Congestion Detection in Wireless Sensor Networks Based on Artificial Neural Network and Support Vector Machine
Jonnel Alejandrino, Ronnie Concepcion, Sandy Lauguico, Maria Gemel B. Palconit, Argel A. Bandala, Elmer P. Dadios
Abstract
A wireless sensor network (WSN) monitors a certain phenomenon through interconnecting voluminous quantity of interconnected sensor nodes that are implemented in the sensing space and event. Transmission reliability is the key contributing factor in of WSN. Network latency, data packet loss, throughput reduction, and low energy efficiency is caused by a congested network. To solve the network congestion issue, a proposed strategy of congestion detection using ANN (artificial neural network) in comparison to SVM (support vector machine) is presented in this paper. The number of sensor nodes, traffic rate, and node retention are the parameter used. These were generated from the WSN setup in the lettuce smart farm located in Rizal, Philippines. Network Simulator 2 (NS-2) is used for network design, traffic generation, and data gathering. The 3-layer feedforward ANN is optimized using scaled conjugate gradient with sigmoidal function as output activation. SVM is configured using box constraint of 564.22 and kernel scale of 0.1475. There is 0.00776625 CE performance (cross-entropy) and 98.8% accuracy through network training. Conversely, SVM shows 95.18% accuracy. Thus, the developed ANN-based WSN congestion detection model with +3.62% accuracy is an effective tool for agricultural wireless network where array of sensors for crop and environmental monitoring is connected.