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: .
  
  
• 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.
  
  
• 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.
  
  
• SemicNet: A semicircular network for the segmentation of the liver and its lesions  
  by Zhihua Zheng 
  Abstract: The traditional neural network used for medical image segmentation was not clear on the network depth. In view of these problems, we propose a convenient and efficient liver and lesion segmentation system, which uses a double-layer codec semi-circular network to combine the deep and shallow semantic information through dense jump connection, which is easier to be processed by the optimizer; The transition zone between liver and lesion segmentation is designed so that the result of liver segmentation can be effectively transmitted to lesion segmentation; We believe that the selection of complementary loss function combination for in-depth supervision can effectively receive the anti-propagation gradient signal and obtain more regularization effects. Finally, in terms of liver segmentation, in addition to the model with lower accuracy than multiple loss functions for joint decision-making, all other evaluation indexes, including lesions, exceeded the fusion results of multiple models.
  Keywords: Liver and lesion segmentation; Codec network; Dense connections; Transition zone; The depth of the supervision.
  
  
• A computationally efficient authentication and key agreement scheme for multi-server switching in WBAN  
  by Zisang Xu, Cheng Xu, Jianbo Xu, Xiangwei Meng 
  Abstract: Wireless Body Area Network (WBAN) is mainly used in the medical field. The wearable device in WBAN can monitor the physiological information of the patient and send information to a server. The doctor can remotely diagnose the patient by accessing the server in the hospital. As the patient's physiological information is sensitive, transmitting the data in the WBAN may reveal patient's privacy. Hence, WBAN needs a reliable authentication and key agreement scheme. In addition, each hospital or health care provider usually has a server that is independent of each other. Once the patient needs to change hospitals or health care providers to receive medical services, he/she needs to transfer his/her historical data to the new server, the process which is called multi-server switching. Most existing authentication schemes for WBAN either use a single-server model or do not consider multi-server switching issues. Therefore, we propose a computationally efficient mutual authentication and key agreement scheme for multi-server switching in WBAN. Our scheme ensures that patients in WBAN can implement secure switching servers at any time in a multi-server environment, and it is also lightweight enough because only hash function operations, XOR operations, and symmetric encryptions/decryptions are employed. Our scheme proves to be secure under the Real-Or-Random (ROR) model and ProVerif. In addition, compared with related schemes, our scheme solves the server switching problem while reducing the computational cost.
  Keywords: authentication; cryptography; key agreement; WBAN.
  
  
• Novel greedy grid-voting algorithm for optimization Placement of multi-camera  
  by Hocine Chebi 
  Abstract: The optimal placement of surveillance cameras can be used for other purposes such as network performance on information detection, in addition to improving the efficiency of surveillance in public places as well as the cost of the installation. Determining the configuration of exposure routes to ensure optimal coverage is essentially a combinatorial optimization problem. In this paper, we proposed two approaches to identify and locate the location of cameras in an area of interest with planning already done at the site. The major contribution of the paper is to propose the modification on the greedy grid voting algorithm, which can control the overlap between each cameras coverage based on the application requirements. The proposed method gives preference to cover unique regions first. Unlike other existing greedy methods, the greedy grid-voting algorithm doesnt follow any execution order for optimization purposes. We also modified the traditional global greedy algorithm and it produced better coverage of the area of interest compared to the greedy approaches used to calculate the maximum coverage. The proposed methods of optimization methods are evaluated in an examination hall. The results obtained tell us that the approach ensures total coverage with a minimum of sensors than other techniques exist in the literature.
  Keywords: Placement of multi-camera; video surveillance; combinatorial optimization; sensors network.
  
  
• Application of convolutional neural networks and image processing algorithms based on traffic video in vehicle taillight detection  
  by Ning Cao, Wei Huo, Gangshan Wu 
  Abstract: With the sharp increase in car ownership, frequent traffic accidents have caused huge losses to the national economy and people's lives. How to take effective measures to assist the safe driving of vehicles has become a hot issue in today's traffic safety research. The headlight of a vehicle is an important way to exchange information with surrounding vehicles while the vehicle is running. In the process of assisted driving, accurately understanding the linguistic information transmitted by surrounding vehicles is the prerequisite for making correct driving decisions. In this paper, the neural network is partly used for vehicle detection. The recognition of the front vehicle taillights is based on the taillight recognition mechanism and image processing technology. The taillights are then positioned by using their colour and shape characteristics. The RGB and CMY colour spaces are used to establish a taillight recognition mechanism to detect the taillight status of the front car, thereby the driving intention of the front car is understood. The experimental results show that the method can accurately identify the state of the front taillights during the day.
  Keywords: driving assistance; vehicle detection; taillight detection; signal recognition.
  
