Abstract: This paper addresses critical challenges in the deployment and effectiveness of traditional Roadside Units (RSUs) in traffic monitoring systems and proposes a novel, cost-effective approach using Android-based smart RSUs. Leveraging mobile phone architecture, YOLOv8 , the SAHI algorithm and chat gpt-4o, the system provides real-time traffic data collection, vehicle detection, and congestion analysis. This paper evaluates the performance of different cost tiers of mobile devices, discusses traditional traffic monitoring challenges, and identifies key gaps in current RSU technologies. The proposed system offers enhanced scalability, flexibility, and reduced cost, making it an ideal solution for urban traffic management.

Abstract: Two-step verification (2SV), also known as two-factor authentication (2FA), is a crucial security measure in modern cybersecurity practices. This paper explores its importance in preventing unauthorized access, reducing cyber threats, and enhancing digital security. The study highlights real-world cases where 2SV has mitigated security breaches and provides insights into future developments.

Abstract: The community structure is a major feature of bipartite networks, which serve as a typical model for empirical networks consisting of two kinds of nodes. Over the past years, community detection has drawn a lot of attention. Numerous methods for community detection have been put forth. Nevertheless, some of them need a lot of time, which restricts their use in large networks. While several low-time complexity algorithms exist, their practical value in real-world applications is limited since they are typically non-deterministic. Typically, in bipartite networks, a unipartite projection of one part of the network is created, and then communities are detected inside that projection using methods for unipartite networks. Unipartite projections may yield incorrect or erroneous findings as they inevitably include a loss of information. In this paper, BiVoting, a two-mode and deterministic community detection method in bipartite networks is proposed. This method is a consequence of bipartite modularity, which quantifies the strength of partitions and is based on how people vote in social elections. The proposed method’s performance has been evaluated, and comparison with four common community detection methods in bipartite networks shows that for calculating the modularity score in large networks, BiVoting performs better than the best method.

Abstract: Humans often experience unstable blood sugar due to interference factors such as environmental factors, instruments, and abnormal blood sugar. People with diabetes may develop hyperglycemic due to insufficient insulin secretion or physical abnormalities. A balanced diet and regular exercise are the best ways to ensure that blood sugar is within normal control and to reduce the chance of developing diabetes. This paper emphasizes the importance of accurate glucose monitoring for effective diabetes management, and big data analytics play a pivotal role in enhancing diagnostic accuracy and guiding personalized treatment strategies. Methods to reduce the incidence of diabetes and treat prediabetes, Weight management research shows that being overweight or underweight is one of the risk factors for Prediabetes. Healthy Eating Habits Improving your eating habits can help reduce high blood pressure levels. The exercise track helps lower blood pressure levels and increases the body's response to insulin in response to weight loss.

Abstract: Laplacian controllability and observability of a consensus network is a widely studied topic in the area of multi-agent systems, complex networks and large-scale systems. In this paper, this problem is afforded when the communication among nodes is described through a starlike tree topology. After a brief description of the mathematical setting of the problem, which is widely adopted for a wide number of multi-agent systems engineering applications, some novel results are drawn based on node positions within the network only. The resulting methods are graphical, thus effective and exempt from numerical errors, and the final algorithm is provided to perform the analysis by machine computation. Several examples are provided to show the effectiveness of the algorithm proposed.

Abstract: This paper investigates cybersecurity threats in medical IT (Information Technology) systems exposed to the Internet. To that end, we develop a methodology and build a data processing pipeline that allows to gather data from different OSINT (Open Source Intelligence) sources, and processes it to obtain relevant cybersecurity metrics. To validate its operation and usefulness, we apply it to two countries, Germany and Spain, allowing to study the main threats that affect medical IT systems in these countries. Our initial findings reveal that 20% of German hosts and 15% of Spanish hosts tagged as medical devices have at least one CVE (Common Vulnerabilities and Exposures) with a CVSS (Common Vulnerability Scoring System) graded as critical (i.e., value 8 or greater). Moreover, we found that 74% of CVEs found in German hosts are dated from earlier than 2020, whereas for Spanish hosts the percentage is 60%. This indicates that medical IT systems exposed to the Internet are seldom updated, which further increases their exposure to cyberthreats. Based on these initial findings, we finish the paper providing some insights on how to improve cybersecurity of these systems.

