Open AccessLightweight approach to automated fault diagnosis in WSNs
Author(s) -
Swain Rakesh Ranjan,
Dash Tirtharaj,
Khilar Pabitra Mohan
Publication year - 2020
Publication title -
iet networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.466
H-Index - 21
eISSN - 2047-4962
pISSN - 2047-4954
DOI - 10.1049/iet-net.2019.0117
Subject(s) - testbed , computer science , wireless sensor network , timeout , fault (geology) , constant false alarm rate , fault coverage , fault detection and isolation , real time computing , overhead (engineering) , reliability (semiconductor) , checksum , false alarm , embedded system , reliability engineering , distributed computing , computer network , algorithm , engineering , artificial intelligence , actuator , power (physics) , physics , electrical engineering , quantum mechanics , electronic circuit , seismology , geology , operating system
Automated fault diagnosis in wireless sensor networks (WSNs) has always been an essential field for the networks research community. In resource‐related constraints, the design of a lightweight method to automatically diagnose the fault in WSN is highly beneficial. In this study, the authors propose lightweight and less‐overhead approaches to diagnose hard and soft faults in WSNs automatically. Correctly, a lightweight checksum method is implemented for hard or crash fault detection. This method is capable of detecting multiple hard faults within a single path with the help of a timeout mechanism. For diagnosis of soft faults such as permanent, intermittent, and transient faults, they implement the Anderson–Darling (AD) statistical method. The AD test analyses how the sensor readings are fitted in a specific distribution for a tested significance level. Several testbed experiments are conducted to validate their hypotheses and implementations. The performance parameters that are evaluated in this work include fault detection accuracy, false alarm rate, and false‐positive rate. These parameters are evaluated about various fault probabilities in the network. They observe that the proposed lightweight schemes can diagnose both hard and soft faults in O ( 1 ) message complexity over the network, which makes it adaptable in practise.