International Journal of Sensor Networks (41 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: .
  
  
• A (t, n) threshold quantum visual secret sharing  
  by Wenjie Liu, Yinsong Xu, Junxiu Chen, Ching-Nung Yang 
  Abstract: Secure data sharing is a deserving topic in wireless sensor networks. In order to deliver the information securely, we propose a (t, n) threshold quantum visual secret sharing (QVSS) scheme based on Naor et al.'s visual secret sharing (VSS) scheme, which consists of two phases: sharing process and recovering process. In the first process, the color information of each pixel from the original secret image is encoded into an n
  Keywords: threshold; n×m-qubit superposition state; single-pixel parallel processing; visual cryptography.
  
  
• A Global-to-Local Searching-Based Binary Particle Swarm Optimization Algorithm and its Applications in WSN Coverage Optimization  
  by Kanghsun Li, Ying Feng 
  Abstract: The coverage optimization problem of wireless sensor networks, also known as the minimum connectivity coverage node set problem, has always been a popular research topic. Heuristic search algorithms have been applied to the coverage optimization problem of wireless sensor networks in recent years because of their strong search ability and fast convergence speed. This paper proposes an optimization algorithm for a wireless sensor network based on improved binary particle swarm optimization. The position updating formula based on the sigmoid transformation function is adjusted, and a global-to-local search strategy is used in the global-to-local searching-based binary particle swarm optimization algorithm (GSBPSO). Furthermore, to apply GSBPSO to the optimization of wireless sensor networks, a small probability mutation replacement strategy is proposed to replace individuals who do not meet the coverage requirements in the search process. In addition, the fitness function is improved so that the network density can be adjusted by modifying the parameters in the improved fitness function. Experiments show that the proposed algorithm in this paper is effective.
  Keywords: wireless sensor networks; BPSO; coverage optimization; minimum connected coverage set.
  
  
• Limit Equilibrum based Landslide Prediction System using WSN  
  by Adel Thaljaoui, Fayez Abdulrahman ALFAYEZ, Salim El Khediri 
  Abstract: The increased impact of landslides, especially in emerging countries, is largely related to population growth, increasing infrastructure and human activities in landslide-prone areas. Such a problem incited us to investigate new solutions that detect the early signals preceding a landslide. The system we propose is designed around special sensors that capture each real-time change in the soil of the studied field. These changes are sent toward processing modules that compute the real-time Bishop based factor of security and compares it with limit values. Carried experimentations showed the significant response of the proposed system before the slide surface is formed which gave enough time to take appropriate procedures in order to prevent imminent landslide.
  Keywords: Landslide; prediction; factor of safety; WSN.
  
  
• Research on Natural Disaster Emergency Monitoring System  
  by Yang Jianliang, Hou Hanping, Geng Shaoqing, Qiao Shaobo, Wang Yue 
  Abstract: Monitoring is the main emergency information and intelligence department. The natural disaster emergency rescue decision is made on the basis of disaster monitoring information. Obtaining timely and accurate disaster information is the fundamental guarantee for emergency and rapid rescue. Based on the research status of natural disaster emergency monitoring system, this paper proposes the establishment of sky-ground integrated natural disaster emergency monitoring system based on the research method of system management theory, and analyzes quantitatively the system performance. Finally, it points out the operation mode and guarantee of the system. The mechanism provides reference for the construction of a scientific and perfect natural disaster emergency monitoring system, which really shortens the time of black box and grey box, solves the vacuum of rescue and realizes rapid rescue.
  Keywords: natural disasters; emergency monitoring; information and intelligence; sky-ground integrated monitoring system.
  
  
• Using V2X Communications and Data Fusion to Achieve Lane-Level Positioning for Road Vehicles  
  by Tsu-Kuang Lee, Juyi Lin, Jen-Jee Chen, Yu-Chee Tseng 
  Abstract: Precise positioning is a key issue for road vehicles in navigation, safety, and autonomous driving applications. While GPS is widely accepted, it is still a challenge to achieve lane-level positioning. In this work, we consider the fusion of multi-sensory data using Particle Filter (PF), which is ?exible in integrating different information in complex outdoor environments. We focus on three types of popular sensors: Controller Area Network (CAN bus), GPS, and roadside camera. We propose a PF model that can adopt these types of sensory inputs for vehicle positioning. We show that in scenarios where vision sensory inputs are available, lane-level precision can be achieved. When there is no vision coverage, seamless localization with reasonable precision can still be supported by GPS. Field trial results are presented to validate our model.
  Keywords: Data Fusion; Particle Filter; Positioning; Vehicular Network.
  
