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Recently, unmanned aerial vehicle (UAV) acts as the aerial mobile edge computing (MEC) node to help the battery-limited Internet of Things (IoT) devices relieve burdens from computation and data collection, and prolong the lifetime of operating. However, IoT devices can ONLY ask UAV for either computing or caching help, and collaborative offloading services of UAV is rarely mentioned in the literature. Moreover, IoT device has multiple mutually independent tasks, which make collaborative offloading policy design even more challenging. Therefore, we investigate a UAV-enabled MEC networks with the consideration of multiple tasks either for computing or caching. Taking the quality of experience (QoE) requirement of time-sensitive tasks into consideration, we aim to minimize the total energy consumption of IoT devices by jointly optimizing trajectory, communication and computing resource allocation at UAV, and task offloading decision at IoT devices. Since this problem has highly non-convex objective function and constraints, we first decompose the original problem into three subproblems named as trajectory optimization (PT), resource allocation at UAV (PR) and offloading decisions at IoT devices (PO), then propose an iterative algorithm based on block coordinate descent method to cope with them in a sequence. Numerical results demonstrate that collaborative offloading can effectively reduce IoT devices’ energy consumption while meeting different kinds of offloading services, and satisfy the QoE requirement of time-sensitive tasks at IoT devices.
Keywords
UAV, MEC, QoE, collaborative offloading, trajectory, time-sensitivity
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CURRENT STATUS: Published
View the published article at EURASIP Journal on Wireless Communications and Networking.
No community comments so far
Editorial decision: Accept
On 01 Nov, 2020
Review #3 received
Received 25 Oct, 2020
Review #1 received
Received 24 Oct, 2020
Review #2 received
Received 24 Oct, 2020
Reviewer #3 agreed
On 23 Oct, 2020
Reviewers invited
Invitations sent on 22 Oct, 2020
Reviewer #1 agreed
On 22 Oct, 2020
Reviewer #2 agreed
On 22 Oct, 2020
Editor assigned
On 14 Oct, 2020
Submission checks complete
On 13 Oct, 2020
Editor invited
On 13 Oct, 2020
Version 1
Posted 02 Sep, 2020
No comments provided
Review #2 received
Received 16 Sep, 2020
Review #1 received
Received 16 Sep, 2020
Review #3 received
Received 16 Sep, 2020
Reviewers invited
Invitations sent on 15 Sep, 2020
Reviewer #1 agreed
On 15 Sep, 2020
Reviewer #2 agreed
On 15 Sep, 2020
Reviewer #3 agreed
On 15 Sep, 2020
Editor assigned
On 10 Sep, 2020
Editor invited
On 09 Sep, 2020
Submission checks complete
On 01 Sep, 2020
First submitted
On 30 Aug, 2020
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Subject Areas
Systems and Networking
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A new preprint design is coming!
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See the published version of this preprint at EURASIP Journal on Wireless Communications and Networking.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at EURASIP Journal on Wireless Communications and Networking.
No community comments so far
Editorial decision: Accept
On 01 Nov, 2020
Review #3 received
Received 25 Oct, 2020
Review #1 received
Received 24 Oct, 2020
Review #2 received
Received 24 Oct, 2020
Reviewer #3 agreed
On 23 Oct, 2020
Reviewers invited
Invitations sent on 22 Oct, 2020
Reviewer #1 agreed
On 22 Oct, 2020
Reviewer #2 agreed
On 22 Oct, 2020
Editor assigned
On 14 Oct, 2020
Submission checks complete
On 13 Oct, 2020
Editor invited
On 13 Oct, 2020
Version 1
Posted 02 Sep, 2020
No comments provided
Review #2 received
Received 16 Sep, 2020
Review #1 received
Received 16 Sep, 2020
Review #3 received
Received 16 Sep, 2020
Reviewers invited
Invitations sent on 15 Sep, 2020
Reviewer #1 agreed
On 15 Sep, 2020
Reviewer #2 agreed
On 15 Sep, 2020
Reviewer #3 agreed
On 15 Sep, 2020
Editor assigned
On 10 Sep, 2020
Editor invited
On 09 Sep, 2020
Submission checks complete
On 01 Sep, 2020
First submitted
On 30 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Systems and Networking
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at EURASIP Journal on Wireless Communications and Networking.
Recently, unmanned aerial vehicle (UAV) acts as the aerial mobile edge computing (MEC) node to help the battery-limited Internet of Things (IoT) devices relieve burdens from computation and data collection, and prolong the lifetime of operating. However, IoT devices can ONLY ask UAV for either computing or caching help, and collaborative offloading services of UAV is rarely mentioned in the literature. Moreover, IoT device has multiple mutually independent tasks, which make collaborative offloading policy design even more challenging. Therefore, we investigate a UAV-enabled MEC networks with the consideration of multiple tasks either for computing or caching. Taking the quality of experience (QoE) requirement of time-sensitive tasks into consideration, we aim to minimize the total energy consumption of IoT devices by jointly optimizing trajectory, communication and computing resource allocation at UAV, and task offloading decision at IoT devices. Since this problem has highly non-convex objective function and constraints, we first decompose the original problem into three subproblems named as trajectory optimization (PT), resource allocation at UAV (PR) and offloading decisions at IoT devices (PO), then propose an iterative algorithm based on block coordinate descent method to cope with them in a sequence. Numerical results demonstrate that collaborative offloading can effectively reduce IoT devices’ energy consumption while meeting different kinds of offloading services, and satisfy the QoE requirement of time-sensitive tasks at IoT devices.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
The full text of this article is available to read as a PDF.
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