  
• Parallel Cuckoo Search for Cognitive Wireless Sensor Networks  
  by Tong Bang Jiang, Jeng-Shyang Pan, Yu Mo Gu, Shu-Chuan Chu 
  Abstract: In cognitive wireless sensor networks(CWSNs), although each sensor node has the characteristics of cognitive radio, the limited energy of the sensor node is still the core defect that restricts its comprehensive network performance. This paper proposes a novel medoids generation mode named Parallel Cuckoo Search medoids (PCS-medoids) algorithm with a new communication strategy in Cuckoo Search(CS) to manage the energy consumption in CWSNs efficiently. The PCS-medoids can match cluster heads dynamically and get optimal cluster heads in CWSNs. Firstly, in order to speed up the convergence of CS, an improved Parallel Cuckoo Search algorithm(PCS) is proposed, then, the PCS is applied to k-medoids to get cluster heads quickly. Finally, the PCS-medoids is presented to manage the consumption of each sensor node. First experiment results illustrate that PCS tends to get optimal solutions quickly and accurately compared to CS and PSO. The other experiment results demonstrate that PCS-medoids has advantage over energy management in CWSNs compared to low-energy adaptive clustering hierarchy, LEACH-centralized, and hybrid energy-efficient distributed clustering. In addition, the advantage is more obvious with the increase of sensor nodes in CWSNs.
  Keywords: Cognitive Wireless Sensor Networks (CWSNs); Parallel Cuckoo Search medoids (PCS-medoids); Energy management.
  
  
• A Dynamic Resource Assignment Scheme with Aggregation Node Selection and Power Conservation for WBAN Based Applications  
  by Mahfuzulhoq Chowdhury 
  Abstract: Wireless body area network (WBAN) is regarded as one of the crucial\r\ntechnology to monitor patient health status by collecting real-time health-related data with the help of interconnected human body sensors. For real-time health status monitoring and treatment, the development of an efficient resource management scheme is an important research issue for WBAN based medical applications. To cope with the different requirements of WBAN based applications, the coordination of cluster head\r\nselection, dynamic medium access control mechanism (MAC) considering both critical and normal data transfer, end-to-end connectivity, data transfer to multiple processing and destination device, intermediate node selection, and proper path selection, is mandatory.\r\nWBAN sensor nodes energy power conservation and network lifetime maximization is another major research challenge for WBAN based medical applications. The existing resource management scheme suffers from higher delay and energy power consumption due to a lack of coordination between MAC and routing protocol along with the cluster head nodes huge working load. In this paper, a dynamic resource assignment scheme\r\nis proposed that assigns bandwidth and medical server resources to WBAN users for\r\ndata transfer without any backoff delay and incorporates a power conservation approach\r\nfor WBAN members. To minimize the cluster head node working load, the proposed\r\ndynamic resource management scheme selects both the cluster head node and aggregation\r\nsensor node for the aid of WBAN sensors. This paper compares the proposed dynamic\r\nresource assignment scheme against a random resource assignment scheme in terms of\r\ntotal delay overhead, energy overhead, transmission latency, goodput, number of nonalive nodes, packet delivery, remaining energy, network stable, and unstable lifetime. The\r\nevaluation results show that the proposed dynamic resource assignment scheme performs\r\nmuch better results than the compared random resource assignment scheme in terms of\r\nthe delay overhead, energy overhead, goodput, and the network lifetime.
  Keywords: Wireless body area network (WBAN); Resource Assignment Scheme; Power Conservation; Cluster Head Selection; Goodput; Network Lifetime.
  
  
• Realtime Soldier's Health Monitoring system Incorporating low power LoRa communication  
  by Bhargav Jethwa, Milit Panchasara, Abhi Zanzarukiya, Rutu Parekh 
  Abstract: In this paper, we have implemented a low-cost embedded system developed for soldiers assistance. The system consists of interconnected body sensor networks for real-time health monitoring and environmental analysis of the soldier and the communication is done with the base station using LoRa module. The data collected from each sensor is processed using a robust and steady algorithm to decide the health and environmental conditions of the soldier. The health status is further classified into healthy, wounded or dead. All the collected data along with the obtained results about the soldier's health condition are then encrypted and transmitted to the base station securely. The system uses only 3.2 Wh energy making it energy efficient to extend the operational time. If we use a commercially available 50,000 mAh battery, operating at 5 V, with the worst-case current drawing capacity of 90\%, the system could remain functional for about 75 hrs. The information can be transmitted up to 700 meters at 2400 baud rate and could be increased further by reducing the baud rate.
  Keywords: LoRa; BSN; ECG; IMU; NMEA; WBASN; AES encryption; Signal processing.
  
  
• Dual Hop Relaying using CDMA and Reconfigurable Intelligent Surfaces (RIS)  
  by Sami Touati, Rachid Sammouda, Musaed A. Alhussein 
  Abstract: In this paper, we suggest the use of Reconfigurable Intelligent Surfaces (RIS) in dual hop relaying. There are two hops, the first one uses Code Division Multiple Access (CDMA) and the second one uses RIS as transmitter or reflector. We show that the incorporation of RIS allows to increase the throughput of conventional CDMA systems. The throughput of dual hop relaying using a combination of CDMA and RIS is larger than that of dual hop relaying based entirely on CDMA.
  Keywords: Code Division Multiple Access (CDMA); Reconfigurable Intelligent Surfaces (RIS); throughput analysis; Block error probability.rn.
  
