Citation: Wang, X.; Wu, H.; Miao, Y.;
Zhu, H. A Hybrid Routing Protocol
Based on Naïve Bayes and Improved
Particle Swarm Optimization
Algorithms. Electronics 2022, 11, 869.
https://doi.org/10.3390/
electronics11060869
Academic Editors: Martin Reisslein
and Carlos Andrés García-Vázquez
Received: 6 February 2022
Accepted: 6 March 2022
Published: 9 March 2022
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Article
A Hybrid Routing Protocol Based on Naïve Bayes and
Improved Particle Swarm Optimization Algorithms
Xun Wang
1
, Huarui Wu
2,3
, Yisheng Miao
2,3
and Huaji Zhu
2,3,
*
1
School of Information Science and Engineering, Southeast University, Nanjing 211189, China;
xun_wang@seu.edu.cn
2
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China;
wuhr007@nercita.org.cn (H.W.); miaoys007@nercita.org.cn (Y.M.)
3
Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences,
Beijing 100097, China
* Correspondence: zhuhj007@nercita.org.cn; Tel.: +86-01-5150-3619
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.
Keywords:
wireless sensor network; routing protocol; clustering; energy consumption optimization;
channel model; naïve Bayes
1. Introduction
A wireless sensor network (WSN) is a multi-hop self-organizing network composed
of a large number of microsensors with a dynamic topology. Data gathering and trans-
mission are the main goals of WSNs [
1
]. By integrating different types of sensors, such as
temperature, humidity, and pressure sensors, for data acquisition and monitoring, WSNs
can be used in medical treatments, industry, environmental protection, agriculture, etc. [
2
].
Because WSN nodes are powered by a battery, energy-efficient utilization is the key com-
ponent of WSN design and optimization [
3
,
4
]. On the other hand, due to the complex
occlusion and other characteristics of the network’s application environment, the data
transmission process between WSN nodes is vulnerable to interference and the influence of
the surrounding environment, and the link characteristic is a probabilistic connectivity state
of time connection and time disconnection. In recent years, many researchers have carried
out energy consumption optimization research on routing path selection in wireless sensor
networks. Cluster head multi-hop hybrid routing based on a cluster structure is a current
research hotspot [
5
]. In this hybrid routing structure, the nodes are first clustered locally,
then the data collected by the sensing nodes are collected by the cluster head, and finally the
data are returned to the base station (BS) through multiple hops between cluster heads [
6
].
The advantage of the hybrid routing structure is that it can simplify the network’s structure
Electronics 2022, 11, 869. https://doi.org/10.3390/electronics11060869 https://www.mdpi.com/journal/electronics
