A Novel Range Free Localization Algorithm in Wireless Sensor Networks Based on Connectivity and Genetic Algorithms

Najeh, Tawfik; Sassi, Hichem; Liouane, Noureddine

doi:10.1007/s10776-017-0375-y

Cited by 47 publications

(28 citation statements)

References 14 publications

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“…There is a clear trade-off between range-based and range-free techniques with respect to positioning accuracy and implementation cost. [24][25][26] So, to balance this trade-off, the node localization is defined as a least square problem and optimized using several metaheuristic techniques. Differential evolution algorithm (DEA) proposed by Storn 27 is applied for localization in Harikrishnan et al and Annepu and Rajesh.…”

Section: Relative Workmentioning

confidence: 99%

Implementation of an efficient extreme learning machine for node localization in unmanned aerial vehicle assisted wireless sensor networks

Annepu

Anbazhagan

2019

Int J Communication

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Accurate node localization in wireless sensor networks (WSNs) is an essential for many networking protocols like clustering, routing, and network map building. The classical localization techniques such as multilateration and optimization-based least square localization (OLSL) techniques estimate position of unknown node (UN) from the distance measured between all anchor nodes (ANs) and UNs. On the other hand, node localization using fixed terrestrial ANs suffers from poor localization accuracy because the ground to ground (GG) channel link is not reliable. By contrast, the mobile anchor deployed in unmanned aerial vehicle (UAV) provides high localization accuracy through reliable air to ground (AG) channel link. Still, the nonlinear distortion introduced in the wireless channel makes the distance measurement noisy. This noisy distance measurement also limits localization accuracy of classical localization techniques. Hence, the highly nonlinear artificial neural network (ANN) models such as multilayer perceptron (MLP) models can be applied effectively for node localization in UAV-assisted WSNs. However, the MLP suffers from slow training speed, which limits its usage in real-time applications.So, the extreme learning machine (ELM) is found to be a better alternative because it works on empirical error minimization theory, and its learning process requires only a single iteration. The detailed simulation analysis supports the proposed ELM localization scheme in terms of both localization accuracy and computational complexity.

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Section: Relative Workmentioning

confidence: 99%

Implementation of an efficient extreme learning machine for node localization in unmanned aerial vehicle assisted wireless sensor networks

Annepu

Anbazhagan

2019

Int J Communication

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“…After obtaining range information between an unknown node and its neighbour reference nodes, equations about the location of this unknown node can be gained, and they can be solved by an optimization method, such as maximum likelihood [17], least-square calibration [18], second-order cone programming [19], etc. Because evolutionary algorithms require less computational efforts than traditional optimization algorithms, which are more suitable for implementation on individual sensor node [20], they have been pervasively applied to localization problems, such as artificial neural network [21], genetic algorithm [22], and fuzzy logic and extreme learning machines [23].…”

Section: Related Workmentioning

confidence: 99%

“…Because our algorithm is range-based, anchor-based, and distributed, we compare it with the algorithms of same type, which are iterative multilateration, DV-Distance [9], SPSObased localization [21,22], HPSO [23], and hybrid PSO [24].…”

Section: Performance Analysis and Comparisonsmentioning

confidence: 99%

Localization of Large‐Scale Wireless Sensor Networks Using Niching Particle Swarm Optimization and Reliable Anchor Selection

Cui

Liang

Zhou

et al. 2018

Wireless Communications and Mobile Computing

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Due to uneven deployment of anchor nodes in large-scale wireless sensor networks, localization performance is seriously affected by two problems. The first is that some unknown nodes lack enough noncollinear neighbouring anchors to localize themselves accurately. The second is that some unknown nodes have many neighbouring anchors to bring great computing burden during localization. This paper proposes a localization algorithm which combined niching particle swarm optimization and reliable reference node selection in order to solve these problems. For the first problem, the proposed algorithm selects the most reliable neighbouring localized nodes as the reference in localization and using niching idea to cope with localization ambiguity problem resulting from collinear anchors. For the second problem, the algorithm utilizes three criteria to choose a minimum set of reliable neighbouring anchors to localize an unknown node. Three criteria are given to choose reliable neighbouring anchors or localized nodes when localizing an unknown node, including distance, angle, and localization precision. The proposed algorithm has been compared with some existing range-based and distributed algorithms, and the results show that the proposed algorithm achieves higher localization accuracy with less time complexity than the current PSO-based localization algorithms and performs well for wireless sensor networks with coverage holes.

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“…Range‐free localization technique uses distance approximation algorithm to find the target location. This technique reduces the cost because it does not require additional hardware . Range‐free method is further divided into six types: centroid algorithm, amorphous, DV‐hop, multidimensional scaling (MDS), and approximate point in triangulation (APIT) .…”

Section: Introductionmentioning

confidence: 99%

Multiobjective optimization‐based DV‐hop localization using NSGA‐II algorithm for wireless sensor networks

Kanwar

Kumar

2020

Int J Communication

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Summary Localization is one of the important requirements in wireless sensor networks for tracking and analyzing the sensor nodes. It helps in identifying the geographical area where an event occurred. The event information without its position information has no meaning. In range‐free localization techniques, DV‐hop is one of the main algorithm which estimates the position of nodes using distance vector algorithm. In this paper, a multiobjective DV‐hop localization based Non‐Sorting Genetic Algorithm‐II (NSGA‐II) is proposed in WSNs. Here, we consider six different single‐objective functions to make three multiobjective functions as the combination of two each. Localization techniques based on proposed multiobjective functions has been evaluated on the basis of average localization error and localization error variance. Simulation results demonstrate that the localization scheme based on proposed multiobjective functions can achieve good accuracy and efficiency as compared to state‐of‐the‐art single‐ and multiobjective GA DV‐hop localization scheme.

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A Novel Range Free Localization Algorithm in Wireless Sensor Networks Based on Connectivity and Genetic Algorithms

Cited by 47 publications

References 14 publications

Implementation of an efficient extreme learning machine for node localization in unmanned aerial vehicle assisted wireless sensor networks

Implementation of an efficient extreme learning machine for node localization in unmanned aerial vehicle assisted wireless sensor networks

Localization of Large‐Scale Wireless Sensor Networks Using Niching Particle Swarm Optimization and Reliable Anchor Selection

Multiobjective optimization‐based DV‐hop localization using NSGA‐II algorithm for wireless sensor networks

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