  
• A Novel Offloading Strategy for High Speed Mobile and Data Intensive Intelligent Sensor  
  by Changming Zhao, Mingdong Li, Tiejun Wang, Hao Yang 
  Abstract: In the paper, it proposes a novel offloading computing strategy for the mobile intelligent sensors in the high speed mobile scene. At present, the local computing resources are not able to afford the computing demand for the data intensive sensors. The current offloading computing mechanism are not capable of approaches for offloading computing in high speed scene that it may cause extremely serious delay by the handover delay and the wireless channel fading. In the paper, it proposes a novel strategy named Asynchronous Parallel Offloading Computing Strategy (APOCS). It makes use of one type of spatial computing diversity method to restrain the wireless channel fading.. The simulation results show that it is able to reduce the offloading delay caused by wireless channel fading in the high speed moving scene effectively.
  Keywords: Parallel Computing; Edge Computing; Offloading; fast fading.
  
  
• Low-Delay Fair-Reliability Scheduling in Multi-hop IEEE802.15.4e Time Synchronized Channel Hopping Networks  
  by Junhua Zhang, Yuanyi Wang, Zhenqian Wu 
  Abstract: IEEE802.15.4e is the latest generation of high-reliability, low-power medium access control protocols. As part of IEEE802.15.4e, Time Synchronized Channel Hopping (TSCH) provides support for multi-hop and multi-channel communications. IEEE802.15.4e only defines when a schedule is executed; it does not specify a concrete resource schedule strategy. In this study, an IEEE802.15.4e TSCH scheduling algorithm called Fair Reliability Scheduling Algorithm (FRESA) is proposed to focus on both less delay and fair reliability. Through leaf node prior and multiple nodes sharing the same resource, there is less delay to transmit messages. Balancing the assignment of different qualities of channel offset guarantees fair reliability in message transmission. We design two strategies to ensure fair reliability, one based on per Link and the other based on per Path, and then accordingly divide our algorithm into FRESA-L and FRESA-P. Simulation results demonstrate our algorithms performance. We also observe that FRESA-P can achieve higher reliability than FRESA-L.
  Keywords: IEEE802.15.4e; time synchronized channel hopping; wireless sensor network; industrial Internet of Things; fair reliability.
  
  
• Heuristically Accelerated Reinforcement Learning for Channel Assignment in Wireless Sensor Networks  
  by Mohamed Sahraoui, Azeddine Bilami, Abdelmalik Taleb-Ahmed 
  Abstract: In Wireless Sensor Networks (WSNs), multi-channel communication represents an attractive field due to its multiple advantages in improving performance that has become necessary in many applications in recent fields such as the Internet of Things (IoT). The main axes that determine this performance are throughput and delivery rate, while the major challenge that faces WSNs is the drain of energy. For this reason, various approaches have been proposed for the design of distributed resource allocation schemes in an energy-efficient way. Thus, to overcome the problem of both channel assignment and scheduling in the schedule-based distributed WSNs, Reinforcement Learning (RL) approach for channel assignment is used. However, the use of the RL approach requires a number of iterations to obtain the best solution which in turn creates a communication overhead and time-wasting. In this paper, a Heuristically Accelerated Reinforcement Learning approach for Channel Assignment (HARL CA) in schedule-based distributed WSNs is proposed to reduce the number of learning iterations in an energy-efficient way. The proposal considers the channel chosen by the other neighboring sender nodes as external information and uses it to accelerate the learning process and avoid collisions. Also, it takes into account the bandwidth of the used channel as an important factor in the scheduling process to increase the delivery rate. The results of extensive simulation experiments show the effectiveness of our approach in improving the network lifetime and performance.rn
  Keywords: Wireless sensor networks; Multi-channel; Reinforcement learning; IoT; Energy efficiency.
  
  
• Design and analysis of telemedicine authentication protocol  
  by Lijuan Zheng, Chunlei Song, Rui Zhang, Baoqing Lv, Yujin Liu, Meng Cui, Lili Meng 
  Abstract: In recent years, in order to protect the security of medical privacy data, a large number of telemedicine authentication protocols have been proposed. Some protocols use digital signature methods to achieve two-way authentication among patients, doctors and medical servers, so the execution efficiency of protocol is low. Some protocols fail to achieve patient identity anonymity, the true identity of patient can be analysed by the doctor, so it cant resist security attacks. In order to solve the above problems, a more secure telemedicine authentication protocol is proposed in this paper, which implements two-way identity authentication in the telemedicine authentication model using SHA-1 hash function, time stamp and random number operations. In addition, the introduction of temporary identity can achieve patient identity anonymity and personal medical data security. The protocol has better performance in terms of security, storage and computing overhead, it is more suitable for telemedicine authentication system.
  Keywords: Telemedicine; privacy protection; authentication protocol; digital signature; identity anonymity; hash function; time stamp; random number; temporary identity; performance analysis.
  
