International Journal of Sensor Networks (48 papers in press)

Regular Issues

• Recent Advances in Wireless Sensor Networks with Environmental Energy Harvesting
  by Lei Shu, Wanjiun Liao, Jaime Lloret, Lei Wang 
  Keywords: .
  
  
• Optimum Data Collection and Fusion Schemes in WBSN  
  by Mohammad Mehrani 
  Abstract: in this paper first we define our strategies aiming at minimizing number of communicated data while keeping data integrity in Wireless Body Sensor Networks (WBSNs). In this way, we introduce modified Fisher test, develop Spline interpolation as the behavior function and define controlling parameters. To achieve at significant results we propose three efficient algorithms to perform adaptive sampling over WBSN. Furthermore, at the second step, we represent our method to calculate the priority of vital sign data packets to transmit emergency packets in terms of their priorities. For this purpose we employ Spline interpolation function and define six new controlling parameters for it. At the third step, for correct inference of patients situations and calculating accurate results for monitored patient we introduce our method which develops Adaptive Neuro Fuzzy Inference System (ANFIS) with rncross-validation. This intelligent combination allows the system to track the status of monitored patients, correctly. To evaluate the performance of the proposed approaches we run a number of simulations in MATLAB R2018b. Simulation results demonstrate the optimum performance of our schemes for number of communicated data, network lifetime, priority based data communication and also correct inference of patients situations.
  Keywords: WBSN; Data Collection; Data Fusion; Energy Optimality; Sampling Rate; Packet Priority; ANFIS; Spline;.
  
  
• Throughput Optimization of Multi-antennas CRN-NOMA with Energy Harvesting and Adaptive Transmit Power  
  by Raed Alhamad, Hatem Boujemaa 
  Abstract: In this paper, we optimize the throughput of cooperative Non Orthogonal Multiple Access (NOMA) for Cognitive Radio Networks (CRN). The secondary nodes harvest energy using the received signal on multiple antennas from node A. Secondary nodes adapt their power to generate interference at Primary Destination ($P_D$) less than threshold $I$. The source transmits a combination of symbols dedicated to near and far users. The signal is decoded by a relay node $R$ that regenerates it and transmits it to near and far secondary users. We optimize both harvesting duration and power allocation to near and far users to maximize the secondary total throughput. We also suggest two techniques for users' ranking using average or instantaneous channel gains.
  Keywords: NOMA; Energy harvesting; adaptive transmit power; optimal harvesting duration; optimal power allocation.
  
  
• Varied Density of Vehicles under City, Highway and Rural Area environments in V2V Communication  
  by Mohammed Abdulhakim Al-Absi, Ahmed Abdulhakim Al-Absi, Hoon Jae Lee 
  Abstract: To provide an efficient throughput for V2V communication under different environments, a good radio propagation model is required in order to support the real time implementation. The existing radio propagation path loss models for V2V network adopt mean additional attenuation sophisticated obstacle fading model such as Nakagami, Log normal and so on. These models do not consider the effects of vehicle in modeling LOS among transmitter and receiver and also do not consider evaluation under different environments. The presence of Line of sight component requires the amplification of signal or power. Due to this, here we present an efficient radio propagation path loss model considering obstacle in LOS under different environmental. Experiments are conducted to evaluate the performance of the proposed model in terms of throughput collision and successful packet transmission considering varied number of vehicles under different environments. Result shows that the proposed model is efficient considering varied density.
  Keywords: VANET; V2V; LOS; DSRC.
  
  
• Improved Localization Algorithm based on Markov Chain Monte Carlo-Metropolis Hastings for Wireless Sensor Networks  
  by Yucai Zhou, Munyabugingo Charles, Tong Wang, MIn Song 
  Abstract: Accurate and low-cost sensor localization is the key requirement for deploying Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) in various applications. Researchers are trying their best to find a way to localize mobile nodes in WSNs. To solve the problem of the moment outside the anchoring range or positioning errors, an improved DV-Hop location algorithm based on the Markov chain Monte Carlo Metropolitan Hastings algorithm (MMDV-Hop) is proposed. According to the receiving and transmitting power of RSSI, anchor information is taken into account when calculating the distance between unknown nodes and anchor nodes. From the different percentages of anchor nodes and unknown nodes, node density, and node connectivity, MMDV-Hop shows better position error than traditional algorithms.
  Keywords: Wireless Sensor Networks; Localization; DV-Hop; RSSI.
  
  
• Self-Organizing Cooperative Clustering Protocol for Tracking and Monitoring  
  by Yousef Ali, Uthman Baroudi 
  Abstract: Real time tracking and monitoring using smartphones mobile applications are being increasingly adapted in many systems. Due to the ubiquitous nature and the sophisticated technologies of smartphones, using smartphones in such tracking systems would be time and cost effective. However, the limited\r\nbattery capacity in smartphones could cause interruption in reporting the tracking information especially in situations when users spend long time without recharging batteries. In this paper, we propose a self-organizing and cooperative approach that utilizes the coexistence of different communication technologies (Bluetooth, WiFi and 3/4G) in smartphones. Neighboring smartphones\r\nare cooperatively group themselves to form multiple clusters. Only the cluster head (CH) in each cluster, uses positioning and internet services to send tracking data to the server for all its cluster members. While other members use only Bluetooth to communicate locally and report to the CH. We propose a\r\nclustering algorithm that assure distributed organization and fairness for all nodes. We study the power consumption used to perform the tracking task in our solution. Based on the power consumption analysis, the clustering problem is modeled as a Mixed Integer Programming model (MIP) then solved by the\r\nalgebraic modeling system GAMS. We evaluate our proposed clustering algorithm through extensive simulations compared to two clustering algorithms in the literature. Results show that our proposed solution achieved substantial improvement in terms of energy saving compared to other algorithms while guarantee the fairness.
  Keywords: IoT Networks; Cooperative Clustering; Energy efficiency; Hybrid networks; Bluetooth-WiFi networks; Large scale tracking.
  
  
• A Comparative Study of the Effect of Node Distributions on 2D and 3D Heterogeneous WSN  
  by Yousef Jaradat, Mohammad Masoud, Saleh Al-Jazzar 
  Abstract: Two comparative studies of the impact of different node deployment strategies on 2D and 3D heterogeneous wireless sensor network are conducted. Uniform, normal, and exponential node deployment distributions are utilized. Three 3D network geometries are introduced, namely, cube, sphere, and cylinder networks. Stable election protocolrn(SEP) is used to evaluate the performance of different node distributions in terms of network energy, throughput, stability period and network lifetime. Broadly speaking, it is noticed that normal distribution of nodes outperforms other distributions in the 2Drnand 3D comparative study, and in the study of different 3D network geometries with the exception of sphere network in which the uniform distribution performs almost the same as the normal distribution. It is also noticed that any node distribution performs better in a particular 3D network geometry than others. The paper also introduces the optimal cylinder network parameters for optimal network performance.
  Keywords: 3D WSN; Stable election protocol; Maximum likelihood estimation;rnPerformance Analysis; node distributions; Comparative study.
  
