Cooperative event detection in wireless sensor networks

Wittenburg, Georg; Dziengel, Norman; Adler, Stephan; Kasmi, Zakaria; Ziegert, Marco; Schiller, Jochen

doi:10.1109/mcom.2012.6384461

Cited by 36 publications

(17 citation statements)

References 11 publications

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“…However, this centralised data processing scheme suffers from energy-consuming multi-hop communication and is fragile to the failure of several critical relaying sensor nodes. To enable scalable data processing in a large-scale WSN, decentralised data processing has received extensive research interests recently (Predd et al, 2006;Wittenburg et al, 2012;Schmidt et al, 2012;Laitrakun and Coyle, 2013). In the decentralised data processing scheme, the sensor nodes no longer send their measurements to the fusion centre.…”

Section: Related Workmentioning

confidence: 99%

Joint event detection and environment perception in decentralised wireless sensor networks

Zhu

Xue

et al. 2016

IJSNET

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In an event detection application, sensor nodes measure signals that are emitted from multiple events and attenuated by the environment, and then collaborate to estimate the locations and magnitudes of the events. Existing event detection algorithms often assume that the number of the events is known in advance and/or the attenuation coefficient of the environment is given. This paper considers the case that both the number of the events and the attenuation coefficient of the environment are unknown. Through exploiting the sparse nature of the events, we propose an ℓ-norm regularised least squares formulation that automatically estimates the number of the events as well as their locations and magnitudes; the attenuation coefficient of the environment also appears as an optimisation variable. We develop a decentralised algorithm and its accelerated variant to solve the joint event detection and environment perception problem using the alternating direction method of multipliers (ADMM).

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Section: Related Workmentioning

confidence: 99%

Joint event detection and environment perception in decentralised wireless sensor networks

Zhu

Xue

et al. 2016

IJSNET

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“…Therefore, the local detection decisions of each node regarded as evidences can be fused together using D-S combination rules to improve the accuracy of event detection. while( T not end) (10) if abnormal data then (11) the number l is increased by one (12) if l ≥ C th then break (13) end if (14) else if normal data (15) the number q is increased by one (16) end if (17) end while (18)…”

Section: Event Decisionmentioning

confidence: 99%

“…In fact, because the environmental noise or hardware failures often make the sampling data collected at nodes uncertain, the spatio-temporal correlation characteristics between nodes can be used to achieve fault-tolerant event detection at the node. Such use of spatial-temporal correlation between nodes to achieve event detection is also mentioned in references [13][14][15][16]. Cao et al [13] proposed a spatial-temporal correlation-based event region detection algorithm that uses spatial-temporal correlation between nodes to improve the accuracy of event detection, which attempts to achieve event detection under unbiased conditions.…”

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Event Detection in Wireless Sensor Networks using Evidence Theory

Liu¹,

Tian²,

Ma³

et al. 2015

KSII TIIS

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Event detection is one of the key issues in many wireless sensor network (WSN) applications. The uncertainties that are derived from the instability of sensor node, measurement noise and incomplete sampling would influence the performance of event detection to a large degree. Many of the present researches described the sensor readings with crisp values, which cannot adequately handle the uncertainties inhered in the imprecise sensor readings. In this paper, a fault-tolerant event detection algorithm is proposed based on Dempster-Shafer (D-S) theory (also called evidence theory). Instead of crisp values, all possible states of the event are represented by the Basic Probability Assignment (BPA) functions, with which the output of each sensor node are characterized as weighted evidences. The combination rule was subsequently applied on each sensor node to fuse the evidences gathered from the neighboring nodes to make the final decision on whether the event occurs. Simulation results show that even 20% nodes are faulty, the accuracy of the proposed algorithm is around 80% for event region detection. Moreover, 97% of the error readings have been corrected, and an improved detection capability at the boundary of the event region is gained by 75%. The proposed algorithm can enhance the detection accuracy of the event region even in high error-rate environment, which reflects good reliability and robustness. The proposed algorithm is also applicable to boundary detection as it performs well at the boundary of the event.

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“…[ 26 ], they classify different proposals according to whether the notification decision of the event is made on a sink node or on the node itself. However, the authors of [ 27 ] take into consideration the network architecture, the scope of the decision, and the nature of the detection algorithm.…”

Section: Introductionmentioning

confidence: 99%

D2R-TED: Data—Domain Reduction Model for Threshold-Based Event Detection in Sensor Networks

León‐García

Muñoz

Olivares

2018

Sensors

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The reduction of sensor network traffic has become a scientific challenge. Different compression techniques are applied for this purpose, offering general solutions which try to minimize the loss of information. Here, a new proposal for traffic reduction by redefining the domains of the sensor data is presented. A configurable data reduction model is proposed focused on periodic duty–cycled sensor networks with events triggered by threshold. The loss of information produced by the model is analyzed in this paper in the context of event detection, an unusual approach leading to a set of specific metrics that enable the evaluation of the model in terms of traffic savings, precision, and recall. Different model configurations are tested with two experimental cases, whose input data are extracted from an extensive set of real data. In particular, two new versions of Send–on–Delta (SoD) and Predictive Sampling (PS) have been designed and implemented in the proposed data–domain reduction for threshold–based event detection (D2R-TED) model. The obtained results illustrate the potential usefulness of analyzing different model configurations to obtain a cost–benefit curve, in terms of traffic savings and quality of the response. Experiments show an average reduction of 76% of network packages with an error of less than 1%. In addition, experiments show that the methods designed under the proposed D2R–TED model outperform the original event–triggered SoD and PS methods by 10% and 16% of the traffic savings, respectively. This model is useful to avoid network bottlenecks by applying the optimal configuration in each situation.

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Cooperative event detection in wireless sensor networks

Cited by 36 publications

References 11 publications

Joint event detection and environment perception in decentralised wireless sensor networks

Joint event detection and environment perception in decentralised wireless sensor networks

Fault-Tolerant Event Detection in Wireless Sensor Networks using Evidence Theory

D2R-TED: Data—Domain Reduction Model for Threshold-Based Event Detection in Sensor Networks

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