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<p>Wireless Sensor Networks (WSNs) play a vital role in diverse applications, including environmental monitoring, industrial automation, and military surveillance. The effectiveness of these networks largely depends on sensor deployment strategies, which influence coverage, energy efficiency, communication reliability, and operational costs. This study proposes a multi-objective optimization framework for strategically deploying heterogeneous sensor nodes in WSNs. A structured hexagonal grid-based arrangement is adopted to optimize coverage while minimizing redundancy and interference. The deployment optimization is formulated as a multi-objective problem, considering critical performance metrics such as signal-to-noise ratio (SNR), packet error rate (PER), bit error rate (BER), network lifetime, total delay, energy efficiency, and overall system cost. The proposed framework employs the Non-Dominated Sorting Particle Swarm Optimization (NSPSO) algorithm, with a two-stage initialization strategy, to generate Pareto-optimal trade-off solutions. Simulations in a 20 × 10&#xa0;m<sup>2</sup> region with 12 heterogeneous sensors show that NSPSO improves coverage (75.2% → 86.6%), reduces overlap (18.4% → 4.3%), boosts SNR (18.7 dB → 22.1 dB), lowers delay (65.3 ms → 54.6 ms), and extends lifetime (840 → 1102 cycles). Error rates also drop (BER: 0.0041 → 0.0029; PER: 0.0083 → 0.0051), confirming enhanced efficiency and reliability. These results confirm the proposed methodology’s effectiveness, demonstrating its potential for large-scale IoT, 5G/6G, and smart city WSN deployments.</p>

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Performance-aware deployment of heterogeneous sensors in wireless sensor networks via multi-objective NSPSO

  • 期刊论文

  • 发表日期：

    2026年1月14日

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  • Kaki Ramya Sree,
  • Vinay Kumar Pamula

摘要

Wireless Sensor Networks (WSNs) play a vital role in diverse applications, including environmental monitoring, industrial automation, and military surveillance. The effectiveness of these networks largely depends on sensor deployment strategies, which influence coverage, energy efficiency, communication reliability, and operational costs. This study proposes a multi-objective optimization framework for strategically deploying heterogeneous sensor nodes in WSNs. A structured hexagonal grid-based arrangement is adopted to optimize coverage while minimizing redundancy and interference. The deployment optimization is formulated as a multi-objective problem, considering critical performance metrics such as signal-to-noise ratio (SNR), packet error rate (PER), bit error rate (BER), network lifetime, total delay, energy efficiency, and overall system cost. The proposed framework employs the Non-Dominated Sorting Particle Swarm Optimization (NSPSO) algorithm, with a two-stage initialization strategy, to generate Pareto-optimal trade-off solutions. Simulations in a 20 × 10 m2 region with 12 heterogeneous sensors show that NSPSO improves coverage (75.2% → 86.6%), reduces overlap (18.4% → 4.3%), boosts SNR (18.7 dB → 22.1 dB), lowers delay (65.3 ms → 54.6 ms), and extends lifetime (840 → 1102 cycles). Error rates also drop (BER: 0.0041 → 0.0029; PER: 0.0083 → 0.0051), confirming enhanced efficiency and reliability. These results confirm the proposed methodology’s effectiveness, demonstrating its potential for large-scale IoT, 5G/6G, and smart city WSN deployments.