Published September 25, 2020 | Version v1
Journal article Open

COVERAGE OPTIMIZED AND TIME EFFICIENT LOCAL SEARCH BETWEENNESS ROUTING FOR HEALTH MONITORING IN WSN

  • 1. Department of MCA, KSR College of Arts and Science, Tiruchengode, Namakkal – Dt
  • 2. Department of Computer science, Thiruvalluvar Govt Arts college, Rasipuram, Namakkal Dt.

Description

Wireless Sensor Networks has been widely used for monitoring and control applications in our daily life due to its appealing characteristic like low cost, power efficient, easy to implement in many areas such as war zone, medical monitoring optimization and so on. However, Wireless Sensor Networks (WSN) requires a new routing algorithm for health monitoring. In this paper, an efficient routing model called Local Search and Enhanced Betweenness Routing (LS-EBR) in WSN to improve routing efficiency by increasing sensor nodes coverage and minimizing the time for routing is presented. The LS-EBR model in WSN for health monitoring uses enhanced betweenness routing that measures energy consumption of the neighboring nodes in a local search manner. First, a novel Local Search model that does not require global information about the entire network and in turn divert routing from sensor nodes that are more frequently used is used. The aim is to reduce the time for routing. Second, an Enhanced Betweenness Routing model that not only considers routing overhead but also considers the remaining energy of sensor node into account to ensure higher number of sensor nodes to be monitored, thus achieving much higher coverage is designed. Finally, working together with the above Local Search and Enhanced Betweenness Routing by applying Enhanced Betweenness Routing algorithm, reduces the average energy consumed. Simulation results showed that the proposed routing model has advantages over opportunistic routing with respect to coverage and improving routing efficiency by reducing time for routing in wireless sensor networks.

Files

6116ijcsa04.pdf

Files (322.7 kB)

Name Size Download all
md5:3ec70f52ebc947d96d0514f090234349
322.7 kB Preview Download