable and transparent. The main feature of our paper is the use of the tau-computing paradigm along with hotspots to improve availability, Internet backhaul-agnostic network coverage, and synchronous update capability, and tau-aware leasing to strengthen and improve coverage. We also discuss the key challenges and future roadmap that require further investment and deployment.

In the rapidly evolving landscape of wireless communication, each successive generation of networks has achieved significant technological leaps, profoundly transforming the way we connect and interact. From the analog simplicity of 1G to the digital prowess of 5G, the journey of mobile networks has been marked by constant innovation and escalating demands for faster, more reliable, and more efficient communication systems. As 5G becomes a global reality, laying the foundation for an interconnected world, the quest for even more advanced networks leads us to the threshold of the sixth-generation (6G) era. This paper presents a hierarchical exploration of 6G networks, poised at the forefront of the next revolution in mobile wireless technology. This study delves into the technological advancements that underpin the need for 6G, examining its key features, benefits, and the key enabling technologies. We dissect the intricacies of cutting-edge innovations like terahertz communication, ultra-massive MIMO, artificial intelligence (AI), machine learning, quantum communication, and re-configurable intelligent surfaces. Through a meticulous analysis, we evaluate the strengths, weaknesses, and state-of-the-art research in these areas, offering a wider view of the current progress and potential applications of 6G networks. Central to our discussion is the transformative role of AI in shaping the future of 6G networks. By integrating AI and machine learning, 6G networks are expected to offer unprecedented capabilities, from enhanced mobile broadband to groundbreaking applications in areas like smart cities and autonomous systems. This integration heralds a new era of intelligent, self-optimizing networks that promise to redefine the parameters of connectivity and digital interaction. This paper also addresses critical challenges in the deployment of 6G, from technological hurdles to regulatory concerns, providing a holistic assessment of potential barriers. By highlighting the interplay between 6G and AI technologies, this study not only maps out the current landscape but also lights the path forward in this rapidly evolving domain. This paper aims to be a cornerstone resource, providing essential insights, addressing unresolved research questions, and stimulating further investigation into the multifaceted realm of 6G networks. By highlighting the synergy between 6G and AI(Artificial Intelligence) technologies, we aim to illuminate the path forward in this rapidly evolving field.

The new Internet of Things (IoT) applications are enabling the development of projects that help monitoring people with different diseases in their daily lives. Alzheimer’s is a disease that affects neurological functions and needs support to maintain maximum independence and security of patients during this stage of life, as the cure and reversal of symptoms have not yet been discovered. The IoT-based monitoring system provides the caregivers’ support in monitoring people with Alzheimer’s Disease (AD). This paper presents an ontology-based computational model which receives physiological data from external IoT applications, allowing to identify of potentially dangerous behaviors for patients with AD. The main scientific contribution of this work is the specification of a model focusing on Alzheimer’s disease using the analysis of Context Histories and Context Prediction, which considering the state of the art, it is the only one that uses analysis of Context Histories to perform predictions. The research also proposes a simulator to generate activities of the daily life of patients allowing the creation of datasets. These datasets were used to evaluate the contributions of the model and were generated according to the standardization of the ontology. The simulator generated 1025 scenarios applied to guide the predictions, which achieved average accurary of 97.44%. The experiments also allowed the learning of 20 relevant lessons on technological, medical and methodological aspects of DCARE that are recorded in this article.

A large number of smart devices (things) are being deployed with the swift development of Inter- net of Things (IOT). These devices, owned by different organizations, have a wide variety of services to offer over the web. During a natural disaster or emergency (i.e., a situation), for example, relevant IOT services can be found and put to use. However, appropriate service matching methods are required to find the relevant services. Organizations that manage situation responses and organizations that provide IOT services are likely to be independent of each other, and therefore it is difficult for them to adopt a common ontological model to facilitate the service matching. Moreover, there exists a large conceptual gap between the domain of discourse for situations and the domain of discourse for services, which cannot be adequately bridged by existing techniques. In this paper, we address these issues and propose a new method, WikiServe, to identify IOT services that are functionally relevant to a given situation. Using concepts (terms) from situation and service descriptions, WikiServe employs Wikipedia as a knowledge source to bridge the conceptual gap between situation and service descriptions and match functionally relevant IOT services for a situation. It uses situation terms to retrieve situation related articles from Wikipedia. Then it creates a ranked list of services for the situation using the weighted occurrences of service terms in weighted situation articles. WikiServe performs better than a commonly used baseline method in terms of Precision, Recall and F measure for service matching.

