Relocation of wireless sensor network nodes using a genetic algorithm

Qu, Yipeng; Georgakopoulos, Stavros V.

doi:10.1109/wamicon.2011.5872882

Cited by 29 publications

(32 citation statements)

References 11 publications

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“…Besides, a force-based genetic algorithm was introduced to utilize the sum of the forces used by the neighbors to choose the direction of the mobile nodes [19]. Also, a multi-objective genetic algorithm running on a base station to determine where the mobile nodes can move to maximize the coverage and minimize the travelled distance [20]. While, a cluster-based WSN has used the genetic algorithm to find the best positions for the cluster heads that cover maximum number of nodes and so maximizing the area coverage [21].…”

Section: Related Workmentioning

confidence: 99%

Optimizing the Routing of Wireless Sensor Networks for Obstacles-avoidance

Mohamed¹,

Nassar²

2017

IJCA

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In the recent years, wireless sensor networks (WSNs), have become essential part in a huge number of the modern applications. Researchers have developed a lot of work to improve their performance. But, practically WSNs still face with different kinds of obstacles those cause main challenges for their reliability. Therefore, finding an optimum obstacleavoiding route path for the WSNs is considered an important research problem. The present work introduces a new optimum routing algorithm based on the cluster-based method for the WSNs with obstacles. The proposed system uses the cluster-based method and the mobile sink to decrease the power consumptions and increase the lifetime of the WSNs. Besides, it uses the genetic algorithm to optimize the avoiding-obstacles routing path. Suggested system has been applied for a WSN used to communicate between a discoveryradiation robot and its operating system as a case of study. Simulation results for the tested WSN and their comparison with three other route algorithms have proved the effectiveness of the proposed novel method.

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Section: Related Workmentioning

confidence: 99%

Optimizing the Routing of Wireless Sensor Networks for Obstacles-avoidance

Mohamed¹,

Nassar²

2017

IJCA

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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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“…Genetic Algorithms (GA) [6] are heuristic algorithms that mimic the process of natural selection. Starting from an initial population (called chromosomes), new generations are produced which hopefully contain better chromosomes than the previous generation.…”

Section: Related Workmentioning

confidence: 99%

Efficient multi-objective optimization of wireless network problems on wireless testbeds

Mehari

Poorter

Couckuyt

et al. 2014

10th International Conference on Network and Service Management (CNSM) and Workshop

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Abstract-A large amount of research focuses on experimentally optimizing performance of wireless solutions. Finding the optimal performance settings typically requires investigating all possible combinations of design parameters, while the number of required experiments increases exponentially for each considered design parameter. The aim of this paper is to analyze the applicability of global optimization techniques to reduce the optimization time of wireless experimentation. In particular, the paper applies the Efficient Global Optimization (EGO) algorithm implemented in the SUrrogate MOdeling (SUMO) toolbox inside a wireless testbed. The proposed techniques are implemented and evaluated in a wireless testbed using a realistic wireless conference network problem. The performance accuracy and experimentation time of an exhaustively searched experiment is compared against a SUMO optimized experiment. In our proof of concept, the proposed SUMO optimizer reaches 99.51% of the global optimum performance while requiring 10 times less experiments compared to the exhaustive search experiment.

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Relocation of wireless sensor network nodes using a genetic algorithm

Cited by 29 publications

References 11 publications

Optimizing the Routing of Wireless Sensor Networks for Obstacles-avoidance

Optimizing the Routing of Wireless Sensor Networks for Obstacles-avoidance

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

Efficient multi-objective optimization of wireless network problems on wireless testbeds

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