International Journal of Sensor Networks (64 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: .
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.
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.
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;.
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.
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).
A Petri nets based approach for the optimization of surveillance patrols
by Marwa GAM, Dimitri Lefebvre, Lotfi Nabli, Achraf Jabeur Telmoudi
Abstract: The aim of this paper is to prevent the risks in several industrial domains. A systematic monitoring approach is proposed that ensures security and safety by performing a set of the surveillance tasks with mobile agent and sensors. For this purpose, we propose contributions to configure the mobile wireless sensor networks system and to plan the trajectories of the mobile agents. The Petri nets is used as a modeling tool to monitor the environment and to describe and compute the trajectories of the robots. The objectives are reformulated as, on the one hand, an initial marking optimization problem and as a firing sequence calculation, within the framework of the Petri network on the other hand.
Keywords: Security; Monitoring; surveillance; patrol; mobile robots; Petri nets; initial marking optimization; trajectory planning.
A survey on state-of-the-art road surface monitoring techniques for intelligent transportation systems
by Rishu Chhabra, C. Rama Krishna, Seema Verma
Abstract: Intelligent Transportation Systems (ITS) aims at maximizing the convenience, safety, and efficiency of the transportation system with the help of advanced technologies while reducing the investment in transportation infrastructure. It refers to the embedment of technology in the standard conventional transportation framework. With the advancement of the road network in all countries across the globe, road surface condition information has become a significant factor in preventing road accidents. Road condition monitoring is a critical aspect of road infrastructure management and affects the safety and control of the entire transportation system. In this paper, we present a survey of the road surface monitoring techniques for ITS. The techniques presented in this paper; focus on detecting the presence of road anomalies like potholes, manholes, speed bumps, and cracks, etc. that can lead to road accidents. Due to the shortcomings of the manual inspection method, automatic and semi-automatic road surface monitoring techniques have been proposed in the literature. In this paper, we have categorized the road surface monitoring techniques into four categories based on the equipment used for data acquisition. The four categories are vision-based, optical technology-based, smartphone-based, and specially designed hardware-based techniques. The vision-based techniques are less expensive but are constrained by illumination conditions. The optical technology-based techniques are highly accurate but exhibit high cost. However, the smartphone-based techniques are ubiquitous and offer a low-cost solution for road surface monitoring. Finally, we conclude by presenting future research directions to improve the process of road surface monitoring.
Keywords: crack; ITS; manhole; pothole; road surface; speed bump.
BSAD: Bus-based services advertisement protocol for vehicular ad hoc network (VANET)
by Kifayat Ullah, Abdul Haseeb
Abstract: The deployment of emerging Vehicular Ad hoc Networks (VANETs) will unfold the possibilities for a large number of safety and non-safety applications. An attractive no-safety application is the advertisement of roadside services to the drivers and passengers. The provision of roadside services would enable the business owners to reach out to a large number of customers while, on the other side, it will also help the drivers and passengers to discover these services. Current solutions make use of private cars for advertisement dissemination. However, the main problem with these solutions is the provision of incentives. It would not be possible for the business owners to give incentives to all participating car owners. To address this problem, we proposed a novel protocol for roadside services advertisement using public buses. Our proposed solution would enable the business owners to register their services with a near-by Road Side Unit (RSU) through a broker. The RSU would then broadcast these services to the buses. These buses will store the advertisements for a predefined time and will offer them to the drivers and passengers, upon request. To implement, test, and evaluate the performance of our proposed solution, we used a multi-lane highway scenario with high traffic density by using state-of-the-art simulators. We carried out several experiments and analyzed the results to get important insights. Besides, we also compared the performance of our protocol with existing solution under various traffic conditions. The results of our experiments revealed that: (a) broadcast frequency and speed of the buses influenced the advertisement messages received by the buses; (b) by using a broadcast frequency of 5\s, the buses were able to receive all the advertised services.
Keywords: VANET; Services Advertisement; ITS.
Dairy cows localization and feeding behavior monitoring using a combination of IMU and RFID network
by Samir Aoughlis, Rafik Saddaoui, Brahim Achour, Mourad Laghrouche
Abstract: Localization systems have become recently important for the monitoring of cow's health. However, the main limitations of these systems lie in the lack of information about cows activities such as feeding and drinking in the monitored zone. To overcome these limits, we have developed a non-invasive ear-attached sensor based on a combination of a low-cost radio frequency identification system with an inertial measurement unit. With this procedure, the cows are localized and their feeding behaviors are classified. The localization is realized by means of an accelerometer and a gyroscope permitting the trajectory estimation thanks to the dead reckoning and the sensor fusion method. However, problems related to instability emerge which are solved with the particle filter algorithm. In order to monitor the standing and the feeding behaviors, the decision tree technique is adopted to filter them. The obtained results proved that the algorithm has achieved high classification and localization rates.
Keywords: wireless sensor networks; dairy cow; radio frequency identification; FRID; inertial measurement unit; IMU; particle filter; dead reckoning; decision tree; feeding behavior.
A Node Localization Method for Wireless Sensor Networks Based on an Improved Bee Colony Algorithm
by Hao-Min Shan
Abstract: To overcome the problems of inaccurate positioning and large time consumption for traditional node localization in wireless sensor networks, a node localization method based on an improved bee colony algorithm is proposed in this paper. This paper analyzes the principle of the bee colony algorithm, adopts a traditional method combined with a classical multidimensional expansion method to construct a static distance matrix, uses the improved bee colony algorithm to obtain the original coordinates of the static nodes in a wireless sensor network, and determines the actual positions of these nodes. The paper utilizes the bee colony release principle, a positioning constraint threshold, and the mean value of the hop distance of the beacon node. It also uses the offset speed and a time sequence method to construct an offset compensation model of dynamic sensor nodes. Combined with the uniform measurement method of node target positioning, the optimal positioning of the dynamic sensor nodes is determined. The experimental results show that the accuracy of the proposed method is approximately 98%, and the shortest time is approximately 2.2 s.
