WSN Node Localization Algorithm Based on Adaptive Particle Swarm Optimization

Gao, Ying; Zhao, Wanxin; Cheng, Jian; Ren, Wei

doi:10.4028/www.scientific.net/amm.143-144.302

Cited by 11 publications

(7 citation statements)

References 5 publications

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“…In recent years, some intelligent algorithms have been used to improve DV-Hop; for example, the particle swarm optimization (PSO) is one of the most popular algorithms. The PSO was used to improve the localization accuracy of DV-Hop [7][8][9][10]. However, the PSO algorithm is weak in solving discrete and combinatorial optimization problems, especially for some nonrectangular coordinate systems, and the parameter control of the PSO is not flexible.…”

Section: Related Workmentioning

confidence: 99%

DV-Hop Node Location Algorithm Based on GSO in Wireless Sensor Networks

Song

Zhao

2019

Journal of Sensors

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Node location is one of the most important problems to be solved in practical application of WSN. As a typical location algorithm without ranging, DV-Hop is widely used in node localization of wireless sensor networks. However, in the third phase of DV-Hop, a least square method is used to solve the nonlinear equations. Using this method to locate the unknown nodes will produce large coordinate errors, poor stability of positioning accuracy, low location coverage, and high energy consumption. An improved localization algorithm based on hybrid chaotic strategy (MGDV-Hop) is proposed in this paper. Firstly, a glowworm swarm optimization of hybrid chaotic strategy based on chaotic mutation and chaotic inertial weight updating (MC-GSO) is proposed. The MC-GSO algorithm is used to control the moving distance of each firefly by chaos mutation and chaotic inertial weight when the firefly falls into a local optimum. The experimental results show that MC-GSO has better convergence and higher accuracy and avoids the premature convergence. Then, MC-GSO is used to replace the least square method in estimating node coordinates to solve the problem that the localization accuracy of the DV-Hop algorithm is not high. By establishing the error fitness function, the linear solution of coordinates is transformed into a two-dimensional combinatorial optimization problem. The simulation results and analysis confirm that the improved algorithm (MGDV-Hop) reduces the average location error, increases the location coverage, and decreases and balances the energy consumption as compared to DV-Hop and the location algorithm based on classical GSO (GSDV-Hop).

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Section: Related Workmentioning

confidence: 99%

DV-Hop Node Location Algorithm Based on GSO in Wireless Sensor Networks

Song

Zhao

2019

Journal of Sensors

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“…Sezaki used a differential error correction scheme designed to reduce the location error accumulated over multiple hops [16]. Gao put forward a WSN localization algorithm based on the adaptive particle swarm optimization [17].…”

Section: Existing Improved Dv-hop Algorithmsmentioning

confidence: 99%

Improved DV-Hop Node Localization Algorithm in Wireless Sensor Networks

Xiao

Zhang

2012

International Journal of Distributed Sensor Networks

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An improved algorithm is put forward to improve the poor locating performance of the DV-Hop algorithm, which is one of the range-free algorithms in wireless sensor networks. Firstly, we set some anchor nodes at the border land of monitoring regions. Secondly, the average one-hop distance between anchor nodes is modified, and the average one-hop distance used by each unknown node for estimating its location is modified through weighting the received average one-hop distances from anchor nodes. Finally, we use the particle swarm optimization to correct the position estimated by the 2D hyperbolic localization algorithm, which makes the result closer to the actual position. The simulation results show that the proposed algorithm has better localization performance in the localization precision and stability than the basic DV-Hop algorithm and some existing improved algorithms.

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“…to the minimized weighting factor min w max min max ww w w t T   (16) t is the current iteration number and T is the total iteration number. In order to find out the most optimized solution in iteration, fitness value will be acquired as:…”

Section: Relative Coordinate Calculationmentioning

confidence: 99%

“…Therefore, it will be closer to practical distance. Meanwhile, particle swarm optimization algorithm [16] will be used to overcome non solution phenomenon probably caused by MDS so it can effectively improve the precision of node localization.…”

Section: Introductionmentioning

confidence: 99%

Improved MDS-based Sensor Localization Algorithm in Wireless Sensor Networks

Zhang¹,

Tan²

2013

JNW

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Sensor localization technology is the principle problem for configuration and operation of wireless sensor network. Since existing multidimensional scaling localization algorithm has its limitation in localization accuracy, a novel method based on node distance correction (DCMDS) is put forward. The improved multidimensional scaling-based sensor localization algorithm partly divides the location areas of the whole network. It achieves the relative position by distance information among the nodes. Then the distance estimations are corrected according to the distance information of anchor nodes. When the corrected estimations distance matrix is completed, relative position is recalculated with classical MDS algorithm. The optimized particle swarm optimization is used to acquire the final localization results. The performances of DCMDS algorithm were validated in different scenario. The experiments revealed that improved method has high accuracy in localization and low complexity for calculation and communication. It also enhances the extensibility of localization algorithm

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WSN Node Localization Algorithm Based on Adaptive Particle Swarm Optimization

Cited by 11 publications

References 5 publications

DV-Hop Node Location Algorithm Based on GSO in Wireless Sensor Networks

DV-Hop Node Location Algorithm Based on GSO in Wireless Sensor Networks

Improved DV-Hop Node Localization Algorithm in Wireless Sensor Networks

Improved MDS-based Sensor Localization Algorithm in Wireless Sensor Networks

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