scholarly journals A Virtual Force Algorithm-Lévy-Embedded Grey Wolf Optimization Algorithm for Wireless Sensor Network Coverage Optimization

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2735 ◽  
Author(s):  
Shipeng Wang ◽  
Xiaoping Yang ◽  
Xingqiao Wang ◽  
Zhihong Qian
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Optimization Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Grey Wolf ◽  
Network Coverage ◽  
Coverage Optimization ◽  
Virtual Force ◽  
Monitoring Area

The random placement of a large-scale sensor network in an outdoor environment often causes low coverage. In order to effectively improve the coverage of a wireless sensor network in the monitoring area, a coverage optimization algorithm for wireless sensor networks with a Virtual Force-Lévy-embedded Grey Wolf Optimization (VFLGWO) algorithm is proposed. The simulation results show that the VFLGWO algorithm has a better optimization effect on the coverage rate, uniformity, and average moving distance of sensor nodes than a wireless sensor network coverage optimization algorithm using Lévy-embedded Grey Wolf Optimizer, Cuckoo Search algorithm, and Chaotic Particle Swarm Optimization. The VFLGWO algorithm has good adaptability with respect to changes of the number of sensor nodes and the size of the monitoring area.

scholarly journals Coverage Optimization Algorithm of Wireless Sensor Network Based on Mobile Nodes

2016 ◽  
Vol 12 (08) ◽  
pp. 45 ◽  
Author(s):  
Li Zhu ◽  
Chunxiao Fan ◽  
Huarui Wu ◽  
Zhigang Wen
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Optimization Algorithm ◽  
Quantitative Relationship ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Mobile Nodes ◽  
Coverage Optimization ◽  
Blind Zone

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; -ms-layout-grid-mode: line; mso-fareast-font-family: SimSun; mso-fareast-theme-font: minor-fareast; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">To reduce the blind zone in network coverage, we propose a coverage optimization algorithm of wireless sensor network based on mobile nodes. This algorithm calculates the irregularity of blind zone in network coverage and obtains the minimum approximate numerical solution by utilizing the quantitative relationship between energy consumption of related nodes and the position of the mobile nodes. After determining the optimal relative position of the mobile nodes, the problem of blind zone between the static nodes is addressed. Simulation result shows that the proposed algorithm has high dynamic adaptability and can address the problem of blind zone maximally. Besides increasing the network coverage, the algorithm also reduces the network energy consumption, optimizes network coverage control and exhibits high convergence. </span>

scholarly journals Wireless Sensor Network Coverage Optimization Based on Fruit Fly Algorithm

2018 ◽  
Vol 14 (06) ◽  
pp. 58 ◽  
Author(s):  
Ren Song ◽  
Zhichao Xu ◽  
Yang Liu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Fruit Fly ◽  
Effective Radius ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Fruit Fly Optimization Algorithm ◽  
Network Coverage ◽  
Coverage Optimization ◽  
Improved Algorithm

<p class="0abstract"><span lang="EN-US">To solve the defect of traditional node deployment strategy, the improved <a name="_Hlk502130691"></a>fruit fly algorithm was combined with wireless sensor network. The optimization of network coverage was implemented. </span><span lang="EN-US">Based on a new type of intelligent algorithm, the change step of fruit fly optimization algorithm (CSFOA)</span><span lang="EN-US">was proposed. At the same time, the mathematical modeling of two network models was carried out respectively. The grid coverage model was used. The network coverage and redundancy were transformed into corresponding mathematical variables by means of grid partition.</span><span lang="EN-US">Among them, the maximum effective radius of sensor nodes was fixed in mobile node wireless sensor network. The location of nodes was randomly cast. The location of sensor nodes was placed in fixed position nodes. The effective radius of nodes can be changed dynamically.</span><span lang="EN-US">Finally, combined with the corresponding network model, the improved algorithm was applied to wireless sensor network.</span><span lang="EN-US">The combination of the optimal solution of the node position and the perceptual radius was found through the algorithm. The maximum network coverage was achieved.</span><span lang="EN-US">The two models were simulated and verified. The results showed that the improved algorithm was effective and superior to the coverage optimization of wireless sensor networks.</span></p>

scholarly journals Coverage Optimization Strategy for WSN based on Energy-aware

2016 ◽  
Vol 11 (6) ◽  
pp. 877 ◽  
Author(s):  
Li Zhu ◽  
Chunxiao Fan ◽  
Zhigang Wen ◽  
Huarun Wu
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Path Loss ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Optimization Strategy ◽  
Network Coverage ◽  
Energy Aware ◽  
Coverage Optimization