  
• TVDD: Topology based Vehicular Data Dissemination scheme for Stability Optimization in IoV  
  by Richa Sharma, Teek Parval Sharma, Ajay Kumar Sharma 
  Abstract: Internet of vehicles (IoVs) nowadays has opened the door to a myriad of advancements in safety, mobility, and in environmental aspects. IoV extends Vehicular Ad-Hoc Networks (VANETs) by integrating IEEE 802.11p with other cellular networks like Long Term Evolution (LTE), 5G etc. Although, due to core challenges like high mobility, variable density, frequent fragmentation and the hostile terrain types in vehicles, there is a greater need to improve data dissemination interfaces for the IoV. However, network disconnection due to vehicle mobility and/or absence of vehicle may result in connectivity loss, and eventually, information dissemination is disrupted. To mitigate these challenges, this paper proposes an efficient group-based topology management vehicular data dissemination (TVDD) scheme that focuses on improving the stability of groups and meeting users quality of service (QoS) requirements. Continuous service availability is achieved by grouping vehicles in intra-grouping and inter-grouping domains. Group heads (GHs) are selected for avoiding continuous connectivity voids by optimizing the stability factor. Furthermore, for holistic data delivery a hybrid data forwarding approach based on LTE-V is proposed. Simulations are carried out using Network Simulator (NS3) and mobility simulator Simulation Mobility (SUMO). Encouraging results are obtained in terms of grouping overhead, grouping stability and packet delivery ratio stable data delivery.
  Keywords: IoV; DSRC; LTE-V; V2X.
  
  
• Trust and grey model based data aggregation algorithm  
  by Wang Jun, Wang Ni, Li Li, Du Yansui 
  Abstract: The reliability of data aggregation is an important problem in wireless sensor networks. Therefore, Trust and Grey Model based Data Aggregation Algorithm (TGDA) is proposed. The algorithm combines trust mechanism, data prediction and data aggregation. Before initiating data aggregation, the cluster heads will detect abnormal nodes whose trust value is lower than a certain value, and add the abnormal nodes into the blacklist. According to the high time dependence of data collected by sensor nodes, the grey model is used to predict the missing data of the abnormal nodes, and finally the cluster heads aggregate the trusted data. Simulation results show that compared with other algorithms, TGDA algorithm can effectively improve the security and accuracy of data aggregation under the premise of ensuring the network life cycle.
  Keywords: trust mechanism;data prediction; grey model; data aggregation.
  
  
• Design and Analysis of Network Adaptive Coding Protocol Based on Dynamic Feedback  
  by Wei Zhang, Yangyang Bai, Xiarui Li, Renjie Zhou 
  Abstract: Traditional wireless network coding methods often fail to achieve ideal collection efficiency under single-point-to-multipoint multi-layer networks, and the Coupon Collectors Problem aggravates the collection delay. This paper divides the network hierarchy into the source node, relay node and sink node, and proves the collection delay effect in the multi-layer network through Markov chain analysis. Then we propose to use a small amount of feedback information to reduce the collection delay and the number of invalid codewords and derive a new degree distribution formula. We propose a dynamic feedback adaptive coding (DFAC) model and propose an adaptive coding protocol (DFACP) based on DFAC. It shows that the DFAC model is superior to the traditional LT Codes and Growth codes protocol in terms of collection efficiency and other indicators, and the scheme has strong adaptive robustness in dynamic node changes.
  Keywords: network coding; dynamic feedback; degree distribution; Coupon Collector’s Problem; multi-layer network; Markov chain of stochastic process theory; real-time collection;.
  
  
• Stable Backbone-based Geographic Routing by Using Traffic Lights as Bridges in Vehicular Ad Hoc Networks  
  by Reyhane Gazori, Ghasem Mirjalily, Laleh Eslami 
  Abstract: Routing is very challenging in vehicular ad hoc networks due to the highly dynamic nature of the vehicles. In this paper, a position-based routing protocol is proposed, which leverages the backbone structure. This protocol contains a bridge node selection algorithm and a route updating algorithm. The first algorithm considers traffic lights at the intersections as the bridge nodes, which evaluate the routes by exchanging control packets, and select the best route regarding the path weights. In the second one, bridge nodes update the route. Destination informs the bridge nodes about its previous and new locations when passing an intersection, before the message is received. Bridge nodes forward the message to the new location of the destination through a more reliable path with lower hop counts. Simulation results demonstrate that the proposed protocol outperforms the similar existing method in terms of the end-to-end delay, packet delivery ratio, and network overhead.
  Keywords: Routing; Geographic routing; Backbone; Traffic Light; Vehicular ad hoc networks.
  
  
• Energy-Aware Rate Adaptation for Computational RFID  
  by Ruiqin Bai, Jumin Zhao, Dengao Li, Xiaoyu Lv, Qiang Wang 
  Abstract: Rate adaptation is an effective method to maximize the transmission goodput, especially for the passive sensing systems with a more variable channel environment and unstable energy states. Most existing approaches share three common drawbacks: excessive energy consumption, not in real-time, and not taking into account the energy of Computational Radio Frequency Identification (CRFID). We propose an Energy-Aware Rate Adaptation Algorithm for CRFID to solve these problems, which use new channel detection methods, and design an adaptive time window algorithm to balance the statistical characteristics of the data and the real-time requirements of the channel. Also, we establish a model of data rate and required energy, considered the impact of different data rates on energy consumption, and design a new CRFID runtime mechanism. We implement an algorithm on the Wireless Identification Sensing Platform (WISP) CRFIDs and commercial RFID reader, compared to the state-of-the-art scheme, the goodput gain and energy efficiency of our design are much improved under the same energy environment.
  Keywords: Rate Adaptation; Computational RFID; Adaptive Time Window Algorithm; Energy Efficient.
  