Keywords: improved bee colony algorithm; dynamic node; static node; node positioning; wireless sensor network.
A Congestion Attack Behavior Recognition Method for Wireless Sensor Networks Based on a Decision Tree
by Wen Feng, Xuefeng Ding
Abstract: To overcome the problems of low attack recognition rates and high congestion identification errors in traditional congestion attack behavior judgment methods for wireless sensor networks (WSNs), a new congestion attack behavior judgment method based on decision trees is proposed in this paper. First, the traffic is processed, a congestion attack detection model is constructed as a decision tree, and the sensor nodes with artificial labels are classified and evaluated. According to the classification results, the network congestion attack behavior is identified according to an attack recognition threshold. The experimental results show that the average recognition rate of congestion attacks is 99.82%, the error rate of congestion identification is only 0.00284, and the packet loss rate is low, indicating that this method can effectively recognize network congestion attack behaviors.
Keywords: Decision tree; WSNs; congestion attack; attack behavior judgment.
An Anomaly Detection Method Based on Feature Mining for Wireless Sensor Networks
by Xuefeng Ding, Wen Feng
Abstract: To overcome the problems of large errors in data feature acquisition and long detection delays in traditional detection methods, this paper proposes an anomaly detection method based on feature mining for wireless sensor networks. In our method, dimensionality reduction is performed on the data, all wireless sensor nodes are classified by a hybrid immune method, and data features are mined through vector set recognition. Moreover, the confidence interval is set by a time series, and the effective detection of abnormal data is conducted by comparison. The experimental results show that the maximum error of anomaly data collection is only 1.9%, the maximum time cost of anomaly detection is 8.4 s, and the P-R value is high, indicating that the proposed method is effective.
Keywords: Wireless sensor networks; Abnormal detection; Abnormal data; Time series; Feature mining; Dimensionality reduction; Confidence interval.
Optimization of Wireless Sensor Networks Using Supervision Information
by Seyed Hossein Khasteh, Hamidreza Rokhsati
Abstract: The Wireless Sensor Network (WSN) is a rapidly growing technology that is used to meet the different requirements of a wide range of applications. However, these sensors have some limitations, including energy and bandwidth constraints and Energy saving in wireless sensor networks (WSNs) is a critical problem for diversity of applications. As a result, WSNs have inspired a resurgence in research on machine learning methodologies with the objective of overcoming the physical constraints of sensors. In many scenarios using WSNs, we have incomplete and imperfect prior knowledge about the problem; however, if this knowledge can be incorporated into the problem-solving process, our performance can be improved. Furthermore, this knowledge could be useful in WSN learning. The main goal of this paper is to demonstrate the positive effect of supervision information, i.e., information such as our prior knowledge about the problem domain, on the performance of machine learning in WSNs. The novelty of this paper is the use of supervision information to improve the performance of machine learning methods in WSNs. To achieve this goal, the routing problem in WSNs is solved with and without supervision information. First, the problem is solved using a very simple routing method, Gossiping. Next, a reinforcement learning-based machine learning technique is used to find the most energy-efficient routes from the sensor nodes to the sink, and the positive effect of supervision information on the performance of routing in WSNs is shown in terms of energy consumption. Our methods are analyzed theoretically and tested using a simulation. The results are highly promising and show that the utilization of supervision information can reduce energy consumption by nearly 60%.
Keywords: Wireless Sensor Networks; Machine Learning; Supervision Information; Energy Conservation; Routing.
Pedestrian Characterization in Urban Environments Combining WiFi and AI
by Antonio Guillen-Perez, Maria-Dolores Cano
Abstract: Knowing how many people there are in a given scenario offers new possibilities for the development of intelligent services. With this goal in mind, the use of sensors and Radio Frequency (RF) signals is becoming an interesting alternative to other classic methods such as image processing for counting people. In this paper we present a novel method for counting, characterizing, and localizing pedestrians in outdoor environments, called iPCW (intelligent Pedestrian Characterization using WiFi). iPCW is a passive, device-based sensor system that incorporates artificial intelligence techniques, more specifically, machine learning techniques. Performance evaluation using intensive computer simulations shows that iPCW achieves excellent results, with moving and static pedestrian detection accuracy above 98% and positioning accuracy above 92%.
Keywords: artificial intelligence; intelligent transportation systems; machine learning; people counting; pedestrians; sensor systems; smart cities; WiFi.
Protection of Surveillance Recordings via Blockchain-Assisted Multimedia Security
by Zhaowei Ma, Li Zhu, F. Richard Yu, Jeremy James Jeremy James
Abstract: Surveillance is being pervasively used, and its recording is extensively applied in practice. Thereby, protection of surveillance recordings, which is related to multimedia security, increasingly attracts research interests. Traditional techniques, such as watermarking, cryptography and steganography, focus on analyzing multimedia content, resulting in high complexity and long latency, which makes them not suitable for protecting real-time surveillance applications. In this paper, we propose a novel blockchain-assisted framework to protect the recordings in real-time surveillance applications. In the proposed framework, we design an algorithm that generates frame fingerprints, which involves real-time extraction of packets from surveillance multimedia streams, SHA3-512 functions, and the verification of frame consistency. We use a blockchain for preserving the frame fingerprints generated by our proposed algorithm. Different from existing works that use simulations to show the performance, we develop a real system using a service-oriented blockchain, virtualization for distributed ledger technology (vDLT), and the effectiveness of this system is demonstrated.
Keywords: Blockchain; surveillance; DPoS; RTP; multimedia; SHA3; cloud Computing.
A Lighting Control System of Art Museum Based on Image Recognition and STM32
by Cong Zhang, Zhisheng Wang, Dan Zhu, Haiwen Gao, Nianyu Zou
Abstract: Intelligent lighting control system plays a vital role in the lighting regulation of art gallery. It is necessary to design an automatic control system of light regulation. This paper proposes a layered design of lighting control system, which can be divided into three major parts. Each part communicates with each other using wireless sensors to realise intelligent lighting regulation. Before the lighting control system works, it also needs to have the initial values of the lighting and lamp parameters. We also design an experiment to simulate. The initial value of the luminaire indicates that the luminaire adopts 30
Keywords: lighting control; image processing; wireless sensor; feedback adjustment; simulation; initial value.
