A Node Localization Approach Using Particle Swarm Optimization in Wireless Sensor Networks

Zhang, Xihai; Wang, Tianjian; Fang, Junlong

doi:10.1109/iiki.2014.25

Cited by 17 publications

(7 citation statements)

References 16 publications

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“…For instance, [113] discusses localization in a WSN for different applications. In the same context, authors in [114] implemented their algorithm by using particle swarm optimization (PSO) and promoted the path planning strategy based on a grid scan.…”

Section: E Localizationmentioning

confidence: 99%

“…For example, Reference [120] proposes a disaster-time system that makes use of a message ferry method to collect and exchange information while improving DTNs' energy efficiency; in particular, the authors introduced a method that allows to relay the messages of mobile terminals with low battery level to the terminals with higher battery level, avoiding the former to fully discharge. Inspired by biological networks of living organisms, the authors in [121] introduced an energy-efficient 2013 WSNs, algorithms [110] 2014 PSO, path planning strategy [114] 2015 Satellite stereo images [111] Radio channel, rescue [112] 2016 Fuzzy logic, range-free localization, WSNs [117] 2017 Vibration damage, WSN [118] 2019 Range-free localization, taxonomy [116] 2020 Gauss-Newton algorithm, accuracy, WSNs [113] Directional source, 3-D WSNs [119] disaster response network (DRN), called Bio-DRN, modeled via an integer linear programming optimization problem. In the same work, the Bio-DRN was developed by means of a sub-optimal heuristics and tested via simulation considering a real disaster-prone region in Nepal.…”

Section: F Energy Efficiencymentioning

confidence: 99%

See 1 more Smart Citation

Post-Disaster Communications: Enabling Technologies, Architectures, and Open Challenges

Matracia¹,

Saeed²,

Kishk³

et al. 2022

Preprint

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The number of disasters has increased over the past decade where these calamities significantly affect the functionality of communication networks. In the context of 6G, airborne and spaceborne networks offer hope in disaster recovery to serve the underserved and to be resilient in calamities. Therefore, this paper surveys the state-of-the-art literature on post-disaster wireless communication networks and provides insights for the future establishment of such networks. In particular, we first give an overview of the works investigating the general procedures and strategies for counteracting any large-scale disasters. Then, we present the possible technological solutions for post-disaster communications, such as the recovery of the terrestrial infrastructure, installing aerial networks, and using spaceborne networks.Afterward, we shed light on the technological aspects of post-disaster networks, primarily the physical and networking issues. We present the literature on channel modeling, coverage and capacity, radio resource management, localization, and energy efficiency in the physical layer and discuss the integrated space-air-ground architectures, routing, delay-tolerant/software-defined networks, and edge computing in the networking layer. This paper also presents interesting simulation results which can provide practical guidelines about the deployment of ad hoc network architectures in emergency scenarios. Finally, we present several promising research directions, namely backhauling, placement optimization of aerial base stations, and the mobility-related aspects that come into play when deploying aerial networks, such as planning their trajectories and the consequent handovers.Maurilio Matracia and Mohamed-Slim Alouini are with the Computer, Electrical, and Mathematical Sciences and Engineering

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Section: E Localizationmentioning

confidence: 99%

Section: F Energy Efficiencymentioning

confidence: 99%

Post-Disaster Communications: Enabling Technologies, Architectures, and Open Challenges

Matracia¹,

Saeed²,

Kishk³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In formula (18), the amount of computation of the matrix is required. It supposes that the number of anchor nodes is anc n , and the matrix A is a ( 1 anc n  ) rows and 2 columns, then T A is a matrix of 2 rows and ( 1…”

Section: Optimization Average Hop Distance In Dv-hopmentioning

confidence: 99%

A New Location Sensing Algorithm Based on DV-Hop and Quantum-Behaved Particle Swarm Optimization in WSN

Zhang

Hai

2021

ASP Trans. Pattern Recognit. Intell. Syst.

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In wireless sensor network, the location sensing of the sensor nodes is practical. If there is no location information of the sensor nodes, the perceived data would have no meaning. In recent years, the range-free location sensing algorithms have got great attention. DV-Hop localization algorithm is one of the important algorithm in range-free location algorithms. It has high efficiency, convenient operation and low energy consumption. However, the localization accuracy cannot meet the requirements in some applications. In this paper, a new localization method is proposed, which is based on DV-Hop and Quantum-behaved Particle Swarm Optimization (QPSO) algorithm. First, it deals with the high influence of average single jumping distance and then modifies the calculation of it in the DV-Hop algorithm. Second, in order to solve the problem of the coordinate optimization in the DV-Hop algorithm, this study chooses QPSO algorithm to optimize the unknown nodes’ coordinates. Simulation results show that the new method can improve the localization accuracy of the unknown nodes obviously in WSN.

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“…Besides two-dimensional sensor networks, WPSO is also applied to localize three-dimensional WSNs [27,31,32] and underwater WSNs [33]. Different from the above algorithms, a mobile anchor-assisted WPSO-based localization algorithm is proposed in [20,34], which only uses one mobile anchor to provide distance range to all unknown nodes while it traverses the sensor network. Besides localization, [35] also applies WPSO to the real-time autonomous deployment of sensor nodes (including anchors and unknown nodes) from an unmanned aerial vehicle.…”

Section: A Survey Of Pso-based Localization Algorithmsmentioning

confidence: 99%

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

Cui

Shu

Song

et al. 2017

Sensors

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Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

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A Node Localization Approach Using Particle Swarm Optimization in Wireless Sensor Networks

Cited by 17 publications

References 16 publications

Post-Disaster Communications: Enabling Technologies, Architectures, and Open Challenges

Post-Disaster Communications: Enabling Technologies, Architectures, and Open Challenges

A New Location Sensing Algorithm Based on DV-Hop and Quantum-Behaved Particle Swarm Optimization in WSN

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

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