  
• Deep Learning Techniques for Noise-Resilient Localization in Wireless Sensor Networks  
  by Nuha Alwan 
  Abstract: Deep learning techniques are highly attractive for extracting meaningful representations of data of the type encountered in wireless sensor network applications. In particular, these techniques lend themselves readily to the implementation of node localization in wireless sensor networks as the present work demonstrates. Anchor-based range-based time-of-arrival-measurement deep learning localization techniques are implemented both in the classification and in the regression modes using convolutional neural networks and standard deep neural networks. A performance comparison against gradient descent localization is carried out. Deep learning techniques exhibited better noise resilience for typical standard deviation ranges of the measurement noise, especially when used in the classification mode. Compressed sampling is also implemented for tracking a mobile node via deep learning regression techniques to increase energy efficiency.
  Keywords: Deep learning; classification; regression; neural network; wireless sensor network localization.
  
  
• CNN-based Anomaly Detection for Packet Payloads of Industrial Control System  
  by Joo-Yeop Song, Rajib Paul, Jeong-Han Yun, Hyoung Chun Kim, Young-June Choi 
  Abstract: Industrial control systems are more vulnerable to cyber threats owing to their network connectivity. More sophisticated cyber-attacks are making rule-based detection inadequate. Therefore, Intrusion Detection System (IDS) has been deployed but the existing IDS primarily uses the packet header information to perform traffic flow detection. IDS is inefficient to detect packet deformation; therefore, we propose the adoption of packet payload in IDS to respond to a variety of attacks and ensure high performance. We consider Convolutional Neural Networks (CNN) models, one of the deep neural networks, well known for image classification. The packet payload is converted to corresponding images to match the CNN inputs. We use N-gram along with a preprocessing method for performance enhancement. Our proposed model detects packet modification and traffic flow by inspecting each packet and sequence of packets. We generate abnormal data to address data imbalances without abnormal traffic during learning and testing. We have analyzed the accuracy of the packet detection and sequence detection models. For evaluation, cross-verification is conducted to increase the reliability of the statistics.
  Keywords: Network security; Intrusion detection system; Anomaly detection; Convolution neural network.
  
  
• Smart Speed Advisory System for Drivers with Priority Assignment for Smart City  
  by Nazbanoo Farzaneh, Omid Shafaiy 
  Abstract: Nowadays, the problems of air pollution, and the non-optimal fuel consumption have become critical in big cities. Using speed advisory systems can reduce the effects of those problems. In such systems, a certain speed is recommended to the driver which helps the driver to cross the intersection with minimum stopping times. In this paper, a method is presented on drivers cell phones in two stations: data-collecting station and data-consuming station. Optimal speed is calculated in a way to let the vehicle pass the intersection with minimum stopping times. Furthermore, the proposed method uses the priority parameter for facilitating the movement of emergency vehicles. The simulation results show that the proposed method, in comparison with no-advisory systems, acts, respectively, 90%, 25%, and 40% better regarding the number of stopping times, pollutants spread, and fuel consumption when the traffic is not heavy. The trip time of emergency vehicles improves up to about 30%.
  Keywords: Internet of thing; Smart city; Intersection management; air pollution; Optimal driving; Priority assignment; Speed advisory system.
  
  
• Ultrasonic Sensor Based Social Distancing Device  
  by Mohit Ghai 
  Abstract: Corona virus disease (Covid-19) is anrngetting part of it .In simple words, it simply getrntransmitted from one person to many person byrnhaving a physical contact. The Covid-19 can bernstopped from spreading by frequent washing handsrnwith soap or sanitizing with hand sanitizers and byrnmaintaining certain social distance while interactingrnwith the other person who might be corona affected.rnAvoiding social gathering, make certain distancernwhile standing in queue, dont touch face unnecessaryrnand most importantly stay hydrated.rninfectious disease which is caused by a newly discoveredrnvirus, known as the corona virus that comes under therncategory of SARS. It has been originated in Wuhan,rnChina and has spread across the whole world like thernfire in the forest. Due to increase in the pandemic dayrnby day, the number of people infected by it is in millionsrnand the deaths are in lakhs. The transmission of virus isrnthrough physical contact from person to person / objectsrnand gets multiply to pass to community. Till date there isrnno medical vaccine that has been prepared to combatrnthis deadly virus. The only technique which can help tornreduce the corona virus is social distancing. This paperrnproposes a device that gives an actual and real timerninformation observed by device. It helps the person tornremind of having social distancing while interacting withrnany person / object. The hardware, run by the Arduinornprogrammed software, is so small that it can be easilyrncarried to any place easily. The main components ofrndevice is an ultrasonic sensor which measures therndistance, an alarm/led is connected to alert the user tornmaintain the social distance from it.rn
  Keywords: Covid-19; Pandemic; Social Distancing,rnArduino; Ultrasonic Sensor; Alarmrn.
  
