On the impact of node failure on network coverage in wireless sensor network

2012 ◽  
Author(s):  
Ashraf Hossain
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Node Failure ◽  
The Impact

scholarly journals Enhancing Network Coverage Using Sensing Models in Wireless Sensor Network

2021 ◽  
Vol 1717 ◽  
pp. 012062
Author(s):  
J Vidhya ◽  
P Prasanna ◽  
M Margarat ◽  
S Jayalakshmy
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Network Coverage

scholarly journals Node optimization coverage method under link model in passive monitoring system of three-dimensional wireless sensor network

2019 ◽  
Vol 15 (8) ◽  
pp. 155014771986987 ◽  
Author(s):  
Zhanjun Hao ◽  
Nanjiang Qu ◽  
Xiaochao Dang ◽  
Jiaojiao Hou
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Particle Swarm Optimization Algorithm ◽  
Three Dimensional ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Swarm Optimization ◽  
Passive Monitoring ◽  
Link Model

3D coverage is not only closer to the actual application environment, but also a research hotspot of sensor networks in recent years. For this reason, a node optimization coverage method under link model in passive monitoring system of three-dimensional wireless sensor network is proposed in this article. According to wireless link-aware area, the link coverage model in three-dimensional wireless sensor network is constructed, and the cube-based network coverage is used to represent the quality of service of the network. This model takes advantage of the principle that the presence of human beings can change the transmission channel of the link. On this basis, the intruder is detected by the data packets transmitted between the wireless links, and then the coverage area is monitored by monitoring the received signal strength of the wireless signal. Based on this new link awareness model, the problem of optimal coverage deployment of the receiving node is solved, that is, how to deploy the receiving node to achieve the optimal coverage of the monitoring area when the location of the sending node is given. In the process of optimal coverage, the traditional genetic algorithm and particle swarm optimization algorithm are introduced and improved. Based on the genetic algorithm, the particle swarm optimization algorithm which integrates the idea of simulated annealing is regarded as an important operator of the genetic algorithm, which can converge to the optimal solution quickly. The simulation results show that the proposed method can improve the network coverage, converge quickly, and reduce the network energy consumption. In addition, we set up a real experimental environment for coverage verification, and the experimental results verify the feasibility of the proposed method.

scholarly journals A Localization Algorithm of Wireless Sensor Network Based on Statistical Uncorrelated Vector

2017 ◽  
Vol 13 (07) ◽  
pp. 57
Author(s):  
Min Wang
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Inner Product ◽  
Localization Algorithm ◽  
Product Matrix ◽  
Ranging Error ◽  
Location Algorithm ◽  
The Impact

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-ansi-language: EN-US; mso-fareast-font-family: 宋体; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">For exploring wireless sensor network - a self-organized network, a new node location algorithm based on statistical uncorrelated vector (SUV) model, namely SUV location algorithm, is proposed. The algorithm, by translating the node coordinate, simplifies the solution to double center coordinate matrix, and gets the coordinate inner product matrix; then it uses statistical uncorrelated vectors to reconstruct the coordinates of the inner product matrix and remove the correlation of inner matrix of coordinates caused by the ranging error, so as to reduce the impact of ranging error on subsequent positioning accuracy. The experimental results show that the proposed algorithm does not consider the network traffic, bust still has good performance in localization. At last, it is concluded that reducing the amount of communication of sensor nodes is beneficial to prolong the service life of the sensor nodes, thus increasing the lifetime of the whole network.</span>

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