  
• A tractable stochastic geometry model of coverage and an approach to energy efficiency estimation in LPWAN networks  
  by Qiaoshou Liu, Edward Ball 
  Abstract: The low-power wide area network (LPWAN) is designed for low-power, wide area, light load, high latency applications. In many use-case applications of traffic being usually less than 1k of bytes transmitted data per day, it is desirable for a user equipment (UE) to work for 10 years, powered by a primary battery. There is neither real test data nor mathematical models to validate a 10 years battery lifetime. Furthermore, the energy consumption is affected by many factors and is very different in diverse networks. In this paper, we consider two types of LPWAN: LoRa wide area network (LoRaWAN) and narrow-band Internet of Things (NBIoT) network. We first propose a framework to calculate the average number of retransmissions in LoRaWAN networks and NBIoT networks based on stochastic geometry. Combining the average number of retransmissions, we give an approximate method to calculate both networks' energy efficiency. Utilizing the energy efficiency we can estimate the battery lifetime in LoRaWAN networks and NBIoT networks. The numerical results show that the battery lifetime is mainly influenced by the number of active UEs and the spreading factor in LoRaWAN networks and sleeping mode in NBIoT networks, when the data size transmitted each day is fixed. In NBIoT networks, the UEs can work for much longer with power saving mode (PSM) than with extended idle-mode discontinuous reception cycle (eDRX), even exceeding LoRaWAN networks in some cases though the transmitting power is higher and protocol is more complex in NBIoT networks. Finally, in LoRaWAN networks, smaller spreading factors can achieve longer battery lifetime, and increasing the number of base stations also extends the battery lifetime, which is not the case for NBIoT networks.
  Keywords: LoRaWAN; NBIoT; Stochastic Geometry; PSM; eDRX;.
  
  
• Wireless Sensor Network Deployment Optimisation based on Coverage, Connectivity and Cost Metrics  
  by Salah Eddine Bouzid, Youssef Serrestou, Kosai Raoof, Mohamed Mbarki, Mohamed Nazih OMRI, Chérif Dridi 
  Abstract: Wireless Sensor Network (WSN) deployment is still facing many challenges. These challenges are related to determining node positions that ensure a trade-off between different metrics such as coverage, k-coverage, connectivity and cost. Due to the high density of WSN, finding an optimal deployment becomes an NP-Hard task. In this paper, we study this problem of determining the optimal spatial node positions of WSN in indoor environments. We formulate this task as a Constrained Multi-Objective optimisation Problem (CMOOP). This formulation is based on mathematical modelling of the different above metrics. We explicit this original modelling and the CMOOP solving by Genetic Algorithm (GA) combined with the weighted-sum method. To prove the interest of the proposed methodology, the results of this work are presented and compared to other studies.
  Keywords: WSN; Indoor Deployment; Multi-Objective optimisation; Coverage; Connectivity; Cost.
  
  
• Network Traffic Reduction and Representation  
  by Loai Kayed B. Melhim, Mahdi Jemmali, Basil AsSadhan 
  Abstract: Efficient and reliable network operation are the major concerns of computer networks monitoring, an objectives that can be achieved by properly analyzing the monitored network traffic. Monitored network traffic contains significant information about computer networks, status and devices. But due to the huge size of this traffic, the analysis process turns into a headache. One of the suggested solutions presented by this paper is to create a true sample of the captured traffic, which will be called a network traffic representative. We claim that analyzing the representative will generate the same information about the monitored networks that the whole network traffic will provide. To proof this, the representative is created by decomposing the TCP traffic into two parts, a representative which will be called later (SNAK) and the rest of the traffic which will be called the data traffic, then visual plots and cross-correlation was used to expose the similarity between SNAK and data traffic in the case of normal and abnormal network traffic. The performed experiments with many types of data sets showed that the presented methodology reduces the volume of the analyzed traffic by a percentage of (30%80%), SNAK and data traffic showed similar behavior in visual plots and cross-correlation calculations, with a result that SNAK traffic leads data traffic. This result allows us to consider SNAK as a true representative of the whole monitored network traffic.
  Keywords: Network Monitoring; Network Performance; Network Traffic; Network Packets; Network Traffic Reduction; TCP Header flags.
  
  
• Analytic Evaluation of Non-uniformities for Coverage Probability Computation of Randomly Deployed Wireless Sensor Network  
  by Anamika Sharma, Siddhartha Chauhan 
  Abstract: The border region of a country is almost a remote hostile geographic region which requires continuous surveillance to prevent from unauthorized intrusion. The surveillance of such regions with the help of human beings is quite difficult. Therefore, sensor nodes can be deployed randomly for the surveillance. The quality of surveillance is measured by the coverage rate. This application imposes many non-uniformities for coverage rate computation such as asymmetrical locations, irregularity in sensing range due to obstacles inside the sensing region, network connectivity and biased coverage due to coverage redundancy and coverage holes. This paper proposes a coverage probability computation (CPC) protocol that considers these non-uniformities while computing the coverage rate. CPC computes the distribution of coverage probabilities within the sensing region of each sensor node using its probabilistic sensing range and then accumulates the coverage probabilities to discern the coverage rate. This paper also derives the lower bound for a minimum number of sensor nodes required to cover the hostile region. The simulation results of CPC show that at an optimum density of sensor nodes the hostile region can be covered up to a threshold level.
  Keywords: Coverage probability; Coverage Redundancy; Non-uniform sensing range; Random deployment; Sensor node density.
  
  
• A Weighted Centroid Correction Method for Wireless Sensor Network based on GSO Algorithm  
  by Zaopeng Cai 
  Abstract: In order to overcome the ambiguity of the location information of wireless sensor network nodes and lead to the low accuracy of weighted centroid localization results, a new weighted centroid correction method based on GSO (Group Search Optimizer) algorithm for wireless sensor networks is proposed in this paper. This method randomly drops sensor nodes into the area to be monitored to form a wireless sensor network. The experimental results show that the network coverage is close to 100%, the energy consumption per unit during receiving and transmitting accounts for 0.31% of the total battery. The absolute positioning error of each node is 6-8.5m, which can achieve the expected goal of this study.
  Keywords: GSO algorithm; Wireless sensor network; Node coordinates; Weighted centroid correction.
  
