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Fault diagnosis and its prediction in wireless sensor networks using regressional learning to achieve fault tolerance
Author(s) -
Swain Rakesh Ranjan,
Khilar Pabitra Mohan,
Dash Tirtharaj
Publication year - 2018
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.3769
Subject(s) - computer science , wireless sensor network , fault tolerance , fault (geology) , node (physics) , fault indicator , real time computing , sensor node , wireless , fault detection and isolation , key distribution in wireless sensor networks , distributed computing , computer network , wireless network , artificial intelligence , engineering , telecommunications , structural engineering , seismology , actuator , geology
Summary Due to the wide range of critical applications and resource constraints, sensor node gives unexpected responses, which leads to various kind of faults in sensor node and failure in wireless sensor networks. Many research studies focus only on fault diagnosis, and comparatively limited studies have been conducted on fault diagnosis along with fault tolerance in sensor networks. This paper reports a complete study on both 2 aspects and presents a fault tolerance approach using regressional learning with fault diagnosis in wireless sensor networks. The proposed method diagnose the different types of faulty nodes such as hard permanent, soft permanent, intermittent, and transient faults with better detection accuracy. The proposed method follows a fault tolerance phase where faulty sensor node values would be predicted by using the data sensed by the fault free neighbors. The experimental evaluation of the fault tolerance module shows promising results with R 2 of more than 0.99. For the periodic fault such as intermittent fault, the proposed method also predict the possible occurrence time and its duration of the faulty node, so that fault tolerance can be achieved at that particular time period for better performance of the network.