Parking Edge Computing: Parked-Vehicle-Assisted Task Offloading for Urban VANETs

Ma, Chunmei; Zhu, Jinqi; Liu, Ming; Zhao, Hong Yan; Liu, Nianbo; Zou, Xinyu

doi:10.1109/jiot.2021.3056396

Cited by 73 publications

(29 citation statements)

References 36 publications

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“…Qiao et al [20] introduced the vehicle edge multi-access network to combine resource-rich vehicles with cloud servers to build a collaborative computing architecture. C. Ma et al [21] organized parked vehicles outside into parking clusters as virtual edge servers to assist edge servers in processing tasks. Additionally, a new task scheduling algorithm was designed to jointly determine the resource allocation of edge servers.…”

Section: Related Workmentioning

confidence: 99%

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Mobile Edge Computing Task Offloading Strategy Based on Parking Cooperation in the Internet of Vehicles

Shen

Chang

Niu

2022

Sensors

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Due to the limited computing capacity of onboard devices, they can no longer meet a large number of computing requirements. Therefore, mobile edge computing (MEC) provides more computing and storage capabilities for vehicles. Inspired by a large number of roadside parking vehicles, this paper takes the roadside parking vehicles with idle computing resources as the task offloading platform and proposes a mobile edge computing task offloading strategy based on roadside parking cooperation. The resource sharing and mutual utilization among roadside vehicles, roadside units (RSU), and cloud servers (cloud servers) were established, and the collaborative offloading problem of computing tasks was transformed into a constraint problem. The hybrid genetic algorithm (HHGA) with a mountain-climbing operator was used to solve the multi-constraint problem, to reduce the delay and energy consumption of computing tasks. The simulation results show that when the number of tasks is 25, the delay and energy consumption of the HHGA algorithm is improved by 24.1% and 11.9%, respectively, compared with Tradition. When the task size is 1.0 MB, the HHGA algorithm reduces the system overhead by 7.9% compared with Tradition. Therefore, the proposed scheme can effectively reduce the total system cost during task offloading.

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Section: Related Workmentioning

confidence: 99%

“…Ref. [21] proposed a new framework but mainly studied the task scheduling and task completion rate between parked vehicles and moving vehicles and took the reduction in total delay as the goal. At present, there are few studies on the time delay and energy consumption of mobile edge computing task offloading on the Internet of vehicles.…”

Section: Related Workmentioning

confidence: 99%

Mobile Edge Computing Task Offloading Strategy Based on Parking Cooperation in the Internet of Vehicles

Shen

Chang

Niu

2022

Sensors

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“…To solve this problem, Xiangjie Kong et al [23] proposed an optimization framework for an edge cooperative network to improve the performance of task offloading. In addition, Chunmei Ma et al [24] incorporated the concept of edge computing offloading into the Internet of Vehicles environment and used parked vehicles as virtual MEC servers to solve the problem of limited computing resources. On this basis, a local task scheduling strategy was proposed to further improve the performance of task offloading.…”

Section: Research On Edge Task Offloadingmentioning

confidence: 99%

“…To integrate the optimization objectives (19a) into the approximate graph, a quota reward parameter r i is allocated to each node of the approximate graph, which is equivalent to the sum of service popularities deployed on the corresponding MEC server, as shown in Formula (24).…”

Section: Quota Problem For Steiner Treementioning

confidence: 99%

SD-AETO: Service Deployment Enabled Adaptive Edge Task Offloading in MEC

Song¹,

Sun²,

Yu³

et al. 2022

Preprint

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In recent years, edge computing, as an important pillar for future networks, has been developed rapidly. Task offloading is a key part of edge computing that can provide computing resources for resource-constrained devices to run computing-intensive applications, accelerate computing speed and save energy. An efficient and feasible task offloading scheme can not only greatly improve the quality of experience (QoE) but also provide strong support and assistance for 5G/B5G networks, the industrial Internet of Things (IIoT), computing networks and so on. To achieve these goals, this paper proposes an adaptive edge task offloading scheme assisted by service deployment (SD-AETO) focusing on the optimization of the energy utilization ratio (EUR) and the processing latency. In the pre-implementation stage of the SD-AETO scheme, a service deployment scheme is invoked to assist with task offloading considering each service's popularity. The optimal service deployment scheme is obtained by using the approximate deployment graph (AD-graph). Furthermore, a task scheduling and queue offloading design procedure is proposed to complete the SD-AETO scheme based on the task priority. The task priority is generated by the corresponding service popularity and task offloading direction. Finally, we analyze our SD-AETO scheme and compare it with related approaches, and the results show that our scheme has a higher edge offloading rate and lower resource consumption for massive task scenarios in the edge network.

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“…For instance, serving PVs as static nodes to extend vehicular network resources and the concept of parked vehicle assistance (PVA) was proposed in [31,32]. In addition, using PVs to assist edge servers in handling offloading tasks was presented in [33], by organizing PVs into parking clusters and abstracting them as virtual edge servers. Eventually, the task offloading performance was effectively improved by a task scheduling strategy and an associated trajectory prediction model.…”

Section: Parked Vehicles Collaborate Vehicular Edgementioning

confidence: 99%

Joint Load Balancing and Offloading Optimization in Multiple Parked Vehicle‐Assisted Edge Computing

Tang

et al. 2021

Wireless Communications and Mobile Computing

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The introduction of mobile edge computing (MEC) in vehicular network has been a promising paradigm to improve vehicular services by offloading computation-intensive tasks to the MEC server. To avoid the overload phenomenon in MEC server, the vast idle resources of parked vehicles can be utilized to effectively relieve the computational burden on the server. Furthermore, unbalanced load allocation may cause larger latency and energy consumption. To solve the problem, the reported works preferred to allocate workload between MEC server and single parked vehicle. In this paper, a multiple parked vehicle-assisted edge computing (MPVEC) paradigm is first introduced. A joint load balancing and offloading optimization problem is formulated to minimize the system cost under delay constraint. In order to accomplish the offloading tasks, a multiple offloading node selection algorithm is proposed to select several appropriate PVs to collaborate with the MEC server in computing tasks. Furthermore, a workload allocation strategy based on dynamic game is presented to optimize the system performance with jointly considering the workload balance among computing nodes. Numerical results indicate that the offloading strategy in MPVEC scheme can significantly reduce the system cost and load balancing of the system can be achieved.

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Parking Edge Computing: Parked-Vehicle-Assisted Task Offloading for Urban VANETs

Cited by 73 publications

References 36 publications

Mobile Edge Computing Task Offloading Strategy Based on Parking Cooperation in the Internet of Vehicles

Mobile Edge Computing Task Offloading Strategy Based on Parking Cooperation in the Internet of Vehicles

SD-AETO: Service Deployment Enabled Adaptive Edge Task Offloading in MEC

Joint Load Balancing and Offloading Optimization in Multiple Parked Vehicle‐Assisted Edge Computing

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