Recurrent Neural Network NARX for Distributed Fault Detection in Wireless Sensor Networks
by Jamila Atiga, Monia Hamdi, Ridha Ejbali, Mourad Zaied
Abstract: \r\nWireless sensor networks (WSNs) comprise a collection of sensors used to collect data, such as temperature, displacement, and acceleration. \r\nThese data allow knowing the state of a zone or a monitored system. Sensor nodes are usually deployed in harsh environments, where node failures are common. The network should be able to distinguish these defective nodes. In this work, we propose a Distributed Fault Detection (DFD) algorithm based on the nonlinear automatic regression recurrent neural network with eXogen input (NARX). The NARX network is used to predict a time series of data based on the previous outputs and the current input. Defective sensors are identified by comparisons between series of actually measured values and their predicted values. The proposed method, in the first step, divides the network into a set of cliques, forming different areas. In the next step, the damaged cliques are identified using the Gaussian distribution theorem. Finally, the NARX approach is applied only to the damaged cliques to determine the defective nodes. The comparisons of simulation results to other existing algorithms show that the proposed method reaches the best results.
Keywords: WSN; fault detection ; DFD algorithm; NARX; Gaussian distribution theorem.
A Prediction Model of Death Probability for Guiding Wireless Recharging in Sensor Networks
by Ping Zhong, Aikun Xu, Shu Lin, Xiaoyan Kui
Abstract: Wireless power transmission (WPT) technology is usually used to maintain the continuous operation of sensor nodes. However, when large amounts of data need to be processed, the node may enter an abnormal death state because it cannot be charged in time. Therefore, a prediction of node death probability is crucial to guide the charging path planning for charging vehicles. In this paper, we build an analysis model based on a Markov fluid queue model with the aim of creating harvest-store-use (HSU) and harvest-then-use (HTU) models of the node. Specifically, the proposed models involve a Markov process, a queuing model, and a successive fluid process. The result shows that the abnormal death probability calculated by the model is approximately 0.1% different from the probability of death obtained by simulation. Meanwhile, by comparing the two modes of energy usage, we find that HTU is better than HSU.
Keywords: abnormal death; MFQ; markov fluid queue; WSN; wireless rechargeable sensor network; WRSN; wireless rechargeable sensor network; WPT; wireless power transmission; WET; wireless energy transmission.
An Efficient Resource Sharing Scheme based on the Linear Feedback Shift Register
by Tianqi Zhou, Jian Shen, Huaqun Wang
Abstract: With the invention of various kinds of electrical equipment, electricity has become an indispensable necessity in human society. The concept of smart grid is proposed to overcome the problems of power waste and low power utilization rate. Resource sharing is regarded as one of the important goals in smart grid. However, how to improve communication efficiency has become the bottleneck of resource sharing in smart grid. In this paper, we focus on optimization of the symmetric balanced incomplete block design (SBIBD) to support efficient communication for resource sharing in smart grid. Note that the linear feedback shift register (LFSR) is employed in the generation of the SBIBD, which is far more efficient than the algorithm implementation. In order to improve efficiency, the characteristic polynomial of the LFSR for generating the SBIBD is defined. Moreover, the one-step state transition matrix of the LFSR is presented and the $s$-step state transition matrix is further deduced. Compared with the one-step state transition matrix, the $s$-step state transition matrix can well support multi-step parallel shift operation thereby increasing efficiency by $s$ times.
Keywords: Smart grid; SBIBD; LFSR; characteristic polynomial; state transition matrix.
LIA-EN: Enhancing the performance of multipath congestion control over lossy networks
by Songyang Zhang, Weimin Lei
Abstract: For a transmission protocol, the most important component is the congestion control module. It provides the endpoint the ability to probe available bandwidth to get channel resource fully utilized and prevents the network falling into severe congestion. But these coupled congestion control algorithms designed for multipath transmission protocol take packet loss event to infer link congestion and they achieve inferior performance if the multipath session traverses route suffering from random loss. Especially, the random packet loss event is not rare in wireless network. For possible performance improvement, we present LIA-EN in this work. The main idea is based on the fact that the congested loss only happens when the buffer is fully occupied and the round trip delay is in its peak in a normal route. By counting the packet loss rate in safe area, LIA-EN can determine whether a path contains link that induces random loss. If the route induces random loss, the congestion window of LIA-EN session will be set according to estimated bandwidth delay product instead of recklessly cutting it by half. Further, a framework is implemented and interested readers can test the performance of other congestion control algorithms for multipath transmission. Extensive experiments are conducted on ns3 to verify the effectiveness of the proposed solution.
Keywords: congestion control; multipath congestion control; coupled congestion control; bandwidth estimation; lossy connection.
An IoT Based Efficient Multi-Objective Real-Time Smart Parking System
by Hitesh Mohapatra, Amiya Kumar Rath
Abstract: Nowadays, finding a parking place is the most frustrating job for drivers in many of the major cities. The improper parking invites many negative side effects on urban roads. In this work, we propose a unique smart parking analyst (SPA) model for adequate parking. The SPA broadly focuses on two types of parking spaces such as public and private space-based smart parking. Unlike existing models, our proposed plan does not give any additional burden on drivers, like carrying a smartphone or driver has to be a techie. Here, we broadcast all the sensor-based real-time parking information through LED displays at the appropriate point for the convenience of all types of people. Additionally, we propose an efficient clear crowd (CC) method which helps to make available parking space most of the time and to generate more revenue from parking areas in case of excessive use. Though this work does not support smart phone-based parking solutions, still we have provided a few additional services through smartphone users as it is a demand of time which is not a core part of the SPA. The result of our execution shows that it achieves high efficiency of 94.23% in judging the appropriate parking slots and high accuracy of 91.76% in making free the occupied parking area. On the other hand the proposed model generates revenue of 1.18 times than the existing conventional parking model.
