Vol. 4, Issue 5, Part G (2018)

Abstract
Wireless Sensor Networks (WSNs) have become a fundamental component of modern communication systems due to their wide applicability in Internet of Things (IoT) environments, including environmental monitoring, healthcare, industrial automation and smart infrastructure. When integrated with the Internet, WSNs must satisfy diverse Quality of Service (QoS) requirements while operating under strict constraints such as limited energy, bandwidth, processing capability and dynamic network topology. Ensuring reliable data delivery, congestion control and efficient routing in such environments remains a major research challenge. This study investigates QoS provisioning in WSN-Internet integrated systems with a focus on congestion control, routing efficiency and performance evaluation. Simulation-based analysis is adopted to evaluate network behavior under varying traffic conditions using metrics such as packet delivery ratio, end-to-end delay, throughput, queue length and packet loss. The Ad hoc On-Demand Distance Vector (AODV) routing protocol is examined to understand its route discovery, maintenance and error handling mechanisms in dynamic wireless environments. Additionally, malware threats and detection techniques using machine learning are discussed, highlighting supervised and unsupervised learning approaches, with particular emphasis on the K-Nearest Neighbors (KNN) algorithm for classification. MATLAB is employed as the primary simulation and analysis tool due to its powerful modeling, visualization and computational capabilities. The study demonstrates that simulation-based evaluation provides an effective and scalable approach for validating QoS models and routing strategies in WSNs. The findings contribute to the development of efficient, scalable and QoSaware network designs suitable for future IoT-enabled WSN deployments.