Distributed homology algorithm to detect topological events via wireless sensor networks

Farah, Christopher; Schwaner, Frederick; Abedi, Ali; Worboys, Michael

doi:10.1049/iet-wss.2011.0083

Cited by 16 publications

(13 citation statements)

References 23 publications

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“…However, both these approaches require the communication network be structured as a triangulation (and in the case of [22] as the Delaunay triangulation). Another approach to monitoring region evolution that does not require a maximal planar communication network is presented in [23]; however, this work does not restrict communication to the boundary, and instead requires coordination between all nodes inside the monitored region. By contrast, the inquire algorithm is assumes only minimal geometric information, in the form of the cyclic ordering of neighbor nodes; and operates purely at the boundary of the monitored region.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Detection of Topological Events in Evolving Spatial Regions

Sadeq

Duckham

Worboys

2012

The Computer Journal

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Qualitative information about topological events, like the merging or splitting of spatial regions, has many important applications in environmental monitoring. Examples of such applications include detecting the emergence of "hot spots" in sea temperature around a coral reef; or the break up and dispersion of an environmental pollution spill. This paper develops and tests an efficient, decentralized spatial algorithm capable of detecting high-level topological events occurring to spatial regions monitored by a wireless sensor network. The algorithm, called INQUIRE, is decentralized because at no point does any single system element possess global knowledge of the entire system state. Instead, INQUIRE relies purely on a sensor node's local knowledge of its own state and the state of its immediate network neighbors. Experimental evaluation of the INQUIRE algorithm demonstrates that our decentralized approach can substantially improve scalability of communication when compared with efficient centralized alternatives.

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Section: Introductionmentioning

confidence: 99%

Decentralized Detection of Topological Events in Evolving Spatial Regions

Sadeq

Duckham

Worboys

2012

The Computer Journal

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“…A model M is a neighborhood model of the model M if is a neighboring node of . If 4 denotes the proportion of correct selection of the model M , when it is true, by this method, then 4 estimates M ( ∈ N max ∪ {∪ ∈N max ( )}). Clearly, 4 ≥ 3 ≥ 2 .…”

Section: Simulation Studymentioning

confidence: 99%

“…One of the unique features of a WSN is random deployment in inaccessible terrains and cooperative effort that offers unprecedented opportunities for a broad spectrum of civilian and military applications, such as industrial automation, military surveillance, national security, and emergency health care [1][2][3]. Sensor networks are also useful in detecting topological events such as forest fires [4].…”

Section: Introductionmentioning

confidence: 99%

Model Selection Approach for Distributed Fault Detection in Wireless Sensor Networks

Nandi

Dewanji

Roy

et al. 2014

International Journal of Distributed Sensor Networks

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Sensor networks aim at monitoring their surroundings for event detection and object tracking. But, due to failure or death of sensors, false signal can be transmitted. In this paper, we consider the problems of distributed fault detection in wireless sensor network (WSN). In particular, we consider how to take decision regarding fault detection in a noisy environment as a result of false detection or false response of event by some sensors, where the sensors are placed at the center of regular hexagons and the event can occur at only one hexagon. We propose fault detection schemes that explicitly introduce the error probabilities into the optimal event detection process. We introduce two types of detection probabilities, one for the center node, where the event occurs, and the other one for the adjacent nodes. This second type of detection probability is new in sensor network literature. We develop schemes under the model selection procedure and multiple model selection procedure and use the concept of Bayesian model averaging to identify a set of likely fault sensors and obtain an average predictive error.

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“…We incidentally report also a class of algorithms that directly focus on detecting topological changes in Wireless Sensor Networks (WSNs), that are explicitly based on measuring geographical phenomena, e.g., wildfires (Farah et al, 2011;Fekete et al, 2004;Jiang and Worboys, 2009). Here, instead, we consider the more general case where environmental or similar measurements are not available to the change detector.…”

Section: Introductionmentioning

confidence: 99%

Distributed detection of topological changes in communication networks

Lucchese

Varagnolo

Johansson

2014

IFAC Proceedings Volumes

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Changes in the topology of communication networks, such as sudden appearance or disappearance of links or nodes, may signal malicious attacks or malfunctions. A topology change detector may thus be useful to trigger alarms or self-reconfiguration procedures. Here we present a novel approach that enjoys several desirable qualities such as fast convergence, intrinsically distributed computations, and scalability w.r.t. communication and computational requirements. We characterize the performance of this technique from analytical and practical points of view, providing theoretical results on its performance. We thus show how it is possible to tune and trade-off the accuracy of the change detection results with the communication requirements of the procedure.

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Distributed homology algorithm to detect topological events via wireless sensor networks

Cited by 16 publications

References 23 publications

Decentralized Detection of Topological Events in Evolving Spatial Regions

Decentralized Detection of Topological Events in Evolving Spatial Regions

Model Selection Approach for Distributed Fault Detection in Wireless Sensor Networks

Distributed detection of topological changes in communication networks

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