Abstract
Scheduling sensor nodes has an important role in real monitoring applications using sensor networks, lowering the power consumption and maximizing the network lifetime, while maintaining the satisfaction to application requirements. Nevertheless, this problem is usually very complex and not easily resolved by analytical methods. In a different manner, genetic algorithms (GAs) are heuristic search strategies that help to find the exact or approximate global optimal solution efficiently with a stochastic approach. Genetic algorithms are advantageous for their robustness to discrete and noisy objective functions, as they are only evaluated at independent points without requirements of continuity or differentiability. However, as explained in this paper, a time-based sensor network schedule cannot be represented by a chromosome with fixed length that is required in traditional genetic algorithms. Therefore, an extended genetic algorithm is introduced with variable-length chromosome (VLC) along with mutation and crossover operations in order to address this problem. Simulation results show that, with help of carefully defined fitness functions, the proposed scheme is able to evolve the individuals in the population effectively and consistently from generation to generation towards optimal ones, and the obtained network schedules are better optimized in comparison with the result of algorithms employing a fixed-length chromosome.
Highlights
We aim to propose a technique find the solution to this problem using a genetic algorithm with variable-length chromosome
These results show that, thanks to the flexibility of variable-length chromosome (VLC), the resultant schedule clearly outperforms that obtained with fixed time blocks
Schedule optimization is critical for the deployment of sensor networks and is a non-trivial problem with large-scale and complex search space, such that it is not suitable to use deterministic approach, but stochastic and metaheuristic ones instead, including particle swarm optimization (PSO) [11], ant colony optimization (ACO) [10], bacterial foraging algorithm (BFA) [14], monarch butterfly optimization (MBO) [15], earthworm optimization algorithm (EWA) [16], elephant herding optimization (EHO) [17], moth search (MS) algorithm [18], slime mould algorithm (SMA) [19], Harris hawks optimization (HHO) [20], etc., besides genetic algorithms (GAs), which is the most basic and well-known algorithm
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Schedule optimization is commonly used in deploying sensor networks with goal to reduce the energy consumption, increase the network lifetime, while guaranteeing its activities to satisfy the constraints specified by applications This is achieved based on timely programming the working modes in each node individually so that they cooperate and accomplish intended tasks. Wang et al [23] proposed an information feedback mechanism that considers the current individuals in the reproduction phase, and those from the last one, two, or three generations This technique can be applied to any metaheuristic optimization algorithm, and its superiority has been proven for many of those, including ACO, PSO, and Sensors 2021, 21, 3990.
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