Localization Research Based on Improved Simulated Annealing Algorithm in WSN

Yu-zeng, LI; Xing, Jianjun; Yang, Qiaohe; Hui-chang, Shi

doi:10.1109/wicom.2009.5301593

Cited by 11 publications

(2 citation statements)

References 3 publications

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“…This document focuses on examining past improvement strategies under a multi-objective position leading to compensation. We exclude known optimization methods [24][25][26][27][28][29][30][31][32] and examine great, accurate measurement improvements based on link disruption, load balancing, and distance.…”

Section: Energy Efficient Optimization With Load Balancingmentioning

confidence: 99%

Survey on wireless sensor networks: energy efficient optimization routing algorithms

Sivasankarareddy¹,

Sundari²

2020

IJEECS

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Wireless Sensor Network is extensively utilized in numerous places, such as protection surveillance. In Wireless Sensor Network, sensing unit networks are particular arbitrarily in addition to likewise in-network relying upon the technique is used to extend the network. As sensing unit nodes make use of strength from batteries for noticing the facts in addition to forwarding data, it uses the capability for those answers. The sizable troubles in cordless networks include power optimization, protection, directing, and project type. In this paper, current procedures in escaping power utilization of Wireless Sensor Network in addition to distinctive protocols and also Methods are researched. Additionally, destiny research have a look at on strength efficiency in Wireless Sensor Network putting forward new terms as well as targets for in addition examination is mentioned. Depiction of optimizing strategies like particle swarm optimization set of rules as well as ant swarm optimization Formula is already possible for lowering the electricity loss and complements the life of sensor community but those strategies take in greater time. This paper gives surveying extraordinary different optimization techniques below the multi-objective facet that takes region in tradeoffs. Information extracting in sensing unit networks is the technique of obtaining software-enabled plans in addition to patterns with gratifying accuracy from a constant, speedy, in addition to probable non-ended flow of facts streams from sensor networks. Various boundaries in preceding optimization Algorithms and suggesting a great deal better treatment through applying Data extracting Strategies for Wireless Sensing unit Networks is carried out.

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Section: Energy Efficient Optimization With Load Balancingmentioning

confidence: 99%

Survey on wireless sensor networks: energy efficient optimization routing algorithms

Sivasankarareddy¹,

Sundari²

2020

IJEECS

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“…A few GA-based node localization algorithms are presented in [19,20,21], that estimate optimal node locations of all onehop neighbors. Simulated Annealing Algorithm (SAA) and GA based two phase centralized localization scheme is presented in [22]. PSO-based algorithm is proposed in [23,24] to minimize the localization error.…”

Section: Literature Surveymentioning

confidence: 99%

Stochastic Algorithms for 3D Node Localization in Anisotropic Wireless Sensor Networks

Kumar

Khosla

Saini

et al. 2012

Advances in Intelligent Systems and Computing

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This paper proposes two range based 3D node localization algorithms using application of Hybrid Particle Swarm Optimization (HPSO) and Biogeography Based Optimization (BBO) for anisotropic Wireless Sensor Networks (WSNs). Target nodes and anchor nodes are randomly deployed with constraints over three layer boundaries. The anchor nodes are randomly distributed over top layer only and target nodes over middle and bottom layers. Radio irregularity factor, i.e., an anisotropic property of propagation media and an heterogenous property (different battery backup statuses) of devices are considered. PSO models provide fast but less mature convergence whereas the proposed HPSO algorithm provides fast and mature convergence. Biogeography is based upon the collective learning of geographical allotment of biological organisms. BBO has a new comprehensive energy based on the science of biogeography and apply migration operator to share selective information between different habitats, i.e., problem solutions. Due to size and complexity of WSN, localization problem is articulated as an NP-hard optimization problem . In this work, an error model in a highly noisy environment is depicted for estimation of optimal node location to minimize the location error using HPSO and BBO algorithms. The simulation results establish the strength of the proposed algorithms by equating the performance in terms of the number of target nodes localized with accuracy, and computation time. It has been observed that existing sensor networks localization algorithms are not significant to support the rescue operations Anil Kumar Panipat Institute of Engg. and Technoly, Panipat, Haryana, India e-mail: anil.rose@rediffmail.com Arun Khosla National Institute of Technology, Jalandhar, Punjab, India e-mail: khosla@nitj.ac.in Jasbir Singh Saini DCR Univ. of Sc. and Tech., Murthal, Sonepat, Haryana, India, e-mail: jssain@rediffmail.com Satvir Singh SBS State Technical Campus, Ferozpur, Punjab, India,e-mail: satvir15@gmail.com 1 involving human lives. Proposed algorithms are beneficial for rescue operations too to find out the accurate location of target nodes in highly noisy environment.

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