Keywords: Wireless Sensor Network; Smart Parking; Revenue Generation; Internet of Things.
Performances Analysis of UAV-assisted Wireless Powered Sensor Network
by Jing Yan, Liming Wang
Abstract: In a wireless sensor network (WSN), the key restrictive factor for network service performance is the energy replenishment of energy-constrained sensor nodes. Moreover, utilizing radio frequency signals for wireless energy transmission is regarded as an effective method for energy-constrained sensor nodes to charge. Furthermore, UAV (Unmanned Aerial Vehicle) has attracted increased attention to wireless sensor networks because of their high flexibility and low-cost by improving energy efficiency through trajectory planning while data collection. In this paper, we propose a novel UAV-assisted wireless rechargeable sensor network (WRSN), where the energy-constrained sensor nodes firstly harvest energy from the RF (Radio Frequency) signals of UAV in the downlink and then forward the information to the sink node with harvesting energy. The closed-form expressions of system throughput and outage probability in AF (Amplify and Forward) and DF (Decode and Forward) modes are both derived. Additionally, the theoretical derivation model is validated with simulation results. In particular, the results show that the DF protocol is superior to the AF protocol with lower probability and higher throughput. Besides, the influences of energy harvest time, the transmission power of UAV, and target signal-to-noise ratio on the throughput and outage probability are systematically analyzed.
Keywords: Wireless Sensor Network; UAVs; Relay; Outage Probability; Throughput.
Multi-Armed bandit Algorithms Over DASH for Multihomed Client
by Ali Hodroj, Marc Ibrahim, Yassine Hadjadj-Aoul
Abstract: There is an expanding request from the mobile clients of video traffic which has come to over the most recent two years the greater part of all the mobile data traffic. The challenge of providing the clients excellent video streaming is expected principally to the constrained transfer speed which makes it important to misuse the rising decent variety of mobile video spilling and get to systems. For enhancing the video quality received by \"multi-homed client\" (i.e., clients with multiple network interfaces), a network selection algorithm based on the Multi-Armed Bandit heuristic is proposed on top of the most broadly utilized standard for video streaming Dynamic Adaptive Streaming over HTTP (DASH).rnFor choosing the best network at each progression powerfully, DASH gives mobile video quality dependent on the apparent exhibition from the pre-owned system association through the Adaptive Bitrate Rules (ABR) without investigating the system conditions through the other network(s) which could give better quality. Subsequently, a few alterations for DASH ABR is required to enhance the video quality.rnTwo of the MAB algorithms (UCB and Epsilon Greedy) were embraced for improving MPEG-Dash. The investigations are performed through a proving ground execution to show that UCB surpasses Epsilon Greedy, in stable system conditions, regarding goodput received by the Dash customer. Additionally, UCB can discover the harmony between investigating new choices, and abusing the triumphant variation.
Keywords: DASH; Multi-homed; video streaming.
An Improved DV-Hop Algorithm Based on Differential Simulated Annealing Evolution
by Fei Tang, Witold Pedrycz
Abstract: To improve the localization accuracy of a DV-Hop localization algorithm in the wireless sensor networks with irregular network topologies, in this study, we propose an improved DV-Hop algorithm based on differential simulated annealing evolution (DSAE). The distance between the unknown node and anchor are main element in the phase of calculating the coordinate of the unknown node, but this is calculated by average hop distance (HD). So, the key behind improving the localization accuracy is to calculate the hop distance between the anchor nodes. In this paper, we innovatively divide the hop distance into the global single-hop average HD between beacon nodes, the corrected the average HD between anchor nodes and the local single-hop average HD between anchor nodes. The defect of computation method of HD, only considering the average HD, draws forth this originality. Combining three kinds of HDs, DSAE based on the HC threshold is used to estimate the average HD. The simulation results show that the improved DV-Hop algorithm can decrease the error of localization and significantly outperforms state of the art localization algorithm by 37.38% in terms of the localization error.
Keywords: wireless sensor networks; the global single-hop average HD; the corrects the average HD; the local single-hop average HD; differential simulated annealing evolution algorithm.
Analysis and Modeling of iBeacon Wireless Signal Propagation in Multiple Environments
by Luchuan Zeng, Zhifei He, ZheRui Tan, Renliang Geng, Menghua Liu, Linglin Xia, Deyu Lin
Abstract: IBeacon positioning accuracy is usually influenced by its signal quality in practical environments. The purpose of this study is to provide users with a reasonable iBeacon networking deployment scheme to improve the accuracy of positioning. We explore the characteristics of iBeacon signal propagation and existing propagation path loss in common environments (corridor, playground, classroom). Firstly, the changes of Received Signal Strength Indicat (RSSI) value of iBeacon wireless signal under three environments are collected, and then the signal samples are filtered numerically. Secondly, a simplized single-slope and a double-slope propagation models are developed to simulate iBeacon signal propagation. Finally, the developed models are used to carry out regression analysis on the sample data under different scenarios, and the best applicable scenario of the two models is determined according to the results. In conclusions, based on the simulation results of the propagation model, this study provides some suggestions for users to deploy ibeacons indoors or outdoors.
Keywords: iBeacon; propagation characteristics; logarithmic distance model; received signal strength indicat (RSSI); multi-environment.
An Inertial and Global Positioning System based Algorithm for Ownship Navigation
by Ihsan Ullah, Bizzat Hussain, Shafaq Nisar, Uzair Khan, Mohsin Ali, Sajjad Manzoor
Abstract: In this paper a fusion algorithm for Global Positioning Systems (GPS) and Inertial Navigation System (INS), using Inertial Measurement Unit (IMU), is proposed for ground vehicle trajectory estimation during a GPS outage. As a standalone system an INS has its limitations and an error growth as a result of bias and drift inherent to this sensor restricts its application. A GPS can be used for vehicle navigation flawlessly, however the navigation performance may suffer due to intentional or unintentional GPS signal outage. The proposed GPS-INS fusion algorithm is able to handle any GPS blackout, restrict the INS error development and estimate a precise ownship position while utilizing information from two route frameworks i.e. the GPS and the INS. In order to validate the proposed GPS-INS based Matched State Cascaded Fusion (MSCF) algorithm, it's root mean squared error (RMSE) of the developed algorithm is analyzed. It is proven using experiments and simulations that a decrease in critical measure of error has been accomplished with the proposed algorithm.
