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ABSTRACT LIBRARY

Neural computing for sensor health audit towards Hill station disaster management

Publisher: USS

Authors: Ramesh Keerthika, Saveetha Engineering college L Logeshwari, Saveetha Engineering college M Vanitha, Saveetha Engineering college

Open Access

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Abstract:

The emerging growth of sensors and integrated device plays an optimum role in recent innovations towards

natural disaster management system. Keeping an underground wireless sensor network (UGWSN). Similar to wireless

sensor network (WSN) the underground sensor network is created with numerous sensor devices integrated with control

unit, communication unit etc. the sensors are placed inside the earthy surface to record and reflect on natural changes inside

the ground surface. Installation of sensor nodes in the slopy area of hill stations undergoes various challenges to make the

communication in spite of rigid surfaces, soil texture hard stones etc. On the other hand, natural consequences such as

heavy rainfall landslides impact the performance of the sensor opted in the surface of the earth. To address these

Constraints, various research frameworks are considered. The goal of proposed model is to make efficient communication

between wireless sensor network (WSN) placed in the underground area. The proposed system measures the network

performance through sensor communication flow without any interrupt, calculates the dead nodes, energy transmission per

iteration and optimize the network during loss of signal. Neural boosters are placed at the pathways to detect and reroute

the network. The bio-inspired behaviour of chimpanzee based behavioural optimization algorithm act upon the strong nodes

during the droppage of energy level and adopt the network performance without making disconnection. The proposed

system achieved 99.86% packet delivery ratio (PDR) on testing with 100 dynamic nodes.

Keywords: Wireless sensor networks,Neural boosters,sensor node,artificial intelligence,,application specific integrated circuits

Published in: IEEE Transactions on Antennas and Propagation( Volume: 71, Issue: 4, April 2023)

Page(s): 2908 - 2921

Date of Publication: 2908 - 2921

DOI: 10.1109/TAP.2023.3240032

Publisher: UNITED SOCIETIES OF SCIENCE