Research of Data Mining and Network Coverage Optimization in Early Warning Model of Chlorine Gas Monitoring Wireless Sensor Network

2014 ◽  
Author(s):  
Shi Yunbo ◽  
Wang Rongxin ◽  
Zhou Yushan ◽  
Liu Congning ◽  
Zhang Yiwei ◽  
...  
Keyword(s):  
Data Mining ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Early Warning ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Coverage Optimization ◽  
Early Warning Model ◽  
Chlorine Gas ◽  
Warning Model

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 Wireless sensor network coverage optimization based on whale group algorithm

2018 ◽  
Vol 15 (3) ◽  
pp. 569-583 ◽  
Author(s):  
Lei Wang ◽  
Weihua Wu ◽  
Junyan Qi ◽  
Zongpu Jia
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Network Performance ◽  
Mathematic Model ◽  
Initial Distribution ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Coverage Optimization

For all of types of applications in wireless sensor networks (WSNs), coverage is a fundamental and hot topic research issue. To monitor the interest field and obtain the valid data, the paper proposes a wireless sensor network coverage optimization model based on improved whale algorithm. The mathematic model of node coverage in wireless sensor networks is developed to achieve full coverage for the interest area. For the model, the idea of reverse learning is introduced into the original whale swarm optimization algorithm to optimize the initial distribution of the population. This method enhances the node search capability and speeds up the global search. The experiment shows that this algorithm can effectively improve the coverage of nodes in wireless sensor networks and optimize the network performance.

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 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.

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