  
• Managing Sensor Data Streams in a Smart Home Application  
  by Johan Jansson, Ismo Hakala 
  Abstract: A challenge in developing an ambient activity recognition system for use in elder care is finding a balance between the sophistication of the system and a cost structure that fits within the budgets of public and private sector healthcare organisations. Much activity recognition research in the context of elder care is based on dense networks of sensors and advanced methods, such as supervised machine learning algorithms. This paper presents the data processing aspects of an activity recognition system based on a simpler, knowledge-based unsupervised approach, designed for a sparse network of sensors. By structuring sensor data management as a streaming system, we provide a simple programming model for the application logic, which facilitates building a fault-tolerant system with the potential for distributed data management within the sensor network. The system, evaluated by a public sector healthcare organisation, constitutes an example of a system that is useful and has a sustainable cost structure.
  Keywords: activity recognition; healthcare; home care; passive infrared sensor; PIR; sensor data; sensor data management; sensor data processing; sensor data streams; smart home; wireless sensor network; WSN.
  
  
• Cooperative Spectrum Sensing with Energy Harvesting for Nakagami fading channels  
  by Raed Alhamad, Hatem Boujemaa 
  Abstract: The paper derives the Detection Probability (DP) of Cooperative Spectrum Sensing (CSS) algorithms with energy harvesting. There are three phases with the same duration. The first phase is dedicated to energy harvesting by Primary User (PU) and relays. They harvest energy from Radio Frequency (RF) signal received from another node H. Node H can be a Base Station. In the second phase, PU transmits its signal that will be received by relays. In the last phase, a selected relay amplifies the PU signal to a Fusion Center (FC) where spectrum sensing is performed using the energy detector. Our analysis is valid for Opportunistic Amplify and Forward (OAF), Partial(PRS) and Reactive Relay Selection (RRS). A Nakagami fading channel is assumed between all nodes. Another contribution of the paper is to show that the DP can be lower bounded using the Cumulative Distribution Function (CDF) of Signal to Noise Ratio (SNR).
  Keywords: Radio Frequency (RF) Energy harvesting; Spectrum Sensing; Nakagami channels.
  
  
• Early detection of foot complications using a thermal profiler of plantar surface  
  by M.M. Manohara Pai, Kushagra Jain, K.N. Manjunath, Sucheta V. Kolekar, Radhika M. Pai 
  Abstract: Foot complications in diabetic patients can become fatal. Hence early detection of the onset of foot complications becomes the need of the hour. Various techniques are being used by diabetologists to know the presence of foot complications. One of the technique used at present in the measurement of temperature variations in the plantar surface of the foot using thermogram images. This paper describes a novel system called Thermal Prolfier System which proles the temperature variations in the plantar surface. The Thermal Proler System consists of a device with 23 sensors embedded at strategic locations which senses the temperature values of the plantar surface. It also consists of a mobile application that gets the temperature data from the device, computes the variations in the temperature values and reports abnormalities to the diabetologist. Further, an experimental study is conducted to validate the values recorded by the device. This study includes temperature data of symptomatic and asymptomatic subjects whose values read from the plantar surface using a Thermal Proler device is compared with the temperature data obtained from thermogram images of the same plantar surface captured using FLIR thermal camera at almost the same time. The statistical analysis performed using paired t-test indicate that the difference between the thermal profiler values and the thermogram images are statistically not signficant (p=0.054).
  Keywords: Plantar surface; Thermal profiler; Tibial arteries; Angiosomes; Thermogram images.
  
  
• Cooperative NOMA: device-to-device mode and outage performance analysis  
  by Thuan Do, Anh-Tu Le, Thi-Anh Hoang 
  Abstract: In this paper, a new approach as combination of relaying model and device-to-device (D2D) scheme to perform non-orthogonal multiple access (NOMA) in the fifth generation (5G). In such NOMA system, we deploy power domain to transfer multiple users signals over same resource at the same time. As important result, two NOMA users communicate with each other as D2D pair and they exhibit different system performance due to different links and dissimilar allocated power levels. More specifically, expression of a closed-form is presented in term of the outage probability. In numerical result, exactness of the theoretical expressions can be confirmed by exploiting Monte Carlo simulations. Compared to many scenarios in such multiple access technique, the proposed NOMA scheme can offer improved spectral efficiency and user fairness by matching the obtained analytical and simulation results.
  Keywords: D2D; non-orthogonal multiple access (NOMA); outage probability.
  