  
• Exploring Real-Time Super-Resolution Generative Adversarial Networks  
  by Xiaoyan Hu, Zechen Wang, Xiangjun Liu, Xinran Li, Guang Cheng, Jian Gong 
  Abstract: Image super-resolution is an essential technology for improving user quality of experience of Internet videos. As the state-of-the-art deep learning- based super-resolution technology, the Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) has the best performance in the perceptual quality of reconstructed images. However, our evaluation suggests that the time-consuming feature of ESRGAN makes it unqualified for super-resolution at the client side during Internet video delivery. The image super-resolution by ESRGAN based on the limited compute capacity at the client side, nowhere near as powerful as GPU, can not satisfy the real-time playback demand. In contrast, the Efficient Sub-Pixel Convolutional Neural network (ESPCN) has the best real-time performance. However, users still can not enjoy smooth watching experiences with ESPCN and the perceptual quality of reconstructed images is much lower. This work proposes Real-Time Super-Resolution Generative Adversarial Network, referred to as RTSRGAN. RTSRGAN takes the advantages of ESRGAN in image perceptual quality and ESPCN in real-time performance so as to simultaneously satisfy the demands on the real-time performance and the resulting pleasant artifacts of super-resolution at the client side. Our experimental studies demonstrate that the reconstruction speed of RTSRGAN is as fast as, on the average, 15 images per second on a single 2.3GHz CPU (only 6 images per second by ESPCN), and its reconstructed images have a relatively acceptable perceptual quality. Namely, our proposed RTSRGAN can be used for super-resolution at the client side to enhance the real-time performance as well as ensure the image perceptual quality. We also find that RTSRGAN is suitable for restoring images with regularly changing texture features without requiring training for individual image categories.
  Keywords: Image Super-resolution; Real-time; ESRGAN; ESPCN; Network Structure.
  
  
• A variable tap-length DILMS algorithm with variable parameters for wireless sensor networks  
  by Ghanbar Azarnia, Mostafa Hassanlou 
  Abstract: In most applications, the estimation of the filter length, along with the adoption of the filter coefficients, is an exact requirement. Accordingly, followed the stand-alone adaptive filters, the variable tap-length adaptive estimation has recently been considered in wireless sensor networks. Among these, the fractional tap-length (FT) distributed incremental least mean square (DILMS) algorithm is of particular interest due to its superior performance. However, the main drawback of this method is that its performance, especially under high noise conditions, depends on the suitable selection of the length adaption parameters. On this basis, in this paper, a variable tap-length algorithm with variable parameters is proposed where the parameters of the FT algorithm is adapted according to the error correlation. Such an adaptation will lead to good performance, even under high noise conditions. The performed simulations also confirm such improved performance.
  Keywords: Wireless sensor networks; Variable tap-length; Incremental topology; Adaptive parameters; DILMS algorithm.
  
  
• Securing Wake Up Radio for Green Wireless Sensor Networks Against Denial of Sleep Attacks  
  by Amine Kardi, Rachid Zagrouba 
  Abstract: This paper proposes the Distributed Cooperation Model (DCM) based Hierarchical Smart Routing Protocol (HSRP) to counter the Denial of Sleep (DoSL) attacks in Green Wireless Sensor Networks (GWSNs). DCM solution combined with the Dynamic Elliptic Curves-RSA (DECRSA) algorithm is used to provide high security level and protect Wake-Up Radio (WUR) for GWSNs against DoSL attack targeting the energy resources of sensors. The used distributed schema ensures the load balancing in the network through the selection of a new Supervisor Node at the beginning of each round based on a special equation taking into account different metrics such as the remaining energy and the number of neighbor nodes. Simulation results conducted under the NS3 simulator show that the proposed DCM helps to effectively protect the GWSNs against the DoSL attacks and improves the network lifetime by more than 60% when three malicious nodes are used. Obtained results show also that the DCM extends either the period of stability and instability to the maximum and helps to counter the fall in the number of alive nodes which cause the problem of isolated area.
  Keywords: Wireless Sensor Networks; Deny of Sleep attack; Hierarchical Smart Routing Protocol; secure routing.
  
  
• Vehicular cloud computing networks: availability modeling and sensitivity analysis  
  by Gabriel Araújo, Laécio Rodrigues, Kelly Oliveira, Iure Fé, Razib Khan, Francisco Airton Silva 
  Abstract: Vehicle ad hoc networks (VANETs) have emerged to make traffic more efficient andintelligent. Equipment known as Road Side Units (RSUs) can help control the flowof vehicle traffic. RSUs can act as sensors and as a provider of route information forvehicles. RSUs have processing, storage, and communication capabilities. However, RSUscan suffer from peak requests and non-functional data demands. Also, RSUs must beavailable as long as possible for traffic control to work satisfactorily. To overcome thisdeficiency, cloud computing can act as an additional resource, processing part of therequests. This combined architecture is called vehicular cloud computing (VCC). Thispaper uses stochastic Petri nets (SPNs) and Reliability Block Diagrams (RBD) to assessa VCC architectures availability and reliability with multiple RSUs. Two sensitivityanalyzes were performed that made it possible to identify the models components thathave the most significant impact. In addition to a base model, extended models withgreater redundancy were also proposed. The results showed that the extended model hadincreased the systems availability (A). The base model has obtained A = 97.68%, andthe extended model obtained A = 99.19%. Therefore, the models aim to help networkadministrators plan more optimized VANET architectures, reducing failures.
  Keywords: analytical modeling; VANET; sensors; smart traffic control; availability; stochastic Petri net.
  
