Learning-Based Computation Offloading for IoT Devices With Energy Harvesting
TL;DR: A reinforcement learning (RL) based offloading scheme for an IoT device with EH to select the edge device and the offloading rate according to the current battery level, the previous radio transmission rate to each edge device, and the predicted amount of the harvested energy.
read more
Abstract: Internet of Things (IoT) devices can apply mobile edge computing (MEC) and energy harvesting (EH) to provide high-level experiences for computational intensive applications and concurrently to prolong the lifetime of the battery. In this paper, we propose a reinforcement learning (RL) based offloading scheme for an IoT device with EH to select the edge device and the offloading rate according to the current battery level, the previous radio transmission rate to each edge device, and the predicted amount of the harvested energy. This scheme enables the IoT device to optimize the offloading policy without knowledge of the MEC model, the energy consumption model, and the computation latency model. Further, we present a deep RL-based offloading scheme to further accelerate the learning speed. Their performance bounds in terms of the energy consumption, computation latency, and utility are provided for three typical offloading scenarios and verified via simulations for an IoT device that uses wireless power transfer for energy harvesting. Simulation results show that the proposed RL-based offloading scheme reduces the energy consumption, computation latency, and task drop rate, and thus increases the utility of the IoT device in the dynamic MEC in comparison with the benchmark offloading schemes.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Nguyen Cong Luong,Dinh Thai Hoang,Shimin Gong,Dusit Niyato,Ping Wang,Ying-Chang Liang,Dong In Kim +6 more
TL;DR: This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking, and presents applications of DRL for traffic routing, resource sharing, and data collection.
1.9K
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
TL;DR: By consolidating information scattered across the communication, networking, and DL areas, this survey can help readers to understand the connections between enabling technologies while promoting further discussions on the fusion of edge intelligence and intelligent edge, i.e., Edge DL.
867
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
TL;DR: In this paper, a survey on the relationship between edge intelligence and intelligent edge computing is presented, and the practical implementation methods and enabling technologies, namely DL training and inference in the customized edge computing framework, challenges and future trends of more pervasive and fine-grained intelligence.
790
A survey of multi-access edge computing in 5G and beyond : fundamentals, technology integration, and state-of-the-art.
Quoc-Viet Pham,Fang Fang,Vu Nguyen Ha,Md. Jalil Piran,Mai Le,Long Bao Le,Won-Joo Hwang,Zhiguo Ding +7 more
TL;DR: In this article, the authors provide a comprehensive overview of mobile edge computing (MEC) and its potential use cases and applications, as well as discuss challenges and potential future directions for MEC research.
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks
TL;DR: In this article, a Deep Reinforcement Learning-based Online Offloading (DROO) framework is proposed to optimize task offloading decisions and wireless resource allocation to the time-varying wireless channel conditions.
718
References
•Book
Reinforcement Learning: An Introduction
Richard S. Sutton,Andrew G. Barto +1 more
- 01 Jan 1988
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
A Survey on Transfer Learning
Sinno Jialin Pan,Qiang Yang +1 more
TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.
A Survey on Mobile Edge Computing: The Communication Perspective
TL;DR: A comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management is provided in this paper, where a set of issues, challenges, and future research directions for MEC are discussed.
4.6K
•Posted Content
A Survey on Mobile Edge Computing: The Communication Perspective
TL;DR: A comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management and recent standardization efforts on MEC are introduced.
3.1K