A New Method to Improve Energy Consumption in Wireless Camera Sensor Networks
Subject Areas : Multimedia Processing, Communications Systems, Intelligent Systemsjavad bayat 1 , Shiva Karimi 2
1 - MSc Student, Faculty of Electrical and Computer Engineering, Zanjan Branch, Islamic Azad University, Zanjan, Iran
2 - Assistant Professor, Faculty of Electrical and Computer Engineering, Zanjan Branch, Islamic Azad University, Zanjan, Iran
Keywords: Wireless Camera Sensor Networks, Harris Hawk Optimization Algorithm, energy consumption, Clustering, Genetic Algorithm,
Abstract :
Introduction: In the development of wireless camera sensor networks, there are unique challenges such as the need for high bandwidth, low latency for processing, high energy consumption, and real-time control. Each wireless camera sensor node is able to process image data locally and extract suitable data and cooperate with other cameras based on the desired application. In these networks, high bandwidth is demanded to transmit visual data, and high volume calculations in these networks must be possible with low power. In this article, a new model based on Harris‘s Hawk optimization algorithm is proposed to improve energy consumption in wireless camera sensor networks. The optimization algorithm of Harris‘s Hawk is one of the meta-heuristic algorithms that was invented in 2019.
Method: Harris‘s Hawk optimization algorithm was used to form optimal clustering. Each vector generated in Harris‘s Hawk optimization algorithm is calculated based on the fitness function and the most optimal vectors are selected for clustering. In the proposed model, factors such as intra-cluster distance and extra-cluster distance, and energy consumption have been considered.
Discussion: In wireless camera sensor networks, the imbalance of energy consumption among nodes is an effective factor in the network lifetime. In order to balance the energy consumption among nodes, clustering algorithms have been proposed for uniform energy distribution. In this paper, we proposed a new model for clustering camera sensor nodes based on Harris‘s Hawk optimization algorithm. In the proposed model, we paid attention to parameters such as intra-cluster distance, extra-cluster distance, and residual energy of sensor nodes. The cluster quality criterion is based on the intra-cluster distance, which depends on the position of the cluster head in the clusters. In the proposed model, because the distance criterion is taken into account and the distance of non-cluster nodes with the cluster head node is evaluated and the closest nodes to the cluster head are selected.
Results: Evaluations in the environment of 150×150 m2 and 300×300 m2 with a different number of nodes show that the proposed model has better efficiency compared to PADT and genetic algorithm (GA).
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