A Framework and Classification for Fault Detection Approaches in Wireless Sensor Networks with an Energy Efficiency Perspective

Abstract

Wireless Sensor Networks (WSNs) are more and more considered a key enabling technology for the realisation of the Internet of Things (IoT) vision. With the long term goal of designing fault-tolerant IoT systems, this paper proposes a fault detection framework for WSNs with the perspective of energy efficiency to facilitate the design of fault detection methods and the evaluation of their energy efficiency. Following the same design principle of the fault detection framework, the paper proposes a classification for fault detection approaches. The classification is applied to a number of fault detection approaches for the comparison of several characteristics, namely, energy efficiency, correlation model, evaluation method, and detection accuracy. The design guidelines given in this paper aim at providing an insight into better design of energy-efficient detection approaches in resource-constraint WSNs.

Inter-Process Correlation Model based Hybrid Framework for Fault Diagnosis in Wireless Sensor Networks

“…The framework distributes diagnostic workload in a decentralized way that is energy efficient and produces lesser communication overhead. As, more energy is spent in wireless communication than in processing [27]. For overhead comparison, ECDF function in MATLAB has been used.…”

A Survey on Proactive, Active and Passive Fault Diagnosis Protocols for WSNs: Network Operation Perspective

“…It would be even worse to implant proactive information-collecting schemes in a network because they would increase the depletion of energy and hastily reduce the overall lifetime of the network. Pan et al and Zhang et al [ 72 , 73 ] recommended online diagnosis because it is lightweight with respect to the network and to monitoring network symptoms for the sink. This technique uses a probabilistic inference model to effectively detect and deduce the root cause of abnormal behavior occurring in the network.…”

The Distributed Convergence Classifier Using the Finite Difference

“…Thus, the WSN nodes are characterized by a significantly higher fault rates in comparison with the devices used within other semiconductor-based systems [4]. Especially it is for distinctive features such as [5] resource constraints, lack of well-defined models, network dynamics etc. The listed facts are reasons of why the mechanisms to detect failures within these systems significantly differ from the traditional systems [6].…”