# Learning-Based Computation Offloading for IoT Devices with Energy Harvesting

arXiv: https://arxiv.org/abs/1712.08768 · HTML: https://mlsanity.com/paper/1712.08768/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2017.

## Built on by (most cited first, 15 of 15)

- [A Distributed Deep Reinforcement Learning Technique for Application Placement in Edge and Fog Computing Environments](https://mlsanity.com/paper/2110.12415/) (2021)
- [Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks](https://mlsanity.com/paper/2202.10541/) (2022)
- [Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence](https://mlsanity.com/paper/2201.11410/) (2022)
- [Adaptive Edge Offloading for Image Classification Under Rate Limit](https://mlsanity.com/paper/2208.00485/) (2022)
- [A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing System](https://mlsanity.com/paper/2307.08474/) (2023)
- [Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications](https://mlsanity.com/paper/2109.09503/) (2021)
- [PeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement Learning](https://mlsanity.com/paper/2403.17637/) (2024)
- [Energy-Efficient Federated Learning for AIoT using Clustering Methods](https://mlsanity.com/paper/2505.09704/) (2025)
- [FAuNO: Semi-Asynchronous Federated Reinforcement Learning Framework for Task Offloading in Edge Systems](https://mlsanity.com/paper/2506.02668/) (2025)
- [Computation Rate Maximum for Mobile Terminals in UAV-assisted Wireless Powered MEC Networks with Fairness Constraint](https://mlsanity.com/paper/2109.05767/) (2021)
- [A Closer Look at Personalization in Federated Image Classification](https://mlsanity.com/paper/2204.11841/) (2022)
- [Mobility-Aware Computation Offloading for Swarm Robotics using Deep Reinforcement Learning](https://mlsanity.com/paper/2308.11154/) (2023)
- [dRG-MEC: Decentralized Reinforced Green Offloading for MEC-enabled Cloud Network](https://mlsanity.com/paper/2402.00874/) (2024)
- [Unsupervised Federated Optimization at the Edge: D2D-Enabled Learning without Labels](https://mlsanity.com/paper/2404.09861/) (2024)
- [Reinforcement Learning-based Task Offloading in the Internet of Wearable Things](https://mlsanity.com/paper/2510.07487/) (2025)

## Abstract (arXiv)

Internet of Things (IoT) devices can apply mobile-edge computing (MEC) and energy harvesting (EH) to provide the satisfactory quality of experiences for computation intensive applications and prolong the battery lifetime. In this article, we investigate the computation offloading for IoT devices with energy harvesting in wireless networks with multiple MEC devices such as base stations and access points, each with different computation resource and radio communication capability. We propose a reinforcement learning based computation offloading framework for an IoT device to choose the MEC device and determine the offloading rate according to the current battery level, the previous radio bandwidth to each MEC device and the predicted amount of the harvested energy. A "hotbooting" Q-learning based computation offloading scheme is proposed for an IoT device to achieve the optimal offloading performance without being aware of the MEC model, the energy consumption and computation latency model. We also propose a fast deep Q-network (DQN) based offloading scheme, which combines the deep learning and hotbooting techniques to accelerate the learning speed of Q-learning. We show that the proposed schemes can achieve the optimal offloading policy after sufficiently long learning time and provide their performance bounds under two typical MEC scenarios. Simulations are performed for IoT devices that use wireless power transfer to capture the ambient radio-frequency signals to charge the IoT batteries. Simulation results show that the fast DQN-based offloading scheme reduces the energy consumption, decreases the computation delay and the task drop ratio, and increases the utility of the IoT device in dynamic MEC, compared with the benchmark Q-learning based offloading.