Genetic Algorithm Application in Optimization of Wireless Sensor Networks

Norouzi, Ali; Zaim, Abdül Halim

doi:10.1155/2014/286575

Cited by 69 publications

(33 citation statements)

References 18 publications

(20 reference statements)

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“…Basically, GA is based on natural selection and the biological evolution. There have been some studies using GA to solve the coverage problem of optimal node deployment in [13], [14]. In [15] a GA based multiple objectives methodology was implemented for a self-organizing wireless sensor network.…”

Section: Related Workmentioning

confidence: 99%

Covering the Target Objects with Mobile Sensors by Using Genetic Algorithm in Wireless Sensor Networks

Ta¹,

Huang²,

Bình³

2015

JCP

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Wireless sensor networks have several applications, such as target detection and tracking, and monitoring battlefields. Coverage is one of the most important performance metrics for wireless sensor networks since it reflects how appropriate an event can be detected and monitored in the sensing field. To achieve optimal coverage, an efficient algorithm should be employed to find the best positions of sensor node deployment. In this paper, an efficient genetic algorithm is proposed to solve the coverage problem of the target objects. The optimal number of sensor nodes starts from fewer randomly deployed nodes and increases gradually in subsequence generations. The performance of the proposed genetic algorithm was evaluated, and the simulation results show that this approach can cover all the target objects as well as minimize the number of additional mobile sensor nodes.

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Section: Related Workmentioning

confidence: 99%

Covering the Target Objects with Mobile Sensors by Using Genetic Algorithm in Wireless Sensor Networks

Ta¹,

Huang²,

Bình³

2015

JCP

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“…A Genetic Algorithm (GA) [6] is used to generate energy-efficient hierarchical clusters. The base station broadcasts the GA-based clusters configuration, which is received by the sensor nodes and the network is configured accordingly.GA also known as a global heuristic algorithm.…”

Section: Steady State Phasementioning

confidence: 99%

A Survey on Cluster Based Energy Efficient Routing Protocols

2015

Icrbit - 2015

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Abstract-Wireless sensor networks (WSNs) have been mainly deployed in remote areas for usually environment monitoring purposes, military application, infrastructure monitoring etc. It is very difficult to maintain or repair sensor nodes every now and then in these environment. Moreover, sensor nodes have limited resources. Hence it's very necessary to find an approach that is very effective in routing data among the nodes and preserve the lifetime of the network. So in the course of time this document presents a survey on clustering technique designed to preserve energy as well as route in an energy efficient manner within wireless sensor networks. Before routing is done, a clustering algorithm is applied to the node locations to aggregate nodes into cluster whose diameter is comparable to the node communication radius. Each cluster elects a cluster-head node, and routing is done only among the cluster-heads. This is advantageous for a variety of reasons, including the possibility of using simpler communication protocols within a cluster, recycling of resources among disjoint clusters, saving power and reducing the number of nodes from participating in routing. All these advantage will lead to distribute the load evenly among the nodes in network and thereby avoiding drainage of energy of any node that becomes the hot-spot while routing.

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“…For instance, the 2-Opt [6] algorithm improves the current solution by replacing two edges with two new ones to reduce the tour length. Other examples are given by Tabu [7], [8] and Genetic [9], [10] algorithms. In this paper, we will compare the solutions provided by the 2-Opt algorithm, a Genetic algorithm and an Hybridation of these last two algorithms, with the optimal one for various configurations.…”

Section: Related Workmentioning

confidence: 99%

Optimized trajectory of a robot deploying wireless sensor nodes

Khoufi

Livolant

Minet

et al. 2014

2014 IFIP Wireless Days (WD)

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Mobile robots can be used to deploy static wireless sensor nodes to achieve the coverage and connectivity requirements of the applications considered. Many solutions have been provided in the literature to compute the set of locations where the sensor nodes should be placed. In this paper, we show how this set of locations can be used by a mobile robot to optimize its tour to deploy the sensor nodes to their right locations. In order to reduce both the energy consumed by the robot, its exposure time to a hostile environment, as well as the time at which the wireless network becomes operational, the optimal tour of the robot is this minimizing the delay. This delay must take into account not only the time needed by the robot to travel the tour distance but also the time spent in the rotations performed by the robot each time it changes its direction. This problem is called the Robot Deploying Sensor nodes problem, in short RDS. We first show how this problem differs from the well-known traveling salesman problem. We then propose an integer linear program formulation of the RDS problem. We propose various algorithms relevant to iterative improvement by exchanging tour edges, genetic approach and hybridization. The solutions provided by these algorithms are compared and their closeness to the optimal is evaluated in various configurations.

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Genetic Algorithm Application in Optimization of Wireless Sensor Networks

Cited by 69 publications

References 18 publications

Covering the Target Objects with Mobile Sensors by Using Genetic Algorithm in Wireless Sensor Networks

Covering the Target Objects with Mobile Sensors by Using Genetic Algorithm in Wireless Sensor Networks

A Survey on Cluster Based Energy Efficient Routing Protocols

Optimized trajectory of a robot deploying wireless sensor nodes

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