Keywords: Sensor Fusion; Global Positioning Systems (GPS); Inertial Navigation System (INS) and Inertial Measurement Unit (IMU).
The Flaws of Internet of Things (IoT) Intrusion Detection and Prevention Schemes
by Saher Ghayyad, Shengzhi Du, Anish Kurien
Abstract: For years, the Internet has been a major tool in our lives but is always a target for hackers who intend to take advantage of individuals, corporates, governments, banks and other institutions. As the Internet evolved to include the Internet of Things (IoT), each physical system with an IP address on this wide network can communicate without human intervention. These cyber-physical systems can vary from a smart grid to a smartphone and are attractive to hackers leading to a high risk of compromise of these systems on core infrastructure and network communication security. This paper addresses a Denial-of-Service attack (DoS), one of the major attacks on IoT Networks. The flaws are discovered in existing IoT intrusion detection and prevention schemes combating DoS attacks on Wireless Sensor Networks (WSN). Live DoS attacks on IoT Sensors are demonstrated using Kali Linux Platform. Moreover, the flaws in existing solutions are revealed using a demo Cisco Meraki MR33 and a more effective intrusion detection scheme is proposed by an active in-depth-defence strategy against DoS attacks.
Keywords: Internet of Things (IoT); Hackers; Denial-of-Service (DoS) Attack; Wireless Sensor Networks; Intrusion Detection and Prevention Schemes; Cisco Meraki MR33; Air Marshal.
Performance optimization of multichannel MAC in large-scale wireless sensor network
by Chuchu Dong
Abstract: Efficient multichannel medium access control (MAC) protocols can significantly prolong the lifetime of sensor networks by reducing the energy consumption of packet transmission among neighbor nodes. In this paper, an energy-efficient Prediction-based Multichannel MAC protocol (PM-MAC) is proposed for large-scale wireless sensor networks. PM-MAC minimizes sensor node's idle listening time without introducing any multihop time synchronization and channel assignment overhead by introducing a prediction mechanism of the target node's wakeup channel and wakeup time. PM-MAC introduces a dynamic channel allocation to achieve precise and quick resynchronization when a prediction error in the wakeup time occurs. In addition, in PM-MAC, the load can be well balanced on all channels. Finally, its prediction-based retransmission mechanism can achieve more energy efficiency than traditional strategies such as Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) in resolving severe wireless collisions caused by concurrent traffic in large-scale sensor network. The PM-MAC not only reduces multihop packet latency and energy consumption, but also increases network capacity and throughput. Experimental results confirm the outstanding performance of PM-MAC in all testbeds.
Keywords: wireless sensor network; MAC protocol; channel assignment; energy efficiency.
Hybrid Visible Light Communication Power Optimization in Indoor Environment
by Choong Florence, Tan Ching Han, Yandan Lin
Abstract: Traditional way of handling more traffic is by having higher cell density, higher spectral efficiency and carrier aggregation. Many network optimizations have been put in place to overcome this high growth in data traffic demand. However, the network electricity consumption also increases tremendously. Hybrid visible light communication (HVLC) rides on solid-state light emitting diode lighting infrastructure with higher energy efficiency. HVLC has the advantages of high data rates, higher bandwidth, un-licence requirement, lower cost and more energy efficient. This paper focuses on recent breakthrough and advancement in HVLC especially for indoor applications. Two indoor lighting topologies were studied in detail in order to determine their power optimization performances. The power optimization through linear programming methods were used to simulate and compare the performance of square and ring topology design. The square topology showed better performance as compared to ring topology. Also, the number of users versus received power and channel assignments performance is also better for square topology. The findings demonstrated the feasibility of HVLC for indoor application.
Keywords: HVLC; radio frequency; visible light communication; square topology; ring topology; bandwidth; power optimization; energy efficiency; lighting; LED.
An IoT Smart Clothing System for the Visually Impaired using NFC Technology
by Reham Alabduljabbar
Abstract: The selection of appropriate clothing to match ones daily life is an important ritual for individuals across the globe. But this can be a Herculean task, especially for the visually impaired. Together with the rise of the Internet of Things (IoT) paradigm and enabling technologies, solutions to improve the quality of life for visually impaired people have arisen. This paper proposes an IoT-based smart clothing system (EZwear) to enable visually impaired individuals to select and find appropriate clothing within their closets. The main underlying technical components in the solution are the use of an NFC (Near Field Communication) and a smartphone and its applications. By combining these technologies, we can transform a smartphone into an NFC reader that can provide sound information to the visually impaired user. The system enhances the ability of the visually impaired person to independently and comfortably manage their closets. The results show that the system performed reasonably and achieved the usability requirements.
Keywords: IoT; Internet of Things; visually impaired; smart clothing; mobile application; NFC; assistive technology; sensors; network.
Optimal fusion rule for distributed detection with channel errors taking into account sensors unreliability probability when protecting coastlines
by Parfenov Vladimir Ivanovich, Le Van Dong
Abstract: In this paper, algorithms that make decisions on the presence or absence of penetration on a protected object with both ideal and non-ideal communication channels are synthesized and analyzed. Local sensor decision rules based on an energy detector and its performance characteristics are presented. We determine the dependencies of the total error probability on the energy parameter taking into account the energy emitted from the target at the level of local sensors and the channels signal-to-noise ratio. The gain of the optimal fusion rule in efficiency compared to known fusion rules such as the LRT-BER and Chair-Varshney fusion rule is shown. The detection algorithm under possible sensor failure and its detection performance is also presented. With the unknown target location on a coastline, the detection rules are given and the detection performance is analyzed
Keywords: wireless sensor networks (WSN); sensor; coastline; Bézier curve; decision fusion; likelihood ratio test; total error probability; unreliability probability; location estimation; generalized maximum likelihood method.
