Scholarlink

Abstract <p>The Centroid Localization Algorithm (CLA) is a commonly used technique in wireless sensor networks (WSN) to detect the location of target nodes. Nonetheless, the localization errors associated with CLA are typically substantial, which can reduce its effectiveness in real-life WSN applications. To address this limitation, while achieving the basic localization goal of accurately identifying unknown nodes in the WSN, this paper proposes a new localization approach, namely, SLnA-CLA, by integrating the CLA and the sea lion optimization algorithm (SLnA), which is a bioinspired technique based on sea lion social behavior. We compare the performance of our proposed SLnA-CLA algorithm with the basic CLA and SLnA for nodes localization algorithms. In this study, we make sure to evaluate the three algorithms, SLnA-CLA, CLA, and SLnA, using the same deployment of anchor and target nodes. This way, we confirm that any performance discrepancies are attributable to the algorithms, not to any biases introduced by the various network topologies. The results demonstrate that the proposed algorithm effectively reduces localization error by up to 98.7% when compared to CLA, albeit with a longer computation time, and outperforms SLnA in both accuracy and computation time.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Optimization of Centroid-Based Location Using Sea Lion Optimization Algorithm in Wireless Sensor Networks

  • 期刊论文

  • 发表日期：

    2025年10月1日

查看全文
  • Amina Tabbi,
  • Seddik Rabhi

摘要

Abstract

The Centroid Localization Algorithm (CLA) is a commonly used technique in wireless sensor networks (WSN) to detect the location of target nodes. Nonetheless, the localization errors associated with CLA are typically substantial, which can reduce its effectiveness in real-life WSN applications. To address this limitation, while achieving the basic localization goal of accurately identifying unknown nodes in the WSN, this paper proposes a new localization approach, namely, SLnA-CLA, by integrating the CLA and the sea lion optimization algorithm (SLnA), which is a bioinspired technique based on sea lion social behavior. We compare the performance of our proposed SLnA-CLA algorithm with the basic CLA and SLnA for nodes localization algorithms. In this study, we make sure to evaluate the three algorithms, SLnA-CLA, CLA, and SLnA, using the same deployment of anchor and target nodes. This way, we confirm that any performance discrepancies are attributable to the algorithms, not to any biases introduced by the various network topologies. The results demonstrate that the proposed algorithm effectively reduces localization error by up to 98.7% when compared to CLA, albeit with a longer computation time, and outperforms SLnA in both accuracy and computation time.