Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Energy-Efficient Adaptive Sensing Scheduling in Wireless Sensor Networks using Fibonacci Tree Optimization Algorithm

Version 1 : Received: 31 May 2021 / Approved: 1 June 2021 / Online: 1 June 2021 (08:35:20 CEST)

How to cite: Wu, L.; Cai, H. Energy-Efficient Adaptive Sensing Scheduling in Wireless Sensor Networks using Fibonacci Tree Optimization Algorithm. Preprints 2021, 2021060002 (doi: 10.20944/preprints202106.0002.v1). Wu, L.; Cai, H. Energy-Efficient Adaptive Sensing Scheduling in Wireless Sensor Networks using Fibonacci Tree Optimization Algorithm. Preprints 2021, 2021060002 (doi: 10.20944/preprints202106.0002.v1).

Abstract

Wireless sensor networks are attractive largely because they need no wired infrastructure. But precisely this feature makes them energy constrained. Recent studies find that sensing behaviors that are otherwise deemed efficient consume comparable energy with communication. The duty cycle scheduling is perceived as contributing to achieving energy efficiency of sensing. Because of different research assumptions and objectives, various scheduling schemes have various emphases. This paper designed an adaptive sensing scheduling strategy. The objective function of the scheduling strategy includes minimizing average energy expenditure and maximizing sensing coverage (reducing event miss-rate), and it requires relatively loose assumptions. We determine the functional relationship between the variables of the objective function and the step-size parameters of the proposed strategy through the numerical fitting. We found that the objective function aggregated by the fitting functions is a bivariate multi-peak function that favors the Fibonacci tree optimization algorithm. Once the optimization of parameters is done, the strategy can be easily deployed and behaves consistently in the coming hours. We name the proposed strategy as “FTOS”. The experimental results show that the Fibonacci tree optimization algorithm gets a better optimistic effect than the comprehensive learning particle swarm optimization (CLPSO) algorithm and differential evolution (DE) algorithm. The FTOS strategy is superior to the fixed time scheduling strategy in achieving the scheduling objectives. It also outperforms other strategies with the same scheduling objectives such as LDAS, BS, DSS and PECAS.

Subject Areas

sensing; energy-saving; duty cycles; Fibonacci tree optimization

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