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- W4312354927 abstract "In an edge cloud environment, data processing in self-driving vehicles goes through local processing, communication, and remote processing. How to schedule these data for timely processing is critical to guaranteeing the safety of self-driving vehicles. This scheduling problem is related to the flow shop scheduling problem, which is NP-complete. In this paper, different from those in the literature that focus on minimizing makespan, our objective is to develop algorithms that produce schedules to minimize the average waiting time <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$AWT$</tex> and by taking the priorities of the data types into account since vehicle data are time-sensitive and different data types have different emergency levels. In regard to this objective, we propose two heuristic algorithms: the Priority and AWT-based NEH (PAN) algorithm, and the Priority-based Aalla's <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$(PAA)$</tex> algorithm. Simulation results show that our proposed algorithms outperform the preexisting ones and while PAN is a better algorithm when considering our metrics, <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$PAA$</tex> is more efficient while still producing similarly viable results." @default.
- W4312354927 created "2023-01-04" @default.
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- W4312354927 date "2022-10-01" @default.
- W4312354927 modified "2023-09-27" @default.
- W4312354927 title "Self-Driving Vehicle Data Scheduling in Edge-Clouds" @default.
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- W4312354927 doi "https://doi.org/10.1109/mass56207.2022.00099" @default.
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