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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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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 languageEnglish
Title of host publication2023 18th Iberian Conference on Information Systems and Technologies (CISTI)
PublisherIEEE Computer Society
Number of pages6
Publication statusE-pub ahead of print - 15 Aug 2023


  • VEC
  • Task Allocation


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