Intelligence at the Edge of Complex Networks: The Case of Cognitive Transmission Power Control

The rapid proliferation of new devices has led to the Internet of Things (IoT), a network where virtually any object equipped with a radio interface can be connected. Accordingly, networks are exploding in terms of the number of devices but also in complexity. The key issue arises from the increasing density in wireless communications, which the deterministic nature of current protocols can no longer handle. Herein, we explore ways in which the latest development in artificial intelligence (AI) and particularly machine learning may help address the complex requirements of IoT communications, highlighting the crucial role of predictive communications. We illustrate the software architectures and the fundamental mechanisms that can enable AI processes in communications. Finally, we introduce an exemplary case study where machine learning is successfully used to find the delicate balance between spectrum and energy efficiency in wireless sensor networks. The emerging panorama for cognitive communications is one in which intelligent processes must start at the very edge and need to transfer metalearned information in a peer-to-peer fashion.