Abstract: In the recent past, vehicular networks (VANETs) have emerged as promising technology for enabling communication between vehicles and infrastructures to improve road safety and driving experience. However, the dynamic nature of VANETs, characterised by rapid changing traffic conditions and varying network load, poses significant challenges for reliable communication. Congestion control is a critical aspect in VANETs to prevent network saturation, reduce packet loss, and enhance overall system performance. In this context, the application of fuzzy-logic-based approaches offers a flexible and adaptive solution to dynamically adjust the network performance. This research introduced a fuzzy-logic-based congestion control mechanism for VANEts. The approach focused on dynamically adjusting the beacon busy ratio, road segment, and vehicle speed to address the fluctuating traffic condition, thereby mitigating congestion and enhancing vehicular network efficiency. Leveraging fuzzy logic, the proposed system can make route suggestions through the communication between roadside units based on input variables such as beacon busy ratio, road segment, and vehicle speed. On the result and analysis, the performance analysis of the system-based implemented Network Simulator-3 (NS3) and Simulation for Urban Mobility (SUMO) network simulation tool is used. Through simulation, the efficacy of the approach is demonstrated, showing its ability to adapt to evolving traffic dynamics and alleviate congestion on VANETs for enhancing network performance and reliability. The simulation result shows that our proposed system achieves a packet delivery ratio of 95%, throughput of 110 Kbps, and end-to-end delay of 1.93 seconds. This result shows that our scheme is feasible and effective.

Abstract: The rapid proliferation of wireless mobile networks has necessitated robust and scalable authentication mechanisms to ensure secure communication and user privacy. Traditional centralized authentication systems, while effective, are increasingly vulnerable to single points of failure, scalability issues, and privacy concerns. This paper explores the potential of blockchain technology to enable decentralized authentication in wireless mobile networks. By leveraging the inherent properties of blockchain—such as immutability, transparency, and distributed consensus—we propose a novel framework that eliminates the need for a central authority, thereby enhancing security and resilience. The proposed system utilizes smart contracts to automate authentication processes, ensuring tamper-proof and efficient user verification. Additionally, the decentralized nature of the framework mitigates risks associated with data breaches and unauthorized access. Through a combination of theoretical analysis and simulation-based experiments, we demonstrate the feasibility and advantages of blockchain-based decentralized authentication in terms of security, scalability, and performance. The results indicate that this approach not only addresses the limitations of traditional systems but also paves the way for more secure and privacy-preserving authentication in future wireless mobile networks.

Abstract: Nowadays, due to the popularity of portable computers and the increasing demands of users to access computing services better,. Mobile ad hoc networks (MANET) are self-configuring wireless networks with no established infrastructure. Due to constantly changing network architecture, a lack of central monitoring, and insufficient security mechanisms, MANETs are vulnerable to a variety of attacks. The primary objectives of this study are to detect a node's misbehavior in a MANET and also effectively validate the selfish node by using an algorithm for detecting selfish nodes. Retransmission is reduced, and all network metrics performance is increased resulting from the discovery. AODV was utilised as the routing algorithm in this study. The proposed algorithm is implemented using the NS2 simulation tool. In the presence of selfish nodes and without selfish nodes, our proposed algorithm improves the packet delivery ratio and throughput and minimises delay and packet drop, which are all network metrics that are compared and examined. The simulation analysis evaluated based on the routing performance was enhanced in the proposed AODV protocol in terms of packet dropped, packet delivery ratio, end-to-end delay, and throughput. However, the study of the simulation result showed an improvement in packet delivery ratio from 85.60 to 87.6638, an improvement of packet dropped from 34.40 to 32.38, the throughput improved from 674.52 to 724.521, and end-to-end delay improved from 1.902 to 1.08. We concluded that all performance parameters investigated by the proposed Selfish node detection algorithm demonstrate improvement.

Abstract: Autonomous Vehicles (AVs) are revolutionizing transportation by integrating advanced sensors, artificial intelligence, and communication networks to enhance safety and efficiency. This review explores the architecture of AVs, focusing on perception, localization, path planning, and control. A detailed analysis of AV sensors, including LiDAR, radar, cameras, and inertial navigation systems, highlights their roles, advantages, and limitations. Additionally, the paper examines in-vehicle and inter-vehicle communication networks, such as CAN, LIN, FlexRay, and Ethernet, which facilitate real-time data exchange. The study also addresses the key challenges AVs face, including cybersecurity threats, data processing, legal policies, and ethical concerns. By synthesizing recent advancements and ongoing challenges, this paper provides a comprehensive understanding of the state of AV technologies and their future prospects.

