Abstract

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.

Highlights

  • The widespread digitization of the physical world and the Internet of Things (IoT) trend to connect virtually any object equipped with a radio interface, are creating ever more complex systems

  • Cooperative Transmission Power Control (TPC) scheme based on reinforcement learning (RL) agents, whereby each wireless sensor node iteratively learns its minimum energy level

  • EXPERIMENTS AND RESULTS This section shows the experiments conducted on a real testbed in which the multi-agent Cognitive Q-Learning TPC (CQL-TPC) strategy has been implemented in the sensor nodes

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Summary

INTRODUCTION

The widespread digitization of the physical world and the Internet of Things (IoT) trend to connect virtually any object equipped with a radio interface, are creating ever more complex systems. In most challenging IoT applications, sensor data needs to be collected, analyzed and correlated with historical performance data to make decisions in real-time [2]; as an example you can consider swarms of industrial drones or remote facilities filled with smart sensors and actuators that need to communicate and coordinate with each other to accomplish tasks without being connected to a remote AI cloud service [3],[4]. In such cases, embedding intelligence at the source of the sensing is the most sensible way to obtain timely actionable reactions. In this work we only present the results related to a specific sensor platform due to the space limits

GENERAL SOFTWARE ARCHITECTURES FOR LEARNING
LEARNING MECHANISMS FOR CONSTRAINED DEVICES
COGNITIVE TRANSMISSION POWER CONTROL
EXPERIMENTS AND RESULTS
Testbed setup and working parameters
CONCLUSION

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