Enhanced QoS Optimization of Opportunistic Networks using Decision Tree

Opportunistic networks are a type of dynamic wireless network that lack a fixed infrastructure and rely on intermittent connections between nodes to facilitate communication. Quality of Service (QoS) is an essential factor for the efficient operation of opportunistic networks, as it ensures that data is delivered in a timely and reliable manner. This paper proposes an enhanced QoS optimization approach for opportunistic networks using a Decision Tree algorithm. The algorithm considers various network parameters such as node mobility, data transmission rate, and available storage capacity to predict the optimal routing path for data transmission. The Decision Tree algorithm uses a classification technique to build a model based on historical data to identify the most suitable routing path for each data transmission. This approach allows for dynamic adaptation to changing network conditions, improving the overall performance and QoS of the opportunistic network. By incorporating the Decision Tree algorithm into the routing decisions of the opportunistic network, we can achieve better QoS optimization than traditional routing protocols. Our experimental results show that the proposed approach outperforms existing routing protocols regarding data delivery ratio, end-to-end delay, and energy consumption, making it a promising solution for enhancing QoS in opportunistic networks.