Obstacle‐resistant hybrid localisation algorithm

Kargar, Amin; Mahani, Ali

doi:10.1049/iet-wss.2020.0052

Cited by 5 publications

(2 citation statements)

References 41 publications

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“…From the experimental results, the research focus of the above localization algorithm is mainly on how to improve localization accuracy, thus consuming too much energy. However, the firefly optimized localization algorithm (FA) [ 29 ], the K-value common line and gray wolf optimized localization algorithm (DCK-GWO) [ 30 ], the quantum optimized localization algorithm (QA) [ 31 ], and the anti-barrier hybrid localization algorithm (D-PSO and D-C) [ 32 ] have been studied in terms of energy consumption and are able to save more energy while obtaining higher accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Quantum Annealing Bat Algorithm for Node Localization in Wireless Sensor Networks

Zhu

2023

Sensors

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Node localization in two-dimensional (2D) and three-dimensional (3D) space for wireless sensor networks (WSNs) remains a hot research topic. To improve the localization accuracy and applicability, we first propose a quantum annealing bat algorithm (QABA) for node localization in WSNs. QABA incorporates quantum evolution and annealing strategy into the framework of the bat algorithm to improve local and global search capabilities, achieve search balance with the aid of tournament and natural selection, and finally converge to the best optimized value. Additionally, we use trilateral localization and geometric feature principles to design 2D (QABA-2D) and 3D (QABA-3D) node localization algorithms optimized with QABA, respectively. Simulation results show that, compared with other heuristic algorithms, the convergence speed and solution accuracy of QABA are greatly improved, with the highest average error of QABA-2D reduced by 90.35% and the lowest by 17.22%, and the highest average error of QABA-3D reduced by 75.26% and the lowest by 7.79%.

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Section: Related Workmentioning

confidence: 99%

A Quantum Annealing Bat Algorithm for Node Localization in Wireless Sensor Networks

Zhu

2023

Sensors

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“…The location computations of TNs based on the degree of collinearity and GWO algorithm can reduce the number of iterations and energy consumptions. The combination of GWO-FA algorithms addresses the anisotropic properties of SNs in finding the location coordinates using a single AN and multiple virtual ANs [23]. An improved version of WOA has clustering intelligence to optimize the node localization process and enhance the positioning accuracy compared to RSSI based methods and other swarm intelligence algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Gaussian-newton Localization Through Multilateration Algorithm for Wireless Sensor Networks

Reddy

Chandra²

2023

Preprint

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With the continuous prevalence of wireless sensor network (WSN) applications in the recent days, localization of sensor nodes became an important aspect in research in terms of its accuracy, communication overhead and computational complexity. Localization plays an important role in location sensitive applications like object tracking, nuclear attacks, biological attacks, fire detection, traffic monitoring systems, intruder detections, and finding survivors in post-disasters, etc. The objective of localization is to identify the coordinates of target nodes using information provided by anchor nodes. Precision improvement of the sensor node positions is a key issue for an effective data transmission between sensor nodes and save the node’s energy as well as enhance the network lifetime. In this article, a cost-effective localization algorithm with minimal number of anchor nodes is proposed that uses nature inspired optimization techniques to enhance the localization accuracy compared to the state-of-the-art localization algorithms. The performance metrics considered for simulations and comparison with the existing algorithms include average localization accuracy, communication range, and the number of anchor nodes. The simulation results prove that the proposed gaussian-newton localization through multilateration algorithm (GNLMA) enhances the mean localization accuracy to 92.8% and the range measurement error is limited to 1.22meters. Depending on the communication range of sensor nodes, the average localization accuracy is achieved up to 94.4% using the proposed GNLMA.

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Bluetooth low energy indoor localization for large industrial areas and limited infrastructure

Szyc

Nikodem²,

Zdunek³

2023

Ad Hoc Networks

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Obstacle‐resistant hybrid localisation algorithm

Cited by 5 publications

References 41 publications

A Quantum Annealing Bat Algorithm for Node Localization in Wireless Sensor Networks

A Quantum Annealing Bat Algorithm for Node Localization in Wireless Sensor Networks

Gaussian-newton Localization Through Multilateration Algorithm for Wireless Sensor Networks

Bluetooth low energy indoor localization for large industrial areas and limited infrastructure

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