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Concoction Node Fault Discovery (CNFD) on Wireless Sensor Network Using the Neighborhood Density Estimation in SHM - 2020

Research Area:  Wireless Sensor Networks

Abstract:

Structural Health Monitoring (SHM) has been considered to afford up-to-date information about the state of structures by assessing structural vibration responses and further physical phenomena and disorders. Determining the structure conditions is not an easy task which is associated with complex civil structures. The main objective of SHM is earlier damage detection, inspection cost reduction and lifetime estimation. The modern improvements of wireless sensor networks paid attention in the field of structural health monitoring. The deployment of sensors are expected to provide the rich information about civil structures in an effective manner. The main limitations of sensors (small memory size, small communication throughput, limited speed of the CPU) reduce the effectiveness of SHM. We propose concoction node fault discovery approach in order to provide reliable communication in the wireless environment specifically having lot of obstacles. The HRPD algorithm select the Line of sight (LOS) nodes based on extracted RSS features which is higher than non LOS. The selected intermediate LOS nodes transmit the collected sensing information to the base station with reduced energy consumption. The Statistical link parameter estimation supports to detect the optimal path through continuous analysis of link metrics such like link bandwidth, response time and queue size. The LOS fault tolerant approach determines the faulty LOS nodes, and direct the system towards survivability. The simulation results shows that our proposed approach minimize the energy depletion and enhances the sensor nodes life time. The application of the proposed work helps to monitor the structural health of buildings, bridges and towers with high quality monitoring mechanism.

Author(s) Name:  S. Surya,R. Ravi

Journal name:  Wireless Personal Communications

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/s11277-020-07623-5

Volume Information:  volume 113, pages 2723–2746 (2020)