# A reinforcement learning approach to improve communication performance and energy utilization in fog-based IoT

arXiv: https://arxiv.org/abs/2106.00654 · HTML: https://mlsanity.com/paper/2106.00654/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [Implications of Decentralized Q-learning Resource Allocation in Wireless Networks](https://mlsanity.com/paper/1705.10508/) (2017)
- [Self-organized Low-power IoT Networks: A Distributed Learning Approach](https://mlsanity.com/paper/1807.09333/) (2018)
- [Optimization of the Energy-Efficient Relay-Based massive IoT Network](https://mlsanity.com/paper/1805.12278/) (2018)
- [Using Deep Q-learning To Prolong the Lifetime of Correlated Internet of Things Devices](https://mlsanity.com/paper/1902.02850/) (2019)

## Abstract (arXiv)

Recent research has shown the potential of using available mobile fog devices (such as smartphones, drones, domestic and industrial robots) as relays to minimize communication outages between sensors and destination devices, where localized Internet-of-Things services (e.g., manufacturing process control, health and security monitoring) are delivered. However, these mobile relays deplete energy when they move and transmit to distant destinations. As such, power-control mechanisms and intelligent mobility of the relay devices are critical in improving communication performance and energy utilization. In this paper, we propose a Q-learning-based decentralized approach where each mobile fog relay agent (MFRA) is controlled by an autonomous agent which uses reinforcement learning to simultaneously improve communication performance and energy utilization. Each autonomous agent learns based on the feedback from the destination and its own energy levels whether to remain active and forward the message, or become passive for that transmission phase. We evaluate the approach by comparing with the centralized approach, and observe that with lesser number of MFRAs, our approach is able to ensure reliable delivery of data and reduce overall energy cost by 56.76% -- 88.03%.