Citation :

S. Archana, V. Jayapradha, "Reliable Data Communication using Bayes Optimal Classifier in MWSN," International Journal of Electronics and Communication Engineering, vol. 13, no. 7, pp. 231-240, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I7P116

Abstract

Mobile Wireless Sensor Networks (MWSNs) are extensively used in mission-critical and dynamic applications such as intelligent transportation systems, disaster management, and environmental monitoring. Though abnormal sensor node behaviour, such as malicious or selfish sensor nodes, may extremely degrade Quality of Service (QoS) in MWSNs due to sensor node mobility, energy limitations, and unpredictable climatic conditions. To enhance network reliability and QoS in MWSNs, this work introduces a Reliable Data Communication using Bayes Optimal Classifier (RCBC) in MWSN. The proposed approach uses multidimensional characteristics, including residual energy level, packet received ratio, selfishness rate, and delay, to probabilistically describe sensor node behaviour. The RCBC approach uses the bayes optimum classifier isolates normal and abnormal sensor nodes under ambiguity. By classifying recognized abnormal sensor nodes from data forwarding and routing procedures, throughput and residual energy are improved. Simulation findings expose that the proposed RCBC technique outperforms machine learning classifiers in terms of abnormal node detection accuracy, false positive and false negative ratio, making it ideal for dynamic MWSN scenarios.

Keywords

Mobile Wireless Sensor Networks, Abnormal node detection, Bayes Optimal Classifier, Quality of Service, Network Reliability.

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