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Wireless network (radio) virtualization and its synergy with ML/AI-based technologies is a novel concept that can efficiently address problems of legacy networks, such as flash crowds. This paper discusses the integration aspects of intelligence-based technologies with Sate-of-the-Art end-to-end reconfigurable, flexible and scalable network architecture, capable of handling demands in flash crowd scenarios. The presented results, demonstrate that advanced solutions based on ML can significantly improve the network proactivity and adaptivity by reliably predicting flash crowd scenarios. The results also show that in case of low dataset fidelity, conventional statistical models are a more suitable option.

Energy efficiency is a process under development and execution in all levels of society and economy, mostly driven by the environment protection interests. One of dilemmas in this process is the interest of the electricity provider companies, what kind of model to use in order to secure their profitability and benefits from energy efficiency projects deployment? This paper is presenting an ICT model for energy efficiency, model with scalable development, starting on a level of fundamental and currently available resources. The model is consumer centric and integrates communication tools. Using the approach of cooperative game theory, we are analyzing if this model is beneficial for all stakeholders in energy efficiency chain, the providers and the consumers. Having in mind the diversity of markets for electricity, in our case we deal with the simplest scenario, considering provider – consumer relation in two regimes of electricity network stage, peak and normal load, as the baseline from where the specific commercial cases could be further developed.

The next generation communication networks are envisioned to be flexible, scalable, reliable and secure. Software Defined Networking introduces mechanisms to build and orchestrate efficient, flexible and adaptable networks. This is achieved by the separation of software and hardware functionalities in the network devices. This paper evaluates the overall Software Defined Networking performances with respect to the allocated network resources, i.e. different CPU’s and bandwidth’s allocations. The performance results clearly show that the overall system performances can be highly influenced by the network setup and chosen topology.

The CHEST demo platform facilitates a solution that can help patients suffering from COPD to adjust the conditions in their home environment and everyday life. The developed demo offers a simple and user-friendly solution, which consists of a sensor network that measures the relevant COPD parameters (number of steps; indoor temperature; level of carbon dioxide; air humidity; temperature), a developed cloud solution for data management, and a graphical user interface (GUI) that displays daily measured parameters, parameters’ statistics and interacts with the patients via specific notifications.

The endeavor of categorizing the existing Cognitive-MAC (C-MAC) protocols requires definition of general classification frameworks or layouts that merge most of the aspects of the protocols in a single unified presentation. This chapter introduces the C-MAC cycle as a general classification and systematization layout for C-MAC protocols. The C-MAC cycle originates form the idea that the MAC layer in spectrally heterogeneous environments should provide support for three generic technical features: radio environmental data acquisition; spectrum sharing; and control channel management. The inclusion of these generic technical features is necessary in Cognitive Radio Networks (CRNs) for improving the network performance and achieving spectrum efficiency gain while providing maximal level of protection for the primary system. This chapter presents extensive survey on the state-of-art advances in C-MAC protocol engineering by reviewing existing technical solutions and proposals, identifying their basic characteristics and placing them into the C-MAC cycle, with emphasis on the modularity of the C-MAC cycle. It provides overview of large number of technical details concerning the three generic functionalities (i.e. the radio environmental data acquisition, the spectrum sharing and the control channel management) as the main building blocks of the C-MAC cycle. Three uses cases (each in different generic functional group), illustrate the capabilities of the proposed C-MAC cycle layout. In more detail, the first use case theoretically presents and practically evaluates cooperative spectrum sensing based on Estimated Noise Power. The results illustrate the effect of estimating the noise variance on the detection capabilities of the Majority Voting and Equal Gain Combining cooperative spectrum sensing strategies. The second use case presents advanced and computationally efficient horizontal spectrum sharing strategy for secondary systems based on Node Clustering and Beamforming. Finally, the last use case presents and assesses a multiuser quorum-based multiple rendezvous strategy for control channel establishment in distributed Cognitive Radio Networks.

Spectrum sensing is a fundamental feature of cognitive radio technologies facilitating detection and localization of spectrum opportunities. Combined with centralized database storage, it fosters reliable spatial spectrum measurements and Radio Environmental Maps (REMs) construction that can provide an accurate insight in the spectrum utilization over time, space and frequency. The spectrum sensing process often combines different devices with different capabilities. This paper introduces novel REM construction platform that integrates several mid- and low-end spectrum sensing devices into a single heterogeneous testbed. The devices of interest are USRP2s, TI RF2500 and Sun SPOTs, which are upgraded with custom developed software for providing versatile spectrum sensing capabilities. The paper evaluates their performances and discusses their integration into a single heterogeneous testbed platform. Additionally, the paper evaluates the realized heterogeneous spectrum sensing testbed in terms of accurate REM derivation and additional statistics gathering for indoor environments.