The subject of study in the article is the method of industrial equipment vibration diagnostics using Allan variance. The goal is to increase the precision and accuracy of industrial equipment's vibration diagnostics processes by developing and implementing IoT-oriented solutions based on intelligent sensors and actuators per the IEEE 1451.0-2007 standard. Tasks: justify the feasibility of using platform-oriented technologies for vibration diagnostics of industrial equipment and choose a cloud service for the implementation of the platform; develop software and hardware solutions of the IoT platform for vibration diagnostics of industrial equipment; calibrate the vibration diagnostics system and check the measurement precision and accuracy. The methods used are the microservice approach, multilevel architecture, and assessing equipment state-based Allan variance. We obtained the following results. The architecture of the IoT system for vibration diagnostics of industrial equipment developed and presented in the article is three-level. The level of autonomous sensors provides readings of vibration acceleration indicators and transmits data to the Hub level, which is implemented based on a BeagleBone single-board microcomputer through the BLE digital wireless data transmission channel. BeagleBone computing power provides work with artificial intelligence algorithms. At the third level of the server platform, the tasks of diagnosing and predicting the condition of the equipment are solved, for which the Dictionary Learning algorithm implemented in the Python programming language is applied. Verifying the accelerometer calibration method for vibration diagnostics of industrial equipment was performed using a unique stand. Correct operation of the entire system is confirmed by the coincidence of expected and measured results. In the next step, we plan the development of additional microservices that will provide the possibility of using time series analysis methods and modern artificial intelligence technologies for complex diagnostics and forecasting of the equipment state.

The article is of a research nature. The aim of the article is to identify the role of social media in shaping personal brand. To this end, the first part discusses the concept of personal brand, components of brand capital in case of famous people, including consumer-based capital. Attention was also paid to the great importance of social media and the growing role of their users in the process of shaping personal brand. Based on the analysis of the source literature, a research gap was identified, related to the lack of empirical verification of the relationship between users’ online activity and the brand capital of famous people, also known as celebrities, associated with artistic and cultural activities. The article uses the results of direct research carried out in the years 2019-2020. The second (empirical) part of the article presents research hypotheses, methodology, as well as results and conclusions from the research. Based on 26 in-depth individual interviews that were conducted with people famous in Poland (mainly engaged in artistic and cultural activities) and surveys on a group of 324 social media users, it was shown, among others, that online activity of Internet users stimulates the brand capital of famous people. Statistically significant relationships were observed for such components of the personal brand as awareness / associations with the personal brand and for the relationship regarding the perception of the quality of activities carried out by a famous person.

In recent years, the individualized demand of customers brings small batches and diversification of orders towards enterprises. The application of enabling technologies in factory, such as the Industrial Internet of Things (IIoT) and Cloud Manufacturing (CMfg), enhances the ability of customer requirement automatic elicitation and the manufacturing process control. The job shop scheduling problem with random job arrival time dramatically increases the difficulty in process management. Thus, how to collaboratively schedule the production and logistics resources in the shop floor is very challenging, and it has a fundamental and practical significance of achieving the competitiveness for an enterprise. To address this issue, the real-time model of production and logistics resources is built firstly. Then, the task entropy model is built based on the task information. Finally, the real-time self-adaption collaboration of production and logistics resources is realized. The proposed algorithm is carried out based on a practical case to evaluate its effectiveness. Experimental results show that our proposed algorithm outperforms three existing algorithms.

It is strongly believed that technology can reap the best only when it can be tamed by all stakeholders. Big data technology has no exception for this and even after a decade of emergence, the technology is still a herculean task and is in nascent stage with respect to applicability for many people. Having understood the gaps in the technology adoption for big data in the contemporary world, the present exploratory research work intended to highlight the possible prospects of big data technologies. It is also advocated as to how the challenges of various fields can be converted as opportunities with the shift in the perspective towards this evolving concept. Examples of apex organizations like (IMF and ITU) and their initiatives of big data technologies with respect to the Sustainable Development Goals (SDGs) are also cited for a broader outlook. The intervention of the responsible organizations along with the respective governments is also much sought for encouraging the technology adoption across all the sections of the market players.

