As a novel concept in technology and communication world, “Internet of Things (IoT)” has been emerged. In such modern technology, the capability to transmit data through data communication networks (such as Internet or Intranet) is provided for each organism (e.g. human being, animals, things, and so forth). Due to the limited hardware and communication operational capability as well as small dimensions, IoT undergoes quite a few challenges. Such inherent challenges not only cause fundamental restrictions in the efficiency of aggregation, transmission, and communication between nodes; but they also degrade routing performance. To cope with the reduced availability time and unstable communications among nodes, data aggregation and transmission approaches in such networks are designed more intelligently. In this paper, a distributed method is proposed to set child balance among nodes. In this method, the height of the network graph is increased through restricting the degree; and network congestion is reduced as a result. Besides, a dynamic data aggregation approach -named as LA-RPL- is proposed for RPL networks. More specifically, each node is equipped with learning automata in order to perform data aggregation and transmissions. Simulation results demonstrate that the proposed approach outperforms previously suggested based approaches in terms of energy consumption, network control overhead, and packet loss rate.

Adequate knowledge about the development and operation of the components of water systems is of high importance in order to optimize them. For this reason, forecasting of future events becomes greatly significant due to making the appropriate decision. Moreover, operational river management severely depends on accurate and reliable flow forecasts. In this regard, current study inspects the accuracy of support vector regression (SVR), and SVR regulated with fruit fly optimization algorithm (FOASVR) and M5 model tree (M5), in river flow forecasting. Monthly data of river flow in two stations of the Lake Urmia Basin (Vaniar and Babarud stations on the Aji Chay and the Barandouz Rivers) were utilized in the current research. Additionally, the influence of periodicity (π) on the forecasting enactment was examined. To assess the performance of mentioned models, different statistical meters were implemented, including root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Bayesian information criterion (BIC). Results showed that the FOASVR with RMSE (4.36 and 6.33 m3/s), MAE (2.40 and 3.71 m3/s) and R (0.82 and 0.81) values had the best performances in forecasting river flows in Babarud and Vaniar stations, respectively. Also, regarding BIC parameters, Q_t-1 and π were selected as parsimonious inputs for predicting river flow one month ahead. Overall findings indicated that, although both FOASVR and M5 predicted the river flows in suitable accordance with observed river flows, the performance of FOASVR was moderately better than the M5 and periodicity noticeably increased the performances of the models; consequently, FOASVR can be suggested as the accurate method for forecasting river flows.

Greenhouse Gases (GHGs) emission has considerable impact on global warming and climate change. Since energy systems and their features noticeably influence on the amount of GHGs emission, it can be modeled based on the specifications of energy sources utilized by the countries. In addition, economic activity is another factor which should be considered in GHG emission modeling. In this work, Artificial Neural Network (ANN) is used for estimating carbon dioxide emission, as one of the most abundant GHGs, on the basis of shares of various energy sources used as primary energy supply and GDP as an indicator for economic activities. Five countries including Iran, Kuwait, Qatar, Saudi Arabia and United Arab Emirates (UAE) are considered as case studies. Comparing between the estimated data by the achieved model and actual quantities showed acceptable precision of the ANN model for prediction of carbon dioxide emission. The average absolute relative error and the R-squared values of the GMDH model are approximately 2.28% and 0.9998, respectively. The obtained values for the mentioned statistical criteria show the precision of the model in forecasting the emission of Co2.

The combination of artificial intelligence algorithms and numerical methods has recently become popular in the prediction of macroscopic and microscopic hydrodynamics parameters of bubble column reactors. The multi inputs and outputs machine learning can cover small phase interactions or large fluid behavior in industrial domains. This numerical combination can develop the smart multiphase bubble column reactor with the ability of low-cost computational time. It can also decrease case studies for the optimization process when big data is appropriately used during learning. There are still many model parameters that need to be optimized for a very accurate artificial algorithm, including data processing and initialization, the combination of inputs and outputs, number of inputs and model tuning parameters. For this study, we aim to train four inputs big data during learning process by an adaptive neuro-fuzzy inference system or adaptive-network-based fuzzy inference system (ANFIS) method, and we consider the superficial gas velocity as one of the input variables, while for the first time, one of the computational fluid dynamics (CFD) outputs named gas velocity is used as an output of the artificial algorithm. The results show that the increasing number of input variables improves the intelligence of the ANFIS method up to , and the number of rules during learning process has a significant effect on the accuracy of this type of modeling. The results also show that propper selection of model parameters results in more accuracy in prediction of the flow characteristics in the column structure.

