Distributed event region detection in wireless sensor networks

Fang, Jun; Li, Hongbin; Quoraishee, Shafik; Hohil, Myron E.

doi:10.1117/12.777387

Cited by 7 publications

(7 citation statements)

References 14 publications

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“…Although a BN can be derived from a GMRF or from the model in [21] to find an ICN for the model, it will be interesting to see if special cases of our algorithms can be designed to exploit special properties, specifically linearity, of these models.…”

Section: Related Prior Workmentioning

confidence: 99%

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Graphical Models for Distributed Inference in Wireless Sensor Networks

Trivedi

2009

2009 Third International Conference on Sensor Technologies and Applications

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Distributed information fusion is an active area of research; however, fusion in the large, dynamic, unpredictable, and power-scarce setting of wireless sensor network (WSN) requires more than just distributed fusion algorithms. Some of the important questions that need to be addressed are dynamic mapping of inference responsibilities to sensor nodes in a distributed manner and cost-effective, fault-tolerant exchange of fusion information. Graphical models provide compact representation of joint probability distributions; they help in drawing inference more efficiently. We propose two graphical models called Inference Cost Network (ICN) and Dynamic Inference Cost Network (DICN) that generalize Bayesian networks and dynamic Bayesian networks for truly distributed implementation. We also propose distributed inference algorithms for ICN/DICN that address the unique requirements of information fusion in WSN. We prove correctness of the model and the algorithms and demonstrate that the algorithms are lightweight in terms of computational complexity.

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Section: Related Prior Workmentioning

confidence: 99%

“…Fang and Li [21] proposed a method for distributed event-region detection in WSNs, where spatial dependence between sensor nodes is modeled using graphical models. Their method also involves solving a linear system of equations.…”

Section: Related Prior Workmentioning

confidence: 99%

Graphical Models for Distributed Inference in Wireless Sensor Networks

Trivedi

2009

2009 Third International Conference on Sensor Technologies and Applications

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“…Moreover, sensing noise at different sensors might vary, and consequently their observation data. Such sensing noise can be modelled using Gaussian distribution [5]. Let the sensing noise at the i-th sensor be n i which follows a Gaussian distribution with µ s mean and σ 2…”

Section: The Fault and Noise Modelmentioning

confidence: 99%

QoS Support in Event Detection in WSN through Optimal k-Coverage

Alam

Karmakar

Murshed

et al. 2011

Procedia Computer Science

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Wireless sensor networks promise to guarantee accurate, fault tolerant and timely detection of events in large scale sensor fields. To achieve this the notion of k-coverage is widely employed in WSNs where significant redundancy is introduced in deployment as an event is expected to be sensed by at least k sensors in the neighborhood. As sensor density increases significantly with k, it is imperative to find the optimal k for the underlying event detection system. In this work, we consider the detection probability, fault tolerance and latency as the Quality of Service (QoS) metrics of an event detection system employing k-coverage and present a probabilistic model to guarantee given QoS support with the minimum degree of coverage taking into account the noise related measurement error, communication interference and sensor fault probability. This work eventually resolves the problem of over or under deployment of sensors, increases scalability and provides a well defined mechanism to tune the degree of coverage according to performance needs.

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“…In the sensing process of WSNs, detection is the most important and an initial step [6,7]. For example, in the monitoring of an environment, the presence of contaminant (e.g., radioactive material) is detected first before finding the level of contamination [8]. Mostly in large scale WSNs, the signal level produced by such events/targets/contamination may spread over a portion of the whole region, which is known as the event region [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…For example, in the monitoring of an environment, the presence of contaminant (e.g., radioactive material) is detected first before finding the level of contamination [8]. Mostly in large scale WSNs, the signal level produced by such events/targets/contamination may spread over a portion of the whole region, which is known as the event region [8,9]. In other words, the event region spreads over an area that includes just a subset of all the sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

Two Stage Multi Sensor Detection Scheme for Large Scale WSN Using Spatial Proximity

Ali¹,

Shah²,

Badshah³

2022

murjet

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Intuition suggests that in a large-size sensor network, event signal spreads over a sub-region known as event region. The sub-set of sensors inside the event region is located close to each other in the form of a cluster. On the contrary, the reports from the rest of the sensors that are far away from the event will be less credible and prone to faulty detection. In this work, such fact is exploited to improve the detection performance of the detection schemes at the fusion center by proposing a two-stage detection mechanism. In other words, the proposed two-stage detection scheme exploits the spatial proximity of the event region sensors. Numerical results are provided to assess the detection performance of the proposed scheme that shows that the proposed detection scheme has a superior performance over the traditional detection schemes.

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Distributed event region detection in wireless sensor networks

Cited by 7 publications

References 14 publications

Graphical Models for Distributed Inference in Wireless Sensor Networks

Graphical Models for Distributed Inference in Wireless Sensor Networks

QoS Support in Event Detection in WSN through Optimal k-Coverage

Two Stage Multi Sensor Detection Scheme for Large Scale WSN Using Spatial Proximity

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