Existence of dumb nodes in stationary wireless sensor networks

Misra, Sudip; Kar, Pushpendu; Roy, Arijit; Obaidat, Mohammad S.

doi:10.1016/j.jss.2013.12.039

Cited by 32 publications

(16 citation statements)

References 20 publications

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“…Such behavior is denoted by Ψ d . Mathematically [1], , which is less than d min . Thus, due to this shrinkage in communication range, node A fails to transmit any data packet at time instant t j to any activated neighbor node and it becomes dumb.…”

Section: B Dumb Nodesmentioning

confidence: 95%

“…In the present context, the dumb misbehavior [1] of sensor nodes is considered. In a WSN, collaboration among sensor nodes is crucial for the self-organization and proper functioning of the entire network.…”

Section: B Contributionmentioning

confidence: 99%

“…Boano et al show how the communication is affected by temperature with real outdoor sensor deployment [13]. Due to the presence of such environmental impact on the sensor nodes, the communication range of a node gets reduced, and consequently, the sensor nodes get dumb [1]. Existing approaches to detection of malicious or selfish nodes in the literature are not suitable for detecting dumb nodes.…”

Section: Related Workmentioning

confidence: 99%

“…However, with the resumption of favorable environmental conditions a node can perform its normal operations. This temporary behavior of a node characterizes it to be dumb [1].…”

Section: Introductionmentioning

confidence: 99%

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Detection of dumb nodes in a stationary wireless sensor network

Roy

Kar

Misra

2014

2014 Annual IEEE India Conference (INDICON)

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A sensor node is termed as "dumb" [1], if at a certain time instant it can sense its surroundings, but is unable to communicate with any of its neighbors due to the shrinkage in communication range. Such isolation occurs because of the presence of adverse environmental effects. However, the node starts its normal operation with the resumption of favorable environmental conditions. Thus, the detection of dumb nodes is essential in order to re-establish network connectivity. However, the temporal behavior of a dumb node in a network makes the detection of such a node challenging. In the present work, we address a plausible solution to this problem by taking into account the evidences from neighboring nodes.

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Section: B Dumb Nodesmentioning

confidence: 95%

Section: B Contributionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…However, with the resumption of favorable environmental conditions a node can perform its normal operations. This temporary behavior of a node characterizes it to be dumb [1].…”

Section: Introductionmentioning

confidence: 99%

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Detection of dumb nodes in a stationary wireless sensor network

Roy

Kar

Misra

2014

2014 Annual IEEE India Conference (INDICON)

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“…After deployment, sensor nodes form an ad-hoc network using a nearby node discovery process. In WSNs, the deployed sensor nodes are prone to various faults such as transceiver unit fault, sensor unit fault, processing unit or microcontroller unit fault and the power unit fault Lee and Choi, 2008b;Chen et al, 2012;Misra et al, 2014 ;and Bari et al, 2012 ). Faulty sensors produce erroneous data during the normal operation of the network which can reduce the number of available multihop paths in the network.…”

Section: Introductionmentioning

confidence: 99%

Mobile sink based fault diagnosis scheme for wireless sensor networks

Chanak

Banerjee

Sherratt

2016

Journal of Systems and Software

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a b s t r a c tNetwork diagnosis in Wireless Sensor Networks (WSNs) is a difficult task due to their improvisational nature, invisibility of internal running status, and particularly since the network structure can frequently change due to link failure. To solve this problem, we propose a Mobile Sink (MS) based distributed fault diagnosis algorithm for WSNs. An MS, or mobile fault detector is usually a mobile robot or vehicle equipped with a wireless transceiver that performs the task of a mobile base station while also diagnosing the hardware and software status of deployed network sensors. Our MS mobile fault detector moves through the network area polling each static sensor node to diagnose the hardware and software status of nearby sensor nodes using only single hop communication. Therefore, the fault detection accuracy and functionality of the network is significantly increased. In order to maintain an excellent Quality of Service (QoS), we employ an optimal fault diagnosis tour planning algorithm. In addition to saving energy and time, the tour planning algorithm excludes faulty sensor nodes from the next diagnosis tour. We demonstrate the effectiveness of the proposed algorithms through simulation and real life experimental results.

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D3: distributed approach for the detection of dumb nodes in wireless sensor networks

Roy

Kar

Misra

et al. 2015

Int J Communication

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SUMMARYIn this work, we propose D3-a distributed approach for the detection of 'dumb' nodes in a wireless sensor network (WSN). A dumb node can sense its surroundings, but is unable to transmit these sensed data to any other node, due to the sudden onset of adverse environmental effects. However, such a node resumes its normal operations with the resumption of favorable environmental conditions. Due to the presence of dumb nodes, the network is unable to provide the expected services. Therefore, it is prudent to re-establish connectivity between dumb and other nodes, so that sensed data can be reliably transmitted to the sink. Before the re-establishment of connectivity, a node needs to confirm its actual state of being dumb. Dumb behavior is dynamic in nature, and is, thus, distinct from the traditional node isolation problem considered in stationary WSNs. Therefore, the existing schemes for the detection of other misbehaviors are not applicable for detecting a dumb node in a WSN. Considering this temporal behavior of a dumb node, we propose an approach, D3, for the detection of dumb nodes. In the propose scheme, we uses cumulative sum test, which helps in detecting the dumb behavior. The simulation results show that there is 56% degradation in detection percentage with the increment in the detection threshold, whereas energy consumption and the message overhead increase by 40% with the increment in detection threshold.

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Existence of dumb nodes in stationary wireless sensor networks

Cited by 32 publications

References 20 publications

Detection of dumb nodes in a stationary wireless sensor network

Detection of dumb nodes in a stationary wireless sensor network

Mobile sink based fault diagnosis scheme for wireless sensor networks

D3: distributed approach for the detection of dumb nodes in wireless sensor networks

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