A survey on sensor localization

Wang, Jing; Ghosh, Rumi; Das, Sajal K.

doi:10.1007/s11768-010-9187-7

Cited by 273 publications

(139 citation statements)

References 47 publications

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“…Thus, the attractiveness β is defined as follows which is corresponding to the light intensity judged or seen by fireflies. (5) Here the represents attractiveness between fireflies when distance r is 0. The Cartesian distance method is used to calculate distance between two fireflies.…”

Section: B Firefly Algorithmmentioning

confidence: 99%

“…The estimation of the position of sensor nodes is one of the important issues of the WSN and is known as localization problem [4]. Localization can be defined as determining the coordinates of unknown nodes called as target nodes using the position of known nodes called as anchor nodes or beacons [5] based on the measurements such as Time of arrival (TOA), Time difference of arrival (TDOA), Angle of arrival measurement (AOA), etc. [6].…”

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confidence: 99%

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Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

Kaur¹,

Arora²

2017

IJIMAI

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Localization is one of the most important factors highly desirable for the performance of Wireless Sensor Network (WSN). Localization can be stated as the estimation of the location of the sensor nodes in sensor network. In the applications of WSN, the data gathered at sink node will be meaningless without localization information of the nodes. Due to size and complexity factors of the localization problem, it can be formulated as an optimization problem and thus can be approached with optimization algorithms. In this paper, the nature inspired algorithms are used and analyzed for an optimal estimation of the location of sensor nodes. The performance of the nature inspired algorithms viz. Flower pollination algorithm (FPA), Firefly algorithm (FA), Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) for localization in WSN is analyzed in terms of localization accuracy, number of localized nodes and computing time. The comparative analysis has shown that FPA is more proficient in determining the coordinates of nodes by minimizing the localization error as compared to FA, PSO and GWO.

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Section: B Firefly Algorithmmentioning

confidence: 99%

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confidence: 99%

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

Kaur¹,

Arora²

2017

IJIMAI

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“…Due to space limit, we only review the previous works on mobile-beacon assisted approaches. A comprehensive survey covering other localization methods can be found in [15].…”

Section: Related Workmentioning

confidence: 99%

“…Existing non-GPS-based sensor localization algorithms can be classified as range-based or range-free [15]. Range-based localization relies on signal features such as Angle of Arrival (AoA), Time of Arrival (ToA), Time Difference of Arrival (TDoA), or Received Signal Strength (RSS) for ranging (i.e., finding nodal relative distance or angle).…”

Section: Introductionmentioning

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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“…Recent works document the wireless network technique (Ahmed et al 2012;Ahn et al 2010;Dang et al 2008;Erdogan 2010;Guibin et al 2010;Muhamed et al 2009;Park et al 2010;Rahman et al 2012;Wang et al 2010;Zheng 2011). Generally, there are four techniques that can be used for range estimation: Angle of Arrival (AoA), Time-of-Arrival (ToA), Time-Difference-ofArrival (TDoA) and Received-Signal-Strength-Indicator (RSSI).…”

Section: Introductionmentioning

confidence: 99%