Genetic Algorithm Based Node Deployment in Hybrid Wireless Sensor Networks

Banimelhem, Omar; Mowafi, Moad; Aljoby, Walid

doi:10.4236/cn.2013.54034

Cited by 57 publications

(43 citation statements)

References 18 publications

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“…To evaluate whether the nodes are uniformly distributed over the deployment field an evaluation metric named pair correlation function was introduced in [9]. The Genetic Algorithm (GA) was introduced as a solution for coverage holes problem in WSN [10]. This approach found the optimal positions and the number of mobile nodes that have to be added to the initial deployment schema.…”

Section: Literature Surveymentioning

confidence: 99%

An Efficient Deployment Approach for Improved Coverage in Wireless Sensor Networks Based on Flower Pollination Algorithm

Hajjej¹,

Ejbali

Zaied

2016

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Section: Literature Surveymentioning

confidence: 99%

An Efficient Deployment Approach for Improved Coverage in Wireless Sensor Networks Based on Flower Pollination Algorithm

Hajjej¹,

Ejbali

Zaied

2016

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…Several recent works proposed scalable optimization algorithms for efficient deployment of WSN nodes. In [8], Banimelhem et al propose a genetic algorithm (GA) to solve the problem of coverage holes and deterministic 2D deployment in WSNs. The goal is to minimize the number of mobile nodes.…”

Section: Related Work On the 2d-3d Deployment Problem Using Optimizamentioning

confidence: 99%

A new multi-agent particle swarm algorithm based on birds accents for the 3D indoor deployment problem

et al. 2019

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h i g h l i g h t s• Proving the efficiency of optimization algorithms in solving real-world problems.• A new concept of accent birds introduced to the particle swarm optimization. • A new hybrid scheme that integrates PSO and MAS.• The hybridization improves the performance of the original tested algorithms. • A comparison between simulation and experimental validation is given. a r t i c l e i n f o Keywords: Accent based PSO Multi-agent Many-objective optimization Experimental validation 3D indoor deployment DL-IoT collection networks a b s t r a c tThe 3D indoor deployment of sensor nodes is a complex real world problem, proven to be NP-hard and difficult to resolve using classical methods. In this context, we propose a hybrid approach relying on a novel bird's accent-based many objective particle swarm optimization algorithm (named acMaPSO) to resolve the problem of 3D indoor deployment on the Internet of Things collection networks. The new concept of bird's accent is presented to assess the search ability of particles in their local areas. To conserve the diversity of the population during searching, particles are separated into different accent groups by their regional habitation and are classified into different categories of birds/particles in each cluster according to their common manner of singing. A particle in an accent-group can select other particles as its neighbors from its group or from other groups (which sing differently) if the selected particles have the same expertise in singing or are less experienced compared to this particle. To allow the search escaping from local optima, the most expert particles (parents) ''die'' and are regularly replaced by a novice (newborn) randomly generated ones. Moreover, the hybridization of the proposed acMaPSO algorithm with multi-agent systems is suggested. The new variant (named acMaMaPSO) takes advantage of the distribution and interactivity of particle agents. Experimental, numerical and statistical found results show the effectiveness of the two proposed variants compared to different other recent state-of-the-art of many-objective evolutionary algorithms.✩ This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. * Correspondence to: bureau C107, Batiment C, 1 Place Georges positions and the topology of the network. In this study, we are interested in the deployment of nodes in a three-dimensional space that reflects the real topology of the RoI (region of interest) better than the two-dimensional case. We are particularly interested in improving the initial 3D indoor deployment by adding new nodes while optimizing a set of objectives such as coverage, connectivity, localization, quality of links and network utilization. These objectives will be detailed in the modeling section. To resolve the deployment problem in WSN, the topology of the network can be modeled as an identification problem in a graph as addressed in [1] and [2]. In this static case, the deployment algorithm is run off-...

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“…The binary sensing model assumes that if an event occurs at a distance less than or equal to sensing range, the sensor will deterministically detect this event. According to the binary sensor model, addressed in [18], the probability of sensing an event occurred at point P(x, y) by sensor s i is denoted by C xy s i ð Þ:…”

Section: Coverage Models In Wireless Sensor Networkmentioning

confidence: 99%

Coverage Aware Scheduling in Wireless Sensor Networks: An Optimal Placement Approach

Jamali

Hatami

2015

Wireless Pers Commun

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Area coverage is an important issue in wireless sensor networks, which reflects how well an area is monitored or tracked by sensors. But, since a sensor network has restricted energy resources, energy efficiency is vital for this area coverage. One of the most efficient solutions to provide energy-aware area coverage is scheduling. That is, without any assumption about sensors' locations, only a distributed and parallel scheduling method determines which sensors should be on and which ones should be off in each decision period. The ultimate objective is to maximize network lifetime and keeping a target level of area coverage. A major part of the algorithms proposed in this field, schedule a sensor node activity based on its neighbors' information. Such information includes the distances of a node from its neighbors, the number of its active neighbors, etc. Indeed, message exchange is essential in the implementation of these algorithms which causes to increased energy consumption. In this paper, we propose a distributed scheduling algorithm, at which, each node itself decides to make its sensor on or off based on its location information and the node density over the target area. For this purpose, we first compute the minimum number of nodes that are enough to cover the target area. Then we obtain the best locations for theses nodes. Based on these computed location the area is partitioned into some sub-area, each one coverable by only one sensor. Then in each sub-area, a local scheduling procedure schedules the activation order of sensor. Simulation results show that the proposed algorithm, called CAOP, can maximize the network lifetime while maintaining complete area coverage.

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Genetic Algorithm Based Node Deployment in Hybrid Wireless Sensor Networks

Cited by 57 publications

References 18 publications

An Efficient Deployment Approach for Improved Coverage in Wireless Sensor Networks Based on Flower Pollination Algorithm

An Efficient Deployment Approach for Improved Coverage in Wireless Sensor Networks Based on Flower Pollination Algorithm

A new multi-agent particle swarm algorithm based on birds accents for the 3D indoor deployment problem

Coverage Aware Scheduling in Wireless Sensor Networks: An Optimal Placement Approach

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