Traffic and mobility aware resource prediction using cognitive agent in mobile ad hoc networks

In this paper, we propose a CA-based Resource Prediction mechanism considering Mobility (CA-RPM) that predicts the resources using agents through the resource prediction agency consisting of one static agent, one cognitive agent and two mobile agents. Agents predict the traffic, mobility, buffer space, energy, and bandwidth effectively that is necessary for efficient resource allocation to support real-time and multimedia communications. The mobile agents collect and distribute network traffic statistics over MANET whereas a static agent collects the local statistics. CA creates static/mobile agent during the process of resource prediction. Initially, the designed time-series Wavelet Neural Networks (WNNs) predict traffic and mobility. Buffer space, energy, and bandwidth prediction use the predicted mobility and traffic. Simulation results show that the predicted resources closely match with the real values at the cost of little overheads due to the usage of agents. Simulation analysis of predicted traffic and mobility also shows the improvement compared to recurrent WNN in terms of mean square error, covariance, memory overhead, agent overhead and computation overhead. We plan to use these predicted resources for its efficient utilization in QoS routing is our future work.