  
• Linear Models for Total Coverage Problem with Connectivity Constraints using Multiple Unmanned Aerial Vehicles  
  by Amani Lamine, Fethi Mguis, Hichem Snoussi, Khaled Ghédira 
  Abstract: The use of Unmanned Aerial Vehicles (UAVs) has recently increased both in civilian and military operations, and the planning of their routes is critical. This research investigates a routing problem in which a UAV network, equipped with sensors, covers a given area and maintains connectivity with its neighbouring UAVs and the base station, while respecting to the UAVs lifetime. To cover the area, two integer linear programming models are formulated to solve two problems optimally. In the first one, covering means that all positions should be visited. However, in the second one, covering means that every position should be covered at least by one UAV. Due to the limited communication radius of the UAVs, connectivity then has to find inter-UAVs routing paths to satisfy the communication between UAVs and the base. We verify by experiments that the models, using Cplex, can provide an optimal solution of different area dimensions.
  Keywords: Unmanned Aerial Vehicle (UAV); Communication; Area coverage; UAV route planning; Integer linear model; Exact algorithm.
  
  
• Distributed Clustering and Operational State Scheduling in Wireless Rechargeable Sensor Networks  
  by Shamsuddeen Abdullahi Mikail, Jianxin Wang, Shigeng Zhang 
  Abstract: Replacement of exhausted batteries in wireless sensor networks might lead to temporal disruption of network operations. Wireless rechargeable sensor networks (WRSNs) can mitigate this problem by recharging nodes before they run out of batteries. However, because WSRN nodes cannot perform recharging and task monitoring simultaneously, it is challenging to ensure continuous network operations while maintaining low-cost and low processing-power requirements of nodes. It is necessary to design an effective and energy-efficient node scheduling scheme to schedule nodes to either monitoring or recharging state without adversely reducing the network lifetime and throughput. In this paper, we propose a distributed clustering and operational state scheduling algorithm (DCOS) to maximize the overall network lifetime. Nodes needing energy can be replenished within time intervals when they are not in the state of sensing and transmitting. We conducted extensive simulations to evaluate the performance of DCOS. The results show that DCOS outperforms most of the state-of-the-art methods.
  Keywords: Wireless rechargeable sensor networks; wireless charging; clustering; algorithm; operational state scheduling.
  
  
• A Parking Space Allocation Algorithm Based on Distributed Computing  
  by Guanlin Chen, Huajian Pang, Huang Xu, Wujian Yang, Yong Chen 
  Abstract: In order to make it easier for drivers to find a parking slot, optimize the resources of urban parking slots, and alleviate the problem of parking slot shortage, a distributed parking allocation algorithm was proposed. The algorithm collects the parking requests of user, this parking requests including the current position coordinate information of the users and destination coordinate information, the algorithm allocates parking spaces to users by analyzing the available state of parking spaces, then return the parking route planning to the client. Compared with the traditional algorithm, the distributed parking algorithm has a higher ability to withstand pressure and global search capability, and it can ensure the real-time and validity of the parking information, so it can reduce the problem of the parking space shortage and unavailable parking space. The simulation results show that this algorithm can find the solution set more quickly and accurately under the circumstance of high demand. It also has application value and practicality.
  Keywords: city traffic; parking allocation; distributed computing; matching algorithm; smart city.
  
  
• DSP: A Deep Learning Based Approach to Extend the Lifetime of Wireless Sensor Networks  
  by Jack Press, Suzan Arslanturk 
  Abstract: Wireless Sensor Networks (WSNs) equipped with batteries and solar panels enabled applications in various areas such as environmental monitoring, agricultural, military, and medical systems. Research has shown that batteries often fail earlier than their projected lifetime due to external parameters affecting the battery life. Sensor-nodes with solar panels placed in areas with sufficient sunlight can have their batteries recharged and can stay online for longer periods. However, sensor-nodes placed in areas with insufficient sunlight may need to adjust how often they send data in order to stay online for longer periods. In this study, we present a Dynamic Sleep Protocol (DSP) to forecast the lifetime of a sensor-node by dynamically adjusting the sleep period between transmissions. We have used a deep recurrent neural network with Long Short Term Memory (LSTM) units to forecast the lifetime of the batteries and have discussed potential optimization functions to adjust the sleep period. Our results have shown that an accurate identification of the battery lifetime with accurate adjustments help us obtain longer operating hours without sacrificing the system performance.
  Keywords: WSN; Deep Learning; Machine Learning; Solar; Battery; Optimization; Scheduling.
  
  
• Distributed Relay Selection for Energy Harvesting Systems  
  by Nadhir Ben Halima, Hatem Boujemaa 
  Abstract: In this paper, we suggest a Distributed Relay Selection (DRS) algorithm for Energy Harvesting (EH) systems. Each candidate relay amplify the source packet only when its SNR exceeds Signal to Noise Ratio (SNR) threshold $gamma_{th}$. Relay node harvest energy from Radio Frequency (RF) signals received from the source. Both harvesting duration and SNR threshold $gamma_{th}$ are optimized to maximize the throughput. Our results are compared to Opportunistic Amplify and Forward (OAF) and Uniform Relay Selection (URS).
  Keywords: Distributed Relay Selection; Energy harvesting; cooperative systems.
  DOI: 10.1504/IJSNET.2020.10029555
   
  
• Machine Learning Based Low-rate DDoS Attack Detection for SDN enabled IoT Networks  
  by Haosu Cheng, Jianwei Liu, Tongge Xu, Bohan Ren, Jian Mao, Wei Zhang 
  Abstract: The SDN enabled IoT architecture is deployed in many industrial systems. The ability of SDN to intelligently route traffic and use underutilized network resources, enables IoT networks to cope with data onslaught smoothly. SDN also eliminates bottlenecks and helps to process IoT data efficiently without placing a larger strain on the network. The large amount of IoT devices are exposing the network infrastructure to increasingly disruptive distributed denial-of-service attacks (DDoS). Low- rate DDoS attacks have significant ability of concealing there traffic. The SDN-enabled IoT network behaviors are different from traditional networks, which makes the detection of low-traffic DDoS attacks more difficult. In this paper, we propose a learning-based detection approach that deploys learning algorithms and utilizes stateful and stateless features from OpenFlow packages to identify attack traffics in SDN control and data planes. Our prototype approach and experiment results show that our system identified the low-rate DDoS attack traffic accurately with relatively low system performance overheads.
  Keywords: Internet of Things; Software-defined Networking; Industrial System; Low-rate DDoS; Machine Learning.
  
  
• Collaborative Filtering Recommendation Algorithm Based on Deep Neural Network Fusion  
  by Juan Fang, Baocai Li, Mingxia Gao 
  Abstract: In order to accurately obtain potential features and improve the recommendation performance of the collaborative ?ltering algorithm, this paper puts forward a Collaborative Filtering Recommendation Algorithm Based on Deep Neural Network Fusion(CF-DNNF). CF-DNNF makes the best of the implicit attributes of data, where the text attributes and the other attributes are extracted from the data through the LSTM network and the deep neural network, respectively, so as to obtain the feature matrix that contains the user and item attribute information. DBN uses the feature matrix and outputs the probability. Besides, this paper initially discusses an Interpretable Collaborative Filtering Recommendation Algorithm Based on Deep Neural Network Fusion(ICF-DNNF). The paper compares the CF-DNNF algorithm with PMF, SVD, and RBM-CF algorithms on the MovieLens dataset and the Amazon product dataset. Results indicate that the RMSE of CF-DNNF is improved by 2.015%, and the MAE is improved by 2.222%.
  Keywords: recommendation; algorithm; feature; interpretable; fusion; neural network; collaborative ?ltering; deep learning; MovieLens; RBM; CFDNNF.
  
