Path planning of mobile landmarks for localization in wireless sensor networks

Koutsonikolas, Dimitrios; Das, Saumitra M.; Hu, Y. Charlie

doi:10.1016/j.comcom.2007.05.048

Cited by 215 publications

(143 citation statements)

References 33 publications

(65 reference statements)

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“…The mobile beacon in its travels uses the following predefined trajectories: SCAN, DOUBLE SCAN, HILBERT, CIRCLE and S-CURVES [27][28]. We denote the predefined trajectory by ti , i = 1……..nt, where nt is the number of trajectories.…”

Section: Outlier Anchor Detectionmentioning

confidence: 99%

Mobile-Assisted Anchor Outlier Detection for Localization in Wireless Sensor Networks

Abukhalaf¹,

Wang

Zhang

2016

IJFGCN

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Section: Outlier Anchor Detectionmentioning

confidence: 99%

Mobile-Assisted Anchor Outlier Detection for Localization in Wireless Sensor Networks

Abukhalaf¹,

Wang

Zhang

2016

IJFGCN

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“…Most of them have used straight lines, triangles, or squares as the basis for constructing routing paths [7][8][9][10]. Only three regular geometric shapes, namely, triangle, square, and hexagon, can be used to tessellate a two-dimensional plane.…”

Section: Related Workmentioning

confidence: 99%

Node-Gosper Curve-Based Unknown Sensor Localization Using Single Mobile Anchor in Wireless Sensor Networks

Chen

Wang

2016

International Journal of Distributed Sensor Networks

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The node-Gosper curve is a fractal space-filling curve constructed by recursively replacing each node by a seven-segment generator curve. Here, we propose a novel scheme for describing the path of a node-Gosper curve; the scheme can be used to calculate the coordinates of each turning point of the curve efficiently. The node-Gosper curve serves as a routing path for a single mobile anchor to help unknown sensors locate themselves in the wireless sensor network. In our proposed localization method, the mobile anchor travels along the node-Gosper curve, which covers the entire sensing field, and when it reaches the turning points of the node-Gosper curve, it broadcasts its current location to unknown sensors within a preset communication range. Each unknown sensor estimates its location when it receives at least three messages from the mobile anchor from different locations. Experimental results show that the proposed method outperforms several well-known mobile anchor localization methods in terms of localization accuracy and energy consumption.

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“…Pre-defined 'S' shape trajectory spanning the entire sensory field is suggested to support the two algorithms. In [19], [20], other types of fixed trajectories are suggested under the assumption of known sensory field boundary. In [7], [12], the beacon node first collects the topology information of the network, then with this global view, it identifies the areas where additional distance measures are needed so that in the end the whole network becomes a globally rigid region and is thus localizable by a range-free algorithm.…”

Section: Related Workmentioning

confidence: 99%

Mobile-Beacon Assisted Sensor Localization with Dynamic Beacon Mobility Scheduling

Mitton

Simplot-Ryl

et al. 2011

2011 IEEE Eighth International Conference on Mobile Ad-Hoc and Sensor Systems

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Abstract-In mobile-beacon assisted sensor localization, beacon mobility scheduling aims to determine the best beacon trajectory so that each sensor receives sufficient beacon signals with minimum delay. We propose a novel DeteRministic bEAcon Mobility Scheduling (DREAMS) algorithm, without requiring any prior knowledge of the sensory field. In this algorithm, beacon trajectory is defined as the track of depth-first traversal (DFT) of the network graph, thus deterministic. The mobile beacon performs DFT under the instruction of nearby sensors on the fly. It moves from sensor to sensor in an intelligent heuristic manner according to RSS (Received Signal Strength)-based distance measurements. We prove that DREAMS guarantees full localization (every sensor is localized) when the measurements are noise-free. Then we suggest to apply node elimination and topology control (Local Minimum Spanning Tree) to shorten beacon tour and reduce delay. Through simulation we show that DREAMS guarantees full localization even with noisy distance measurements. We evaluate its performance on localization delay and communication overhead in comparison with a previously proposed static path based scheduling method.

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Path planning of mobile landmarks for localization in wireless sensor networks

Cited by 215 publications

References 33 publications

Mobile-Assisted Anchor Outlier Detection for Localization in Wireless Sensor Networks

Mobile-Assisted Anchor Outlier Detection for Localization in Wireless Sensor Networks

Node-Gosper Curve-Based Unknown Sensor Localization Using Single Mobile Anchor in Wireless Sensor Networks

Mobile-Beacon Assisted Sensor Localization with Dynamic Beacon Mobility Scheduling

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