Localization in wireless sensor networks using particle swarm optimization

Gopakumar, A.; Jacob, Lillykutty

doi:10.1049/cp:20080185

Cited by 167 publications

(88 citation statements)

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“…These algorithms became popular from the last decade as they can easily adjust to frequently changing environment and have high efficiency [10]. The various algorithms like Particle Swarm Optimization (PSO) [11], Firefly Algorithm (FA) [12] [13], Genetic Algorithm (GA) [14], Grey Wolf Optimization (GWO) [15], Flower pollination Algorithm [16], etc. have been used to determine positions of target nodes.…”

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confidence: 99%

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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confidence: 99%

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

Kaur¹,

Arora²

2017

IJIMAI

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“…The accuracy of this approach is not as good as the PSO algorithm in [3] according to the performance evaluation. A two-phase localization algorithm for WSN presented in [4,5] is centralized and not feasible for large-scale sensor networks.…”

Section: Introductionmentioning

confidence: 93%

An Improved PSO Algorithm for Distributed Localization in Wireless Sensor Networks

Wen

2015

International Journal of Distributed Sensor Networks

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Accurate and quick localization of randomly deployed nodes is required by many applications in wireless sensor networks and always formulated as a multidimensional optimization problem. Particle swarm optimization (PSO) is feasible for the localization problem because of its quick convergence and moderate demand for computing resources. This paper proposes a distributed twophase PSO algorithm to solve the flip ambiguity problem, and improve the efficiency and precision. In this work, the initial search space is defined by bounding box method and a refinement phase is put forward to correct the error due to flip ambiguity. Moreover, the unknown nodes which only have two references or three near-collinear references are tried to be localized in our research. Simulation results indicate that the proposed distributed localization algorithm is superior to the previous algorithms.

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“…Novel computational techniques are also proving helpful in overcoming specific localization problems in WSNs (Kulkarni et al, 2011). For example, stochastic particle swarm optimization was recognized as an efficient tool to use to solve localminimum problems for localization and tracking in mobile WSN environments (Gopakumar and Jacob, 2008).…”

Section: B Wireless Sensor Networkmentioning

confidence: 99%

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Deng

Rauchenstein

et al. 2016

Review of Scientific Instruments

127

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Locating the position of fixed or mobile sources (i.e., transmitters) based on measurements obtained from sensors (i.e., receivers) is an important research area that is attracting much interest. In this paper, we review several representative localization algorithms that use time of arrivals (TOAs) and time difference of arrivals (TDOAs) to achieve high signal source position estimation accuracy when a transmitter is in the line-of-sight of a receiver. Circular (TOA) and hyperbolic (TDOA) position estimation approaches both use nonlinear equations that relate the known locations of receivers and unknown locations of transmitters. Estimation of the location of transmitters using the standard nonlinear equations may not be very accurate because of receiver location errors, receiver measurement errors, and computational efficiency challenges that result in high computational burdens. Least squares and maximum likelihood based algorithms have become the most popular computational approaches to transmitter location estimation. In this paper, we summarize the computational characteristics and position estimation accuracies of various positioning algorithms. By improving methods for estimating the time-of-arrival of transmissions at receivers and transmitter location estimation algorithms, transmitter location estimation may be applied across a range of applications

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Localization in wireless sensor networks using particle swarm optimization

Cited by 167 publications

References 0 publications

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

An Improved PSO Algorithm for Distributed Localization in Wireless Sensor Networks

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

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