Fuzzy Rough Set Inspired Rate Adaptation and resource allocation using Hidden Markov Model (FRSIRA-HMM) in mobile ad hoc networks

Data transmission in ad hoc networks necessitates different quality of services (QoS) for ensuring reliable throughput of the network. This reliability in network performance is achievable through a rate adaptation scheme that aids in effective allocation of resource for ensuring QoS, based on network demand. In this approach, the available bandwidth is forecasted based on Hidden Markov Model for improving the efficacy in resource allocation and the new users are facilitated to utilize the resources based on the estimation of currently available resources determined through rough set theory. The rate of adaptation is forecasted based on the present load condition of the network by Rough set theory, which forms the input of the Hidden Markov Model. The fuzzy Rough set concept is mainly used in this technique as they are reliable in analyzing data that not require any basic or auxiliary information pertaining to data than its fuzzy set theory, Bayesian theory and Shafer–Dempster theory counter parts that necessitate the assignment of probability. Fuzzy Rough set concept determines the rate of adaptation through the estimation of queuing state, queuing delay, channel utilization rate, and bandwidth availability. This fuzzy Rough set concept also considers the resource allocation as the significant entity as the channel allocation in dynamic networks purely relies on the dynamic channel variation that alternates in real time distribution of data.