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Energy-efficient coverage enhancement strategy for 3D WSNs based on an imitator-inspired optimization algorithm
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OA
AI
J
Jin RenY
Yibo Li DOI：10.23919/jcn.2025.000109.png)
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Abstract
En 中文
Coverage and energy consumption are highly nonconvex and challenging optimization problems in wireless sensor networks (WSN)s deployment. Traditionally, these problems are optimized separately. In this paper, we present a multi-objective optimization model that addresses both issues concurrently, enhancing its relevance for real-world applications such as seabed monitoring, tactical surveillance, and traffic direction systems in 3D WSNs, where balancing coverage and energy efficiency is critical. While conventional intelligent optimization algorithms (e.g., PSO, GWO) often excel in benchmark function tests, they struggle with premature convergence and computational inefficiency in practical engineering problems like WSN deployment. Therefore, we propose a novel heuristic optimization algorithm with low computational complexity, termed the imitator-inspired optimization algorithm (IOA). To validate IOA's single-objective optimization performance, it is applied to coverage optimization in 2D WSNs and compared with five other popular algorithms. For energy-efficient coverage enhancement in 3D WSNs, we introduce a multi-objective imitator-inspired optimization algorithm (MOIOA), which simultaneously optimizes the conflicting objectives of coverage and energy consumption. MOIOA's performance is benchmarked against three well-known multi-objective algorithms. Simulation results demonstrate that the proposed algorithms exhibit both reliability and superiority in single-objective and multi-objective optimization, effectively solving the energy-efficient coverage enhancement problem in 3D WSNs.
Keywords:
Coverage enhancement
energy consumption
imitator-inspired optimization algorithm (IOA)
multi-objective optimization
wireless sensor networks (WSN)s
Journal
J
IF:
3.2
Papers:
47
Citations:
0
Organization
Cited Papers