Genetic Algorithm–based Sensor Deployment With Area Priority

Kalayci, Tahir Emre; Uğur, Aybars

doi:10.1080/01969722.2011.634676

Cited by 33 publications

(19 citation statements)

References 7 publications

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“…This section presents the applied DSLC algorithm to the deployment optimization of WSN based on node coverage technique. The experimental results of the DSLC algorithm are compared with the SLC, GA [40], and FA [41] algorithms for the coverage of node layout in the deployment of WSN.…”

Section: Applied Dslc For Deployment Optimization In Wsnmentioning

confidence: 99%

Diversity Teams in Soccer League Competition Algorithm for Wireless Sensor Network Deployment Problem

et al. 2020

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The drawback of several metaheuristic algorithms is the dropped local optimal trap in the solution to complicated problems. The diversity team is one of the promising ways to enhance the exploration of searching solutions in algorithm to avoid the local optimum trap. This paper proposes a diversity-team soccer league competition algorithm (DSLC) based on updating team member strategies for global optimization and its applied optimization of Wireless sensor network (WSN) deployment. The updating team consists of trading, drafting, and combining strategies. The trading strategy considers player transactions between groups after the ending season. The drafting strategy takes advantage of draft principles in real leagues to bring new players to the association. The combining strategy is a hybrid policy of trading and drafting one. Twenty-one benchmark functions of CEC2017 are used to test the performance of the proposed algorithm. The experimental results of the proposed algorithm compared with the other algorithms in the literature show that the proposed algorithm outperforms the competitors in terms of having an excellent ability to achieve global optimization. Moreover, the proposed DSLC algorithm is applied to solve the problem of WSN deployment and achieved excellent results.

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Section: Applied Dslc For Deployment Optimization In Wsnmentioning

confidence: 99%

Diversity Teams in Soccer League Competition Algorithm for Wireless Sensor Network Deployment Problem

et al. 2020

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“…In the studied literature, we can deduce that Genetic Algorithm represents a good result to obtain the optimal solution for 3D indoor localization. The authors in [6] develop a Genetic Algorithm in order to obtain the optimized deployment of sensors in a particular context. The studied space in their paper is divided in parts and priorities area fixed for each part.…”

Section: Accuracymentioning

confidence: 99%

3d Indoor Localization Through a Wireless Acoustic sensor Networks

Nasri¹,

Rached²,

Chenini³

et al. 2018

PIER B

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GPS is well recognized as the best procedure for outdoor localization. However, it presents limits in indoor localization due to particular geometric difficulties that necessitate specific solution to locate a target inside a building. Also, radio frequency technologies have many disadvantages in indoor localization. Bluetooth and Radio Frequency Identification (RFID) are unsuitable for realtime localization because of latency. Ultra-wideband (UWB) localization needs an expensive hardware. Zigbee presents a high interference with wide range of signal frequency because it operates in unlicensed Industrial, Scientific and Medical ISM bands. Light waves also present some limitations due to interferences from fluorescent light and sunlight. The IR based indoor system has expensive system hardware and maintenance cost. To overcome limits and non-availability of radio waves and light waves, an acoustic solution using an array of microphones is presented as a solution for indoor localization, and an optimized deployment is used to improve precision and restrain error. The aim of this work is to propose a 3D indoor audio localization approach inspired by the principle of functioning of the human ear. In order to achieve our goal, we will use a genetic algorithm to obtain the optimized deployment of the used hardware.

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“…In [22], a simulated annealing algorithm is used for sensors placement within a grid to minimize the error of maximum distances between target monitored by the same sensors under the constraint of cost. Some genetic algorithms have also been used to cope with the sensors placement problem [3,[23][24][25][26][27][28][29][30]. In [30], a genetic algorithm is proposed to find the position and the minimum number of mobile sensors that has to be deployed to fill the holes of coverage generated by the random deployment of static sensors.…”

Section: Related Workmentioning

confidence: 99%

Optimization of sensor deployment using multi-objective evolutionary algorithms

Njoya

Dipandà

Tonyé

2016

J Reliable Intell Environ

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Many designs of wireless sensor network applications require the determination of the optimal locations of sensor nodes to be placed in a sensor field. Coverage enables us to evaluate the supervision quality of each point within an area of interest. In this paper, we address the problem of target coverage in wireless sensor networks. This concern is trivial if each target must be covered by a single sensor. However, it becomes an NP-complete problem when the choice of the position of the sensor must take into account the targets that it should cover in its vicinity. Using a multi-objective evolutionary-based approach, we propose a stochastic method to search for network configurations that achieve good coverage with the fewest sensors. A comparative experimental study of the model with wellknown multi-objective algorithms such as NSGA-II, SPEA2, SMSEMOA and MOEA/D indicate that NSGA-II performs better than others on most of the test instances.

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Genetic Algorithm–based Sensor Deployment With Area Priority

Cited by 33 publications

References 7 publications

Diversity Teams in Soccer League Competition Algorithm for Wireless Sensor Network Deployment Problem

Diversity Teams in Soccer League Competition Algorithm for Wireless Sensor Network Deployment Problem

3d Indoor Localization Through a Wireless Acoustic sensor Networks

Optimization of sensor deployment using multi-objective evolutionary algorithms

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