Intelligent Edge Computing in Internet of Vehicles: A Joint Computation Offloading and Caching Solution

Ning, Zhaolong; Zhang, Kaiyuan; Wang, Xiaojie; Guo, Lei; Hu, Xiping; Huang, Jun; Hu, Bin; Kwok, Ricky Y. K.

doi:10.1109/tits.2020.2997832

Cited by 267 publications

(131 citation statements)

References 33 publications

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“…The cluster based MAC protocol in [15] has high delay and low throughput because after a failed transmission during unreserved time slots, the sender waits for the next frame for retransmission even if the channel is idle. Real-time traffic management is discussed in [16]- [21]. In [22]- [23], only sm is considered.…”

Section: Introductionmentioning

confidence: 99%

Performance Optimization of Cluster-Based MAC Protocol for VANETs

et al. 2020

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Recently, a novel cluster-based medium access control (CB-MAC) protocol has been proposed for vehicular ad hoc networks (VANETs). Though performance of CB-MAC protocol is affected by cluster size, no mechanism is provided to manage cluster size efficiently. In this paper, the performance of CB-MAC protocol is optimized for VANETs by optimizing transmission probability with cluster size. Each vehicle should adopt the optimum transmission probability in the cluster which can be obtained by tuning the number of clusters in VANET. Therefore, optimum number of clusters is defined based on the number of vehicles in VANETs. An analytical study based on Markov chain model is provided. Optimum transmission probability, and optimum number of clusters expressions are derived. The microscopic mobility model is generated in SUMO for practical scenario. Simulation result s are presented which verify analytical (theoretical) analysis and show that the performance of CB-MAC protocol is maximized in terms of throughput, packet dropping rate (PDR), and delay. Throughput is remarkably increased, whereas PDR and delay are decreased.

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Section: Introductionmentioning

confidence: 99%

Performance Optimization of Cluster-Based MAC Protocol for VANETs

et al. 2020

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“…In addition, vital signs monitoring based on [24] has brought great changes to personalized medical treatment. Real-time monitoring of vital signs [25]- [28] can timely understand the patient's physical condition [29] and analyze and make decisions on diagnosis and treatment information, which is helpful to study human diseases and provide preventive measures. In the case that patients do not have time to go or where they cannot visit doctors, the emerging telemedicine and mobile medicine based on smart-phones [30], laptops, tablet computers and other devices [31] have made great contributions, optimized the business process of the hospital, improved the work efficiency and resource utilization, which is the main direction of the application of the IoMT at present.…”

Section: Applications Of Iomtmentioning

confidence: 99%

Edge-Cloud Computing and Artificial Intelligence in Internet of Medical Things: Architecture, Technology and Application

et al. 2020

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With the booming development of medical informatization and the ubiquitous connections in the fifth generation mobile communication technology (5G) era, the heterogeneity and explosive growth of medical data have brought huge challenges to data access, security and privacy, as well as information processing in Internet of Medical Things (IoMT). This article provides a comprehensive review of how to realize the timely processing and analysis of medical big data and the sinking of high-quality medical resources under the constraints of the existing medical environment and medical-related equipment. We mainly focus on the advantages brought by the cloud computing, edge computing and artificial intelligence technologies to the IoMT. We also explore how to rationalize the use of medical resources and the security and privacy of medical data, so that high-quality medical services can be provided to patients. Finally, we discuss the current challenges and possible future research directions in the edge-cloud computing and artificial intelligence related IoMT. INDEX TERMS Internet of medical things (IoMT), deep learning, edge of computing, computation offloading.

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“…In addition, a fully connected wireless network is required such that all nodes communicate with each others (reference nodes (RN) and unknown nodes to be localized), and the completion preprocessing step needs to be performed online. Online multi-decision making schemes have been used in [25], [26], [27], to solve a related problem. However, indoor localization systems require low energy consumption and low computational complexity to meet the IoT constraints.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Data Recovery for Localization

et al. 2020

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In this paper, we study the problem of euclidean distance matrix (EDM) recovery aiming to tackle the problem of received signal strength indicator sparsity and fluctuations in indoor environments for localization purposes. This problem is addressed under the constraints required by the internet of things communications ensuring low energy consumption and reduced online complexity compared to classical completion schemes. We propose EDM completion methods based on neural networks that allow an efficient distance recovery and denoising. A trilateration process is then applied to recovered distances to estimate the target's position. The performance of different deep neural networks (DNN) and convolutional neural networks schemes proposed for matrix reconstruction are evaluated in a simulated indoor environment, using a realistic propagation model, and compared with traditional completion method based on the adaptative moment estimation algorithm. Obtained results show the superiority of the proposed DNN based completion systems in terms of localization mean error and online complexity compared to the classical completion.

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Intelligent Edge Computing in Internet of Vehicles: A Joint Computation Offloading and Caching Solution

Cited by 267 publications

References 33 publications

Performance Optimization of Cluster-Based MAC Protocol for VANETs

Performance Optimization of Cluster-Based MAC Protocol for VANETs

Edge-Cloud Computing and Artificial Intelligence in Internet of Medical Things: Architecture, Technology and Application

Deep Learning Based Data Recovery for Localization

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