Leveraging the Cloud to Achieve Near Real-time Processing for Drone-Generated Data

Sarkar, Sayani; Totaro, Michael W.; Elgazzar, Khalid

doi:10.1109/wieforum47344.2019.8981670

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…During the execution of task j,n , the UAV is assumed to hover in the sky at the constant flying altitude n to avoid frequent ascending and descending for minimized energy consumption, where n is the minimum altitude suitable for the working area and can avoid all collisions and blockages [8]. Thereby, we only 1 When vehicles are not available in the UAV's communication range or the computing resources of vehicles are not sufficient, UAV's computation tasks can be alternatively offloaded to the remote cloud, as in conventional works [6], [7].…”

Section: B Mobility Modelmentioning

confidence: 99%

“…• Price-Aware Auction (PAA) scheme: every task is assigned to the candidate bidder with the minimum bidding price, and UAV's flying speed is randomly selected from [V min , V max ]. • Cloud-based offloading scheme [6]: the computing tasks of the UAV are offloaded to the remote cloud for processing at each location. Here, φ cloud = 8 and χ j cloud = 10 GC/s.…”

Section: Economic Efficienciesmentioning

confidence: 99%

“…Thereby, efficient computation offloading is urgently needed for UAVs in performing persistent missions. In the literature, UAV's computation missions are conventionally offloaded to the remote cloud for processing [6], [7], and then the computing results are delivered back to the UAV. Nevertheless, it incurs a long network delay in data transmission to/from the remote cloud, especially for latencysensitive tasks such as traffic monitoring.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Towards Online Privacy-preserving Computation Offloading in Mobile Edge Computing

Pang

Wang

et al. 2022

IEEE INFOCOM 2022 - IEEE Conference on Computer Communications

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Due to the limited battery and computing resource, offloading unmanned aerial vehicles (UAVs)' computation tasks to ground infrastructure, e.g., vehicles, is a fundamental framework.Under such an open and untrusted environment, vehicles are reluctant to share their computing resource unless provisioning strong incentives, privacy protection, and fairness guarantee. Precisely, without strategy-proofness guarantee, the strategic vehicles can overclaim participation costs so as to conduct market manipulation. Without the fairness provision, vehicles can deliberately abort the assigned tasks without any punishments, and UAVs can refuse to pay by the end, causing an exchange dilemma. Lastly, the strategy-proofness and fairness provision typically require transparent payment/task results exchange under public audit, which may disclose sensitive information of vehicles and make the privacy preservation a foremost issue. To achieve the three design goals, we propose SEAL, an integrated framework to address Strategy-proof, fair, and privacy-prEserving UAV computation offLoading. SEAL deploys a strategy-proof reverse combinatorial auction mechanism to optimize UAVs' task offloading under practical constraints while ensuring economic-robustness and polynomial-time efficiency. Based on smart contracts and hashchain micropayment, SEAL implements a fair on-chain exchange protocol to realize the atomic completion of batch payments and computing results in multi-round auctions. In addition, a privacy-preserving offchain auction protocol is devised with the assistance of the trusted processor to efficiently protect vehicles' bid privacy. Using rigorous theoretical analysis and extensive simulations, we validate that SEAL can effectively prevent vehicles from manipulating, ensure privacy protection and fairness, improve the offloading efficiency, and reduce UAV's energy costs and expenses with low overheads.

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