- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Learning-based Computation Offloading for IoT Devices with Energy Harvesting
摘要： 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.
关键词： Mobile edge computing,energy harvesting,reinforcement learning,computation offloading,Internet of Things
更新于2025-09-23 15:22:29
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[IEEE 2018 Asia Communications and Photonics Conference (ACP) - Hangzhou, China (2018.10.26-2018.10.29)] 2018 Asia Communications and Photonics Conference (ACP) - Energy efficient Placement of Baseband Functions and Mobile Edge Computing in 5G Networks
摘要： We propose and compare different potential placement schemes for baseband functions and mobile edge computing on their energy efficiency. Simulation results show that NFV enabled flexible placement reduces more than 20% power than traditional solutions.
关键词： Mobile edge computing placement,5G optical transport,Baseband functions placement,energy efficiency
更新于2025-09-23 15:22:29
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A framework involving MEC: imaging satellites mission planning
摘要： Satellite will play an important role in many important industries and exist as a carrier of information transmission in the era of Internet of Things. Massive data can be used in planning and scheduling processes A general data-driven framework- imaging satellite mission planning framework (ISMPF) for solving imaging mission planning problems is proposed. ISMPF mainly includes three parts: task assignment, planning and scheduling and task execution. The framework gives a general solution to the problem of satellite mission planning. The two core parts of the planning and scheduling module are machine learning algorithms and planning and scheduling algorithms, which greatly affect the quality of the results. Machine learning algorithm is mainly used to quickly obtain feasible initial solution. This idea can be used to quickly analyze and model the imaging satellite observation mission planning, imaging satellite measurement and control, data downlink mission planning problems. It has a strong generality and is suitable for most situations of imaging satellites. In order to verify the validity of ISMPF, we designed test examples for measurement and control, data downlink missions. Experimental verification demonstrates the effectiveness of our proposed framework.
关键词： Framework,Imaging satellite,Mission planning,Mobile edge computing
更新于2025-09-19 17:15:36
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[IEEE OCEANS 2019 - Marseille - Marseille, France (2019.6.17-2019.6.20)] OCEANS 2019 - Marseille - Experiments of Recreating the Frequency Domain Properties of Seawater Channel for Underwater Optical Communication
摘要： Mobile Edge Computing (MEC) has recently emerged as a promising technology to push the cloud frontier to the network edge, provisioning network services in close proximity of mobile users. Serving users at edge clouds has many advantages, such as reducing service delay, lower operational cost, and improved network resource availability. Furthermore, providing virtualized network service in MEC can improve user service experience, simplify network service deployments, and ease network resource management. However, provisioning reliable and seamless virtualized network services for mobile users while meeting their individual stringent delay requirements is of signi?cant importance and challenging. In this paper, we study mobile users requesting for virtualized network function services in MEC. We ?rst formulate two novel user request admission problems that take into account user mobility and service delay requirements. One is to minimize the admission cost of all user requests, assuming that there are suf?cient resources in MEC to meet user resource demands; the other is to maximize the accumulative network utility, subject to resource capacities of cloudlets, where the utility gain by admitting a request is determined by its resource demand and delay requirement, and the requested resource utilization in MEC. We then devise ef?cient approximation algorithms for the two problems. We ?nally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising.
关键词： Network Function Virtualization,MEC,delay-sensitive,NFV,user mobility,Mobile Edge Computing,VNF service replication
更新于2025-09-11 14:15:04