Optimization of the Task Allocation Process in VEC with the GWO Bioinspired Algorithm

Douglas Dias Lieira; Matheus Sanches Quessada; Luis Hideo Vasconcelos Nakamura; Sandra Sampaio; Robson  E. De Grande; Rodolfo Ipolito Meneguette

doi:10.23919/CISTI58278.2023.10211659

Optimization of the Task Allocation Process in VEC with the GWO Bioinspired Algorithm

Douglas Dias Lieira, Matheus Sanches Quessada, Luis Hideo Vasconcelos Nakamura, Sandra Sampaio, Robson E. De Grande, Rodolfo Ipolito Meneguette

Information Management

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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Abstract

Vehicular Edge Computing (VEC) helps intelligent transportation systems deliver information and process data efficiently, at low latency. However, with the continuous exponential increases in number of interconnected intelligent vehicles,
managing massive amounts of data generated in vehicular networks becomes a great challenge. This work proposes ATARY, a method for optimizing task allocation processes in VECs using the Grey Wolf Optimization (GWO) algorithm. GWO has been especially adapted to model VEC task allocation as wolves’ hunting behaviour. Through a number of vehicle mobility and communication simulations, we show that ATARY is more efficient than some of the most widely used state-of-the-art mechanisms in number of allocated tasks, denied/lost services and resource usage.

Original language	English
Title of host publication	2023 18th Iberian Conference on Information Systems and Technologies (CISTI)
Publisher	IEEE Computer Society
Number of pages	6
DOIs	https://doi.org/10.23919/CISTI58278.2023.10211659
Publication status	E-pub ahead of print - 15 Aug 2023

Keywords

VEC
Task Allocation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.23919/CISTI58278.2023.10211659

Cisti_2023___GWO_VEC_SimulatorAccepted author manuscript, 916 KBLicence: CC BY

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Dias Lieira, D., Sanches Quessada, M., Vasconcelos Nakamura, L. H., Sampaio, S., E. De Grande, R., & Ipolito Meneguette, R. (2023). Optimization of the Task Allocation Process in VEC with the GWO Bioinspired Algorithm. In 2023 18th Iberian Conference on Information Systems and Technologies (CISTI) IEEE Computer Society . Advance online publication. https://doi.org/10.23919/CISTI58278.2023.10211659

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title = "Optimization of the Task Allocation Process in VEC with the GWO Bioinspired Algorithm",

abstract = "Vehicular Edge Computing (VEC) helps intelligent transportation systems deliver information and process data efficiently, at low latency. However, with the continuous exponential increases in number of interconnected intelligent vehicles,managing massive amounts of data generated in vehicular networks becomes a great challenge. This work proposes ATARY, a method for optimizing task allocation processes in VECs using the Grey Wolf Optimization (GWO) algorithm. GWO has been especially adapted to model VEC task allocation as wolves{\textquoteright} hunting behaviour. Through a number of vehicle mobility and communication simulations, we show that ATARY is more efficient than some of the most widely used state-of-the-art mechanisms in number of allocated tasks, denied/lost services and resource usage.",

keywords = "VEC, Task Allocation",

author = "{Dias Lieira}, Douglas and {Sanches Quessada}, Matheus and {Vasconcelos Nakamura}, {Luis Hideo} and Sandra Sampaio and {E. De Grande}, Robson and {Ipolito Meneguette}, Rodolfo",

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doi = "10.23919/CISTI58278.2023.10211659",

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Optimization of the Task Allocation Process in VEC with the GWO Bioinspired Algorithm. / Dias Lieira, Douglas; Sanches Quessada, Matheus; Vasconcelos Nakamura, Luis Hideo et al.
2023 18th Iberian Conference on Information Systems and Technologies (CISTI). IEEE Computer Society , 2023.

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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T1 - Optimization of the Task Allocation Process in VEC with the GWO Bioinspired Algorithm

AU - Dias Lieira, Douglas

AU - Sanches Quessada, Matheus

AU - Vasconcelos Nakamura, Luis Hideo

AU - Sampaio, Sandra

AU - E. De Grande, Robson

AU - Ipolito Meneguette, Rodolfo

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N2 - Vehicular Edge Computing (VEC) helps intelligent transportation systems deliver information and process data efficiently, at low latency. However, with the continuous exponential increases in number of interconnected intelligent vehicles,managing massive amounts of data generated in vehicular networks becomes a great challenge. This work proposes ATARY, a method for optimizing task allocation processes in VECs using the Grey Wolf Optimization (GWO) algorithm. GWO has been especially adapted to model VEC task allocation as wolves’ hunting behaviour. Through a number of vehicle mobility and communication simulations, we show that ATARY is more efficient than some of the most widely used state-of-the-art mechanisms in number of allocated tasks, denied/lost services and resource usage.

AB - Vehicular Edge Computing (VEC) helps intelligent transportation systems deliver information and process data efficiently, at low latency. However, with the continuous exponential increases in number of interconnected intelligent vehicles,managing massive amounts of data generated in vehicular networks becomes a great challenge. This work proposes ATARY, a method for optimizing task allocation processes in VECs using the Grey Wolf Optimization (GWO) algorithm. GWO has been especially adapted to model VEC task allocation as wolves’ hunting behaviour. Through a number of vehicle mobility and communication simulations, we show that ATARY is more efficient than some of the most widely used state-of-the-art mechanisms in number of allocated tasks, denied/lost services and resource usage.

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KW - Task Allocation

U2 - 10.23919/CISTI58278.2023.10211659

DO - 10.23919/CISTI58278.2023.10211659

M3 - Conference contribution

BT - 2023 18th Iberian Conference on Information Systems and Technologies (CISTI)

PB - IEEE Computer Society

ER -

Optimization of the Task Allocation Process in VEC with the GWO Bioinspired Algorithm

Abstract

Keywords

UN SDGs

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