Wireless sensor network energy efficient coverage method based on intelligent optimization algorithm

Abstract

As a basic and fundamental problem in wireless sensor network (WSN), the network coverage greatly reflects the performance of information transmission in WSN. In order to achieve a good balance between target coverage and energy consumption, in this paper, we propose a novel wireless sensor network energy efficient coverage method based on genetic algorithm. Particularly, the goal of this work is cover a 2D sensing area via selecting a minimum number of sensors. Moreover, the deployed wireless sensors should be connected to let each sensor be connected a path to the base station. Afterwards, genetic algorithm is used to compute the minimum number of potential position to let all target be k-covered and all sensor nodes be m-connected, and each chromosome is set to be the number of potential positions. Finally, we provide a simulation to test the performance of the proposed method, and simulation results demonstrate that the proposed method can achieve high degree of target coverage without wasting extra energy.

Study on Secrecy Capacity of Wireless Sensor Networks in Internet of Things Based on the Amplify-and-Forward Compressed Sensing Scheme

“…Moreover, with the decrease of noise interference intensity, the channel estimation accuracy of this kind of legal receiver increases linearly with SNR. As a result of comparison, it can be seen that the accuracy of the algorithm proposed in literature [14] is about 10 −2 dB. Therefore, the algorithm proposed in this paper has better differentiation for channel estimation of legitimate receiver and illegal receiver, which make the WSN secure communication effect better.…”

“…Due to the importance of channel estimation in the perceptual phase, we increase the channel estimation training process and compare it with the literature [14]. It is assumed that both the legitimate user and the illegal user can use the training sequence signal for channel estimation, and the illegal user can also acquire the training sequence.…”

Target Coverage and Network Connectivity Challenges in Wireless Sensor Networks

“…The Nash Q-Learning node scheduling algorithm [ 9] for coverage and connectivity maintenance where each node learns its optimal action autonomously to enhance coverage rate and establish a communication link in a network. The author in [10] proposes a novel energyefficient wireless sensor network coverage approach based on a genetic algorithm, to achieve the desired balance between target coverage and energy consumption. In particular, the purpose of this work is to cover the 2D sensing region by selecting a limited number of sensors.…”

An Adaptive, Discrete Space Oriented Wolf Pack Optimization Algorithm for a Movable Wireless Sensor Network

“…In [12], Yarinezhad et al proposed a fixed parameter tractable (FPT) approximation algorithm with an approximation factor of 1.2 for the load-balanced clustering problem (LBCP) and an energy-efficient, balanced routing algorithm, which effectively solved the load-balanced clustering problem and maximized the network’s lifetime by reducing the energy consumption. In [13], Chen et al proposed a novel wireless sensor network with energy-efficient coverage that achieved a good balance between target coverage and energy consumption by fusing the genetic algorithm (GA) and WSN. In [14], Somaieh et al proposed the distributed energy-aware hexagon-based clustering algorithm to improve coverage (DEHCIC), which considers energy and topological features such as the number of mobile neighbor nodes and number of neighbor nodes to elect cluster heads and attempts to cover holes as much as possible by static sensor nodes, while the closest mobile node is used to cover holes if this is not possible; furthermore, the proposed algorithm retains sensor nodes in an active mode that covers interest points and puts others into a low-power sleep mode.…”