scholarly journals Unequal clustering and scheduling in Wireless Sensor Network using Advance Genetic Algorithm

2020 ◽  
Vol 1530 ◽  
pp. 012076
Author(s):  
Ruwaida M Yas ◽  
Soukaena H Hashem
Author(s):  
Pawan Singh Mehra

AbstractWith huge cheap micro-sensing devices deployed, wireless sensor network (WSN) gathers information from the region and delivers it to the base station (BS) for further decision. The hotspot problem occurs when cluster head (CH) nearer to BS may die prematurely due to uneven energy depletion resulting in partitioning the network. To overcome the issue of hotspot or energy hole, unequal clustering is used where variable size clusters are formed. Motivated from the aforesaid discussion, we propose an enhanced fuzzy unequal clustering and routing protocol (E-FUCA) where vital parameters are considered during CH candidate selection, and intelligent decision using fuzzy logic (FL) is taken by non-CH nodes during the selection of their CH for the formation of clusters. To further extend the lifetime, we have used FL for the next-hop choice for efficient routing. We have conducted the simulation experiments for four scenarios and compared the propound protocol’s performance with recent similar protocols. The experimental results validate the improved performance of E-FUCA with its comparative in respect of better lifetime, protracted stability period, and enhanced average energy.


Author(s):  
Jin Yong-xian

To improve the energy efficiency of the wireless sensor network (WSN), and extend the network life. This paper proposes an improved unequal clustering multipath routing algorithm (UCMRA). The algorithm improves the formula of cluster head selection probability and competition radius, and considers the energy factor, node density, optimal number of cluster heads, etc. Experimental results show that, compared with the traditional algorithm, UCMRA has more stable cluster head distribution, less energy consumption and longer network lifetime.


2020 ◽  
Vol 111 (4) ◽  
pp. 2703-2732 ◽  
Author(s):  
Naveen Muruganantham ◽  
Hosam El-Ocla

2019 ◽  
Vol 15 (8) ◽  
pp. 155014771986987 ◽  
Author(s):  
Zhanjun Hao ◽  
Nanjiang Qu ◽  
Xiaochao Dang ◽  
Jiaojiao Hou

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.


2018 ◽  
Vol 14 (11) ◽  
pp. 16
Author(s):  
Qing Wan ◽  
Ying Wang

To realize the exploration of wireless sensor network (WSN) based on cloud computing, the application service of WSN is taken as the starting point, the resource advantage of the cloud platform is used, and a WSN service framework based on cloud environment is proposed. Based on this framework, the problems of data management and reconstruction, network coverage optimization and monitoring, and edge recognition of holes are solved. In view of the node deployment of WSN and coverage problem of operation and maintenance optimization, the genetic algorithm is used to adjust the dormancy and energy state of nodes, and a parallel genetic algorithm for covering optimization in the cloud environment is proposed. For the operation and maintenance requirements of WSN, a parallel data statistics method for network monitoring is proposed. The experimental results show that the parallel algorithm is greatly improved in terms of the accuracy and time efficiency.


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