  
• A Data Dissemination Scheme for a Wireless Nanosensor Network towards IoNT  
  by Mohamed Mostafa A. Azim 
  Abstract: Nanotechnology has gained significant importance in many fields, especially biological, industrial, military, and environmental sectors. Rapid advancements in nanotechnology have led to the development of wireless nanosensor networks (WNSNs). Wireless nanosensor networks consist of nano-sized communication devices, including nano-nodes, nanorouters, nano-micro interfaces, and gateways. One of the most important goals for wireless nanosensor networks is data collection and delivery. However, data collection and delivery face two main challenges: resource limitation and dynamic channel state. Nanoscale devices have low limited capacity in terms of storage, computation, energy, and communication. The dynamic channel state occurs due to nanorouter links that are sensitive to molecular absorption. Most researchers focus on addressing resource limitation constraints and ignore the dynamic channel state. Therefore, in this paper, we design a fine-grained, lightweight, and energy-efficient end-to-end data dissemination scheme for WNSN that takes into consideration the dynamic channel state problem and energy efficiency. The proposed scheme divides the data dissemination process into two phases: a setup phase and a data collection phase. The setup phase aims to discover the nanonetwork topology and assign a forwarder for both the nanorouter and nanosensor nodes, while the data collection phase focuses on aggregating, disseminating data, and adopting a dynamic channel state. To validate the performance of the proposed scheme, a network simulator was developed using MATLAB to compare our proposed scheme with one of the benchmarking schemes known as the TEForward scheme. The TEForward scheme is considered to be the first solution that addresses the dynamic channel state as well as resource constraints. Simulation results showed that the proposed scheme outperforms TEForward in terms of energy consumption and packet delivery ratio. Moreover, we investigate the effect of increasing the number of gateways in the network on energy consumption. Our simulations indicate that a small-sized network (a networ with a small number of nodes) using one gateway consumes less energy than those using more than one. For large-sized networks (networks with a large number of nodes) using more than one gateway is more appropriate from the energy consumption point of view than depending on only one gateway. Hence, we conclude that the number of gateways in the network has an effect on energy consumption.
  Keywords: Wireless Nanosensor Network; WNSN; Internet of Things; Internet of NanornThings; IoT; IoNT; gateway; nanosensor; nanorouter.
  
  
• Anisotropic Diffusion Based on FermiDirac Distribution Function and its Application in the ShackHartman Wavefront Sensor  
  by Yanyan Zhang, Chengsheng Pan, Luyao Wang, Suting Chen 
  Abstract: In this study, the anisotropic diffusion technique is applied to estimate spots in the noise signals of the ShackHartmann wavefront sensor. Based on the analysis of the classical anisotropic diffusion function and on an improved algorithm, a diffusion function is proposed based on the FermiDirac distribution. It is proved mathematically that the new function has a higher convergence speed and a better performance. Monte Carlo simulations are used to verify the applicability of the new function subject to the noise limit and signal level. The simulation and experimental results show that the anisotropic diffusion algorithm can effectively filter out the noise. The integrity of the spots can be maintained, and the centroid detection accuracy and signal-to-noise ratio are also improved.
  Keywords: Shack–Hartmann wavefront sensor? noise? anisotropy? diffusion function.
  
  
• Distributed Mobile Wireless Sensor Node Localization using RSSI-aided Monte Carlo Method  
  by Timoteo Cayetano-Antonio, M. Mauricio Lara, Aldo G. Orozco-Lugo 
  Abstract: Localization, also known as positioning, is a key issue in mobile wireless sensor networks. There are different positioning algorithms for low-cost sensor nodes in the literature; but most of them are focused on the basic idealized scenario of the free-space radio propagation model. In this paper, a new algorithm is proposed based on Monte Carlo localization for positioning mobile wireless sensor nodes in the more challenging scenario of the shadowing radio path loss propagation model. The received signal strength indicator (RSSI) is integrated into the Monte Carlo algorithm as an undemanding method of distance estimation. Besides, multilateration based on the concept of radical axes and the use of Least Squares is also proposed to increase the number of localized nodes. The key difference with previous works comes from an extension of the concept of neighborhood of nodes which is more suitable for shadowing channels. The proposed algorithms show an improvement in the localization precision compared with other works in the literature.
  Keywords: Mobile Wireless Sensor Node Localization; Shadowing; Monte Carlo Localization; RSSI Localization; Positioning.
  
  
• IoTtalk Experience on Building Commercial IoT/AI Applications  
  by Yi-Bing Lin, Tai-Hsiang Yen 
  Abstract: Many smart applications have been developed using the Internet of Things (IoT) technology. Unfortunately, some of them are not sustainable and cannot be commercialized. This paper describes our observation on mistakes made in IoT application development, and introduces an IoT application development platform called IoTtalk that supports sustainable applications. Specifically, to achieve the above goal, we introduce two powerful mechanisms of IoTtalk: MapTalk and the IoTtalk control board. In the summary, we show how a 2019 Novel Coronavirus (2019-nCoV) monitoring system with privacy can be conveniently built in IoTtalk.
  Keywords: 2019 Novel Coronavirus (2019-nCoV); Internet of Things; Smart City; sustainable applications.
  
  
• Wind Turbine Blades Icing Failure Prognosis Based on Balanced Data and Improved Entropy  
  by Cheng Peng, Qing Chen, Xiaohong Zhou, Songsong Wang, Zhaohui Tang 
  Abstract: To improve the accuracy of icing failure prediction, which is often limited due to unbalanced condition data, a novel balancing algorithm based on boundary division synthetic minority oversampling technology (BD-SMOTE) and a method for predicting the icing failure of wind turbine blades in the short term based on multiple neural network combination are presented. First, the original data set obtained by sensors is balanced by BD-SMOTE. Then, the key features are extracted by multivariate and multiscale entropy based on a continuous smooth coarse (CSMMSE) algorithm, and the values of three kinds of features in the near future are predicted by the Elman neural network (ENN). Finally, a back-propagation (BP) neural network is adopted to predict the icing failure of wind turbine blades. Compared with the results of other methods, the prediction deviation of the ENN is smaller; the prediction results demonstrated the effectiveness and superiority of the proposed method.
  Keywords: sensors; icing failure; BD-SMOTE; boundary division synthetic minority oversampling technology; CSMMSE; multivariate and multiscale entropy based on a continuous smooth coarse; ENN; Elman neural network; BP; back propagation.
  
