Abstract
Although adoption pace of wireless sensor network has increased in recent times in many advance technologies of ubiquitous-ness, but still there are various open-end challenges associated with energy efficiencies among the sensor nodes till now. We reviewed the existing research approaches towards energy optimization techniques to explore significant problems. This paper introduces MOMEE i.e. Manifold Optimized Modeling of Energy Efficiency that offers novel clustering as well as novel energy optimized routing strategy. The proposed system uses analytical modeling methodology and is found to offer better resiliency against traffic bottleneck condition. The study outcome of MOMEE exhibits higher number of alive nodes, lower number of dead nodes, good residual energy, and better throughput as compared to existing energy efficient routing approaches in wireless sensor network.
Highlights
The concept of wireless sensor network has undergone a tremendous change in last decade
This section discusses about the algorithm that has been used for the purpose of implementing proposed MOMEE concept in wireless sensor network
Wireless sensor network has been increasingly used in modern times for remote monitoring system
Summary
The concept of wireless sensor network has undergone a tremendous change in last decade. Majority of the clustering mechanism carried out till date converges towards a concept of cluster head selection process, where 99% of the work done is by considering maximum residual energy as the prominent criteria towards selection of cluster head. Majority of the sensor applications are used in a scenario where incoming traffic generation is quite uncertain (e.g. habitat monitoring, natural calamities monitoring, health monitoring, etc) This causes uneven rate of dissipation of energy per clusters during each rounds of data aggregation. There are various research work towards optimization in wireless sensor network, there is a less benchmarked work with respect to energy efficiency This problem has multi-facet negative effect on communication performance with all forms of declinations towards Quality-ofService (QoS) parameters. Summarization of the work and findings are briefed in conclusion in Section VII and section VIII concludes future work of the research
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