Learning How to Communicate in the Internet of Things: Finite Resources and Heterogeneity

TL;DR

This paper explores various learning frameworks for IoT devices to operate autonomously, addressing challenges like resource constraints and heterogeneity, and introduces a novel cognitive hierarchy-based framework for efficient device cooperation.

Contribution

It presents a comprehensive analysis of existing learning methods for IoT and introduces a new cognitive hierarchy framework to handle device heterogeneity and resource variability.

Findings

Cognitive hierarchy theory effectively models IoT device heterogeneity.

Different learning frameworks have varying computational complexities.

The proposed framework improves resource utilization and device cooperation.

Abstract

For a seamless deployment of the Internet of Things (IoT), there is a need for self-organizing solutions to overcome key IoT challenges that include data processing, resource management, coexistence with existing wireless networks, and improved IoT-wide event detection. One of the most promising solutions to address these challenges is via the use of innovative learning frameworks that will enable the IoT devices to operate autonomously in a dynamic environment. However, developing learning mechanisms for the IoT requires coping with unique IoT properties in terms of resource constraints, heterogeneity, and strict quality-of-service requirements. In this paper, a number of emerging learning frameworks suitable for IoT applications are presented. In particular, the advantages, limitations, IoT applications, and key results pertaining to machine learning, sequential learning, and reinforcement learning are studied. For each type of learning, the computational complexity, required information, and learning performance are discussed. Then, to handle the heterogeneity of the IoT, a new framework based on the powerful tools of cognitive hierarchy theory is introduced. This framework is shown to efficiently capture the different IoT device types and varying levels of available resources among the IoT devices. In particular, the different resource capabilities of IoT devices are mapped to different levels of rationality in cognitive hierarchy theory, thus enabling the IoT devices to use different learning frameworks depending on their available resources. Finally, key results on the use of cognitive hierarchy theory in the IoT are presented.