  
• Automated Real-Time Anomaly Detection of Temperature Sensors through Machine-Learning  
  by Debanjana Nayak, Harry Perros 
  Abstract: Fast identification of faulty sensors is necessary for guaranteeing their robust functions in diverse applications ranging from extreme weather prediction to energy saving to healthcare. We present an automated machine-learning based framework that can detect anomalies of temperature sensor data in real-time. We adopted a purely temporal approach that utilizes a univariate time-series (UTS) generated by a single sensor. The framework divides the UTS into subsequences, models each subsequence stochastically as an autoregressive function, and finally mines the function parameters with a One-Class Support Vector Machine (OC-SVM) that classifies any outlier as an anomaly. Extensive experimentation showed that the framework identifies both normal and anomalous data correctly with high degrees of accuracy.
  Keywords: Univariate Time-Series; Anomaly Detection; Temperature Sensors; One-Class Support Vector Machines; Autoregression.
  
  
• Throughput and delay optimization for NOMA systems with energy harvesting  
  by Nadhir Ben Halima, Hatem Boujemaa 
  Abstract: In this paper, we analyze and optimize the total throughput and average delay in Non Orthogonal Multiple Access (NOMA) with energy harvesting. The source harvest energy from Radio Frequency (RF) signals received from another node $A$. Node A can be any node transmitting RF signals. The frame with duration $T$ contains two time slots. The first slot has duration $\\delta T$ and is dedicated to energy harvesting where $0< \\delta
  Keywords: NOMA; energy harvesting; Rayleigh channel; optimal power allocation; optimal harvesting duration; packets\' waiting time; delay analysis.
  DOI: 10.1504/IJSNET.2020.10030152
   
  
• Improving Activity Recognition for Multiple-Node Wireless Sensor Network System Based on Compressed Sensing  
  by Duo Yang, Jiangtao Huangfu 
  Abstract: The article proposes a multiple-node activity recognition wireless sensor network system based on compressed sensing (CS), where effective use of transmission resources, the real-time performance, low data recovery errors, and high activity recognition accuracy are achieved. A WSN system model for data compression-transmission-recovery (CTR-WSN) in the case of data loss is built. It performs different degrees of compression according to different types of activities characteristics. With consideration of data loss, transmission resources are effectively used, and the most time-saving data recovery methods are evaluated based on SP/SAMP algorithms, and the error range of different activities are all basically controlled within
  Keywords: activity recognition; compressed sensing; data loss; multiple-node; wireless sensor network.
  
  
• A Dynamic Data Driven Indoor Localization Framework based on Ultra High Frequency Passive RFID System  
  by Sara Masoud, Young-Jun Son, Chieri Kubota, Russell Tronstad 
  Abstract: Better monitoring of workers and the materials flow within a production system can potentially enhance any facilitys productivity and efficiency. This paper proposes a data driven framework to affordably localize indoor workers and materials using a passive Radio Frequency Identification (RFID) system in large scale. Here, indoor wireless sensor networks are developed via passive Ultra-High Frequency (UHF) tags, where Received Signal Strength Indicator (RSSI) is measured by different access points (APs) to generate a fingerprinting database. Then, this database not only translates the signal strength reported by APs to distance through regression models but also helps to localize each tag utilizing our proposed k-nearest neighbors (KNN) algorithm. Our improved KNN algorithm dynamically defines different neighbourhoods, in terms of size and topology considering environment status. Results from multiple experiments under different scenarios reveal that our proposed methods can detect and localize objects with an error as low as 0.36 m.
  Keywords: RFID; radio frequency identification; Passive UHF RFID; WSNs; wireless sensor networks; RFID–sensor networks; distance estimation; localization; KNN; Dynamic Data Driven; Machine learning; Statistical Modeling.
  
  
• A Contactless Sensing System for Indoor Fall Recognition Based on Channel State Information  
  by Wei Zhuang, Yixian Shen, Jiefeng Zhang, Chunming Gao, Yi Chen, Dong Dai 
  Abstract: This paper introduces the designs and the implementation of a non-invasive indoor fall recognition system based on Channel State Information (CSI) in the Wi-Fi physical layer. We use a wireless router and a laptop computer equipped with an Intel Wi-Fi Link 5300 network card (802.11n) to setup a hardware platform. The platform receives and stores CSI data under various circumstances when a person in the Wi-Fi covered area stands up, sits down, walks, and falls. The CSI data are then processed and analyzed using MATLAB tools. Feature variables such as signal offset strength, period of motion, normalized standard deviation, median absolute deviation, interquartile range, and signal entropy are examined and best feature variables are chosen. Finally, cross validation algorithm and support vector machine are used to establish the pattern recognition model. We tested the system in a laboratory environment and the experimental results showed that the fall incidents were effectively differentiated from other movements.
  Keywords: CSI; movement recognition; fall detection; support vector machine.
  
  
• A Brain-Computer Interface System for Smart Home Control Based on Single Trial Motor Imagery EEG  
  by Wei Zhuang, Yixian Shen, Lu Li, Chunming Gao, Dong Dai 
  Abstract: With the development of science and technology in recent years, many researches on brain signal recognition and brain-computer interface control have made great progress. By analysing electroencephalogram (EEG), a specific brain activity can be detected and the signal can be used to control smart devices and help people to complete many difficult and complicated tasks, especially for people with disabilities. This paper presents the design and implementation of a novel brain-computer interface system for smart home control using single trial motor imagery EEG. The system adopts STM32 MCU (Microcontroller Unit) and TGAM (ThinkGear Asic Module) modules to realize the acquisition and recognition of EEG signals. It can transfer the signals to portable devices through low power consumption Bluetooth modules. Three main EEG features including Alpha, Beta, and Gamma waves are discussed. It is tested during simple actions such as blinking in various situations. The experimental results show that the implemented system is suitable for extracting specific EEG signals to control smart home devices.
  Keywords: EEG recognition; STM32 embedded control; TGAM; smart home control.
  
  
• Triplet Erasing-Based Data Augmentation for Person Re-identification  
  by Wei Sun, Xu Zhang, Xiaorui Zhang, Guoce Zhang, Nannan Ge 
  Abstract: Occlusion is a fundamental yet challenging problem in person re-identification task. Previous work like Random Erasing randomly selects a rectangle region in an image and erases its pixels with random values, which does not consider the correlation between images when triplet loss is employed. To address the problem, we propose an end-to-end approach called Triplet Erasing-based data augmentation for person re-identification. We apply this approach to train a CNN network with two branches. Local distance branch determines the location of the part that needs to be erased in the image, then Triplet Erasing branch erases a rectangle region in the determined part. By generating a variety of occlusion samples, Triplet Erasing improves the robustness of the model to occlusion. Triplet Erasing can increase the distance between the positive sample pairs and decrease the distance between the negative sample pairs, which improves the generalization ability of the network.
  Keywords: deep learning; person re-identification; triplet loss; occlusion.
  