Abstract: In order to solve the problems of effective resource allocation in low-power wide-area networks, this thesis investigates the scheduling of end devices in Internet of Things applications using LoRaWAN technology. The main goal of this research is to use RL to improve QoS measures including energy efficiency, throughput, latency, and dependability. This was accomplished by using a simulation-based approach that evaluated the effectiveness of the RL-based scheduling algorithm using NS3 simulations. The main findings show that, in comparison to current scheduling practices, the RL agent greatly improves data transmission reliability and improves network throughput. Furthermore, the suggested approach efficiently lowers average system latency and overall energy usage, improving network resource utilization. These findings imply that using reinforcement learning (RL) for job scheduling in LoRaWAN networks can offer a reliable and expandable solution to present problems, resulting in more intelligent and environmentally friendly IoT systems. In the end, this study finds that using RL-based techniques can help improve resource management in contexts that are dynamic and resource-constrained.

Abstract: Unmanned Aerial Vehicle (UAV) swarms offer a promising solution for revolutionizing Smart Metering Infrastructure (SMI) by enabling efficient, scalable, and cost-effective data collection. However, the deployment of UAV swarms in critical infrastructure applications raises significant security concerns. This paper presents a comprehensive security model designed to protect a UAV swarm-based SMI against various threats throughout its operational phases. We identify key vulnerabilities and propose a multi-layered security framework that incorporates bidirectional entity authentication, secure communication channels using IPSec (Internet Protocol Security), and proactive measures to mitigate specific attacks, such as denial-of-service and man-in-the-middle attacks. We analyze the effectiveness of our proposed solutions in addressing potential threats during different operational phases: DMC (Data Management Center) interaction, in-flight operations, and data collection. We present a comparative analysis highlighting the advantages of our approach over existing security schemes for UAV swarms. Our findings provide valuable insights into securing UAV swarm-based critical infrastructure and contribute towards building more resilient and trustworthy smart city applications.

Abstract:

As the Internet of Things (IoT) continues expanding its footprint across various sectors, robust security systems to mitigate associated risks are more critical than ever. Intrusion Detection Systems (IDS) are fundamental in safeguarding IoT infrastructures against malicious activities. This systematic review aims to guide future research by addressing six pivotal research questions that underscore the development of advanced IDS tailored for IoT environments. Specifically, the review concentrates on applying Machine Learning (ML) and Deep Learning (DL) technologies to enhance IDS capabilities. It explores various feature selection methodologies aimed at developing lightweight IDS solutions that are both effective and efficient for IoT scenarios. Additionally, the review assesses different datasets and balancing techniques, which are crucial for training IDS models to perform accurately and reliably. Through a comprehensive analysis of existing literature, this review highlights significant trends, identifies current research gaps, and suggests future studies to optimize IDS frameworks for the ever-evolving IoT landscape.

Abstract: The paper studies the distribution of relative displacement of a composite plate with integrated fiber Bragg gratings. The analysis of the methods for manufacturing composite plates with embedded optical fibers containing FBG sensors, as well as the spectral characteristics of the gratings under various bending conditions, is performed. The effect of sensor arrangement on the accuracy of determining stresses and relative elongations of the material is experimentally studied. The features of spectral shifts that occur under non-uniform stresses are revealed, which can reduce the accuracy of measurements when using interrogators. The patterns of change in the central wavelength of Bragg gratings depending on the type and magnitude of plate bending are established. The research results confirm that the use of a network of embedded FBG sensors allows one to accurately determine the areas of maximum deformations, as well as the nature and magnitude of bending of composite structures. The data obtained can be used to develop more accurate systems for monitoring the stress-strain state of composite materials.

Abstract: The rapid evolution of smart cities is driven by the integration of the Internet of Things (IoT) and Information and Communication Technologies (ICT), aiming to enhance urban efficiency, sustainability, and citizen well-being. One of the key enablers of smart cities is Smart Metering Infrastructure (SMI), which facilitates monitoring, data collection, and intelligent control. This paper explores the concept of smart metering, its essential components—including smart meters, communication networks, and data management centers—and its role in smart city applications. Furthermore, it discusses the technological advancements in smart metering, the security and privacy concerns, and the challenges related to deployment and scalability. Additionally, the paper examines the potential integration of Unmanned Aerial Vehicles (UAVs) as part of smart metering systems to enhance operational efficiency. The study concludes with insights into the future trends, opportunities, and policy implications required to optimize smart metering infrastructure within smart cities.

