Genetic algorithm based cluster head optimization using topology control for hazardous environment using WSN

Roslin, S. Emalda

doi:10.1109/iciiecs.2015.7193039

Cited by 5 publications

(1 citation statement)

References 20 publications

(13 reference statements)

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“…Shijun He et al proposed a novel method that combines genetic algorithm and fuzzy C-means clustering algorithm (FCM) to improve the sensitivity of the initial value of FCM [1] which uses the membership to describe the possibility of cluster. In this paper [2], an energy efficient sensor network is obtained by using effective topology control in case of any node failure in hazardous environment. Based on residual energy, bandwidth and memory capacity, cluster head is selected using genetic algorithm Shiyuan Jin et al [3] present an optimization algorithm called genetic algorithm to minimize the total communication distance using clustering process in a sensor network.…”

Section: Introductionmentioning

confidence: 99%

Reinforce Coverage Area of Sensor Mote in Wireless Ad-Hoc Networks Using Genetic Algorithm

Hemalatha¹,

Srinivasan²

2018

IJET

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Wireless sensor based communication is everlasting growing sector within the industry of communication. In WSN improving the life expectancy of the network depends on the energy dissipation of senor devices. Diminishing the energy dissipation of sensor device will enhance the lifetime and device failure which helps in better availability and coverage area of sensor network. One of the dynamic research fields in wireless sensor network is that of coverage. Coverage can be defined as how well each point of interest is monitored by sensor network. In this paper, we investigate the cluster head selection issue, particularly focusing on applications where the upkeep of full network coverage is the fundamental prerequisite. Coverage maintenance for extended period is a pivotal issue in wireless sensor network because of the constrained inbuilt battery in sensors. Coverage maintenance may be prolonged by utilizing the network energy efficiently, by keeping an adequate number of sensors in sensor covers. The clustering algorithm is a solution to reduce energy consumption which can be helpful to the scalability and network lifetime. Assuming serious energy rebalancing with additional clustering algorithm, a Genetic algorithm (GA) based clustering algorithm which evaluates the fitness function by considering the two major parameters distance and energy has been proposed in this paper. Simulation result shows that the proposed solution finds the optimal cluster heads and has prolonged network lifetime and maximum coverage.

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Section: Introductionmentioning

confidence: 99%