  
• A multi-objective emergency vehicle scheduling optimization model  
  by Jiao Yao 
  Abstract: Various types of emergencies occur frequently, which may cause casualties and huge economic losses to certain extent. How to optimize the dispatch of emergency rescue vehicles and improve the rescue efficiency quickly and effectively after the occurrence of emergency incidents is of great significance. Based on the analysis of the influencing factors of emergency vehicle scheduling, to overcome its unreasonable problems, the emergency vehicle scheduling optimization model was firstly established, oriented to achieve three objects, minimizing total travel time, total cost of dispatching travel, and maximizing the path reliability. Moreover? model was solved by genetic algorithm based on non-dominated sorting with elite strategy (NSGA-II algorithm). Finally, the local road network in Huangpu District of Shanghai was taken as an example to verify the model in this study. The results show that the optimal solution of the model can satisfy the optimal total travel time, total travel cost of the scheduling scheme and the path reliability can also achieve requirements, which means good validity and practicability.
  Keywords: Emergency vehicle; multi-objective planning; NSGA-II algorithm; path reliability; effectiveness coefficient comprehensive evaluation method.
  
  
• Road Traffic Optimization based on a Learning Approach  
  by Ahmed Mejdoubi, Hacene Fouchal, Ouadoudi Zytoune, Mohamed Ouadou 
  Abstract: Road traffic management is an important issue for authorities, road operators and road users. Researchers from optimisation area have worked for many years in order to propose smart solutions. Many of them have been adopted by road operators and applied on road signaling or on traffic light management. During the last decade, Cooperative Intelligent Transport Systems (C-ITS) have emerged and are being deployed. They permit on one hand to increase the number of connected vehicles and on another hand to deploy RSU (Road Side Units) along roads and unctions. RSUs together with central road operator servers compose the road infrastructure used for Vehicle to Infrastructure (V2I) or Infrastructure to Vehicle (I2V). RSUs aim is to disseminate continuously messages about road safety and trafic flows to vehicles. C-ITS have a high dissemination potential about road traffic informations. Vehicles and RSUs are important actors in C-ITS. They are able to measure, to compute, and to disseminate. In this study, we add to these actors the ability to learn mainly in the purpose to provide the optimal path in terms of travel time for vehicles seeking to reach their destination The solution is based on a reinforcement learning technique, in particular Q-learning, that is used to learn the best action to take into account according to various situations. The transit delay from one location to another is used to determinate the rewards. The simulation results confirm that the proposed Q-learning approach outperforms the existing greedy algorithm with better performances in terms of transit delay.
  Keywords: C-ITS ; VANETs ; Reinforcement learning ; Distributed traffic management ; Travel time.
  
  
• LAVR: Link failure Avoidance and Void Recovery Routing Protocol for Underwater Sensor Networks  
  by Zinal Patel, Keyur Rana 
  Abstract: Recently, Underwater Wireless Sensor Network (UWSN) has paved the way for a new era of exploring ocean realm. The researchers are attracted towards UWSN due to the envisioned landscape of underwater applications including oceanographic exploration, assisted navigation, disaster prevention, underwater pollution monitoring, tactical surveillance, mine reconnaissance, etc. These applications can be made viable through underwater communication by using acoustic waves as a communication medium since radio waves propagate at extra low frequencies in water and optical waves suffer heavily due to scattering. The change in communication medium gives rise to several design challenges like limited bandwidth, high bit error rate, large and variable propagation delay, frequent loss of connectivity, multi-path scattering and fading, etc. which leads to the need of designing efficient and reliable routing protocols for UWSN. Reliable data delivery in UWSN is always a tempting issue to be addressed, which is jeopardized by link failures and communication void problem. Under this context, Link failure Avoidance and Void Recovery (LAVR) routing protocol is proposed to achieve reliability. Link quality is computed using Triangle Metric and routing is done on the basis of link quality to avoid link failure. A void node recovery procedure is proposed to displace the void node to a new depth to increase the packet delivery ratio. Simulations are performed in Aqua-Sim simulator and the performance of LAVR is evaluated by comparing it with existing protocols. It is observed that the proposed protocol outperforms other protocols in terms of packet delivery ratio and network lifetime.
  Keywords: Communication Void; Link Failure; Routing Protocol; Underwater Wireless Sensor Networks; Void Recovery.
  
  
• Optimized Joint Resource Allocation for NOMA MIMO-basedWireless Powered Sensor Networks  
  by Qiang Wang, Hai-Lin Liu 
  Abstract: Wireless powered sensor network has captured a lot of attentions due to its higher lifetime compared with the battery powered sensor network. The multiple-input multiple-output(MIMO) can be used to improve the wireless energy transfer efficiency by beamforming. In this paper, we investigate the resource allocation in the non-orthogonal multiple access (NOMA)MIMO-based wireless powered sensor networks in terms of the wireless energy transfer time allocated and power allocation to maximize the system throughput. The joint resource allocation is\r\nformulated as a nonconvex optimization problem which is difficult to solve due to its high computational complexity. To reduce the complexity, the original optimization problem is decomposed into two optimization subproblems, namely the time allocation subproblem and power allocation subproblem. Specifically, the particle swarm optimisation (PSO) is adopted to solve the time allocation subproblem. When the time allocation is fixed, the power allocation subproblem is still a nonconvex optimization problem. The DC (difference of two convex functions) programming method is adopted to solve it. We first transform the objective function as a difference of two convex functions and then the objective function is approximated as a convex function by the first order Taylor expansion. The Lagrangian dual method is used to solve the\r\napproximated convex optimization problem. Simulation results illustrate that the proposed joint resource allocation scheme can significantly improve the system throughput.
  Keywords: Resource allocation; MIMO; Wireless Sensor Networks; Evolutionary algorithm.
  
  
• Energy Balanced, Delay Aware Multi-Path Routing using Particle Swarm Optimization in Wireless Sensor Networks  
  by Priti Maratha, Kapil Gupta, Pratyay Kuila 
  Abstract: World-wide use of wireless sensor networks has urged the need for energy-efficient, distributed routing algorithms. The interest of researchers from the past decade is in energy-efficient routing. In this paper, sequential quadratic programming (SQP) based multi-path routing formulation focusing on improving lifetime and delay is represented, namely EBDA-DEFL. This SQP based formulation is solved using the optimization tool after that same formulation is solved using particle swarm optimization (PSO). Also, a quota strategy for traffic load distribution is also introduced to mitigate the negative effects of multi-path routing. The proposed work is experimented and compared with existing algorithms to analyze its quality over previous work. Comparison has been done in terms of first node death, half node death, last node death, delay, and time consumed by Fminimax and PSO. Simulation results confirm the supremacy of proposed work over the existing ones.
  Keywords: Wireless sensor networks; residual energy; network lifetime; delay; load; particle swarm optimization.
  
