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

Abstract

Mobile Ad hoc NETwork (MANET) characteristics such as limited resources, shared channel, unpredictable mobility, improper load balancing, and variation in signal strength affect the routing of real-time multimedia data that requires Quality of Service (QoS) provisioning. Accurate prediction of the resource availability assists efficient resource allocation before the routing of such data. Most of the published work on resource prediction in MANET focuses on either bandwidth or energy without considering mobility effects. Adoption of intelligent software agent such as Cognitive Agent (CA) for the accurate resource prediction has a significant potential to solve the challenges of resource prediction in MANET. The intelligence provided in CA is similar to the logical thinking like a human for decision-making. The predominant CA architecture is the Belief-Desire-Intention (BDI) model, which performs the various tasks on behalf of the human user as an assistant.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.