Adequate knowledge about the development and operation of the components of water systems is of high importance in order to optimize them. For this reason, forecasting of future events becomes greatly significant due to making the appropriate decision. Moreover, operational river management severely depends on accurate and reliable flow forecasts. In this regard, current study inspects the accuracy of support vector regression (SVR), and SVR regulated with fruit fly optimization algorithm (FOASVR) and M5 model tree (M5), in river flow forecasting. Monthly data of river flow in two stations of the Lake Urmia Basin (Vaniar and Babarud stations on the Aji Chay and the Barandouz Rivers) were utilized in the current research. Additionally, the influence of periodicity (π) on the forecasting enactment was examined. To assess the performance of mentioned models, different statistical meters were implemented, including root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Bayesian information criterion (BIC). Results showed that the FOASVR with RMSE (4.36 and 6.33 m3/s), MAE (2.40 and 3.71 m3/s) and R (0.82 and 0.81) values had the best performances in forecasting river flows in Babarud and Vaniar stations, respectively. Also, regarding BIC parameters, Qt-1 and π were selected as parsimonious inputs for predicting river flow one month ahead. Overall findings indicated that, although both FOASVR and M5 predicted the river flows in suitable accordance with observed river flows, the performance of FOASVR was moderately better than the M5 and periodicity noticeably increased the performances of the models; consequently, FOASVR can be suggested as the accurate method for forecasting river flows.

Radio Frequency Identification (RFID) devices use radio waves to relay identifying information to an electronic reader using low-cost RFID Tag. RFID is expected to replace the conventional bar-code identification system due to its advantage like real-time recognition of a considerable number of objects. However, in RFID systems an attacker can get the tag that may lead to various security threats, and the limited computation power of RFID tags can cause delay. Lightweight authentication protocols proposed using cryptographic algorithms (one-way hash function, symmetric key encryption/decryption, and exclusive-OR) in order to cope with these problems. One such lightweight cryptographic protocol has been presented by Gope and Hwang using RFID systems. However, it analyzed in this article that their protocol is infeasible and vulnerable to Collision Attack, Denial-of-service (DoS), and Stolen verifier Attacks. A realistic, lightweight authentication protocol has been presented in this article to ensure protection against the mentioned attacks for IoT based RFID system. The proposed protocol has been formally analyzed using BAN logic and ProVerif as well as also analyzed informally using security requirement. The results show that the proposed protocol outperforms the existing protocols not only in security enhancements but also in terms of computation and communication complexity. Furthermore, the proposed protocol has also been analyzed for storage complexity.

Solar energy is a renewable resources of energy which is broadly utilized and have the least pollution impact between the available alternatives of fossil fuels. In this investigation, machine leaening approaches of neural networks (NN), neuro-fuzzy and least squares support vector machine (LSSVM) are used to build the models for prediction of the thermal performance of a photovoltaic-thermal solar collector (PV/T) by estimating its efficiency as an output of the model while inlet temperature, flow rate, heat, solar radiation, and heat of sun are input of the designed model. Experimental measurements was prepared by designing a solar collector system and 100 data extracted. Different analyses are also performed to examine the credibility of the introduced approaches revealing great performance. The suggested LSSVM model represented the best performance regarding the mean squared error (MSE) of 0.003 and correlation coefficient (R²) value of 0.99, respectively.

Due to low working temperature, high energy density and low pollution, proton exchange fuel cells have been investigated under different operating conditions in different applications. Using platinum catalyst in methanol fuel cell leads to increasing the cost of this kind of fuel cells which is considered as a barrier to commercialism of this technology. For this reason, a lot of efforts have been made to reduce the loading of the catalyst required on different supports. In this study, carbon black (CB) and carbon nanotubes (CNT) have been used as catalyst supports of the fuel cell as well as using the double-metal combination of platinum-ruthenium (PtRu) as anode electrode catalyst and platinum (Pt) as cathode electrode catalyst. The performance of these two types of the electro-catalyst in oxidation reaction of methanol has been compared based on electrochemical tests. Results showed that the carbon nanotubes increase the performance of the micro-fuel cell by 37% at maximum power density, compared to the carbon black. Based on thee-electrode tests of chronoamperometry and voltammetry, it was found that oxidation onset potential of methanol for CNT has been around 20% less than CB, leading to the kinetic improvement of the oxidation reaction. In addition, the active electrochemical surface area of catalyst has been increased up to 90% by using CNT compared to CB which shows the significant rise of the electrocatalytic activity in CNT supported catalyst with 62% increase in current density of methanol oxidation reaction respect to CB supported one. Moreover, the resistance of CNT supported sample to poisonous intermediate species has been found 3% more than CB supported one. According to the chronoamperometry test results, it was concluded that the performance and sustainability of NCT electro-catalyst shows remarkable improvement compared to CB electro-catalyst in long term.