Several studies have reported a relationship between autonomic dysfunction　 during tasks and Internet addiction. Changes within 24 h regarding heart rate variability (HRV) may be a more sensitive indicator than short term observations. We evaluated the influence of excessive Internet usage on HRV using a 24 h electrocardiogram (ECG) and examined acute changes during computer game tasks. A total 13 high school students and 22 young adults were recruited, and divided into excessive Internet user and non- excessive user groups based on Internet addiction test scores. The ECG was recorded continuously for 24 h by a wearable sensor and during two computer games (the Iowa gambling task and each participant’s preferred game) using a standard type ECG system. Multiple psychometric tests were also conducted. The excessive Internet user group exhibited clinically significant depressive symptoms, and higher self-reported scores for anxiety, impulsivity, autistic tendencies, and sleep problems than the non- excessive internet user group. There were weak differences within the 24 h high-frequency (HF) band power between two groups. During the Iowa gambling task, younger participants exhibited a reduced HF response, which may be related to weaker top- down self- regulatory mechanisms.

Vehicle-to-Vehicle (V2V) communication plays a pivotal role in modern intelligent transportation systems, enabling seamless information exchange among vehicles to enhance road safety, traffic efficiency, and overall driving experience. However, the secure transmission of sensitive data between vehicles remains a critical concern due to potential security threats and vulnerabilities. This research paper focuses on investigating the security protocols employed in Vehicle-to-Vehicle communication systems. A comprehensive review and analysis of relevant literature and research papers is conducted to gather information on existing V2V communication security protocols and techniques. The analysis encompasses key areas, including authentication mechanisms, encryption algorithms, key management protocols, and intrusion detection systems specifically applicable to V2V communication networks. Within the context of real-world V2V environments, this study delves into the challenges and limitations associated with implementing these protocols. Moreover, to foster a deeper understanding, the paper investigates present communication protocols in the field of Internet of Things (IoT) that are tailored for V2V. Various parameters, such as band-width consumption, energy consumption, latency, and message size, are considered during the evaluation of these protocols to gauge their effectiveness. The research outcomes aim to provide a comprehensive understanding of the strengths and weaknesses of the current V2V communication security protocols. Furthermore, based on the findings, this paper will propose improvements and recommendations to enhance the security measures of the V2V communication protocol. Ultimately, this research will contribute to the development of more secure and reliable V2V communication systems, propelling the advancement of intelligent transportation technology.

The transmission and analysis of data is one of the challenges of the 21st century. In the field of environmental measurement technology, existing broadband and wireless technologies have not been able to transmit data reliably and cost-effectively over long distances and in hard-to-reach places. LoRaWAN, an IoT technology, could be an energy-efficient, cost-effective and secure alternative as a narrowband technology in combination with battery-powered sensors and thus make an important contribution to the intelligent, largely wireless networking of objects, plants and machines (IoT), for example in the municipal sector. In addition to ecological and economic benefits, the quality of life in modern, intelligently networked cities can be enhanced by real time data acquisition. However, the prerequisite is that the quality of the data acquired via this method is sufficiently good. This paper therefore addresses the question of the quality of particulate matter data collected by low-cost sensors. To determine this, an SDS011 particulate matter sensor from Nova Fitness was ported to LoRaWAN. The sensor was installed next to a governmental measurement station. In a test that lasted five weeks, data from the SDS011 sensor were compared with those from the governmental station. Differences were identified and a correction approach was developed and applied. The efficiency of the approach was verified. Based on the results, it can be seen that the use of the low cost sensors has weaknesses. Problems can only be partially reduced. Nevertheless, the use of the low-cost sensors can be helpful for a flexible and cost effective collection of environmental data.

A smartphone plummeted from a stratospheric height of 36 km (~119,000 feet), providing a complete record of its rapid descent and abrupt deceleration when it hit the ground. The smartphone was configured to collect internal sensor data at high rates. We discuss the state-of-the-art of smartphone environmental and sensing capabilities at the closing of year 2020 and present a flexible mobile sensor data model. The associated open-source application programing interface (API) and python software development kit (SDK) used in this work is transportable to any hardware platform and operating system.

