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Multi-objective target Q-coverage in directional sensor network with deterministic deployment
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DOI：10.1080/17445760.2026.2619415.png)
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Abstract
En 中文
This paper focuses on the target Q-coverage and sensor deployment problem, where the coverage requirement of each target is different. Previous studies in the literature solved the Q-coverage optimization as a single-objective problem considering random sensor deployment. In this work, Q-coverage problem in directional sensor network is solved as a multi-objective optimization problem, where objectives are the maximization of the overall target coverage and the minimization of the number of active sensors. Maintaining their generic structures the same, three existing and well-known multi-objective genetic algorithms (MOGAs): strength Pareto evolutionary algorithm 2 (SPEA2), nondominated sorting genetic algorithm II (NSGA-II), and multi-objective evolutionary algorithm based on decomposition (MOEA/D) are modified to solve the proposed multi-objective Q-coverage problem. Since the sensor positions can significantly improve the overall coverage, two different sensor deployment algorithms are proposed to find the suitable positions of sensors, one is heuristic, and the other is based on particle swarm optimization. The MOGAs determine the optimal orientations of the sensors. For the implementation of the modified MOGAs, a new mutation operator, compatible for implementing the problem, has been designed. The impact of five different network parameters: the number of targets, the number of sensors, the number of orientations, the sensing radius values, and the coverage requirement, on the two objectives are analyzed. The performances of the three modified MOGAs are compared based on three performance metrics, hypervolume (HV), inverted generational distance (IGD), and spread. To analyze the robustness of the modified MOGAs, the sensitivity analysis is performed. In addition, the performances of the three modified MOGAs are compared with a genetic algorithm and a greedy algorithm, existing in the literature. Experimental results show that the modified MOGAs need, on an average, 16.28% fewer sensors than the existing genetic algorithm and 2.9% fewer sensors than the existing greedy algorithm, while achieving 7.8% and 1.18% higher target coverage, respectively. To show the scalability and effectiveness of the proposed MOGAs, they are executed on both a large scale network and a real network. Finally, the results are validated using the Wilcoxon signed rank test based on the performance metrics.
Keywords:
Directional sensor network (DSN)
Q-coverage
deterministic sensor deployment
multi-objective genetic algorithm (MOGA)
Journal
I
IF:
0.7
Papers:
50
Citations:
266