A Bee Colony-Based Algorithm for Task Offloading in Vehicular Edge Computing

Souza, Alisson Barbosa de; Rego, Paulo A. L.; Chamola, Vinay; Carneiro, Tiago; Rocha, Paulo H. G.; Souza, José Neuman de

doi:10.1109/jsyst.2023.3237363

Cited by 9 publications

(5 citation statements)

References 41 publications

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“…Compared with the other three algorithms, the MHORA algorithm in this paper improves the resource utilization of service vehicles and edge nodes in the network through multi-hop task offloading and server resource allocation, so as to achieve the lowest offloading delay. Although the random multi-hop offloading algorithm RMHORA achieves lower delay cost than the MHORA algorithm when the vehicle speed is (20,25) km/h, similar experimental results are not obtained in the other four sets of speed-delay experiments. According to the experimental results of figure 6, figure 7 and figure 8, the MHORA algorithm of the proposed scheme can better reduce the task delay cost and improve the satisfaction of vehicle users compared with the other three comparison algorithms in the case of unstable vehicle speed and sufficient edge resources or idle vehicle resources.…”

Section: ) Delay Cost Comparison Experimentsmentioning

confidence: 74%

“…Based on the above analysis, it mainly depends on the moving speed of the vehicle. In order to obtain the mobile communication link of vehicles at different speeds, we set up a time comparison experiment under low speed, medium speed and high speed, in which the moving speeds of consumer and service provider vehicles are (5,10)km/h, (15,20)km/h and (35,40)km/h in turn. The numerical result curves correspond to V2V_A (V2R_A), V2V_B (V2R_B), and V2V_C (V2R_C) in sequence.…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

“…The mobility of vehicles can greatly affect the quality of communication links and interrupt the offloading transmission process of tasks. Therefore, vehicle mobility and communication delay have always been important factors to be considered in vehicle computing offloading [16][17][18][19][20] . In order to cope with the rapid change of network structure caused by vehicle movement, Zhang et al [16] described the task offloading of vehicles as a multi-armed slot machine problem, and proposed a distributed online decision algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…Published by Francis Academic Press, UK -36-frequent link disconnection and connection caused by fast movement of network nodes, the authors of [20] proposed a bee colony based task allocation algorithm for vehicle edge computing to reliably reduce the execution time of applications in vehicle edge computing systems.…”

Section: Related Workmentioning

confidence: 99%

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Multi-Hop Assisted Offloading Strategy Based on Mobile Awareness in the Internet of Vehicles

2023

AJCIS

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The rapid development of Internet information technology and automobile economy has spawned a large number of computation-intensive and time-delay sensitive vehicle applications. In the face of high amount of computing data and real-time response requirements of applications, how to improve the utilization of network resources and reduce the computing delay of vehicle applications has become an urgent problem to be solved. Most of the existing research focuses on the problem of vehicle task offloading within the coverage of edge servers, and the idle vehicle resources outside the one-hop range are not fully utilized. In this paper, we formulate a multi-hop offloading scheme for tasks with mobility awareness. Based on the high-speed movement characteristics of vehicles, the real-time location of vehicles and the multi-hop-assisted offloading of tasks are modeled as a Markov decision process, and a multi-hop-assisted task offloading and resource allocation algorithm is proposed to ensure the reliability of each hop link and minimize the computation delay of tasks. Simulation results show that the proposed multi-hop assisted offloading scheme can significantly improve the response delay of the task. When the vehicle speed is high, the proposed scheme can reduce the response delay of the task by at least 17.2% compared with other algorithms (such as local and single-hop offloading algorithm and random multi-hop offloading algorithm).

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Section: ) Delay Cost Comparison Experimentsmentioning

confidence: 74%

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Multi-Hop Assisted Offloading Strategy Based on Mobile Awareness in the Internet of Vehicles

2023

AJCIS

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“…Vehicles installed with wireless transceivers called On Board Units (OBUs) connect with the RSUs during their journey. RSUs have the storage capacity where they cache the required data for the vehicles on the road 11 , 12 . RSUs are either installed by Traffic Control Centers (TCC) or the automobile manufacturers for the support of vehicles on the road 13 .…”

Section: Introductionmentioning

confidence: 99%

Efficient content caching for 5G assisted vehicular networks

Ahmed,

Alsamani,

Alkhathami

et al. 2024

Sci Rep

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Traffic congestion is one of the major challenges faced by daily commuters in smart cities. An autonomous transportation system with a 5 G-based Cellular Vehicle-to-Everything (C-V2X) communication system is the solution to meet the traffic challenges faced in smart cities. Vehicular networks provide wireless connectivity to enable a large number of connected vehicle applications. Vehicular networks allow vehicles to share their emergency and infotainment traffic by following vehicle-to-vehicle (V2V) or by using vehicle-to-infrastructure (V2I) communication. The infrastructure of vehicular networks mainly comprises multiple Road Side Units (RSUs). Fog computing nodes are placed adjacent to these RSUs to provide quick access to vehicles. For infotainment traffic, vehicles intend to download their required content from the content provider. Caching the same contents from the nearby fog computing node significantly reduces delay with improved quality of service. As there are millions of contents with varying sizes, caching all demanded contents on these fog nodes is not possible due to their limited caching capacity. In this work, we propose an improved content caching scheme for fog nodes to satisfy vehicles and content providers for fair content placement. The proposed algorithm is based on a modified Gale–Shapley technique that considers factors such as content popularity, vehicle connectivity, and quality of the communication channel to optimize the content caching process. Simulation results show that the proposed technique caches a higher number of popular contents with lower downloading time.

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