Path coverage enhancement in wireless sensor networks based on CVT model

2021 ◽  
pp. 1-13
Author(s):  
Guangxu Yu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Detection Probability ◽  
Coverage Rate ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Current Path ◽  
Coverage Model ◽  
Path Coverage ◽  
Low Coverage

In order to overcome the problems of low detection probability, low coverage uniformity and low coverage of current path coverage enhancement methods in wireless sensor networks, a new path coverage enhancement method based on CVT model is proposed in this paper. Firstly, the node perception model and network coverage model are constructed. On the basis of the node awareness model and network coverage model, CVT model is used to adjust the connection mode, density and location of nodes in wireless sensor networks, so as to improve the coverage performance of nodes in the detection area in wireless sensor networks, and realize the effective enhancement of path coverage in wireless sensor networks. Experimental results show that, compared with the traditional methods, the proposed method has high detection probability, high coverage uniformity and coverage rate, and the highest coverage rate reaches 97%, which has higher practical application performance.

scholarly journals Improved whale optimization algorithm and its application in heterogeneous wireless sensor networks

2021 ◽  
Vol 17 (5) ◽  
pp. 155014772110181
Author(s):  
Yinggao Yue ◽  
Hairong You ◽  
Shuxin Wang ◽  
Li Cao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Coverage Optimization ◽  
Network Cost ◽  
Whale Optimization ◽  
Heterogeneous Wireless Sensor Networks

Aiming at the problems of node redundancy and network cost increase in heterogeneous wireless sensor networks, this article proposes an improved whale optimization algorithm coverage optimization method. First, establish a mathematical model that balances node utilization, coverage, and energy consumption. Second, use the sine–cosine algorithm to improve the whale optimization algorithm and change the convergence factor of the original algorithm. The linear decrease is changed to the nonlinear decrease of the cosine form, which balances the global search and local search capabilities, and adds the inertial weight of the synchronous cosine form to improve the optimization accuracy and speed up the search speed. The improved whale optimization algorithm solves the heterogeneous wireless sensor network coverage optimization model and obtains the optimal coverage scheme. Simulation experiments show that the proposed method can effectively improve the network coverage effect, as well as the utilization rate of nodes, and reduce network cost consumption.

Formal Probabilistic Analysis of Detection Properties in Wireless Sensor Networks

2015 ◽  
pp. 228-258
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Probabilistic Analysis ◽  
Detection Probability ◽  
Scheduling Algorithm ◽  
Wireless Sensor ◽  
Higher Order Logic ◽  
Security Monitoring ◽  
Detection Delay ◽  
Mission Critical

In the context of Wireless Sensor Networks (WSNs), the ability to detect an intrusion event is the most desired characteristic. Due to the randomness in nodes scheduling algorithm and sensor deployment, probabilistic techniques are used to analyze the detection properties of WSNs. However, traditional probabilistic analysis techniques, such as simulation and model checking, do not ensure accurate results, which is a severe limitation considering the mission-critical nature of most of the WSNs. In this chapter, the authors overcome these limitations by using higher-order-logic theorem proving to formally analyze the detection properties of randomly deployed WSNs using the randomized scheduling of nodes. Based on the probability theory, described in Chapters 5, they first formally reason about the intrusion period of any occurring event. This characteristic is then built upon to develop the fundamental formalizations of the key detection metrics: the detection probability and the detection delay. For illustration purposes, the authors formally analyze the detection performance of a WSN deployed for border security monitoring.

scholarly journals Bioinspired Evolutionary Algorithm Based for Improving Network Coverage in Wireless Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Mohammadjavad Abbasi ◽  
Muhammad Shafie Bin Abd Latiff ◽  
Hassan Chizari
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Evolutionary Algorithm ◽  
Sensor Node ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Target Area ◽  
Network Coverage ◽  
Detection Model

Wireless sensor networks (WSNs) include sensor nodes in which each node is able to monitor the physical area and send collected information to the base station for further analysis. The important key of WSNs is detection and coverage of target area which is provided by random deployment. This paper reviews and addresses various area detection and coverage problems in sensor network. This paper organizes many scenarios for applying sensor node movement for improving network coverage based on bioinspired evolutionary algorithm and explains the concern and objective of controlling sensor node coverage. We discuss area coverage and target detection model by evolutionary algorithm.

