Multi-strategy Improved Northern Goshawk Optimization for WSN Coverage Enhancement

TL;DR

Maximizing $Cov$, the coverage rate, in Wireless Sensor Networks is NP-hard. The paper introduces the INGO algorithm, which combines Diverse Chaotic Map Initialization Strategy (DCMIS) for diverse starting points and Bidirectional Population Evolutionary Dynamics (BPED) to balance exploration and exploitation. Experimental results show INGO outperforms NGO, ABC, IWHO, and FA, achieving a peak coverage of 92.81% and full (100%) connectivity, with faster convergence. This approach offers a scalable, robust deployment framework for WSNs and can be extended to 3D spaces, mobile scheduling, and energy-aware optimization.

Abstract

To enhance the coverage rate of Wireless Sensor Networks (WSNs), this paper proposes an advanced optimization strategy based on a multi-strategy integrated Northern Goshawk Optimization (NGO) algorithm. Specifically, multivariate chaotic mapping is first employed to improve the randomness and uniformity of the initial population. To further bolster population diversity and prevent the algorithm from stagnating in local optima, a bidirectional population evolutionary dynamics strategy is incorporated following the pursuit-and-evasion phase, thereby facilitating the attainment of the global optimal solution. Extensive simulations were conducted to evaluate the performance of the proposed multi-strategy NGO in WSN coverage. Experimental results demonstrate that the proposed algorithm significantly outperforms existing benchmarks in terms of both coverage enhancement and node connectivity.

Figures (7)

• Figure 1: Wireless Sensor Network Coverage
• Figure 2: NGO algorithm exploration and pursuit phase diagram
• Figure 3: INGO-WSN Coverage Overall Flowchart
• Figure 4: Algorithm Performance Comparison - Simulated Boxplot
• Figure 5: Wireless Sensor Network Coverage