Diagnosability evaluation for a system-level diagnosis algorithm for Wireless Sensor Networks

Weber, Andréa; Kutzke, Alexander Robert; Chessa, Stefano

doi:10.1109/iscc.2010.5546808

Cited by 10 publications

(8 citation statements)

References 10 publications

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“…Weber et al [8] propose a strategy of mutual tests among the sensor nodes in a region where t numbers of faulty sensor nodes are present such that the system graph representing the region of the WSNs is t-diagnosable. Weber et al presented a diagnosis approach namely energyefficient test assignment without reciprocal tests [7] which are based on their previous work [8].Chen et al [9] propose and evaluate a localized fault detection algorithm named Distributed Fault Detection to identify the faulty sensors. Mahapatro and Khilar [10] propose an On-line distributed fault diagnosis method called cluster-based distributed fault diagnosis algorithm.…”

Section: Related Workmentioning

confidence: 99%

An Improved Distributed Fault Diagnosis Algorithm in Wireless Sensor Networks

Lin¹,

Huang

Zhang

2017

Proceedings of the 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)

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Abstract-In wireless sensor networks (WSNs), nodes fault diagnosis is the important measure for the continuous monitoring service. Efficient and accurate methods for diagnose nodes fault is a hot research topic in the field of WSNs in the recent years. This paper propose an improved distributed fault diagnosis method namely Cross-Space Distributed Fault Diagnosis (CS-DFD) which follows the DFD algorithm, building high-dimensional vector space model through the collected information from each node, establishing the cross type of sliding window through its historical data and the data from neighbor nodes, setting the cross direction customizable weights of fault. Ultimately, achieving the goal of fault diagnosis by detecting abnormal vector and threshold. The experimental results show that it has reduced the amount of calculation; simplified harsh conditions of fault judgments; reduced the consumption of electricity, etc. which in DFD algorithm. Comparing with the traditional DFD algorithm, the fault diagnosis accuracy improved by 5.14%, the fault of false alarm ratio (FAR) decreased by 2.01% and the time of diagnosis has shortened obviously.

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Section: Related Workmentioning

confidence: 99%

An Improved Distributed Fault Diagnosis Algorithm in Wireless Sensor Networks

Lin¹,

Huang

Zhang

2017

Proceedings of the 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)

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“…Krishnamachari et al have presented a Bayesian fault recognition model to solve the fault-event disambiguation problem in sensor networks [18]. Weber et al [19] consider the problem of determining a test strategy of the sensors in a WSN in order to ensure a desired level of diagnosability of the system. Since the diagnosability of a diagnostic graph depends on whether the graph defines reciprocal tests among units or not, they discuss two strategies namely testing strategy without reciprocal tests and testing strategy with reciprocal tests.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, node 8 marks 10 as hard faulty in F T 8 and node 10 marks 8 as hard faulty in F T 10 . However, node 21 has detected 8 as fault-free and 19 has detected 10 as fault-free. Since 8 , 10 , 19 , and 21 are under same parent node 15 , thus, node 15 rectifies the incorrect decision made by 8 and 10 by comparing the received fault tables.…”

Section: Dissemination Phasementioning

confidence: 99%

“…However, node 21 has detected 8 as fault-free and 19 has detected 10 as fault-free. Since 8 , 10 , 19 , and 21 are under same parent node 15 , thus, node 15 rectifies the incorrect decision made by 8 and 10 by comparing the received fault tables. Once the sink node has the global diagnosis view, the synchronization phase is triggered and the global view is embedded in the time sync packet of the sink node.…”

Section: Dissemination Phasementioning

confidence: 99%

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Online fault diagnosis of wireless sensor networks

Mahapatro

Khilar

2014

Open Computer Science

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This paper proposes an adaptive online distributed solution for fault diagnosis in wireless sensor networks (WSNs). Fault diagnosis is achieved by comparing the heartbeat message generated by neighboring nodes and dissemination of decision made at each node. Time redundancy is used to detect the intermittent faults since an intermittent fault will not occur consistently. The diagnosis performance degradation due to intermittent faults in sensing and transient faults in communication is analyzed. A near optimal trade-off between detection latency and number of tests required to detect intermittent faults is obtained. Simulation results are provided and they show that this work performs better, from both time and energy complexity viewpoint. Keywords

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“…The cluster head takes a decision by comparing the test results sent by its member sensor nodes. Weber et al [14] consider the problem of determining a test strategy of the sensors in a WSN in order to ensure a desired level of diagnosability of the system. Since the diagnosability of a diagnostic graph depends on whether the graph defines reciprocal tests among units or not, they discuss two strategies namely testing strategy without reciprocal tests and testing strategy with reciprocal tests.…”

Section: Related Workmentioning

confidence: 99%

Energy‐efficient distributed approach for clustering‐based fault detection and diagnosis in image sensor networks

Mahapatro¹,

Khilar²

2013

IET wirel. sens. syst.

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This study proposes an algorithm for the design of a distributed protocol for detecting and diagnosing permanent faults in wireless image sensor networks (WISNs). This work explicitly takes into account the possibility of faults in different sections of the image sensor node and communication channel. The fault diagnosis is achieved by disseminating local decision made at each node. Theoretical analysis and simulation results show that high level (>0.95) of diagnosis accuracy can be achieved for a wide range of fault rates. Correctness and complexity proofs are provided, and they show that the proposed algorithm performs better than the existing protocols from both communication and energy complexity perspective. Analytical study shows that time complexity is O(n c) and message complexity O(n) for an n-node WISN where n c is the number of cluster heads.

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Diagnosability evaluation for a system-level diagnosis algorithm for Wireless Sensor Networks

Cited by 10 publications

References 10 publications

An Improved Distributed Fault Diagnosis Algorithm in Wireless Sensor Networks

An Improved Distributed Fault Diagnosis Algorithm in Wireless Sensor Networks

Online fault diagnosis of wireless sensor networks

Energy‐efficient distributed approach for clustering‐based fault detection and diagnosis in image sensor networks

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