Learning-Based Computation Offloading for IoT Devices with Energy Harvesting
نویسندگان
چکیده
Internet of Things (IoT) devices can apply mobileedge 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” Qlearning 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.
منابع مشابه
Design and Evaluation of a Method for Partitioning and Offloading Web-based Applications in Mobile Systems with Bandwidth Constraints
Computation offloading is known to be among the effective solutions of running heavy applications on smart mobile devices. However, irregular changes of a mobile data rate have direct impacts on code partitioning when offloading is in progress. It is believed that once a rate-adaptive partitioning performed, the replication of such substantial processes due to bandwidth fluctuation can be avoid...
متن کاملStochastic Control of Computation Offloading to a Helper with a Dynamically Loaded CPU
Due to densification of wireless networks, there exist abundance of idling computation resources at (network) edge devices (e.g., access points and handheld computers). These resources can be scavenged by offloading heavy computation tasks from small IoT devices (e.g., sensors and wearable computing devices) in proximity, thereby overcoming their limitations and lengthening their battery lives....
متن کاملCoded Cooperative Computation for Internet of Things
Cooperative computation is a promising approach for localized data processing for Internet of Things (IoT), where computationally intensive tasks in a device could be divided into sub-tasks, and offloaded to other devices or servers in close proximity. However, exploiting the potential of cooperative computation is challenging mainly due to the heterogeneous nature of IoT devices. Indeed, IoT d...
متن کاملComputation Rate Maximization for Wireless Powered Mobile-Edge Computing with Binary Computation Offloading
Finite battery lifetime and low computing capability of size-constrained wireless devices (WDs) have been longstanding performance limitations of many low-power wireless networks, e.g., wireless sensor networks (WSNs) and Internet of Things (IoT). The recent development of radio frequency (RF) based wireless power transfer (WPT) and mobile edge computing (MEC) technologies provide promising sol...
متن کاملIFCIoT: Integrated Fog Cloud IoT Architectural Paradigm for Future Internet of Things
We propose a novel integrated fog cloud IoT (IFCIoT) architectural paradigm that promises increased performance, energy efficiency, reduced latency, quicker response time, scalability, and better localized accuracy for future IoT applications. The fog nodes (e.g., edge servers, smart routers, base stations) receive computation offloading requests and sensed data from various IoT devices. To enh...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- CoRR
دوره abs/1712.08768 شماره
صفحات -
تاریخ انتشار 2017