Reliable Computation Offloading for Edge-Computing-Enabled Software-Defined IoV

Hou, Xiangwang; Ren, Zhiyuan; Wang, Jingjing; Cheng, Wenchi; Ren, Yong; Chen, Kwang-Cheng; Zhang, Hailin

doi:10.1109/jiot.2020.2982292

Cited by 222 publications

(64 citation statements)

References 58 publications

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“…According to (3) and ( 6), P * s,n is calculated in (7), which represents the advantage of assigning the n th task to the s th sensor. The result matrix is shown in formula (8). D * is a binary task allocation plan matrix.…”

Section: System Modelmentioning

confidence: 99%

“…The traffic conditions of different road sections are also different, and the tasks assigned by the sensors are also different. To better detect the traffic condition in the case of limited traffic sensor resources, an efficient task allocation strategy can collect traffic to a greater extent information for monitoring traffic conditions more effectively [7][8][9]. The focus of the research is to develop new task allocation schemes to maximize the network throughput and revenue of intelligent transportation wireless sensor networks (ITWSNs).…”

Section: Introductionmentioning

confidence: 99%

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An Improved Adaptive Clone Genetic Algorithm for Task Allocation Optimization in ITWSNs

Zha

Xiao

et al. 2021

Journal of Sensors

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Research on intelligent transportation wireless sensor networks (ITWSNs) plays a very important role in an intelligent transportation system. ITWSNs deploy high-yield and low-energy-consumption traffic remote sensing sensor nodes with complex traffic parameter coordination on both sides of the road and use the self-organizing capabilities of each node to automatically establish the entire network. In the large-scale self-organization process, the importance of tasks undertaken by each node is different. It is not difficult to prove that the task allocation of traffic remote sensing sensors is an NP-hard problem, and an efficient task allocation strategy is necessary for the ITWSNs. This paper proposes an improved adaptive clone genetic algorithm (IACGA) to solve the problem of task allocation in ITWSNs. The algorithm uses a clonal expansion operator to speed up the convergence rate and uses an adaptive operator to improve the global search capability. To verify the performance of the IACGA for task allocation optimization in ITWSNs, the algorithm is compared with the elite genetic algorithm (EGA), the simulated annealing (SA), and the shuffled frog leaping algorithm (SFLA). The simulation results show that the execution performance of the IACGA is higher than EGA, SA, and SFLA. Moreover, the convergence speed of the IACGA is faster. In addition, the revenue of ITWSNs using IACGA is higher than those of EGA, SA, and SFLA. Therefore, the proposed algorithm can effectively improve the revenue of the entire ITWSN system.

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Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Adaptive Clone Genetic Algorithm for Task Allocation Optimization in ITWSNs

Zha

Xiao

et al. 2021

Journal of Sensors

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“…Table 1 lists the recent works in this area. Hou et al [22] proposed a model for horizontal offloading between autonomous vehicles and road infrastructure, and optimized for latency using a heuristic algorithm. Ren et al [8] assumed a vertical offloading model for edge and cloud, and optimized for latency using convex optimization.…”

Section: Previous Workmentioning

confidence: 99%

Comparative Analysis of 5G Mobile Communication Network Architectures

et al. 2020

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Mobile communication technology is evolving from 4G to 5G. Compared to previous generations, 5G has the capability to implement latency-critical services, such as autonomous driving, real-time AI on handheld devices and remote drone control. Multi-access Edge Computing is one of the key technologies of 5G in guaranteeing ultra-low latency aimed to support latency critical services by distributing centralized computing resources to networks edges closer to users. However, due to its high granularity of computing resources, Multi-access Edge Computing has an architectural vulnerability in that it can lead to the overloading of regional computing resources, a phenomenon called regional traffic explosion. This paper proposes an improved communication architecture called Hybrid Cloud Computing, which combines the advantages of both Centralized Cloud Computing and Multi-access Edge Computing. The performance of the proposed network architecture is evaluated by utilizing a discrete-event simulation model. Finally, the results, advantages, and disadvantages of various network architectures are discussed.

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“…1, we consider an MEC system with multiple UEs and ESs. To facilitate the multiple ESs, a software-defined networking (SDN) architecture could be introduced to the MEC system [24], [25]. The SDN controller, referred to as an edge orchestrator, keeps the global status information updating in the system, e.g., channel state information (CSI) between UEs and ESs, available computing .…”

Section: System Model and Problem Formulation A System Modelmentioning

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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Reliable Computation Offloading for Edge-Computing-Enabled Software-Defined IoV

Cited by 222 publications

References 58 publications

An Improved Adaptive Clone Genetic Algorithm for Task Allocation Optimization in ITWSNs

An Improved Adaptive Clone Genetic Algorithm for Task Allocation Optimization in ITWSNs

Comparative Analysis of 5G Mobile Communication Network Architectures

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

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