Reliability-Oriented Optimization of Computation Offloading for Cooperative Vehicle-Infrastructure Systems

Zhou, Jianshan; Wang, Yunpeng; Sheng, Zhengguo; Duan, Xuting; Leung, Victor C. M.

doi:10.1109/lsp.2018.2880081

Cited by 25 publications

(21 citation statements)

References 18 publications

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“…Other objectives found in the works include: reduce the loss of quality of service experienced by the user [136], increase offloading reliability [150], and enrich the user experience [151].…”

Section: G: Othermentioning

confidence: 99%

“…There is also a variant of the integer programming for which some, but not all, variables must present integer values, called mixed-integer programming. Some works also formulate the problem as integer and mixed-integer programming [103], [120], [136], [148], [166], [167] and well know algorithmic approaches, such as branch-and-bound [168], cutting planes [169], and dynamic programming [150] can be seen.…”

Section: C: Mathematical Programmingmentioning

confidence: 99%

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Computation Offloading for Vehicular Environments: A Survey

et al. 2020

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With significant advances in communication and computing, modern day vehicles are becoming increasingly intelligent. This gives them the ability to contribute to safer roads and passenger comfort through network devices, cameras, sensors, and computational storage and processing capabilities. However, to run new and popular applications, and to enable vehicles operating autonomously requires massive computational resources. Computational resources available with the current day vehicles are not sufficient to process all these demands. In this situation, other vehicles, edge servers, and servers in remote data centers can help the vehicles by lending their computing resources. However, to take advantage of these computing resources, computation offloading techniques have to be leveraged to transfer tasks or entire applications to run on other devices. Such computation offloading can lead to improved performance and Quality of Service (QoS) for applications and for the network. However, computation offloading in a highly dynamic environment such as vehicular networks is a major challenge. Therefore, this survey aims to review and organize the computation offloading literature in vehicular environments. In addition, we demystify some concepts, propose a taxonomy with the most important aspects and classify most works in the area according to each category. We also present the main tools, scenarios, subjects, strategies, objectives, etc., used in the works. Finally, we present the main challenges and future directions to guide future research in this active research area.

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Section: G: Othermentioning

confidence: 99%

Section: C: Mathematical Programmingmentioning

confidence: 99%

Computation Offloading for Vehicular Environments: A Survey

et al. 2020

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“…Liu et al [15] jointly minimize the transmission failure probability and latency allowing multiple ESs cooperatively to complete a task. Zhou et al [16] maximize the successful transmission probability within latency constraints by optimizing the transmitted data bits in each time slot. Wang et al [17] consider the successful transmission probability under channel condition in the worse case and leverage the conditional value-at-risk approach to ensure the reliable transmission for computation offloading.…”

Section: Related Workmentioning

confidence: 99%

Using Imperfect Transmission in MEC Offloading to Improve Service Reliability of Time-Critical Computer Vision Applications

Liu

Zhang

2020

IEEE Access

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The emerging time-critical Internet-of-Things (IoT) use cases, e.g., augmented reality (AR), virtual reality (VR), autonomous vehicle etc., on the one hand, involve computation intensive computer vision (CV) components in the services, on the other hand, the computation task should be completed within the stringent latency constraint, otherwise, the service reliability will deteriorate. The state-of-theart work has shown that it is promising to tackle this challenge by offloading the computation tasks to the edge servers (ESs) using mobile edge computing (MEC). However, offloading tasks from local IoT devices to remote ESs could cause communication errors, thereby resulting in transmission failure even service timeout. The existing work mainly requires perfect transmission during task offloading at physical layer or transport layer. In fact, CV algorithms for, e.g., image classification and recognition, are able to tolerate certain level distortion of the input image to maintain the required inference accuracy. In this paper, we focus on the service reliability at application layer and study how feasible it is to improve the service reliability of the time-critical CV services in MEC system by allowing imperfect transmission. The service reliability is modeled by the transmission failure probability, service timeout probability and inference accuracy. The optimization goal is to maximize the service reliability, subject to the latency constraint. Due to the nonconvexity, we solve this problem by the semi-definite relaxation based algorithm for a multiuser scenario. We evaluate the algorithm considering the practical scenarios, i.e., object detection with SSD and YOLOv2. The proposed algorithm achieves the performance closed to the exhaustive method but at a much lower complexity.

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“…In [35], the authors mainly considered the random and dynamic characteristics of vehicles, and they proposed a computation offloading algorithm from vehicles to infrastructures focused on improving reliability. Misra et al [36] generated a task offloading problem and a computation results downloading problem separately, because the vehicles were continuously moving.…”

Section: Background and Related Workmentioning

confidence: 99%

Computation offloading through mobile vehicles in IoT-edge-cloud network

Long

Luo

Zhu

et al. 2020

Preprint

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With the developing of Wireless Sensor Networks (WSNs), more and more sensing devices are widely deployed in the smart city. These sensing devices generate various kinds of tasks, which need to be sent to cloud to process. Usually, the sensing devices do not equip with wireless modules, because it is neither economical nor energy saving. Thus, it is a challenging problem to find a way to offload tasks for sensing devices. However, many vehicles are moving around the city, which can communicate with sensing devices in an effective and low-cost way. In this paper, we propose a computation offloading scheme through mobile vehicles in IoT-edge-cloud network. The sensing devices generate tasks and transmit the tasks to vehicles, then the vehicles decide to compute the tasks in the local vehicle, MEC server or cloud center. The computation offloading decision is made based on the utility function of the energy consumption and transmission delay, and the deep reinforcement learning technique is adopted to make decisions. Our proposed method can make full use of the existing infrastructures to implement the task offloading of sensing devices, the experimental results show that our proposed solution can achieve the maximum reward and decrease delay.

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Reliability-Oriented Optimization of Computation Offloading for Cooperative Vehicle-Infrastructure Systems

Cited by 25 publications

References 18 publications

Computation Offloading for Vehicular Environments: A Survey

Computation Offloading for Vehicular Environments: A Survey

Using Imperfect Transmission in MEC Offloading to Improve Service Reliability of Time-Critical Computer Vision Applications

Computation offloading through mobile vehicles in IoT-edge-cloud network

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