A robust boundary detection algorithm based on connectivity only for 3D wireless sensor networks

Zhou, Hongyu; Wu, Hongyi; Jin, Miao

doi:10.1109/infcom.2012.6195529

Cited by 27 publications

(15 citation statements)

References 24 publications

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“…An event sensor declares itself on the event boundary if it has non-event sensors in its neighbourhood. While the basic idea appears straightforward, event boundary detection is challenging, due to limited sampling density, noisy sensor readings, lossy data delivery, and low computation power of individual sensors [13], [14], calling for efficient information processing and modeling techniques to analyze sensor data, in order to estimate the boundary of events [13]- [17]. The detection of network boundary is to locate the outmost nodes in a sensor network, irrespective of sensor data or events.…”

Section: Boundary Detection In 2d Sensor Networkmentioning

confidence: 99%

Boundary Detection in 2-D and 3-D Wireless Sensor Networks

Pandey¹,

Thalore²

2017

IJAERS

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Section: Boundary Detection In 2d Sensor Networkmentioning

confidence: 99%

Boundary Detection in 2-D and 3-D Wireless Sensor Networks

Pandey¹,

Thalore²

2017

IJAERS

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“…First, in order to delineate the approximate geometry of the 3D sensor network, they constructed a tetrahedral structure, and then produced a set of ''sealed'' triangular boundary surfaces for separating non-boundary nodes and boundary node candidates. There are three features about the proposed algorithm in this paper: it requires connectivity information only, it does not rely on particular communication models, and it is robust to sensor distribution [15]. For real situation, Angeles Serna et al [7] focus on the forest fire fighting operations to investigate the representation of the fire fronts with WSNs.…”

Section: Related Workmentioning

confidence: 99%

BP neural network based continuous objects distribution detection in WSNs

Chen

Wang

et al. 2015

Wireless Netw

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WSNs (Wireless Sensor Networks) are widely applied in environment monitoring. Especially, in large scale environment monitoring, its flexibility in deployment and self-organization are strong points. However for distribution detection of continuous objects in large scale environment monitoring, there are two primary constraints: energy consumption and the accuracy of the detection which relies on the density of the WSNs. Currently, almost all of the continuous object monitoring are based on the boundary detection, and all the energy efficiency solutions only focus on the WSNs itself. Unfortunately, with the boundary detection method, the accuracy of the continuous objects detection highly relies on the density of the sensor nodes. What is worse, it is even impossible to make sure of the density of the sensor nodes in real situation. In order to deal with these issues, we proposed the Optimal Fusion Set based Clustering algorithm based on the continuous characteristics of the targets to enhance the energy efficiency and Global Distribution Status Monitoring (GDSM) algorithm to implement the monitoring with finite sensor nodes. Firstly, a dynamic diffusion model based on the Gaussian Puff model is proposed, and then the characteristics of continuous objects are analyzed. According to the theoretic analysis and simulation results, the GDSM algorithm can achieve stable accuracy with limited sensor nodes.

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“…Distributed boundary detection has two main research thrusts: the identification of network boundaries [13,14] and of events and their boundaries [4][5][6][7][8][9][10][11]. While in the first the aim is to solely identify the nodes at the edge of the network, in the case of events, an algorithm must first detect the event region within the network.…”

Section: A Related Workmentioning

confidence: 99%

How to identify global trends from local decisions? Event region detection on mobile networks

Loukas

Zúñiga

Protonotarios

et al. 2014

IEEE INFOCOM 2014 - IEEE Conference on Computer Communications

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Abstract-The decentralized detection of event regions is a fundamental building block for monitoring and reasoning about spatial phenomena. However, so far the problem has been studied almost exclusively for static networks. This study proposes a theoretical framework with which we can analyze event detection algorithms suitable for large-scale mobile networks. Our analysis builds on the following insight: the inherent trends of spatial events are well captured by the spectral domain of the network graph. Using this framework, we propose novel local algorithms that are location-free; that work with mobile nodes and dynamic events; that operate on 3D topologies; and that are simple to implement. We are not aware of event detection algorithms possessing all these traits. Simulations based on complex oil spill traces showcase the resilience and robustness of our methods. Additionally, we demonstrate their validity for practical scenarios by evaluating them on a 105 node testbed.

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A robust boundary detection algorithm based on connectivity only for 3D wireless sensor networks

Cited by 27 publications

References 24 publications

Boundary Detection in 2-D and 3-D Wireless Sensor Networks

Boundary Detection in 2-D and 3-D Wireless Sensor Networks

BP neural network based continuous objects distribution detection in WSNs

How to identify global trends from local decisions? Event region detection on mobile networks

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