  
• Physical Layer Security of Cooperative Wireless Networks with RF Energy Harvesting  
  by Faisal Alanazi 
  Abstract: The paper derives the Secrecy Outage Probability (SOP) and the Probability of Strictly Positive Secrecy Capacity (SPSC) for wireless networks with energy harvesting. The Base Station (BS) uses the received RF signal to harvest energy. The harvested energy is used to transmit data to the receiver $R$. The eavesdropper $E$ receives the signal from BS and try to decode it. We also derive the SPSC and SOP when there are $K$ relay nodes. The best relay is selected to amplify the BS signal to the receiver $R$.
  Keywords: Energy Harvesting; SOP; SPSC; Security.
  
  
• An Adaptive Leak Localization System based on a Multi-level Analytics Framework in Piping Network  
  by Wen-Hao Png, Horng-Sheng Lin, Chang-Hong Pua, Mau-Luen Tham, Faidz Abdul Rahman 
  Abstract: Leak localization is a growing concern in water distribution system (WDS) to reduce losses due to leakage. The conventional time-correlation analysis incorporates with acoustic sensing is a feasible leak localization technique in single pipeline system. However, the conventional approach is impractical and time-consuming in a piping network due to multi-directional transmission waves from the leak source. In this paper, we propose an adaptive leak localization system incorporating a remote-acoustic sensor network and a Multi-level analytics framework (MLAF) for piping networks. The MLAF overcomes the multi-directional waves issue in piping networks by aggregately analysing the multi-spatial acoustic signals from the sensor network. The system is adopted with an automated flow control algorithm to ensure time-effective localization without needs of human supervision. The performance of MLAF has been evaluated based on several emulated piping networks with various network topologies. The characterization results demonstrated high adaptiveness and location accuracies of the proposed system in dealing with various piping networks. The excellent results of field prediction in a local district metered area (DMA) has further confirmed the feasibility of the proposed leak localization system.
  Keywords: pipeline monitoring; wireless sensor network; leak localization; data analytics; path analysis; clustering analysis.
  
  
• Link Quality Estimation Method Based on Gradient Boosting Decision Tree  
  by Y.A.N. ZHANG, JIAN SHU 
  Abstract: In wireless sensor networks application, link quality estimation is the primary problem to guarantee the reliable transmission of data and the upper layer network protocol's performance. Effective link quality estimation methods can not only objectively evaluate link quality, link stability, and agility, but also provide a guarantee for data transmission. To accurately evaluate link quality, a link quality estimator based on gradient boosting decision tree (GBDT) was proposed. The physical layer parameter average received signal strength indication, mean link quality indicator and mean signal noise rate selected as the input of the GBDT estimator. The nonlinear correlation between physical layer parameters and packet received rate is analyzed by using the maximum information coefficient method. Considering the influence between outliers and different dimensionality of parameters, we used the boxplot method to carry out smoothing and normalization processing to reduce the complexity of the estimator. Finally, the improved particle swarm optimization algorithm is used to select the optimal parameter combination in the GBDT estimator. The experimental results show that compared with the Support Vector Machine (SVM) estimator, the estimator of this study has higher accuracy and stability.
  Keywords: Wireless sensor networks; link quality;gradient boosting decision tree; maximum information coefficient; particle swarm optimization.
  
  
• The QoS Evaluation Model for Cloud Resource Node  
  by Dan Liu, Leilei Zhu, Zetian Zhang, Yan Zeng, Zhengqi Bai, Li Li 
  Abstract: The service quality of resource nodes in a cloud service system can reflect the capability of the system in providing users with services. In this paper, we focus on the evaluation of cloud service quality to build Cloud_MQOSF, a cloud service QoS evaluation framework with availability, reliability, service performance, and scalability as the evaluation indexes. This architecture can rapidly and stably evaluate quantitatively the service capabilities of resource nodes in a cloud service system. Several sets of experiments are carried out using the operational data of a well-known cloud service provider and the Quality of Web Service dataset. The experimental results show that the model not only effectively evaluates the quality of service of resource nodes in a data center, but is also fast and stable. This provides a favorable basis for resource allocation in the cloud.
  Keywords: quality of service; resource node; Cloud_MQOSF.
  
