Learning nodes: machine learning-based energy and data management strategy

Yunmin Kim,Tae-Jin Lee

doi:10.1186/s13638-021-02047-6

Abstract

The efficient use of resources in wireless communications has always been a major issue. In the Internet of Things (IoT), the energy resource becomes more critical. The transmission policy with the aid of a coordinator is not a viable solution in an IoT network, since a node should report its state to the coordinator for scheduling and it causes serious signaling overhead. Machine learning algorithms can provide the optimal distributed transmission mechanism with little overhead. A node can learn by itself by utilizing the machine learning algorithm and make the optimal transmission decision on its own. In this paper, we propose a novel learning Medium Access Control (MAC) protocol with learning nodes. Nodes learn the optimal transmission policy, i.e., minimizing the data and energy queue levels, using the Q-learning algorithm. The performance evaluation shows that the proposed scheme enhances the queue states and throughput.

Highlights

With the advent of the Internet of Things (IoT), wireless communication function is employed in the electronic devices and in every ‘Things’ [1]
Since tons of devices are expected to be deployed in IoT networks, energy should be provided in a sustainable way to maintain long-lasting networks [3]
5 Conclusion We have proposed a new learning Medium Access Control (MAC) protocol for energy-harvesting nodes to resolve the imbalance between energy and data

Summary

Introduction

With the advent of the Internet of Things (IoT), wireless communication function is employed in the electronic devices and in every ‘Things’ [1]. Based on the nature of nodes, we classify them into energy-dominant and data-dominant nodes, and, for each type of node, the multi-slot and high-rate transmission strategies are proposed to mitigate the imbalance problem. Each node learns and selects a different transmission mechanism based on their evolutions of data and energy queue states.

Results

Conclusion