Grid-based Lane Identification with Roadside LiDAR Data
by Jianqing Wu
Abstract: Lane identification is important for many different applications, especially for connected-vehicle technologies. This paper presents a new method for lane identification with the roadside Light Detection and Ranging (LiDAR) serving connected-vehicles. The proposed lane identification method is a revised grid-based clustering method (RGBC). The whole procedure includes background filtering, object clustering, object classification, and RGBC. A location matrix (LM) can be generated to store the location of each lane. The performance of the proposed method was evaluated with the data collected from the real world. The testing results showed that the RGBC can locate 96.73% of vehicles to the correct lane. The RGBC was also compared to the state of the art, showing that the computational load for RGBC is lowest compared to other algorithms, with a cost of slightly reduced accuracy. The time delay for real-time data processing is less than 0.1 ms, which can provide the High-Resolution Micro Traffic Data (HRMTD) for connected-vehicles.
Keywords: Lane identification; Roadside LiDAR; Grid-based Classification.
Trustworthy Collaborative Trajectory Scheme for Continuous LBS
by Yuan Tian, Biao Song, Miada Murad, Najla Al-Nabhan
Abstract: With the high demand for using location-based services (LBSs) in our daily lives, the privacy protection of users trajectories has become a major concern. When users utilize LBSs, their location and trajectory information may expose their identities in continuous LBSs. Using the spatial and temporal correspondences on users trajectories, adversaries can easily gather their private information. Using collaboration between users instead of location service providers (LSPs) reduces the chance of revealing private information to adversaries. However, there is an assumption of a trusting relationship between peers. In this paper, we propose a trustworthy collaborative trajectory privacy (TCTP) scheme, which anonymizes users trajectories and resolves the untrustworthy relationship between users based on peer-to-region LBSs. Moreover, the TCTP scheme provides query content preservation based on a fake query concept in which we conceal the users actual query among a set of queries. The results of several experiments with different conditions confirm that our proposed scheme can protect users trajectory privacy successfully in a trustworthy and efficient manner.
Keywords: Continuous Location-based Services; Anonymity; Trajectory Privacy Protection; Fake Queries; K-anonymization.
Object detection algorithm combined with dynamic and static for air target intrusion
by Yi Xiao, Faming Shao, Fanjie Meng, Jiqing Luo
Abstract: The infrared detection of target intrusion usually faces the problems of complex background and insufficient brightness. This paper proposed a method combining dynamic detection and static detection, which is mainly composed of adaptive local contrast module (ALCM), interval frame semantic layered module (IFSL) and enhancement network module. ALCM, as a static method, uses adaptive sliding box to traverse the original image to obtain relatively simple background targets. As a dynamic method, IFSL can effectively separate moving targets from the background based on spatial semantic information under dynamic conditions. The function of the enhanced network module is to suppress the background clutter and highlight the target. Compared with the traditional detection method, the SNR of the proposed method is improved by 8.65%, and the computing speed is improved by 7.14%. This strongly proves that the method has high detection rate and detection efficiency in this case.
Keywords: infrared target; static detection; dynamic detection; feature fusion; enhanced network.
Energy-efficient Time Synchronization Based on Odd-Even Periodic Data Transmission and Acknowledgement in Wireless Sensor Networks
by Chuang Wang, Zhili Zhang
Abstract: In the working process of wireless sensor networks (WSNs), it is challenging to realize precise and energy-efficient time synchronization on resource-constrained WSNs nodes. In this paper, a fusion method of time synchronization with periodical data transmission and acknowledgement is proposed for WSNs. Moreover, different response intervals are generated according to the parity characteristic of the measurement data's serial number. The maximum likelihood estimators (MLE) of clock skew and fixed delay, and the simplified MLE of clock skew under the Gaussian random delay are derived. Meanwhile, the corresponding Cramer-Rao Lower Bound is calculated. The two proposed MLEs of clock skew show that both of them can work independently of the fixed delay. Simulation results verify the efficiency of these two clock skew estimators and fixed delay estimator, especially the performance and effectiveness of the second simple and practical MLE.
Keywords: Time Synchronization; Clock Skew Estimation; Energy Efficiency; Timed Response; Wireless Sensor Networks.
A Collaborative Charge Scheduling Scheme for Electric Vehicles (EV) Based on Charging Time and Resource Cost-awareness
by Mahfuzulhoq Chowdhury
Abstract: To reduce the greenhouse gas emissions problems of traditional fuel vehicles, Electric vehicles (EVs) are regarded as a promising transportation solution by industry peoples. Although EVs' performances are limited due to their limited battery power, large travelling distance during their journeys, charging delay for service, and resource price. To improve the EV's performance, this paper presents a charging time and resource cost- aware collaborative EV charging policy that selects not only a suitable charging station but also assigns timeslot for charging-discharging operation. In this paper, a decentralized EV charging/discharging network architecture is proposed in which the EV does not only communicate with the local cloud server by using the traditional vehicular network but also utilizes the EV-to-charging station and EV-to-EV interaction. The simulation results indicate that the proposed charging scheduling policy results in a better charging requirement satisfaction ratio, charging delay, and resource costs than the compared traditional schemes.
Keywords: Electric Vehicles (EV); Charging delay; Charge scheduling; Satisfaction ratio,
Utility; Waiting delay; Resource allocation; Revenue-to-required time ratio; Elapsed time,
Resource cost.
