As mobility and computing becomes ever more pervasive in society and business, the non-optimal use of radio resources has created many new challenges for telecommunication operators. Usage patterns of modern wireless handheld devices, such as smartphones and surfboards, have indicated that the signaling traffic generated is many times larger than at a traditional laptop. Furthermore, in spite of approaching theoretical limits by, e.g., the spectral efficiency improvements brought by 4G, this is still not sufficient for many practical applications demanded by end users. Essentially, users located at the edge of a cell cannot achieve the high data throughputs promised by 4G specifications. Worst yet, the Quality of Service bottlenecks in 4G networks are expected to become a major issue over the next years given the rapid growth of mobile devices. The main problems are because of rigid mobile systems architectures with limited possibilities to reconfigure terminals and base stations depending on spectrum availability. Consequently, new solutions must be developed that coexist with legacy infrastructures and more importantly improve upon them to enable flexibility in the modes of operation. To control the intelligence required for such modes of operation, cognitive radio technology is a key concept suggested to be part of the so-called beyond 4th generation mobile networks. The basic idea is to allow unlicensed users access to licensed spectrum, under the condition that the interference perceived by the licensed users is minimal. This can be achieved with the help of devices capable of accurately sensing the spectrum occupancy, learning about temporarily unused frequency bands and able to reconfigure their transmission parameters in such a way that the spectral opportunities can be effectively exploited. Accordingly, this indicates the need for a more flexible and dynamic allocation of the spectrum resources, which requires a new approach to cognitive radio network management. Subsequently, a novel architecture designed at the application layer is suggested to manage communication in cognitive radio networks. The goal is to improve the performance in a cognitive radio network by sensing, learning, optimization and adaptation.

In this work we present algorithms that are capable of calculating paths to destination for electric vehicles. These paths can be based on the simple metrics such as the distance, time or the paths can be based on more advanced metric such as the minimum energy demanding metric. This metric is parameterizable by the physical construction of the electrical vehicle. We also propose a new algorithm that computes energy optimal paths that are more acceptable by the driver, because it also takes into consideration the time metric while computing the path.