Abstract
Wireless sensor networks (WSNs) consist of a number of nodes and one or two base stations (BS). Each node has limited energy. Therefore, the energy each node is very important parameter in network since accessing the nodes and re-charging them are difficult or in some cases, are impossible. Thus, the main purpose of this article is to increase the lifetime of the wireless sensor networks by finding the optimal route to send the data to the base station in order to save the energy of each node. In this paper, a hybrid clustering method called Hybrid based on Bayesian Networks (HBN) is proposed based on Bayesian network which considers the radio range of each nodes. In this algorithm, four different parameters are considered including residual energy, the distance to the base station, distance to the neighbor nodes and the radio range of the sensor nodes. According to the simulation results, this algorithm enables an increase in network lifetime in comparison to other similar algorithms.
Highlights
Wireless Sensor Network are applied in various fields such as environment and wildlife surveillance in which accessing each node and recharging them are impossible [1]
This algorithm consists of four main parameters including residual energy, the distance to the base station and to the neighboring nodes and the radio range of the sensor nodes and utilizes a Bayesian probability
HEED [26] was proposed for Wireless sensor networks (WSNs) clustering and reducing energy consumption by considering two main parameters including residual energy and the communication cost
Summary
Wireless Sensor Network are applied in various fields such as environment and wildlife surveillance in which accessing each node and recharging them are impossible [1]. Some hybrid methods have been proposed to address the disadvantages of the static and dynamic methods For this purpose, the presented algorithm is based on a hybrid method in which clustering is performed in each round according to the dynamic protocols and the clusters remain the same during several rounds of clustering, similar to the static protocols. The presented algorithm is based on a hybrid method in which clustering is performed in each round according to the dynamic protocols and the clusters remain the same during several rounds of clustering, similar to the static protocols This algorithm consists of four main parameters including residual energy, the distance to the base station and to the neighboring nodes and the radio range of the sensor nodes and utilizes a Bayesian probability
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