In order to optimize the wireless sensor network coverage, this paper designs a coverage optimization strategy for wireless sensor network (EACS) based on energy-aware. Under the assumption that the geographic positions of sensor nodes are available, the proposed strategy consists of energy-aware and network coverage adjustment. It is restricted to conditions such as path loss, residual capacity and monitored area and according to awareness ability of sensors, it would adjust the monitored area, repair network hole and kick out the redundant coverage. The purpose is to balance the energy distribution of working nodes, reduce the number of “dead” nodes and balance network energy consumption. As a result, the network lifetime is expanded. Simulation results show that: EACS effectively reduces the number of working nodes, improves network coverage, lowers network energy consumption while ensuring the wireless sensor network coverage and connectivity, so as to balance network energy consumption.

scholarly journals A Novel Coverage Optimization Strategy Based on Grey Wolf Algorithm Optimized by Simulated Annealing for Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yong Zhang ◽  
Li Cao ◽  
Yinggao Yue ◽  
Yong Cai ◽  
Bo Hang
Keyword(s):  
Wireless Sensor Networks ◽  
Simulated Annealing ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Coverage Optimization ◽  
Grey Wolf Algorithm

The coverage optimization problem of wireless sensor network has become one of the hot topics in the current field. Through the research on the problem of coverage optimization, the coverage of the network can be improved, the distribution redundancy of the sensor nodes can be reduced, the energy consumption can be reduced, and the network life cycle can be prolonged, thereby ensuring the stability of the entire network. In this paper, a novel grey wolf algorithm optimized by simulated annealing is proposed according to the problem that the sensor nodes have high aggregation degree and low coverage rate when they are deployed randomly. Firstly, the mathematical model of the coverage optimization of wireless sensor networks is established. Secondly, in the process of grey wolf optimization algorithm, the simulated annealing algorithm is embedded into the grey wolf after the siege behavior ends and before the grey wolf is updated to enhance the global optimization ability of the grey wolf algorithm and at the same time improve the convergence rate of the grey wolf algorithm. Simulation experiments show that the improved grey wolf algorithm optimized by simulated annealing is applied to the coverage optimization of wireless sensor networks. It has better effect than particle swarm optimization algorithm and standard grey wolf optimization algorithm, has faster optimization speed, improves the coverage of the network, reduces the energy consumption of the nodes, and prolongs the network life cycle.

A Survey on Coverage Control Technology in Wireless Sensor Network

2013 ◽  
Vol 774-776 ◽  
pp. 1556-1559 ◽  
Author(s):  
Kai Guo Qian ◽  
Li Mui ◽  
Tian Ma Zuo ◽  
Zhi Qiang Xu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Research Literature ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Coverage Problem ◽  
Research Directions ◽  
And Control ◽  
Monitoring Area

Deployment and coverage, studied how to effectively place and control of sensor nodes and to make WSN covered the monitoring area with the purpose of minimum the energy consumption and prolonging the network life cycle under the premise of guarantee the quality of service (QOS),are the primary problems for construction wireless sensor network. This paper analyzes important factors for resolve the coverage problem such as sensing models and deployment way, followed survey the state-of-the art coverage contro1 techniques and presents an overview and analysis of the solution proposed in recent research literature, Further research directions are pointed out in the end.

scholarly journals Path Optimization Algorithm in Wireless Sensor Network with Obstacle

2018 ◽  
Vol 9 (3) ◽  
pp. 599
Author(s):  
J. S. Praveen ◽  
Mr. Mayilvahanan
Keyword(s):  
Wireless Sensor Network ◽  
Quality Factor ◽  
Sensor Network ◽  
Optimization Algorithm ◽  
Optimization Technique ◽  
Base Station ◽  
Path Optimization ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Hop Count

<p>Clustering is used for prolonging the network lifetime in WSN. It groups sensor nodes into different groups and selects a single node as a cluster head (CH) for all the groups. CHS collect the data from consonant clusters and forward the data to base station. In this paper, we proposed path optimization algorithm in Wireless Sensor Network with an obstacle (POAWSNO) that periodically selects the cluster heads according to quality factor. The quality factor is estimated by three criteria including quality of the link, remaining energy and degree of the node. Path optimization technique determines the shortest path during obstacle present in the WSN. POT is used to reduce the hop count and packet delay. The simulation results demonstrate that this approach improves the throughput and reduce the loss of packets and energy consumption in the network using network simulator.</p>

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