# Resource Allocation for Edge Computing in IoT Networks via Reinforcement Learning

arXiv: https://arxiv.org/abs/1903.01856 · HTML: https://mlsanity.com/paper/1903.01856/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2019.

## Built on by (most cited first, 4 of 4)

- [Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse with Deep Reinforcement Learning](https://mlsanity.com/paper/2303.10288/) (2023)
- [Deep Reinforcement Learning Based Resource Allocation for Cloud Native Wireless Network](https://mlsanity.com/paper/2305.06249/) (2023)
- [A Deep RL Approach on Task Placement and Scaling of Edge Resources for Cellular Vehicle-to-Network Service Provisioning](https://mlsanity.com/paper/2305.09832/) (2023)
- [Simulating Battery-Powered TinyML Systems Optimised using Reinforcement Learning in Image-Based Anomaly Detection](https://mlsanity.com/paper/2403.05106/) (2024)

## Abstract (arXiv)

In this paper, we consider resource allocation for edge computing in internet of things (IoT) networks. Specifically, each end device is considered as an agent, which makes its decisions on whether offloading the computation tasks to the edge devices or not. To minimise the long-term weighted sum cost which includes the power consumption and the task execution latency, we consider the channel conditions between the end devices and the gateway, the computation task queue as well as the remaining computation resource of the end devices as the network states. The problem of making a series of decisions at the end devices is modelled as a Markov decision process and solved by the reinforcement learning approach. Therefore, we propose a near optimal task offloading algorithm based on Q-learning. Simulations validate the feasibility of our proposed algorithm, which achieves a better trade-off between the power consumption and the task execution latency compared to these of edge computing and local computing modes.