IoT Networks for Monitoring and Detection of Leakage in Pipelines
by Seham Bakheder, Ghadah Aldabbagh, Samar Alkhuraiji, Nikos Dimitriou, Mai Fadel, Helen Bakhsh
Abstract: Pipelines have been an important means of oil transport in recent years, especially in Saudi Arabia. Since oil pipelines have extensive length, tracking them is a critical task. Therefore, monitoring pipeline leaks has become a significant field of intelligent sensing systems. There are many challenges when monitoring long-distance pipelines, requiring energy efficient. Wireless sensor networks (WSNs) deployed in oil pipelines provide solutions to address these challenges, but various issues must be considered, such as costs, scalability, and delay issues for monitoring systems. The recent advances in Internet of things (IoTs) and the new LPWAN capabilities have promising benefits over monitoring systems. This paper investigates WSN systems in oil pipelines with IoTs technologies. Also, it presents a LoRa as an LPWAN single-hop model for monitoring aboveground oil pipelines. The LoRa model is evaluated by using two scenarios and analyzing the scalability and capacity of the LoRa and discussing the result.
Keywords: Internet of Things IoT; WSN; LPWAN; LoRa; Long range; Low power.
Outlier node localization in sensor networks based on double layer modified unscented Kalman filter
by Zhonghua Ni, Xinhua Wang
Abstract: Traditional sensor network abnormal node localization has some problems, such as low positioning accuracy, high positioning time cost and so on. The range of motion of abnormal nodes is determined by controlling the distance of abnormal nodes; According to the fading degree of abnormal nodes, the unstructured feature extraction model is constructed, and the minimum mean square error of abnormal nodes is calculated. According to the median change of neighbor nodes, the movement situation of abnormal nodes is analyzed. With the help of the upper unscented Kalman to determine the nonlinear state space of abnormal nodes, the abnormal nodes in sensor networks are located, and then the positioning error is corrected by the lower unscented Kalman to realize the abnormal node location. The results show that the highest accuracy of the proposed method is about 96%, and the shortest positioning time is about 1.1s.
Keywords: Double layer correction; unscented Kalman; abnormal node; control distance; fading degree; average hop distance.
Research on Abnormal Node Detection in a Wireless Sensor Network Based on Random Matrix Theory
by Jibao Hu
Abstract: Because the traditional detection methods have the problems of low recall and precision and long detection time, this paper studies a method of abnormal node detection in a wireless sensor network(WSN) based on random matrix theory. This method uses particle swarm optimization to improve DV-Hop, and uses the improved DV-Hop method to locate WSN nodes. According to the spatiotemporal characteristics of WSN data, a data matrix is built, and the dimensionality of the data matrix is reduced by using a random matrix. The node attributes are judged according to the element correlation between multiple matrices to realize abnormal node detection in a WSN. The test results show that the average recall rate and recall rate of this method are 97.0% and 97.2% respectively, and the detection time is always less than 0.5s, so the practical application effect is good.
Keywords: Random matrix theory; WSN; Abnormal nodes detection; DV-Hop; Particle swarm optimization.
A Self-Organizing Organizational Paradigm for Using Multi-Agent Systems in Traffic Control Application of VANETs
by Mir Bagher Hosseini, Amin Rahmanzadeh, Eslam Nazemi
Abstract: The growth of small systems towards ultra-large-scale ones gradually leads us to leave the control of those systems to themselves. Autonomic computing and the self-adaptation concept have come to help. One of the sub-categories of autonomic computing is self-organization, which is suitable for distributed systems. By controlling a distributed system using agents and turning it into a multi-agent system, we can use self-organization mechanisms to enable the system to control itself. Vehicular Ad-Hoc Networks (VANETs), which can be considered as multi-agent systems, have many applications in the areas of safety and comfort of passenger vehicles. Due to the characteristics of VANETs, it is not easy to manage and maintain them manually. The dynamic nature of these networks makes automatic management necessary. VANETs characteristics, such as including vehicles with computing and energy resources, bring us to the idea of assuming them as multi-agent systems. Having this in mind, we can provide the required automatic management by adding self-organization mechanisms to VANETs, as they are multi-agent systems. In this paper, we present Team-Coalition Traffic Control (TCTC), a self-organizing organizational paradigm to add self-organization features to VANETs for traffic control. The presented organizational paradigm works without any need for roadside traffic control infrastructure, and it is fully distributed. The goal of this paradigm is to reduce the waiting time for vehicles to pass an intersection.
Keywords: Multi-Agent Systems; VANETs; Autonomic Computing; Self-adaptation; Self-organization; Traffic Control.
Real-Time monitoring of Social Distancing with Person Marking and Tracking System using YOLO V3 model
by Pandiyan P, Rajasekaran Thangaraj, Subramanian M, Rahul R, Nishanth M, Indupriya Palanisamy
Abstract: The global economy has affected enormously due to the spread of coronavirus (COVID-19). Even though, there is the availability of vaccines, social distancing in public places is one of the viable solutions to reduce the spreading of COVID-19 suggested by the World Health Organization (WHO) for fighting against the pandemic. This paper presents a YOLO v3 object detection model to automate monitoring of social distancing among persons through CCTV surveillance camera. Furthermore, this research work used to detect and track the person, measure the inter-person distance in the crowd under a challenging environment which includes partial visibility, lighting variations and person occlusion. Moreover, the YOLO V3 model is experimented with Darknet53 and ShuffleNet V2 backbone architecture. Compared with Darknet53 architecture, ShuffleNet V2 achieves better detection accuracy tested on Custom Video Footage Dataset (CVFD), Oxford Town Centre Dataset (OTCD) and Custom Personal Image Dataset (CPID) datasets.
Keywords: Deep Learning; COVID-19; Social Distancing; Surveillance Camera; Crowd Counting; Computer Vision.
