Scholarlink

<p>This article addresses the major challenge of extending the lifetime of wireless sensor networks (WSNs), which are usually limited by power constraints. One promising solution is the use of multiple sinks instead of relying on a single sink, which often leads to increased network traffic, overflow conditions and potential network shutdowns due to excessive energy consumption. In this context, the concept of sink placement is critical, as the optimal number and location of sinks plays a crucial role in improving network lifetime and performance. This study examines how the use of multiple sinks can overcome these challenges and thereby improve data transmission efficiency and network longevity—particularly in applications such as military operations, environmental monitoring and medical systems where data integrity and continuous functionality are of paramount importance. The paper highlights the importance of determining the optimal placement of sinks to balance power constraints and minimize data loss in power-constrained networks. We introduce DSP-ILS (dynamic sink placement using iterated local search), a novel method to identify the optimal locations of sinks to reduce energy consumption and improve network resilience. The effectiveness of this method is then evaluated based on certain performance metrics. To validate the DSP-ILS method, we conducted experiments using topologies from the Internet Topology Zoo. In these experiments, we compared the results of the DSP-ILS method with the ABC-DE, PSOBS and DCRRP methods in terms of energy consumption and survivability criteria. The results show that the DSP-ILS method has a clear advantage over the compared methods, especially for large topologies. Thus, the DSP-ILS method can reduce the cost of energy consumption by up to 12.23% compared to the DCRRP method, 14.48% compared to the ABC-DE method and 22.17% compared to the PSOBS method. Moreover, the DSP-ILS method improves the survivability of the network at different values of <i>R</i> compared to the benchmarked methods.</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

Dynamic sink placement in wireless sensor networks for enhancing network lifetime and improving network survivability

  • 期刊论文

  • 发表日期：

    2025年6月2日

查看全文
  • Ali Abdi Seyedkolaei

摘要

This article addresses the major challenge of extending the lifetime of wireless sensor networks (WSNs), which are usually limited by power constraints. One promising solution is the use of multiple sinks instead of relying on a single sink, which often leads to increased network traffic, overflow conditions and potential network shutdowns due to excessive energy consumption. In this context, the concept of sink placement is critical, as the optimal number and location of sinks plays a crucial role in improving network lifetime and performance. This study examines how the use of multiple sinks can overcome these challenges and thereby improve data transmission efficiency and network longevity—particularly in applications such as military operations, environmental monitoring and medical systems where data integrity and continuous functionality are of paramount importance. The paper highlights the importance of determining the optimal placement of sinks to balance power constraints and minimize data loss in power-constrained networks. We introduce DSP-ILS (dynamic sink placement using iterated local search), a novel method to identify the optimal locations of sinks to reduce energy consumption and improve network resilience. The effectiveness of this method is then evaluated based on certain performance metrics. To validate the DSP-ILS method, we conducted experiments using topologies from the Internet Topology Zoo. In these experiments, we compared the results of the DSP-ILS method with the ABC-DE, PSOBS and DCRRP methods in terms of energy consumption and survivability criteria. The results show that the DSP-ILS method has a clear advantage over the compared methods, especially for large topologies. Thus, the DSP-ILS method can reduce the cost of energy consumption by up to 12.23% compared to the DCRRP method, 14.48% compared to the ABC-DE method and 22.17% compared to the PSOBS method. Moreover, the DSP-ILS method improves the survivability of the network at different values of R compared to the benchmarked methods.