Self-organized Low-power IoT Networks: A Distributed Learning Approach

TL;DR

This paper proposes a lightweight, distributed learning approach for self-organized IoT networks that enhances energy efficiency and communication reliability without relying on centralized control, suitable for large-scale low-power IoT deployments.

Contribution

It introduces a novel distributed learning method for IoT device coordination that improves energy efficiency and reliability, validated through stochastic geometry analysis and simulations.

Findings

Significant improvement in energy efficiency over state-of-the-art methods

Enhanced reliability of data transmissions in noisy and interference-prone environments

Effective adaptation of communication parameters through lightweight learning

Abstract

Enabling large-scale energy-efficient Internet-of-things (IoT) connectivity is an essential step towards realization of networked society. While legacy wide-area wireless systems are highly dependent on network-side coordination, the level of consumed energy in signaling, as well as the expected increase in the number of IoT devices, makes such centralized approaches infeasible in future. Here, we address this problem by self-coordination for IoT networks through learning from past communications. To this end, we first study low-complexity distributed learning approaches applicable in IoT communications. Then, we present a learning solution to adapt communication parameters of devices to the environment for maximizing energy efficiency and reliability in data transmissions. Furthermore, leveraging tools from stochastic geometry, we evaluate the performance of proposed distributed learning solution against the centralized coordination. Finally, we analyze the interplay amongst energy efficiency, reliability of communications against noise and interference over data channel, and reliability against adversarial interference over data and feedback channels. The simulation results indicate that compared to the state of the art approaches, both energy efficiency and reliability in IoT communications could be significantly improved using the proposed learning approach. These promising results, which are achieved using lightweight learning, make our solution favorable in many low-cost low-power IoT applications.