Dynamic Bandwidth Estimation and Congestion Avoidance based on Network Traffic in Mobile Cloud
by TAMIZHSELVI S.P, Vijayalakshmi M
Abstract: Mobile cloud computing (MCC) plays a vital role in digital communication for delivering the network and media services such as live streaming, social network, search engine, GPS navigation, and email. While providing services, the smartphone faces many QoS challenges due to network traffic, bandwidth, congestion, delay, and packet loss. To solve the issues, we propose a novel framework, namely, Network traffic-aware dynamic bandwidth estimation and congestion avoidance in the mobile cloud. To handle the mobile network traffic, delay, and congestion, we propose three algorithms in the cloud, namely, Network Bandwidth Cloud Estimation (NBCE) algorithm, Cloud Estimated Queuing Delay (CEQD) algorithm, Cloud Bandwidth Congestion Avoidance (CBCA) algorithm. Firstly, NBCE utilizes the actual bandwidth based on different mobile network traffic. The second algorithm CEQD determines the delay to minimize the packet loss. Finally, CBCA reduces the congestion with the help of two parameters estimated bandwidth and queue length. The above algorithms were implemented in the public cloud Amazon Web Service (AWS) and evaluated. The experiments conducted in this work observed that NBCE utilizes the actual bandwidth of 88%, 80%, 67% for different network status such as low, medium, and high compared with existing TCP variants to provide improved network performance. CEQD minimizes the average delay to 20 ms when compared with other TCP algorithms. Finally, CBCA improves 5%, 4%, 4% goodput in different network traffic.
Keywords: Network traffic; bandwidth estimation; queueing delay; congestion avoidance;rnmobile cloud; Quality of service; packet loss rate; throughput; goodput; congestion window;.
Improved Bees Algorithm for the Deployment of Homogeneous and Heterogeneous Wireless Sensor Networks
by Hicham Deghbouch, Fatima Debbat
Abstract: Nowadays, countless applications benefit from the services provided by Wireless Sensor Networks (WSNs) in remote surveillance and data gathering. Coverage is considered an important Quality of Service (QoS) criterion for many of these applications. To ensure that the required QoS is achieved, the sensors must be placed in locations that optimise the area coverage and eliminate the coverage holes, especially when deploying sensors with heterogeneous sensing ranges. In this paper, a novel sensor deployment scheme based on the Improved Bees Algorithm (IBA) for optimising the area coverage in both homogeneous and heterogeneous WSNs is proposed. The IBA includes a neighbourhood shrinking procedure that aims to improve the local search efficiency and a site abandonment procedure for preventing the algorithm from the entrapment in a local optimum. Experiments show that the IBA delivers high-quality solutions and optimises the area coverage effectively compared with four state-of-the-art methods in homogeneous and heterogeneous WSNs.
Keywords: wireless sensor networks; bio-inspired algorithms; metaheuristics; coverage redundancy; area coverage; simulation; optimisation.
A quantum key injection scheme for mobile terminals based on commercial quantum key distribution
by Xiaohui Li, Dexin Zhu, Huan Wang, Lifeng Yang, Jianan Wu, Lijun Song
Abstract: In order to satisfy the demand of mobile secure communication for quantum keys, this study proposes a commercial quantum key distribution (QKD) based quantum key injection scheme for mobile terminals. It integrates a quantum network with a classic network, employs a quantum key injection device, and applies an encryption strategy to securely transmit the quantum key from the quantum network to the classic network for injection into a mobile terminal.Experimental results in a real quantum key distribution environment show that the fusion network runs stably, and the injection of the quantum key at the mobile terminal is realized effectively, which can meet the demands of mobile secure communication for quantum keys.
Keywords: quantum key; mobile key injecting; commercial QKD.
Motion Mode Recognition in Multi-storey Buildings Based on the Naive Bayes Method
by Litao Han, Lei Gu, Cheng Gong, Tianfa Wang, Aiguo Zhang
Abstract: When walking within a multi-storey building, pedestrians use a variety of modes of motion, such as going upstairs or downstairs, or walking on a plane. In each of these modes, the step size will be different, and this has a strong impact on the accuracy of pedestrian dead reckoning. In order to identify the patterns of movement in multi-storey buildings, we propose a new method of movement recognition based on smart phones. Firstly, the relationships between the patterns of indoor movement and the changes in the air pressure, acceleration and angular velocity data obtained from the built-in sensors of mobile phones are analysed. The naive Bayes method is then used to identify four different modes of motion: walking up and down stairs, moving on a landing and moving at a constant speed along a corridor. Our experimental results show that the recognition accuracy of our scheme reaches 95.82%.
Keywords: accelerometer; barometer; gyroscope; motion mode recognition; naive Bayes; smart phone.
Online Drone-based Data Gathering Strategies for Ground Sensor Networks
by Celia Yasmine TAZIBT, Nadjib ACHIR, Tounsia DJAMAH
Abstract: This paper proposes two path-planning schemes for data collection in WSN using a drone flying over the sensor nodes to collect their data. We assign a weight to each sensor node corresponding to its priority in the collection process. When the drone selects its destination node, it will choose the one having the highest weight. We have defined utility functions based on the sensor nodes' information disseminated in the Wireless Sensor Network (WSN) using the Optimized Link State Routing protocol (OLSR). The information required to compute the nodes' weight is added to the exchanged packets during the execution of OLSR. The first proposed strategy is Data-driven Data Gathering Strategy (DDG) which uses the amount of stored data in each sensor node buffer. A priority is given to the nodes having the most significant data amount to collect. The second strategy is called Time-driven Data Gathering Strategy (TDG) where the age of the data is considered.
Keywords: Wireless sensor networks; unmanned aerial vehicles; data gathering; path planning.
An IoT-based Scalable River Level Monitoring Platform
by Juan Acosta, Diego Mendez, Daniel Moreno, German Montanez, Luis Trujillo, Mauricio Escobar, Ignacio Gonzalez
Abstract: Due to extreme natural conditions, there is a clear necessity of cost-effective and scalable solutions to continuously monitor the environmental conditions in order to take precautionary actions. In this paper we present the design and implementation of an IoT-based solution for river level monitoring. The implemented system integrates 31 stations with communication capabilities to monitor the status of the Negro and Nare rivers. An embedded system has been designed and implemented from scratch into a single PCB, integrating ultrasonic sensors and power management. In order to consolidate and visualize the data, a cloud platform has been implemented, which is also in charge of generating alerts to local authorities and the population in general. The stations have an autonomy of 22 days in case of complete solar panel damage. The experimental results show that the river dynamics are perfectly captured in real-time, allowing the authorities to timely warn the population.
Keywords: Embedded system; early warning system; floods; river level; IoT.
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