Lightweight approach to automated fault diagnosis in WSNs

Swain, Rakesh Ranjan; Dash, Tirtharaj; Khilar, Pabitra Mohan

doi:10.1049/iet-net.2019.0117

Cited by 6 publications

(3 citation statements)

References 37 publications

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“…Different productive applications provided by VANET are environmental parameter monitoring through VANET [39,40], secure transaction during automatic toll tax collection [41], etc. Optimization of various vehicular parameters [42] and self fault detection [43][44][45][46] in VANET [47][48][49] encourage to use VANET for the wide range of applications. Autonomous applications is successful through VANET due to efficient transmission of physical parameters measured through various sensors.…”

Section: Consideration Of Vanet Based On Applicationsmentioning

confidence: 99%

AI-assisted Emergency Healthcare using Vehicular Network and Support Vector Machine

Senapati

Khilar

Dash

et al. 2023

Wireless Pers Commun

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The COVID-19 pandemic has created an emergency across the globe. The number of corona positive and death cases is still rising worldwide. All countries’ governments are taking various steps to control the infection of COVID-19. One step to control the coronavirus’s spreading is to quarantine. But the number of active cases at the quarantine center is increasing daily. Also, the doctors, nurses, and paramedical staff providing service to the people at the quarantine center are getting infected. This demands the automatic and regular monitoring of people at the quarantine center. This paper proposed a novel and automated method for monitoring people at the quarantine center in two phases. These are the health data transmission phase and health data analysis phase. The health data transmission phase proposed a geographic-based routing that involves components like Network-in-box, Roadside-unit, and vehicles. An effective route is determined using route value to transmit data from the quarantine center to the observation center. The route value depends on the factors such as density, shortest path, delay, vehicular data carrying delay, and attenuation. The performance metrics considered for this phase are E2E delay, number of network gaps, and packet delivery ratio, and the proposed work performs better than the existing routing like geographic source routing, anchor-based street traffic aware routing, Peripheral node based GEographic DIstance Routing . The analysis of health data is done at the observation center. In the health data analysis phase, the health data is classified into multi-class using a support vector machine. There are four categories of health data: normal, low-risk, medium-risk, and high-risk. The parameters used to measure the performance of this phase are precision, recall, accuracy, and F-1 score. The overall testing accuracy is found to be 96.8%, demonstrating strong potential for our technique to be adopted in practice.

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Section: Consideration Of Vanet Based On Applicationsmentioning

confidence: 99%

AI-assisted Emergency Healthcare using Vehicular Network and Support Vector Machine

Senapati

Khilar

Dash

et al. 2023

Wireless Pers Commun

Self Cite

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“…In electrical power systems, communication plays a vital role; even the smart grid also behaves as a data communication network. This work connects sensor devices to the smart grid to transmit the information wirelessly 13,14 . Hence, choosing WSN is appropriate for transmitting the sensed data in a smart grid environment 15 .…”

Section: Introductionmentioning

confidence: 99%

“…This work connects sensor devices to the smart grid to transmit the information wirelessly. 13,14 Hence, choosing WSN is appropriate for transmitting the sensed data in a smart grid environment. 15 WSN is also used for bidirectional delivery, faultless monitoring, power generation, and utilization, which also upgrade the reliability, low-cost solution, and energy efficiency of data transmission.…”

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confidence: 99%

Automatic fault identification in WSN‐based smart grid environment

Murthy

Mahadevaswamy²

2022

Int J Communication

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Wireless sensor network (WSN) plays a vital role in the smart grid (SG) environment. Due to the fault tolerance characteristics, cost reduction, and large-scale convergence, SG introduces many unique challenges caused by system and functional devices. To solve this problem, a WSN-based SG network is used to identify faults. During data transmission, faulty nodes occur in the transmission line. The node failures, calibration, network failures, low battery, dead nodes, environmental changes, software failures, and so on lead to the interruption in data delivery and spoil the entire WSN-based SG network. In order to tackle these problems, the new WSN model is designed to detect the faults in the transmission line based on the SG environment. This paper uses the Adaptive Zigbee-Aquila communication protocol (AZACP) to find the optimal shortest path for transferring data. AZACP finds the shortest optimal path for transmitting the sensed data to the base station with low cost and less time consumption. Fault detection automatically identifies the fault in the transmission line and isolates the faulty nodes to ensure efficient data transmission in WSN. Here, enhanced recurrent equilibrium neural network (ERENN) is introduced to identify the fault in data transmission. The proposed approach provides better performance in terms of evaluating performance metrics like throughput, delay, reliability, average residual energy, number of total transmissions, network lifetime, efficiency, and bit error rate (BER).

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