  
• ST-IFC: Efficient Spatial-Temporal Inception Fully Connected Network for Citywide Crowd Flow Prediction  
  by Yan Kang, Bing Yang, Hao Li, Lan Zhang, Tie Chen 
  Abstract: Traffic flow prediction is important to urban management for the development of smart cities as well as further contribution to public safety. In the era of big data, large amounts of data related to traffic flow have been exponentially produced every day. However, vehicle streaming data may also record the mobility of human traffic. It could reflect urban traffic conditions to a certain extent. This paper analysed the spatial and temporal characteristics of human traffic in depth and proposed an efficient ST-IFC (Spatial-Temporal Inception Fully Connected) network for citywide traffic prediction. An IFC (Inception Fully Connected) unit was proposed to directly capture the spatial dependence and multi-scale characteristics of the entire data set of the urban traffic. In addition, this paper also proposes a multi-level feature fusion strategy to effectively combine the flow features of low-level surface and high-level abstract to avoid feature loss. Therefore, the proposed strategy greatly enhances the utilization of computing resources while ensuring the significant improvements of the prediction results for the proposed model. The simulations were carried out using the trajectory data of Beijing taxis and New York City bicycles. The experimental results show the advantages of our model in predicting the accuracy in addition to operating at a higher speed.
  Keywords: Deep Learning; Urban Computing; Spatio-temporal Data; Traffic Forecast.
  
  
• Compressive Sensing Multi-target Diffusive Source Localization using Sparse Recovery Algorithms in Sensor Networks  
  by Zhang Yong, Zhi Yan, Qi Chen, Teng Fei, Liyi Zhang 
  Abstract: According to the multi-target diffusive source localization in sensor networks, a compressive sensing sparse recovery algorithm was proposed for the mismatching problem of the target sources sparsity and the high-dimensional redundant sampling signals. Firstly, the compressive sensing system model and the related terms were given and explained. Then, the joint optimal estimation of the sparse diffusive source vector and the diffusion distribution state were realized with the variational Bayesian expectation maximization algorithm (VB-EM). In which, the dynamic compressive sensing dictionary model of the real target source sparse representation was designed and adjusted with the grid division parameters optimization for the dictionary mismatch problem solving. Finally, the simulation results show that the proposed compressive sensing method with VB-EM algorithm could effectively achieve the diffusive source parameters estimation and its diffusion distribution state prediction. Compared with the traditional compressive sensing sparse recovery algorithms, it could obtain higher robustness performance for the rapid and accurate localization in complex environment.
  Keywords: sensor network; compressive sensing; variational Bayesian expectation maximization.
  
  
• MPI hardware framework for many-core based embedded systems  
  by Rodrigo Vinicius Mendonça Pereira, Laio Oriel Seman, Marcelo Daniel Berejuck, Douglas Rossi De Melo, Analucia Schiaffino Morales, Eduardo Augusto Bezerra 
  Abstract: Multiprocessor System-on-Chip (MPSoCs) designs interconnected by high-speed networks has a crucial role at embedded systems, leading to next level sensors applications and interfaces' services. This paper presents the results regarding an investigation and evaluation of the services and infrastructure performance of a software and hardware implementation subset of the Message Passing Interface (MPI) standard. The proposal for an efficient MPI Hardware (MPIHW) and MPI Software (MPISW) models, along with the presentation and evaluation of its queuing model, aims at giving the system design a framework to assist. Comparative results are presented between MPI in hardware and software such as silicon consumption, processing time and transfer rate of the system related to the size of buffers. Also, tests in an environment consisting of an MPSoC model integrated on a Network-on-Chip (NoC) were performed, including classical algorithms such as pi calculation and the Dining Philosophers problem, to evaluate the proposed model functionality. Experimental results demonstrated the effectiveness of the proposed approach and the precision of the obtained implementation, although this comes at the cost of increased use of silicon in hardware implementation. This trade-off must be taken into account by the system designer of the sensors network.
  Keywords: MPI Hardware; MPSoC; Queue Model; Analytical Model; Sensor Network.
  
  
• Detection and Recognition of Text Traffic Signs above the Road  
  by Wei Sun, Yangtao Du, Xu Zhang, Guoce Zhang 
  Abstract: Based on the similarity between traffic sign images in the source and target domains, we use the parameters migrated from the source domain as the initial parameters of the Faster-R-CNN network, which is trained for detecting text traffic sign, then fine-tune the network parameters based on the samples in target domain to obtain the final network parameters. Moreover, we convert the traffic sign images from RGB color space to HSV color space and use the converted images in HSV color space as the training samples of the network, thereby overcoming the under-learning problem of model caused by less training samples. We tailor the traditional EAST text detection network model and propose a new recognition model based on the extreme learning machine (ELM) classifier to identify and classify the detected text traffic signs above the road. Experimental results in the natural scene demonstrate the effectiveness of the proposed method.
  Keywords: Text traffic sign; Computer vision; Convolutional neural network; Text detection.
  
  
• An Identity Authentication Method for Ubiquitous Electric Power Internet of Things Based on Dynamic Gesture Recognition  
  by Pingping Yu, Jincan Yin, Yi Sun, Zheng Du, Ning Cao 
  Abstract: This paper presents a novel algorithm for gesture recognition and identity authentica-tion based on continuous hidden Markov model (CHMM) and optical flow method. This study aims to solve the information se-curity problems about ubiquitous electric power Internet of Things. In this system, the optical flow method is used to segment and extract the features of the preprocessed dy-namic gesture information to obtain the fea-tures of the dynamic gesture motion track, and the CHMM is chosen to establish a valid user dynamic gesture model, which leads to ensuring the dynamic gestures are accurately recognized. The proposed method is test on accurately recognize the dynamic gestures and the result is compared with the Dynamic Time Warpring (DTW) algorithm and Practi-cal Swarm Optimisation-Radial Basis Func-tion Network (PSO-RBFN) algorithm. The result of the comparisons illuminates the su-periority of the proposed method in terms of accuracy of identity authentication.
  Keywords: Dynamic gesture recognition; Identity authentication; Ubiquitous electric power Internet of Things; Information safety; Continuous hidden Markov.
  
  
• A Secure and Privacy-Preserving Key Agreement and Mutual Authentication Scheme  
  by Hui Li, Tao Jing, Jin Qian 
  Abstract: The development of manufacturing, communication, and computing technologies has promoted convergence of different network architectures, aiming to make full use of all resources to create valuable information. While heterogeneous integrated networks play a key role in providing highly effective and efficient solutions for medical users, they also raise various security and privacy issues. Strengthen the security and privacy protection and efficiency before transmitting sensitive data is a primary challenge to be addressed. Traditional authentication methods fail to differentiate the privacy sensitivity difference between data collected by distinct devices. Instead of treating all devices just as one ``tool'', context privacy-aware access has been prompted to solve this issue. We propose a secure and efficient key management scheme based on a key-exposure resistant chameleon hash function. Through adopting the devices' privacy sensitivity and users' attribute credential as two factors to establish trust between different entities, we proposed a secure and anonymous authentication protocol. The security analyses indicate that the proposed protocol can satisfy security requirements.
  Keywords: Mutual-authentication; Chameleon Hash Function; Fog Computing; Key Management.
  
