Abstract
Large‐scale wireless sensor networks (LSWSNs) are currently one of the most influential technologies and have been widely used in industry, medical, and environmental monitoring fields. The LSWSNs are composed of many tiny sensor nodes. These nodes are arbitrarily distributed in a certain area for data collection, and they have limited energy consumption, storage capabilities, and communication capabilities. Due to limited sensor resources, traditional network protocols cannot be directly applied to LSWSNs. Therefore, the issue of maximizing the LSWSNs’ lifetime by working with duty cycle design algorithm has been extensively studied in this paper. Encouraged by annealing algorithm, this work provides a new elite adaptive simulated annealing (EASA) algorithm to prolong LSWSNs’ lifetime. We then present a sensor duty cycle models, which can make sure the full coverage of the monitoring targets and prolong the network lifetime as much as possible. Simulation results indicate that the network lifetime of EASA algorithm is 21.95% longer than that of genetic algorithm (GA) and 28.33% longer than that of particle swarm algorithm (PSO).
Highlights
Large-scale wireless sensor networks (LSWSNs) have broad application prospects in various fields, such as agriculture, industry, military, and environmental monitoring because of their real-time data collection and flexibility of deployment methods
For the convenience of representation, we stipulate that the function rowzero represents the number of rows where the first zero element appears in the matrix C, and the duty cycle model of LSWSNs can be expressed as formulas (6) and (7): Objective : f ðCÞ = rowzeroðCÞ − 1, ð6Þ
The elite adaptive simulated annealing (EASA) method we proposed to solve the sensor duty cycle problem will carry out a series of experiments and compare EASA with genetic algorithm (GA) and particle swarm algorithm (PSO) to prove its effectiveness
Summary
Large-scale wireless sensor networks (LSWSNs) have broad application prospects in various fields, such as agriculture, industry, military, and environmental monitoring because of their real-time data collection and flexibility of deployment methods. Sensing nodes are generally randomly deployed in the monitoring area, which is likely to cause uneven distribution of nodes and lead to problems such as coverage blind areas [1] This will affect the coverage quality of LSWSN. The traditional deployment method is to use Journal of Sensors large-scale static nodes to complete the coverage of the target area. This method will cause problems such as high node redundancy. We propose a method EASA to solve the problem of duty cycle, while ensuring full coverage of the monitoring target, and extend the lifetime of the LSWSNs as much as possible to ensure the monitoring effect of the target. Simulation results indicate the proposed algorithm can achieve a higher working life of LSWSNs over GA and PSO [8,9,10,11,12,13]
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