ABSTRACT
The Mobile Ad Hoc Network is a self-configuring wireless network in which the nodes can integrate or leave the network at any time. The optimization of information dissemination and resource discovery in MANET is done through AI-powered chatbots to achieve reliability in the system without certain external interruptions. The important aim of the design of chatbots includes proper assistance of routing in real-time environments in MANETs. The dynamic nature of the system is monitored and maintained properly through an optimization algorithm to obtain higher sustainability. This is obtained through the integration of a decision tree and random forest algorithm. The information dissemination are obtained through considering various parameters such as network congestion and packet delivery rates. The decision tree algorithm functions based on a rule whereas the random forest functions depend on ensemble learning. This helps in obtaining automatic decision-making with improved accuracy. This helps the chatbots to obtain the most significant routing paths and resource discovery with minimum delays. This tends to achieve optimum resource allocation. These chatbots help in the continuous monitoring of the condition of networks by identifying the routing decisions. These chatbots help in obtaining efficient communication through rerouting the data when the existing condition becomes congested in the network. They help in propagating information regarding network conditions with available resources in optimal routes. Thus the integration of the optimization algorithm helps in obtaining reliability and improved performance of the wireless sensor network.
