Maximizing Lifetime of Range-Adjustable Wireless Sensor Networks: A Neighborhood-Based Estimation of Distribution Algorithm

Abstract

Sensor activity scheduling is critical for prolonging the lifetime of wireless sensor networks (WSNs). However, most existing methods assume sensors to have one fixed sensing range. Prevalence of sensors with adjustable sensing ranges posts two new challenges to the topic: 1) expanded search space, due to the rise in the number of possible activation modes and 2) more complex energy allocation, as the sensors differ in the energy consumption rate when using different sensing ranges. These two challenges make it hard to directly solve the lifetime maximization problem of WSNs with range-adjustable sensors (LM-RASs). This article proposes a neighborhood-based estimation of distribution algorithm (NEDA) to address it in a recursive manner. In NEDA, each individual represents a coverage scheme in which the sensors are selectively activated to monitor all the targets. A linear programming (LP) model is built to assign activation time to the schemes in the population so that their sum, the network lifetime, can be maximized conditioned on the current population. Using the activation time derived from LP as individual fitness, the NEDA is driven to seek coverage schemes promising for prolonging the network lifetime. The network lifetime is thus optimized by repeating the steps of the coverage scheme evolution and LP model solving. To encourage the search for diverse coverage schemes, a neighborhood sampling strategy is introduced. Besides, a heuristic repair strategy is designed to fine-tune the existing schemes for further improving the search efficiency. Experimental results on WSNs of different scales show that NEDA outperforms state-of-the-art approaches. It is also expected that NEDA can serve as a potential framework for solving other flexible LP problems that share the same structure with LM-RAS.

An Extraction Method of Volleyball Spiking Trajectory and Teaching Based on Wireless Sensor Network

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Many-Objective Job-Shop Scheduling: A Multiple Populations for Multiple Objectives-Based Genetic Algorithm Approach

“…In the future, we will focus on other types of JSSPs (e.g., flexible JSSPs [49], [50], flow shop scheduling problems [51]- [53]), and extend the MPMO framework with other evolutionary computation [54] (e.g., particle swarm optimization [55]- [57], differential evolution [58]- [60], estimation of distribution algorithm [61], and gravitational search algorithm [62]) for efficiently solving them. Besides, the incorporation with distributed computing technique [63]- [65] or matrix-based technique [66] will be further studied to reduce the running time of the algorithm.…”

SAFE: Scale-Adaptive Fitness Evaluation Method for Expensive Optimization Problems

“…To further investigate the benefits of the SAFE method on other EC algorithms, we test the SAFE method on the estimation of distribution algorithm (EDA) [59], [60]. The results are shown in Table S.XI and their Wilcoxon's rank sum tests are shown in Table S.XII in the supplementary material.…”