  
• Radio-Based Vehicle Dynamic Tracking in GNSS Degraded Environments  
  by Jianqi Liu, Xiuwen Yin, Bi Zeng, Hui Zhang, Wei He 
  Abstract: The vehicular position information plays an important role in the vehicle communication. In open environments without signal blockage, Global Navigation Satellite System (GNSS) has achieved on-road level accuracy and good reliability, However, the vehicle dynamic tracking is difficult in GNSS degraded environments. Usually, vehicle dynamic tracking consists of three portions: static positioning system, maneuvering model and fusion algorithm. A radio-based positioning system aided by Roadside Units (RSUs) is employed as static positioning system, while the adaptive Kalman filter algorithm is used as fusion algorithm. In this paper, a new maneuvering model is studied. Firstly, the problem of current model is analyzed. Secondly, a current-ellipse maneuvering model is proposed to adapt the strong and weak maneuvering simultaneously. The experiments show that it can improve the tracking accuracy. Finally, a vehicle tracking case is discussed, its results show that root mean square error (RMSE) is smaller than 2 meters, and the positioning accuracy can meet the position-based vehicular communication.
  Keywords: Vehicle dynamic tracking;Maneuvering model;RSUs-aided radio-based positioning system;Current-ellipse model.
  
  
• Application of gravity passive aided strapdown inertial navigation in underwater vehicles  
  by Qi Wang, Changsong Yang, Shaoen Wu, Yan Wang 
  Abstract: The area of the ocean accounts for more than 70% of the surface area of our earth, the development and protection of the ocean has become a long-term strategic task of many countries. Underwater vehicle plays an important role in ocean development. Underwater navigation technology is the guarantee to ensure the underwater vehicles complete all tasks. A gravity passive aided strapdown inertial navigation system is proposed in this paper in order to improve the precision of position of autonomous underwater vehicles. Improved unscented Kalman filter is applied in the novel integrated system which is based on gravity gradient characteristics. The main characteristics of strapdown inertial navigation system are presented, gravity-aided navigation system are introduced in this paper, and improved UKF method is applied to the information fusion of integrated navigation system. Simulation experiments were carried out and simulation experiments show that the novel integrated navigation system proposed in the paper can get better performance of position precision comparing to the traditional Kalman filtering methods. The simulation experiment results suggest that the improved UKF method applied in the gravity passive aided navigation system is capable of greatly improving the long-time navigation position precision, attitude precision and velocity precision comparing with the traditional information fusion method.
  Keywords: gravity passive aided; strapdown inertial navigation system; gravity gradient; improved unscented Kalman filter.
  
  
• A High Precision Recognition Method for Abnormal Data in An Optical Network based on Data Mining  
  by Jinkun Sun 
  Abstract: In order to overcome the problem that abnormal data in optical networks affect the quality of data transmission and result in the loss of data information. In this paper, a new high-precision method for abnormal data recognition in an optical network based on data mining is proposed. The acquisition system is used to collect data in the optical network, and photoelectric conversion is carried out. The collected data are filtered and processed by a filter. A segmentation method is used to extract the sequence features of the data. A distance method is used to complete feature matching to realize the recognition of abnormal data in the optical network. The experimental results show that, compared with the three traditional methods for abnormal data recognition in optical networks, the proposed method has higher recognition accuracy and speed, which can detect abnormal data in optical networks quickly and accurately, and ensure the quality of data transmission.
  Keywords: Data mining; Optical network; Abnormal data; High precision; Recognition method.
  
  
• Wireless Energy Harvesting for Nakagami fading channels  
  by Nadhir Ben Halima, Hatem Boujemaa 
  Abstract: This paper deals with wireless energy harvesting using Radio Frequency (RF) signals for Nakagami channels. We derive the Packet Error Probability (PEP) of different Relay Selection (RS) techniques where all nodes harvest energy from RF signal received from node H. Node H can be a base station. We choose the harvesting duration to maximize the throughput. Our analysis is valid for Nakagami fading channels with arbitrary fading figure of different links.
  Keywords: Radio Frequency (RF) Energy harvesting; cooperative communications; optimal harvesting duration; throughput analysis.
  DOI: 10.1504/IJSNET.2019.10026365
   
  
• Multiobjective Optimization Model and Algorithm for Data Center Scheduling in Elastic Optical Networks  
  by Xia Li, Zhanqi Xu, Yuping Wang, Chunxia Ji 
  Abstract: How to properly assign data centers in EONs is a particularly important issue, which determines not only the quality of service (QoS), but also the cost of data centers and their operational expenditures. In this paper, we first propose a multiobjective optimization model for data center scheduling (DDEA/RSS) on EONs and design an evolutionary algorithm using the framework of NSGA-II to solve the model when the total number of data centers deployed in EONs and the call request blocking probability are considered. Then, we design three heuristic routing algorithms to construct routes between a pair of nodes based on deployment of data centers. The experimental results on both small-scale and large-scale network topologies indicate that DDEA/RSS produces the lower call request blocking probability under the same number of data centers in EONs and the smaller number of data centers under the same call request blocking probability.
  Keywords: Elastic Optical Network; Multiobjective Evolutionary Technique; DatarnCenter Placement; Data Center Assignment.
  
