Decision Tree Algorithm for Intelligent Resource Management in Wireless Networks

A wireless network is a distributed network made up of a number of nodes. The battery life, communication range, bandwidth, processing delay, and memory of nodes are constrained. Utilizing wireless network resources effectively is difficult if you want to extend network life, boost throughput, reduce computation time, and reduce control overheads. By using intelligent resource management methods, a number of solutions are suggested to increase performance. Resource allocation, scheduling, and discovery are all important components of efficient and intelligent resource management in wireless networks. This paper presents a Decision tree algorithm for Internet of Things (IoT) resource allotment and balances for the Wireless network load (DTRB). Various variables, including memory, CPU, disk, and network bandwidth, influence the load-balancing process. This approach using the Gini index criteria is much quicker than entropy, and information gain correlation approaches since it requires less computer power.