可变长度多目标社会阶级优化用于无线传感器网络中的信任意识数据采集
Mohammed Ayad Saad1,2, Rosmina Jaafar1, Kalaivani Chellappan1
1Department of Electrical, Electronics & System Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
本研究介绍了一种经过修改的社会阶级多目标粒子集群优化 (SC-MOPSO) 方法,用于在无线传感器网络 (WSN) 中有效和安全地收集数据. 该方法增强了信任,提高了能源效率,减少了旅行时间,超过了现有的算法.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 优化算法 优化算法
背景情况:
- 在无线传感器网络 (WSN) 中收集数据对于物联网 (IoT) 集成至关重要.
- 大规模的WSN部署面临效率挑战和影响数据可靠性的安全威胁.
- 对数据源和路由节点的信任对于WSN中可靠的数据收集至关重要.
研究的目的:
- 开发一个多目标优化方法,用于WSN收集数据.
- 共同优化能源消耗,旅行时间,成本和对WSN数据收集的信任.
- 提出一个修改的社会阶级多目标粒子集群优化 (SC-MOPSO) 算法.
主要方法:
- 引入修改后的SC-MOPSO,其中包括应用依赖的类间操作员.
- 结合解决方案生成,交会点管理和基于阶级的移动策略.
- 从多标准决策 (MCDM) 中利用简单增量权衡 (SAW) 方法从帕雷托前线选择解决方案.
主要成果:
- 在解决方案主导方面,SC-MOPSO和SAW表现出卓越的性能.
- SC-MOPSO实现了一个设置覆盖范围,比NSGA-II占0.06的优势,而NSGA-II仅比SC-MOPSO占0.04的优势.
- 与NSGA-III.III相比,提出的方法表现具有竞争力的表现.
结论:
- 修改后的SC-MOPSO有效地解决了WSN数据收集的多目标性质.
- 将信任作为优化目标的整合提高了WSN中的数据可靠性.
- SC-MOPSO-SAW方法为优化复杂的WSN数据收集场景提供了强大的解决方案.
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