scholarly journals Optimal Coverage Model in Clifford 3-Connected Wireless Sensor Networks

2011 ◽  
Vol 1 ◽  
pp. 66-70
Author(s):  
Wen Ming Cao ◽  
Tian Cheng He
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Full Coverage ◽  
Coverage Model ◽  
Optimal Deployment ◽  
Sensing Radius ◽  
Communication Radius

While moderate loss of coverage can be tolerated by WSN applications, loss of connectivity can be fatal. Moreover, since sensors are subject to unanticipated failures after deployment, it is not sufficient for a wireless sensor network to just be connected, it should be Clifford 3-connected . In this dissertation, we propose optimal deployment patterns to achieve both full coverage and Cliford 3-connectivity, and analyses their optimality for all values of , where is the communication radius and is the sensing radius.

Network Coverage in Interference Limited Wireless Sensor Networks

2019 ◽  
Vol 109 (1) ◽  
pp. 139-153 ◽  
Author(s):  
Sunandita Debnath ◽  
Ashraf Hossain
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Network Coverage

scholarly journals Method for Patching Three-Dimensional Surface Coverage Loopholes of Hybrid Nodes in Wireless Sensor Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Zhanjun Hao ◽  
Hongwen Xu ◽  
Xiaochao Dang ◽  
Nanjiang Qu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Three Dimensional ◽  
Wireless Sensor ◽  
Utilization Rate ◽  
Network Coverage ◽  
Mobile Nodes ◽  
Research Information ◽  
Information Monitoring ◽  
Dimensional Surface

Target sensing and information monitoring using wireless sensor networks have become an important research field. Based on two-dimensional plane research, information monitoring, and transmission for three-dimensional curved target events, due to the uneven deployment of nodes and failures in sensor networks, there are a lot of coverage loopholes in the network. In this paper, a method of detecting and repairing loopholes in monitoring the coverage of three-dimensional surface targets with hybrid nodes is proposed. In the target monitoring area where the hybrid nodes are randomly deployed, the three-dimensional surface cube is meshed, and the coverage loopholes are gradually detected according to the method of computational geometry, and then, the redundant mobile nodes around the coverage loopholes are selected. According to the calculated distance to cover the moving direction and distance of the loophole, the virtual force is used to adjust the mobile nodes to repair the coverage loopholes. Simulation results show that compared with other algorithms, this algorithm has a higher utilization rate of mobile nodes, uses fewer nodes to complete coverage, reduces network coverage costs, meets the overall network coverage requirements, and has lower mobile energy consumption and longer network life. The actual scene further verifies the good connectivity and high coverage of the whole network.

scholarly journals Massive MIMO-Based Distributed Signal Detection in Multi-Antenna Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2005
Author(s):  
Guofeng Wei ◽  
Bangning Zhang ◽  
Guoru Ding ◽  
Bing Zhao ◽  
Yimin Wei ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Network ◽  
Detection Probability ◽  
Massive Mimo ◽  
Large Scale ◽  
Multiple Input Multiple Output ◽  
Wireless Sensor ◽  
Channel State ◽  
Single Antenna

For massive multiple-input multiple-output (MIMO) distributed wireless sensor networks, this paper investigates the role of multi-antenna sensors in improving network perception performance. First, we construct a distributed multi-antenna sensor network based on massive MIMO. By using the anti-fading characteristics of multi-antennas, it is better to achieve accurate detection than the single-antenna sensor network. Based on this, we derive a closed-loop expression for the detection probability of the best detector. Then, we consider the case that the sensor power resources are limited, and thus we want to use finite power to achieve higher detection probability. For this reason, the power was optimized by the alternating direction method of multipliers (ADMM). Moreover, we also prove that only statistical channel state is needed in large-scale antenna scenarios, which avoid the huge overhead of channel state information. Finally, according to the simulation results, the multi-antenna sensor network has better detection performance than the single-antenna sensor network which demonstrates the improved performance of the proposed schemes and also validates the theoretical findings.

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