Agriculture is the primary source of economic growth in many countries in West Africa. It provides food and livelihoods for the population. The purpose of this study is to review the digital agriculture status in five countries, namely Benin, Burkina Faso, Coˆte d’Ivoire, Ghana, and Nigeria. The study consisted of a bibliometric analysis using a database exported from the web of science through a well-defined search string. Additionally, the cases of digital technology deployment in the countries were assessed through a systematic review approach comparing the technologies used in the target countries. Representative articles from the bibliometric review published in the last five years were selected for the comparative analysis. The bibliometric analysis based on 3,249 publications revealed that the research interests have significantly increased since 2014. The top two countries in terms of authors’ nationality were Nigeria and Ghana, respectively. Climate change, Machine Learning (ML) and adoption were the hot topics of discussion. The digital agriculture survey showed that publications in Nigeria were focused on Internet of Things (IoT), Wireless Sensors Networks, blockchain, and Artificial Intelligence (IA) technologies. Ghana also has a strong interest in blockchain, AI, and big data, while Burkina Faso focused on IoT and AI. Cote d’Ivoire and Benin focused only on AI.

Smart farming, as a branch of the Internet of Things (IoT), combines the recognition of agricultural economic competencies, the progress of data and information collected from connected devices with statistical analysis to characterize the essentials of the assimilated information, allowing farmers to make intelligent conclusions that will maximize the harvest benefit. However, the integration of advanced technologies requires the adoption of high-tech security approaches. In this paper, we present a framework that promises to enhance the security and privacy of smart farms by leveraging the decentralized nature of blockchain technology. The framework stores and manages data acquired from IoT devices installed in smart farms using a distributed ledger architecture, which provides secure and tamper-proof data storage and ensures the integrity and validity of the data. The study uses the AWS cloud, ESP32, the smart farm security monitoring framework, and the Ethereum Rinkeby smart contract mechanism, which enables automated execution of pre-defined rules and regulations. As a result of a proof-of-concept implementation, the system can detect and respond to security threats in real time, and the results illustrate its usefulness in improving the security of smart farms.

The potential of digital twin technology is yet to be fully realised due to its diversity and untapped potential. Digital twins enable systems’ analysis, design, optimisation, and evolution to be performed digitally or in conjunction with a cyber-physical approach to improve speed, accuracy, and efficiency over traditional engineering methods. Industry 4.0, factories of the future, and digital twins continue to benefit from the technology and provide enhanced efficiency within existing systems. Due to the lack of information and security standards associated with the transition to cyber digitisation, cybercriminals have been able to take advantage of the situation. Access to a digital twin of a product or service is equivalent to threatening the entire collection. There is a robust interaction between digital twins and artificial intelligence tools, which leads to strong interaction between these technologies, so it can be used to improve the cybersecurity of these digital platforms based on their integration with these technologies. This study aims to investigate the role of artificial intelligence in providing cybersecurity for digital twin versions of various industries, as well as the risks associated with these versions. In addition, this research serves as a road map for researchers and others interested in cybersecurity and digital security.

Sensors and actuators are fundamental units in Cyber-Physical and Internet of Things systems. Because they are included in a variety of systems, using many technologies, it is very useful to characterize their functions abstractly by describing them as Abstract Entity Patterns (AEPs), that are patterns that describe abstract conceptual entities. For concreteness, we study them here in the context of autonomous cars. An autonomous car is a complex system because, in ad-dition to its own complex design, it interacts with other vehicles and with the surrounding in-frastructure. To handle these functions, it must incorporate various technologies from different sources. An autonomous car is an example of a Cyber-Physical System, where some of its func-tions are performed by Internet of Things units. Sensors are extensively used in autonomous cars to measure physical quantities; actuators are commanded by controllers to perform appro-priate physical actions. From AEPs we can derive concrete patterns, a structure combining re-lated AEPs is an Entity Solution Frame (ESF). Both sensors and actuators are susceptible to mali-cious attacks due to the large attack surface of the system where they are used. Our work is in-tended to make autonomous cars more secure, which also increases their safety. Our final objec-tive is to build a Security Solution Frame for sensors and actuators of autonomous cars that will facilitate their secure design. A Security Solution Frame is a solution structure that groups to-gether and organizes related security patterns. This article is the first stage of a secure unit that can be used to design not only secure autonomous cars but also any system where sensors and actuators are used. This paper concentrates on AEPs and ESFs for sensors and actuators; that is, on the functional aspects of these devices.

Immense challenges arise in the Quality Assurance area due to contemporary development in Internet of Things (IoT) technology. Current issues are mainly related to test coverage, test diversity, IoT Stability, Use of Cellular Networks in IoTs, IoT Devices updates, Security, Data Integration, and interoperability. In this paper, we present all those issues with suggestions for tackling those issues.