Fault tolerance in sensor networks using self-diagnosing sensor nodes

Harte, Seán; Rahman, Abdul Hadi Abd; Razeeb, Kafil M.

doi:10.1049/ic:20050211

Cited by 51 publications

(18 citation statements)

References 8 publications

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“…However, those approaches are tightly coupled with specific failures. Harte et al monitor the status of each sensor node to detect physical malfunctions [54]. Ritter et al detect and distinguish network partition from node failure [55].…”

Section: ) Network Self Diagnosismentioning

confidence: 99%

Debugging Low-Power and Lossy Wireless Networks: A Survey

Schoofs

O’Hare

Ruzzelli

2012

IEEE Commun. Surv. Tutorials

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Section: ) Network Self Diagnosismentioning

confidence: 99%

Debugging Low-Power and Lossy Wireless Networks: A Survey

Schoofs

O’Hare

Ruzzelli

2012

IEEE Commun. Surv. Tutorials

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“…In order to improve resilience to sensor node failure, a few approaches propose failure detection mechanisms. Nodes can either detect their own failure [8], or their neighbors failure [9] or compromised nodes [10]. Reputation systems have been developed in order to identify compromised nodes, based on their behavior [11], [12].…”

Section: Related Workmentioning

confidence: 99%

A framework for trust assessment of sensor data

Gomez¹,

Gentile

Riveill

2011

2011 4th Joint IFIP Wireless and Mobile Networking Conference (WMNC 2011)

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International audienceNowadays, Wireless Sensor Networks appear to be mature enough to be used by business applications. As delivered sensor data can strongly impact end users decision taking, business applications must rely on trustworthy sensor data. In this paper, we propose the evaluation of a trust model for sensor data [1] during its life-cycle from acquisition at the nodes, through processing to delivery to business applications. The purpose of this trust framework is the detection of erroneous sensor data which are originated either unintentionally due to defective sensor nodes or intentionally by attackers. For the evaluation of this approach, we use sensor data acquired from herd control experiment, in the context of the WASP (Wireless Accessible for Sensor Population) project[2]. In this experiment, information (e.g. acceleration, activity) of either animals are collected from sensor nodes, and used in order to take a decision by farmers

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“…Sensor nodes gradually take more management responsibility and decision-making in order to achieve the vision of self-managed WSNs. Node self-detection scheme [13] and neighbour coordination [14] have provided us a good example of management distribution, but their focuses are on a small region (a group of nodes) or individual node. Research work as MANNA [4], WinMS [15] etc proposed management architecture to look after the overall network from a central manager scheme.…”

Section: B Distributed Approachesmentioning

confidence: 99%