返回
MeMLO: Mobility-Enabled Multi-level Optimization Sensor Network
DOI:10.1007/s11277-017-4802-2.png)
摘要
En 中文
The paper presents a technique called as Mobility-Enabled Multi Level Optimization (MeMLO) that addressing the existing problem of clustering in wireless sensor net-work (WSN). The technique enables selection of aggregator node based on multiple optimization attribute which gives better decision capability to the clustering mechanism by choosing the best aggregator node. The outcome of the study shows MeMLO is highly capable of minimizing the halt time of mobile node that significantly lowers the transmit power of aggregator node. The simulation outcome shows negligible computational complexity, faster response time, and highly energy efficient for large scale WSN for longer simulation rounds as compared to conventional LEACH algorithm.
Keyword:
Network lifetime
Battery
Clustering
Optimization
Wireless sensor network
AI总结
对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.2
论文数:
742
被引数:
1.2W
机构
引用论文
Algorithms for Bidding Strategies in Local Energy Markets: Exhaustive Search through Parallel Computing and Metaheuristic Optimization
Algorithms
IF0

