Abstract
Clustering of sensor nodes is a prominent method applied to wireless sensor networks (WSNs). In a cluster-based WSN scenario, the sensor nodes are assembled to generate clusters. The sensor nodes also have limited battery power. Therefore, energy efficiency in WSNs is crucial. The load on the sensor node and its distance from the base station (BS) are the significant factors of energy consumption. Therefore, load balancing according to the transmission distance is necessary for WSNs. In this paper, we propose a hybrid routing algorithm based on Naïve Bayes and improved particle swarm optimization algorithms (HRA-NP). The cluster heads (CHs) are selected according to the CH conditional probability, which is estimated by the Naïve Bayes classifier. After the selection of the CHs, the multi-hop routing algorithm is applied to the CHs. The best routing path from each CH to the BS is obtained from an improved particle swarm optimization (PSO) algorithm. Simulations were conducted on evaluation factors such as energy consumption, active sensor nodes per round, the sustainability of the network, and the standard deviation of a load on the sensor node. It was observed that HRA-NP outperforms comparable algorithms, namely DUCF, ECRRS, and FC-RBAT, based on the evaluation factors.
Highlights
A wireless sensor network (WSN) is a multi-hop self-organizing network composed of a large number of microsensors with a dynamic topology
Because WSN nodes are powered by a battery, energy-efficient utilization is the key component of WSN design and optimization [3,4]
In this hybrid routing structure, the nodes are first clustered locally, the data collected by the sensing nodes are collected by the cluster head, and the data are returned to the base station (BS) through multiple hops between cluster heads [6]
Summary
A wireless sensor network (WSN) is a multi-hop self-organizing network composed of a large number of microsensors with a dynamic topology. Cluster head selection and multi-hop path selection between cluster heads are key to determining the overall energy consumption performance of the network [8]. The routing protocol proposed in this paper considers cluster head selection and multiple hops between cluster heads in unreliable link networks. A machine learning method is adopted in order to select the cluster head and the multi-hop path according to the node residual energy, link quality, and hops for the purpose of saving energy. Huang et al [15] proposed a k-center approximation algorithm to select the cluster head that optimizes the distance from the nodes in the cluster to the cluster head and, reduces and balances the energy consumption. Node Pj selects the cluster head with the smallest cost(j,i) to join, so as to ensure that the load is balanced between clusters
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