Enhanced Emergency Message Routing for WBANS Using AI Models

Computer Science Engineering

WBSN aims to distinguish ideal optimal deployment areas of the given sensor nodes with a pre-decided sensing range, and to schedule them to such that the network lifetime is maximum with the required coverage level. Since the upper bound of the network lifetime for a given network can be computed mathematically, WBSN learning is utilized to compute locations of deployment such that the network lifetime is maximum paper. In this paper ultimate objective is to realize an automated monitoring network so that detection applications of various emergency events can be practically implemented. Further, the nodes are scheduled to achieve this upper bound. This proposed framework used for artificial bee colony algorithm and particle swarm optimization for sensor deployment problem followed by a heuristic for scheduling. In exaction, ANT colony optimization technique is utilized to provide maximum network lifetime use. The comparative study demonstrates that artificial ACO performs better than bee colony algorithm for sensor deployment problem. The proposed heuristic was able to achieve the theoretical upper bound in all the tested cases.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.