  
• Real-time detection of burst faults of key nodes in optical transmission networks based on a firework algorithm  
  by Peng Wang, Ningchao Zhang 
  Abstract: In order to overcome the problems of poor real-time performance and low fitting degree of real-time detection methods for burst faults of key nodes in existing optical transmission networks, a new real-time detection method for burst faults of key nodes in optical transmission networks based on fireworks algorithm is proposed. Firstly, this method builds a real-time detection framework for burst faults of key nodes in optical transmission network. Based on this framework, fireworks algorithm is used to identify key nodes in optical transmission network. The recognition results are used as the basis of information of key nodes in optical transmission network. Based on similarity analysis, the trust degree of key nodes is evaluated, and the decision function of burst faults is set up. According to the trust value, the decision function of burst faults is set to determine whether the key nodes have burst faults, and the real-time detection of burst faults of the key nodes is completed. The experimental results show that, compared with the traditional methods, the proposed real-time detection method for burst faults of key nodes in optical transmission network based on fireworks algorithm greatly improves the real-time performance and fitness, fully demonstrating that the proposed real-time detection method for burst faults of key nodes in optical transmission network based on fireworks algorithm has better detection effect.
  Keywords: Fireworks algorithm; Optical transmission; Key nodes; Burst faults; Detection;.
  
  
• Optimization of delay tolerance in wireless sensor networks based on unscented Kalman filter estimation  
  by Zhongli Shen, Guozhu Yao, Qiyue Xie, Fei Jiang 
  Abstract: Delay tolerance in wireless sensor network can be applied to complex and harsh communication environment. In order to improve the communication performance of delay tolerance in wireless sensor network, the optimization of delay tolerance in wireless sensor network based on Unscented Kalman filter estimation is studied. Firstly, the model of delay tolerance in wireless sensor network is established according to the theory of dynamics and position constraints, and then an unscented Kalman filter estimation algorithm is used. Through analyzing the basic state and measurement equation of delay tolerance in wireless sensor network model, the measurement equation of round-trip delay model is obtained. The pre handover time estimated by unscented Kalman filter is set, to effectively reduce the pre handover time and energy consumption, and optimize the delay wireless sensor network. The experimental results show that the delay tolerance of wireless sensor network optimized by the proposed method is within 30ms under different observation noises, and the packet loss rate and energy consumption are low.
  Keywords: Unscented Kalman filter; Estimation; Wireless sensor; Network; Delay tolerance; Optimization research.
  
  
• Energy-Efficient Routing under Delay Constraint in Duty-Cycle Wireless Sensor Networks  
  by Mengmeng Xu, Hai Zhu, Hengzhou Xu, Xiaofei Yang 
  Abstract: The duty-cycle scheme, in which a sensor node could switch between active and dormant states, is widely used in wireless sensor networks (WSNs) to reduce energy consumption and to improve network lifetime. In this paper, we investigate the problem of energy-efficient routing design from a sensor node to a sink node under a given delay constraint. The network topologies in duty-cycle WSNs are changed from one time-slot to another due to each sensor node's active/dormant schedule. A series of network topologies over a period of time are modeled as a virtual time-expanded graph. The time-expanded graph is constituted of the spatial links in each time-slot and the temporal links from one time-slot to another. Then, the problem of routing design on the time-expanded graph is defined, whose aim is to find a space-time path from a sensor node to the sink node with the minimum energy consumption under a delay constraint. Next, an Energy-efficient Routing algorithm in Time-expanded Graph (ERTG) is proposed to find the optimal space-time path. Simulation results validate the effectiveness of the proposed algorithm in energy savings.
  Keywords: Routing; Energy-Efficiency; Delay Constraint; Wireless Sensor Network; Duty-Cycle; Time-Expanded Graph.
  
  
• Cloud Service Security Evaluation of Smart Grid Using Deep Belief Network  
  by Liping Chen, Jun Liu, Weitao Ha 
  Abstract: In this paper, we analyse key security problems in the scalable platform architecture of cloud service of smart grid. We also establish evaluating process according to the lack of evaluation mechanism. They evaluate security risks in 5 respects which are policy and organisational risks, general technical risks, SaaS risks, PaaS risks and IaaS risks. The evaluation model based on deep belief network (DBN) is proposed which is composed of multiple RBMs and a BP neural network. The RBMs are trained by greedy training algorithm, and then BP algorithm is used to fine-tuning. After case verification, it is found that the various errors (including mean absolute error (MAE), mean relative error (MRE), mean square error (MSE), maximum error and minimum error) of DBN model are the smallest compared BP and AE model. It avoids the problem that multilayer neural network is trapped in the local optimum.
  Keywords: smart grid; cloud service security; scalable platform architecture; evaluating process; evaluating index system; evaluation model; DBN deep belief network; RBM; restricted Boltzmann machines; BP neural network; fine tuning.
  
  
• 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 ALABSI 
  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.
  
  
• 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.