  
• A Prediction and Budget-aware Offloading Scheme for Wearable Computing  
  by Mahfuzulhoq Chowdhury 
  Abstract: Wearable devices are very much popular nowadays due to their computing, communication, and sensing abilities. Limited computation, battery power, and storage capabilities hinder the growth of many emerging latency-sensitive wearable device-based applications. Cloud computing has been emerged as a promising technology to wearable devices for their latency-sensitive, energy-aware, and computation-intensive application processing through a process named computation offloading. At present, the existing works on wearable computing suffered from higher latency, energy, and user payment due to lack of wearable device users mobility prediction, budget-aware offloading, deadline, resources availability, cooperation among other users, and energy cost awareness knowledge. This paper studies how to minimize the users payment cost of offloading wearable device tasks and maximize network service providers' profit under the hybrid wired-wireless networks along with how to satisfy wearable device task execution requirements and user budget. This paper proposes a prediction and budget-aware task offloading for wearable computing by investigating the wearable device users' payment, mobility, cloud, and network resource availability, and task requirements. rnThis paper develops a numerical model to analyze the performance of the proposed prediction and budget-aware offloading scheme for wearable computing that consists of the key performance metrics: makespan delay, user payment cost, energy cost, user utility, and service provider profit. The effectiveness of the proposed prediction and budget-aware offloading scheme for wearable computing is evaluated by comparing its results with the baseline greedy and independent resource selection scheme. The experimental results demonstrate that our proposed scheme provides a significant performance gain than the compared greedy and independent selection scheme in terms of makespan delay, user payment cost, users utility, energy cost, and service provider profit.
  Keywords: Wearable devices; Wearable Computing; Task Offloading Scheme; User Payment and Budget; Service Provider Profit.
  
  
• Optimal Deployment of a Distributed IoT Acoustic Surveillance System  
  by Manal Omer Bin Hamza, Leena Alghamdi, Mohamed K. Watfa 
  Abstract: Sensing using sound waves is extensively used in many medical, industrial and surveillance applications and they are especially advantageous in conditions where radio frequency communications are inoperative. In this study, we propose a setup for a high-efficiency surveillance system where we study the deployment of distributed IoT Acoustic Sensors using a real-time scenario. Simulation results indicate that in an ideal situation, with no obstacles, the proposed sensor count remained constant with a 99% efficiency, even with an increase in the count of dangerous events. We observed a rise in the count of sensors with the increase in obstacles and the total area under study. Our study also confirms that any increment beyond the obtained optimal count of the sensors will not necessarily affect efficiency. Moreover, the Total Detection Rate verified that the optimal placement of the acoustic sensors requires fewer sensors than any randomly placed sensors, to achieve a 99% efficiency. To our knowledge, the proposed research work in this paper specific to acoustic surveillance using a non-traditional IoT cloud architecture has not been previously investigated in the literature.
  Keywords: surveillance; acoustic; simulation; IoT; internet of things; sensors; sound detection; regression; obstacles; dangerous events; optimal sensors; optimal deployment.
  
  
• Joint Users\' Pairing and Resource Allocation for Non Orthogonal Multiple Access  
  by Ghassan Alnwaimi 
  Abstract: In Non Orthogonal Multiple Access (NOMA), the base station transmits data to weak and strong users randomly chosen. In this paper, we suggest two strategies for users\' paring maximizing the average or instantaneous total throughput. We also perform joint users\' pairing and resource allocation. We choose the best bandwidth and power allocation as well as user pairing in order to maximize the instantaneous or average total throughput. NOMA average and instantaneous total throughput maximization by smart users\' pairing as well as bandwidth and power allocation is novel and not yet suggested. Our results are based on users\' ranking using instantaneous channel gains for Rayleigh channels. Instantaneous throughput maximization allows 1-6 dB gain with respect to average throughput maximization.
  Keywords: Non Orthogonal Multiple Access (NOMA); Users\' pairing; Resource allocation.
  
  
• Non Orthogonal Multiple Access for DS-CDMA Systems  
  by Raed Alhamad, Hatem BOUJEMAA 
  Abstract: In Direct Sequence Code Division Multiple Access (DS-CDMA), the symbols of a single user are transmitted over a given spreading code. In this paper, we propose to increase the throughput of CDMA systems using Non Orthogonal Multiple Access (NOMA). The base station transmits a combination of $N$ symbols dedicated to $N$ NOMA users using the same spreading code. The $i$-th NOMA user performs despreading then it detects the symbol of weakest user. The $i$-th user performs Successive Interference Cancelation (SIC) to remove the contribution of the signal of weakest user and detects the symbol of the second weakest user. SIC is repeated until the $i$-th user detects its own symbol. We show that CDMA using NOMA offers a larger throughput than CDMA.
  Keywords: NOMA; DS-CDMA; Throughput analysis.
  
  
• Enhanced Physical Layer Security using Reconfigurable Intelligent Surfaces (RIS)
  by Faisal Alanazi 
  Abstract: The paper derives the Secrecy Outage Probability (SOP) and the Probability of Strictly Positive Secrecy Capacity (SPSC) using Reconfigurable Intelligent Surfaces (RIS). We show that the use of RIS improve the security of physical layer by 10-40 dB with respect to conventional wireless systems without RIS. The system model contains a source $S$, a destination $D$, RIS and an eavesdropper $E$. RIS is deployed as a reflector or a transmitter. Reflections on RIS arrive with the same phase at the destination while the eavesdropper receives signals on a direct link between $S$ and $E$.
  Keywords: Physical Layer Security; Secrecy Outage Probability (SOP); Probability of Strictly Positive Secrecy Capacity (SPSC); Reconfigurable Intelligent Surfaces (RIS)