Scholarlink

<p>In the contemporary context, harnessing energy from the environment poses a ubiquitous and formidable challenge. Optimal energy conservation practices dictate the powering down of inactive devices, even when they consume approximately 60% of their peak power. Subsequently, these devices are reactivated when needed. This study addresses this challenge by conceptualizing the Web of Things (WoT) as an <i>M</i>/<i>M</i>/1 retrial queue operating under a working vacation policy, aiming to curtail average power consumption and foster energy conservation within WoT networks. Furthermore, the study delves into the strategic interactions between WoT devices and their users, facilitating mutually beneficial connections to achieve respective objectives. During idle periods, incoming customers are promptly served, whereas the server enters an energy-saving mode until a new client activates it. The study explores numerical examples to assess the impact of various system factors and formulates a cost function, subsequently minimized using particle swarm optimization, artificial bee colony and genetic algorithm techniques. Convergence analysis of these optimization methods is conducted, supported by visual representations. Finally, neuro-fuzzy results generated through the adaptive neuro-fuzzy inference system are compared against validation outcomes.</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

Optimizing energy harvesting in web of things services: a metaheuristic approach with retrial queue under working vacation

  • 期刊论文

  • 发表日期：

    2025年3月1日

查看全文
  • N. Micheal Mathavavisakan,
  • K. Indhira,
  • Sherif I. Ammar

摘要

In the contemporary context, harnessing energy from the environment poses a ubiquitous and formidable challenge. Optimal energy conservation practices dictate the powering down of inactive devices, even when they consume approximately 60% of their peak power. Subsequently, these devices are reactivated when needed. This study addresses this challenge by conceptualizing the Web of Things (WoT) as an M/M/1 retrial queue operating under a working vacation policy, aiming to curtail average power consumption and foster energy conservation within WoT networks. Furthermore, the study delves into the strategic interactions between WoT devices and their users, facilitating mutually beneficial connections to achieve respective objectives. During idle periods, incoming customers are promptly served, whereas the server enters an energy-saving mode until a new client activates it. The study explores numerical examples to assess the impact of various system factors and formulates a cost function, subsequently minimized using particle swarm optimization, artificial bee colony and genetic algorithm techniques. Convergence analysis of these optimization methods is conducted, supported by visual representations. Finally, neuro-fuzzy results generated through the adaptive neuro-fuzzy inference system are compared against validation outcomes.