Abstract:

As cloud computing continues to evolve, the landscape of digital infrastructure is poised for dramatic changes by 2025 and beyond. This article explores the key trends shaping the future of cloud computing, the obstacles that businesses and service providers may encounter, and the new possibilities emerging in this rapidly advancing field. Key trends include the growing adoption of hybrid and multi-cloud environments, the rise of serverless computing, edge computing, and the increasing importance of artificial intelligence (AI) and machine learning (ML) in cloud services. The article also addresses the challenges organizations face, such as data security and privacy concerns, regulatory compliance, vendor lock-in, and the complexity of managing multi-cloud systems. Furthermore, it delves into new opportunities presented by the integration of emerging technologies, the potential for cloud-native development, and the increasing need for sustainable cloud infrastructure. Through a comprehensive analysis, this article provides insights into how businesses can leverage these trends and overcome obstacles to drive innovation and maximize the potential of cloud computing in the years to come.

Abstract: As organizations increasingly shift to the cloud, the adoption of serverless computing has emerged as a promising solution to optimize cloud infrastructure costs while maintaining scalability and performance. This article explores the cost benefits of serverless computing by examining how this cloud model differs from traditional infrastructure approaches, such as Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). Serverless computing operates on a pay-as-you-go model, enabling organizations to eliminate the overhead of idle resources and reduce the complexity of infrastructure management. Through a combination of case studies, industry data, and performance metrics, this article analyzes the cost savings associated with serverless adoption across different organizational sizes and sectors. Key findings reveal that serverless computing can lead to significant reductions in infrastructure costs, with small to medium enterprises experiencing up to 40% savings. However, challenges such as cold start latency and vendor lock-in are also discussed. The article concludes by offering recommendations for businesses considering serverless computing, including strategies to mitigate potential risks and maximize cost benefits.

Abstract: With the growth of IoT technology, connected devices have surged, increasing security risks, especially for devices lacking authentication. Anonymization protection prevents data leaks and control theft but traditional methods lack dynamism, struggle to balance privacy and availability, and remain vulnerable to targeted attacks. Anonymization protection techniques can prevent the leakage of sensitive information and the theft of control privileges, significantly improving the security of devices. However, traditional anonymity protection methods lack dynamism, making it difficult to trade-off between data availability and privacy protection, and attackers can discover system vulnerabilities through reconnaissance and analysis, leaving devices still vulnerable to targeted attacks. In this paper, we propose a device identity anonymization protection method based on address hopping, using the address hopping policy in the Mobile Target Defense (MTD) technique. It collects network topology and node state information, constructs a virtual network topology by backtracking method, and periodically replaces the paths and addresses under the satisfaction of specific constraints, so as to realize the anonymity of network devices. It effectively reduces the risk of device attacks, optimizes network performance, and maintains data availability by dynamically adjusting device addresses in the network. Experiments using Mininet and Ryu controllers show the approach significantly reduces host scans and data exposure compared to unprotected policies.

Abstract: The Controller Area Network (CAN) bus has been a cornerstone in vehicular communication, facilitating robust and efficient data exchange among electronic control units (ECUs). This paper provides a comprehensive review of the classical CAN bus, CAN FD, and their key attributes, including message prioritization, arbitration mechanisms, and error detection. Additionally, the paper explores the IEEE 802.11b wireless standard, emphasizing its potential for extending CAN-based networks into wireless domains. The study categorizes existing literature into wired and wireless CAN applications, highlighting advancements, challenges, and limitations in both areas. A critical gap identified in current research is the lack of performance assessment of ECUs, particularly in autonomous vehicle (AV) applications. Moreover, most wireless implementations of CAN rely on Bluetooth, Zigbee, or IEEE 802.11b, which are constrained by limited data rates and scalability. This review outlines the necessity for more integrated, high-performance wireless CAN solutions to enhance vehicular network efficiency, particularly in AV environments.

Abstract: Autonomous Vehicles (AVs) are poised to revolutionize transportation by integrating advanced sensor technologies, sophisticated control systems, and robust communication networks. This paper presents a comprehensive review of the current state of AV architectures, covering essential components such as perception, localization, path planning, and control. A detailed analysis of sensor technologies—including LiDAR, radar, cameras, and inertial navigation systems—highlights their individual roles, benefits, and limitations. Furthermore, the paper examines both intra-vehicle and inter-vehicle communication networks (e.g., CAN, LIN, FlexRay, MOST, and Ethernet), offering a quantitative performance evaluation through mathematical models and comparative analysis. Critical challenges, including cybersecurity threats, sensor reliability issues, data processing demands, and regulatory hurdles, are discussed alongside potential future directions. The insights provided aim to guide researchers and industry professionals in advancing AV technology while balancing performance, cost, and safety.