A reward based method to wireless sensor network clustering

Zamini, Majid Samad; Zare, Leila

doi:10.1109/icaict.2009.5372614

Cited by 1 publication

(1 citation statement)

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“…The optimal CH selection can reduce the total energy consumption and balance the load. According to the residual energy parameters of CHs and the number of members in the previous process, a reward-based clustering algorithm was proposed to periodically select CHs by learning methods [13]. Under the consideration of energy saving, a CH selection algorithm was proposed to select CHs by the density of member nodes and the distance from CHs, which is more energy efficient than only considering the distance between nodes [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Sensor Association and Data Collection in Power Materials Warehouse Based on Internet of Things

Zhong

et al. 2021

Energies

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In order to realize the intelligent management of a power materials warehouse, the Internet of Things based on wireless sensor networks (WSNs) is a promising effective solution. Considering the limited battery capacity of sensor nodes, the optimization of the topology control and the determination of the amount of collected data are critical for prolonging the survival time of WSNs and increasing the satisfaction of the warehouse supplier. Therefore, in this paper, an optimization problem on sensor association and acquisition data satisfaction is proposed, and the subproblem of the sensor association is modeled as the knapsack problem. To cope with it, the block coordinate descent method is used to obtain the suboptimal solution. A sensor association scheme based on the ant colony algorithm (ACO) is proposed, and the upper and lower bounds of this optimization problem are also obtained. After this, a cluster head selection algorithm is given to find the optimal cluster head. Finally, the experimental simulations show that the algorithms proposed in this paper can effectively improve the energy utilization of WSNs to ensure the intelligent management of a power materials warehouse.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal Sensor Association and Data Collection in Power Materials Warehouse Based on Internet of Things

Zhong

et al. 2021

Energies

View full text Add to dashboard Cite

show abstract

A reward based method to wireless sensor network clustering

Cited by 1 publication

References 23 publications

Optimal Sensor Association and Data Collection in Power Materials Warehouse Based on Internet of Things

Optimal Sensor Association and Data Collection in Power Materials Warehouse Based on Internet of Things

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