Energy-Aware Clustering Routing for Mobile Ad-Hoc Sensor Networks (MASNETS)

Energy efficient operation is often critical for Mobile ad-hoc sensor networks (MASNETs), because sensor network nodes operate on limited batteries. There are many potential applications of MASNETs such as military sensor networks to detect and gather information about enemy movements and some senso...

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主要作者: Amanda, Hendrick
格式: Thesis
語言:English
出版: 2020
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在線閱讀:http://ir.unimas.my/id/eprint/29003/1/Amanda%20Hendrick%20ft.pdf
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總結:Energy efficient operation is often critical for Mobile ad-hoc sensor networks (MASNETs), because sensor network nodes operate on limited batteries. There are many potential applications of MASNETs such as military sensor networks to detect and gather information about enemy movements and some sensor nodes are also mounted on animals or other moving objects, which can sense and collect information such as body temperature. If this information is not properly handled, energy can be wasted due to the changes in network topology. Hence, an efficient and energy-aware clustering routing is required to reduce energy consumption and reliably transmit data to the sink node in the event of frequent topology changes in energy-constrained MASNETs. Clustering is one of the techniques that offer effective ways to prolong the lifetime of the resource constrained sensor networks. The aim of this thesis is to reduce the energy consumption and increase the total packet received in MASNETs. Based on the benchmark identified called Hybrid energy-efficient distributed (HEED) clustering routing, Avrora simulator is used to further energy-efficiency enhancements. This Enhance Intra-Cluster HEED (EICHEED) proposed an adaptive routing that are suitable for MASNETs. Different experiments have been done using different parameters and numbers of results have been achieved to analyze the performance of algorithm. The simulations show that the new enhancement of the HEED; EICHEED is energy efficient and prolongs the network lifetime. Keywords: MASNETs, energy efficiency, clustering