An Edge-Assisted Data Distribution Method for Vehicular Network Services

Wang, Yang; Wang, Sunan; Zhang, Shengyu; Cen, Hongjie

doi:10.1109/access.2019.2946484

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…However, before this possibility is explored, a solution for manually controlling the system is required. For this purpose, the platform must be capable of receiving commands and providing visual and telemetry feedback simultaneously and with minimal latency [6], [7]. Ideally, 100ms latency at 30 frames per second should suffice for inspection tasks [8].…”

Section: Overview Of Control System Topologymentioning

confidence: 99%

The Implementation of a Highly Configurable Control Standard in the Development of a Robotics Platform for the Inspection of Confined Spaces

2023

IJOMAM

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This paper describes a general-purpose control standard and demonstrates its implementation as part of a controller-effector assembly. It elaborates on the specific layers on which the standard is defined: an adaptive control structure, a data transmission protocol and its corresponding instruction set. The aforementioned effector component consists of a reduced form factor robotics platform capable of remote controlled movement and optical inspection.

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Section: Overview Of Control System Topologymentioning

confidence: 99%

The Implementation of a Highly Configurable Control Standard in the Development of a Robotics Platform for the Inspection of Confined Spaces

2023

IJOMAM

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“…Second, VEC systems entail the collaboration of various entities such as vehicles, local edge servers and global cloud servers. While such a multi-component environment can lead to more versatility in task offloading, it also increases the state-space of task offloading complicating the decision to select the most appropriate entity to handle an offloaded task [5], [6]. As the dynamic changes to the VEC systems are difficult to predict or model in advance, an efficient offloading scheme should be able to learn while offloading; it should utilize its historical offloading data to steer its future offloading decisions considering both application-salient characteristics and current status of the VEC system [7].…”

Section: Introductionmentioning

confidence: 99%

Performance Assessment of Context-aware Online Learning for Task Offloading in Vehicular Edge Computing Systems

Al-Tarawneh¹,

Alnawayseh²

2021

IJACSA

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Vehicular Edge Computing (VEC) systems have recently become an essential computing infrastructure to support a plethora of applications entailed by smart and connected vehicles. These systems integrate the computing resources of edge and cloud servers and utilize them to execute computational tasks offloaded from various vehicular applications. However, the highly fluctuating status of VEC resources besides the varying characteristics and requirements of different application types introduce extra challenges to task offloading. Hence, this paper presents, implements and evaluates various task offloading algorithms based on the Multi-Armed Bandit (MAB) theory for VEC systems with predefined application types. These algorithms seek to make use of available contextual information to better steer task offloading. These information include application type, application characteristics, network status and server utilization. The proposed algorithms are based on having either a single MAB learner with application-dependent reward assignment, multiple application-dependent MAB learners or dedicated contextual bandits implemented as an array of incremental learning models. They have been implemented and extensively evaluated using the EdgeCloudSim simulation tool. Their performance has been assessed based on task failure rate, service time and Quality of Experience (QoE) and compared to that of recently reported algorithms. Simulation results demonstrate that the proposed contextual bandit-based algorithm outperforms its counterparts in terms of failure rate and QoE while having comparable service time values. It has achieved up to 73.4% and 21.7% average improvements in failure rate and QoE, respectively, among all application types. In addition, it efficiently utilizes the available contextual information to make appropriate offloading decisions for tasks originating from different application types achieving more balanced utilization of the available VEC resources. Ultimately, employing incremental learning to implement the proposed contextual bandit algorithm has shown a profound potential to cope with dynamic changes of the simulated VEC systems.

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“…Apparently, as the number of vehicles and in-vehicle applications increases, the demand for the networking and hardware resources of the VEC system will also increase. The increased demand for these resources will ultimately lead to increased competition for resources, hindering the decision-making process involved in the operation of workload orchestration [12]. Consequently, predicting the status of hardware resources and their ability to successfully handle offloaded requests is a challenging yet inevitable process.…”

Section: Introductionmentioning

confidence: 99%

Data Stream Classification Algorithms for Workload Orchestration in Vehicular Edge Computing: A Comparative Evaluation

Al-Tarawneh¹

2021

IJFIS

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This paper reports on the use of online data stream classification algorithms to support workload orchestration in vehicular edge computing environments. These algorithms can be used to predict the ability of available computational nodes to successfully handle computational tasks generated from vehicular applications. Several online data stream classification algorithms have been evaluated based on synthetic datasets generated from simulated vehicular edge computing environments. In addition, a multi-criteria decision analysis technique was utilized to rank the different algorithms based on their performance metrics. The evaluation results demonstrate that the considered algorithms can handle online classification operations with various trade-offs and dominance relations with respect to their obtained performance. In addition, the utilized multi-criteria decision analysis technique can efficiently rank various algorithms and identify the most appropriate algorithms to augment workload orchestration. Furthermore, the evaluation results show that the leveraging bagging algorithm, with an extremely fast decision tree base estimator, is able to maintain marked online classification performance and persistent competitive ranking among its counterparts for all datasets. Hence, it can be considered a promising choice to reinforce workload orchestration in vehicular edge computing environments.

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An Edge-Assisted Data Distribution Method for Vehicular Network Services

Cited by 8 publications

References 23 publications

The Implementation of a Highly Configurable Control Standard in the Development of a Robotics Platform for the Inspection of Confined Spaces

The Implementation of a Highly Configurable Control Standard in the Development of a Robotics Platform for the Inspection of Confined Spaces

Performance Assessment of Context-aware Online Learning for Task Offloading in Vehicular Edge Computing Systems

Data Stream Classification Algorithms for Workload Orchestration in Vehicular Edge Computing: A Comparative Evaluation

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