Space/Aerial-Assisted Computing Offloading for IoT Applications: A Learning-Based Approach

Cheng, Xiongwen; Lyu, Feng; Quan, Wei; Zhou, Conghao; He, Hua; Shi, Weisen; Shen, Xuemin

doi:10.1109/jsac.2019.2906789

Cited by 651 publications

(274 citation statements)

References 31 publications

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“…Cheng et. al [25] investigated the computing offloading problem in a space-air-ground integrated network (SAGIN) and proposed a deep reinforcement learning-based computing offloading approach to learn the optimal offloading policy on the fly from dynamic SAGIN environments. They also proposed a joint resource allocation and task scheduling approach for unmanned aerial vehicle-based edge servers to allocate the computing resources to virtual machines and schedule the offloading tasks efficiently.…”

Section: A Related Work and Contributionsmentioning

confidence: 99%

Process Migration-Based Computational Offloading Framework for IoT-Supported Mobile Edge/Cloud Computing

Yousafzai

Yaqoob

Imran

et al. 2020

IEEE Internet Things J.

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Mobile devices have become an indispensable component of Internet of Things (IoT). However, these devices have resource constraints in processing capabilities, battery power, and storage space, thus hindering the execution of computationintensive applications that often require broad bandwidth, stringent response time, long battery life, and heavy computing power. Mobile cloud computing and mobile edge computing (MEC) are emerging technologies that can meet the aforementioned requirements using offloading algorithms. In this paper, we analyze the effect of platform-dependent native applications on computational offloading in edge networks and propose a lightweight process migration-based computational offloading framework. The proposed framework does not require application binaries at edge servers and thus seamlessly migrates native applications. The proposed framework is evaluated using an experimental testbed. Numerical results reveal that the proposed framework saves almost 44% of the execution time and 84% of the energy consumption. Hence, the proposed framework shows profound potential for resource-intensive IoT application processing in MEC.

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Section: A Related Work and Contributionsmentioning

confidence: 99%

Process Migration-Based Computational Offloading Framework for IoT-Supported Mobile Edge/Cloud Computing

Yousafzai

Yaqoob

Imran

et al. 2020

IEEE Internet Things J.

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“…Compared with the terrestrial RAN, DA-RAN advances in following four aspects: 1) The line-of-sight (LoS) probability for the DBS-to-ground (D2G) wireless link is higher than the terrestrial BS-to-user wireless link [4]. Experiments indicate that LoS links probability is the dominating factor to increase network performance [8]; 2) DBSs can be dynamically deployed and dispatched to different controllers/users with respect to the spatial and temporal traffic variations [9]; 3) unlike connected vehicles whose mobility is controlled by drivers or autonomous driving controller, the trajectories of DBSs can be fully controlled by system providers, which empowers DBSs with the dynamic deployment feature [10] [11]; 4) DBS are capable of executing computing tasks by equipping with CPU or caching modules [12] [13]. However, it is challenging to fully utilize the potential of DBSs due to the following two reasons.…”

Section: Introductionmentioning

confidence: 99%

Multi-Drone 3-D Trajectory Planning and Scheduling in Drone-Assisted Radio Access Networks

Shi

Cheng

et al. 2019

IEEE Trans. Veh. Technol.

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Drone base station (DBS) is a promising technique to extend wireless connections for uncovered users of terrestrial radio access networks (RAN). To improve user fairness and network performance, in this paper, we design 3D trajectories of multiple DBSs in the drone assisted radio access networks (DA-RAN) where DBSs fly over associated areas of interests (AoIs) and relay communications between the base station (BS) and users in AoIs. We formulate the multi-DBS 3D trajectory planning and scheduling as a mixed integer non-linear programming (MINLP) problem with the objective of minimizing the average DBS-to-user (D2U) pathloss. The 3D trajectory variations in both horizontal and vertical directions, as well as the stateof-the-art DBS-related channel models are considered in the formulation. To address the non-convexity and NP-hardness of the MINLP problem, we first decouple it into multiple integer linear programming (ILP) and quasi-convex sub-problems in which AoI association, D2U communication scheduling, horizontal trajectories and flying heights of DBSs are respectively optimized. Then, we design a multi-DBS 3D trajectory planning and scheduling algorithm to solve the sub-problems iteratively based on the block coordinate descent (BCD) method. A k-meansbased initial trajectory generation and a search-based start slot scheduling are considered in the proposed algorithm to improve trajectory design performance and ensure inter-DBS distance constraint, respectively. Extensive simulations are conducted to investigate the impacts of DBS quantity, horizontal speed and initial trajectory on the trajectory planning results. Compared with the static DBS deployment, the proposed trajectory planning can achieve 10-15 dB reduction on average D2U pathloss, and reduce the D2U pathloss standard deviation by 68%, which indicate the improvements of network performance and user fairness.

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“…The goal of the two DNNs is to approximate the Q-value in (12). Based on this Q-value, the UAV chooses an action a t according to the current state s t with the proposed QoS-based ǫ-greedy policy, receives the reward r t+1 , and then transfers to the next state s t+1 .…”

mentioning

confidence: 99%

Path Planning for UAV-Mounted Mobile Edge Computing With Deep Reinforcement Learning

Liu

Shi

Sun

et al. 2020

IEEE Trans. Veh. Technol.

201

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In this letter, we study an unmanned aerial vehicle (UAV)-mounted mobile edge computing network, where the UAV executes computational tasks offloaded from mobile terminal users (TUs) and the motion of each TU follows a Gauss-Markov random model. To ensure the quality-of-service (QoS) of each TU, the UAV with limited energy dynamically plans its trajectory according to the locations of mobile TUs. Towards this end, we formulate the problem as a Markov decision process, wherein the UAV trajectory and UAV-TU association are modeled as the parameters to be optimized. To maximize the system reward and meet the QoS constraint, we develop a QoS-based action selection policy in the proposed algorithm based on double deep Q-network. Simulations show that the proposed algorithm converges more quickly and achieves a higher sum throughput than conventional algorithms.

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Space/Aerial-Assisted Computing Offloading for IoT Applications: A Learning-Based Approach

Cited by 651 publications

References 31 publications

Process Migration-Based Computational Offloading Framework for IoT-Supported Mobile Edge/Cloud Computing

Process Migration-Based Computational Offloading Framework for IoT-Supported Mobile Edge/Cloud Computing

Multi-Drone 3-D Trajectory Planning and Scheduling in Drone-Assisted Radio Access Networks

Path Planning for UAV-Mounted Mobile Edge Computing With Deep Reinforcement Learning

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