Optimal periodic radio sensing and low energy reasoning for cognitive devices

Heterogeneous wireless access networks have emerged as a paradigm that offers the potential for increased flexibility, improved QoS and higher data rates. New, dedicated radio channels conveying context information from the network to the terminal as well as terminal observations back to the network have been proposed to facilitate terminal operation in such environments. On the other hand, low energy and “green” aspects are becoming increasingly important, not only for environmental reasons but also because low energy systems offer extended battery duration, reduced operational cost and improved long-term reliability. This work proposes a solution to the problem of low energy periodic sensing of available networks, focusing on the definition of optimal sensing period through Markovian models. Moreover, low energy decision making utilizing a hierarchical fuzzy logic approach consisting of reasoners organized in tree form is introduced. The input parameters of the system are grouped taking into consideration their interrelation, in order to define the position of each reasoner in the structure. Such an approach facilitates utilization of previous knowledge and triggers a minimal number of reasoners, thus considerably reducing energy costs.