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Energy efficient momento based dynamic scheduling for lifetime maximization in WSN - 2021

Energy efficient momento based dynamic scheduling for lifetime maximization in WSN

Research Area:  Wireless Sensor Networks


The problem of routing with scheduling and lifetime maximization in WSN has been well studied. There are number of algorithms discussed earlier for the support of lifetime maximization and scheduling of nodes in WSN. However, they suffer to achieve higher performance in maximization of lifetime of sensor nodes. To improve the performance, an energy efficient momento based dynamic scheduling algorithm (EEMDS) is presented in this article. The proposed method considers energy and previous transmission history of different nodes to perform scheduling. The method first collects the list of sensor which has packets to be transmitted and allocates momento according to the priority of nodes. The node selected has been assigned with the momento which is required for the data transmission. Once the source and destination node has been identified, then according to the topology of nodes, a set of routes has been identified. For each route and the list of intermediate nodes, the method estimates the transmission support and lifetime maximization support values. The transmission support has been measured based on the number of sensors and energy where the lifetime support is measured according to the energy parameter and the previous transmission history. Finally, a small set of nodes are selected and scheduled for working mode. The route available in the selected route has been used to perform data transmission. According to the result of route selection, the list of nodes present in the route is identified. Such nodes present in the selected route are scheduled to be wakeup in the current transmission where the remaining nodes present in the network are scheduled to be in sleep mode. This increases the throughput performance and lifetime of the entire network.


Author(s) Name:  G. Brindha & P. Ezhilarasi

Journal name:  Journal of Ambient Intelligence and Humanized Computing

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/s12652-020-02131-7

Volume Information:  volume 12, pages 5865–5875 (2021)