  
• WQMS - Water Quality Monitoring Station for IoT  
  by Sergio Diaz, Andres Molano, Christian Erazo, Juan C. Monroy 
  Abstract: Water pollution threatens public health with infectious and transmissible diseases. The traditional approach of water quality monitoring consists in collecting samples of water and transporting them to specialized laboratories, which is a waste of manpower. Nowadays, IoT devices are capable of monitoring the water quality and reporting the data to a cloud server without any human intervention. However, related work does not present a comprehensive solution that covers the main design aspects; thus, we designed and built a Water Quality Monitoring Station (WQMS) that includes power management, data measurement, data transmission, and Internet of Things (IoT). We deployed the WQMS in a remote fishpond with tilapias and measured the water quality for seven days in a row. The results show that the pH and dissolved oxygen can be expressed in terms of temperature as a quadratic function; besides, our solar energy harvesting module is a sustainable source of energy as long as there are no failures in the system.
  Keywords: Water Quality; Water Monitoring; Internet of Things.
  
  
• Task Scheduling for Mobile Edge Computing Enabled Crowd Sensing Applications  
  by Jingya Zhou, Jianxi Fan, Jin Wang 
  Abstract: Crowd sensing has emerged as a new promising application paradigm that collects real-time information about the physical world from individuals via their own devices. It effectively solves the dilemma of massive data collection faced by most data-driven applications such as traffic control, air quality prediction, disaster relief, etc. {Most of those applications are latency-sensitive, while current cloud-based crowd sensing systems cannot fully guarantee the response latency due to the restricted bandwidth of the backbone}. In recent years, mobile edge computing (MEC) is proposed to extend the frontier of cloud to the network edge so that it is quite suitable to integrate MEC with current crowd sensing systems. In this paper, we focus on the basic problem of task scheduling in a MEC-enabled crowd sensing system. Though many efforts have been devoted to the task scheduling research, the problem discussed here has some unique challenges, e.g., {edge devices are not dedicated to perform sensing tasks, task scheduling on edge devices and edge servers are highly coupled, and it is hard to achieve long-term objectives}. To address these challenges, we first present a system workflow framework that captures the unique execution logic of sensing tasks. Based on the framework, we propose a staged scheme to decouple the original scheduling problem and divide it into two sub-problems, i.e., task offloading problem and task shifting problem. Moreover, we leverage Lyapunov optimization technique to design a multi-period optimization for long-term objective achievement. The extensive experiments show that our proposed algorithm can effectively reduce cost with the guaranteed latency constraint.
  Keywords: Task scheduling; Crowd sensing; Mobile edge computing; Lyapunov optimization.
  
  
• Instant Messaging User Geolocating Method Based on Multi-Source Information Association  
  by Jiadong Guo, Rui Xu, Wenqi Shi, Pei Zhou, Xiangyang Luo 
  Abstract: The increased intermingling of instant messaging (IM) and the Internet of Things puts forward higher requirements for the security, the research on IM user geolocation technology is particularly important in maintaining the security of IoT. Most of the existing IM user geolocating methods analyze the relationship between the reported and real distance of the user to geolocate, but the number of geolocating users is limited. Therefore, we propose the instant messaging user geolocating method based on multi-source information association. This method converts the geolocating problem of a single user into the association of multi-source IM user information, and solves the problem of automatic and accurate acquisition of user information. The geolocating experiments are carried out for WeChat, Momo, and QQ. The results show that the method can achieve reliable acquisition for user information, and the number of geolocating users can be much greater than the existing geolocating method.
  Keywords: instant messaging; user information acquisition; user geolocating; user associating.
  
  
• Review of Single Image Defogging  
  by Baowei Wang, Bin Niu, Peng Zhao, Neal Xiong 
  Abstract: With the great advance of computer vision technology, the application of images in daily production work is more and more extensive. However, fairly substantial images collected in foggy weather show significant degradation. Image defogging technology is developed to solve this problem. After the defogging operation, the visual effect will be obviously improved, and it will also bring convenience to subsequent processing. This paper discusses the research background and current status of single image defogging strategies, and discusses the advantages and disadvantages of some classical algorithms. At the same time, combined with the analysis of these algorithms, some expectations are proposed.
  Keywords: Image defogging;Image enhancement;Image restoration; Fusion strategy;Retinex theory;Atmospheric scattering model.
  
  
• Towards Green Computing: Intelligent Bio-Inspired Agent for IoT-enabled Wireless Sensor Networks  
  by Sheetal Ghorpade, Marco Zennaro, Bharat Chaudhari 
  Abstract: With the emergence of the Internet of Things and machine to machine communications, massive growth in the IoT-enabled wireless sensor node deployment is expected in the near future. The critical challenges for the sensor network include energy efficiency, optimum route calculation, and the overall transmission cost. Although several kinds of research have been carried out to address such challenges, most of the reported bio-inspired optimizations are based on a single objective function considering other objectives as constraints. Some have considered a multi-objective optimization approach; however, the updating of particle positions was slower and challenging. To avoid the bias toward one of the objectives and also to facilitate ease of position updating, we propose a novel multi-objective optimization agent based on particle swarm gray wolf optimization (PSGWO) and inverse fuzzy ranking. We initially developed an enhanced PSGWO model, and then it is utilized for the development of population and multi-criteria based soft computing algorithm, called fuzzy PSGWO. The inverse fuzzy ranking guides the optimizer in updating the positions of particles better and faster way. The performance of the proposed algorithm is validated and compared with the well-known techniques. The results show that for the proposed algorithm, residual energy of the nodes is much higher than that of other algorithms, and save up to 48% energy along with smaller variation in the standard deviation. The results also demonstrate the smaller average values of fitness function and computationally efficient capabilities of the proposed algorithm.
  Keywords: Bio-inspired optimization; Energy efficiency; Inverse fuzzy ranking; Wireless sensor networksrnrn.
  
  
• Lightning Location Method Based on Improved Fuzzy C-Means Clustering Algorithm  
  by Tao Li 
  Abstract: Location accuracy is an important index for the evaluation of location networks and the localisation algorithm related to the accuracy of the results. Classical location algorithms scarcely correct errors accurately. Moreover, they have poor resistance to error interference and low location accuracy. To achieve error-resistant lightning localisation, weighted rough-fuzzy C-means (WRFCM) was introduced in location calculation. The performance of this localisation algorithm was analysed using a lightning accident case through regional simulation. Results show that the lightning localisation algorithm based on WFCM overcomes the disadvantage in which traditional location algorithms easily diverge; the algorithm also has an improved ability to resist error interference and can solve the lightning points steadily and accurately.
  Keywords: lightning localisation; time difference of arrival; cluster analysis; fuzzy C-means.