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

Mahapatro, Arunanshu; Khilar, Pabitra Mohan

doi:10.1049/iet-wss.2011.0125

Cited by 15 publications

(18 citation statements)

References 35 publications

(64 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hops that are too short lead to excessive receive energy, and hops that are too long lead to excessive path loss [12].…”

Section: Related Workmentioning

confidence: 99%

Location-based Clustering for Skewed-topology Wireless Sensor Networks

Choi¹,

Ryu²,

Kim³

2016

Journal of Digital Convergence

View full text Add to dashboard Cite

The energy consumption problem in wireless sensor networks is investigated. The problem is to expend as little energy as possible receiving and transmitting data, because of constrained battery. In this paper, in order to extend the lifetime of the network, we proposed a location-based clustering algorithm for wireless sensor network with skewed-topology. The proposed algorithm is to deploy multiple child nodes at the sink to avoid bottleneck near the sink and to save energy. Proposed algorithm can reduce control traffic overhead by creating a dynamic cluster. We have evaluated the performance of our clustering algorithm through an analysis and a simulation. We compare our algorithm's performance to the best known centralized algorithm, and demonstrate that it achieves a good performance in terms of the life time.

show abstract

“…Hops that are too short lead to excessive receive energy, and hops that are too long lead to excessive path loss [12].…”

Section: Related Workmentioning

confidence: 99%

Location-based Clustering for Skewed-topology Wireless Sensor Networks

Choi¹,

Ryu²,

Kim³

2016

Journal of Digital Convergence

View full text Add to dashboard Cite

show abstract

“…This approach is motivated by the fact that the system metrics of sensors usually exhibit certain correlation patterns. Mahapatro and Khilar [12] propose a hybrid approach which exploits both the sensor node coordination and sensor node self-detection. In [13], providing LocalDiagnosis (LD2), which conducts the diagnosis process in a local area and achieves diagnosis decision through distributed evidence fusion operations.…”

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)

View full text Add to dashboard Cite

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.

show abstract

“…The dissemination of all diagnostic information was the major requirement to assure the global view of fault status of WSN. Mahapatro and Khilar [17] assumed the cluster-based routing mechanism where the nodes were organized into one-hop clusters. The spanning tree construction spanned all the CHs state of WSN by the faulty nodes leads to the ignorance of other faults generated in a network.…”

Section: Related Workmentioning

confidence: 99%