ISSN 1000-1239 CN 11-1777/TP

    2020 Edge Computing

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    Journal of Computer Research and Development    2020, 57 (9): 1779-1780.   DOI: 10.7544/issn1000-1239.2020.qy0901
    Abstract1092)   HTML361)    PDF (235KB)(728)       Save
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    Multi-Modality Fusion Perception and Computing in Autonomous Driving
    Zhang Yanyong, Zhang Sha, Zhang Yu, Ji Jianmin, Duan Yifan, Huang Yitong, Peng Jie, Zhang Yuxiang
    Journal of Computer Research and Development    2020, 57 (9): 1781-1799.   DOI: 10.7544/issn1000-1239.2020.20200255
    Abstract2138)   HTML59)    PDF (7063KB)(915)       Save
    The goal of autonomous driving is to provide a safe, comfortable and efficient driving environment for people. In order to have wide-spread deployment of autonomous driving systems, we need to process the sensory data from multiple streams in a timely and accurate fashion. The challenges that arise are thus two-fold: leveraging the multiple sensors that are available on autonomous vehicles to boost the perception accuracy; jointly optimizing perception models and the underlying computing models to meet the real-time requirements. To address these challenges, this paper surveys the latest research on sensing and edge computing for autonomous driving and presents our own autonomous driving system, Sonic. Specifically, we propose a multi-modality perception model, ImageFusion, that combines the lidar data and camera data for 3D object detection, and a computational optimization framework, MPInfer.
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    Edge Computing in Smart Homes
    Huang Qianyi, Li Zhiyang, Xie Wentao, Zhang Qian
    Journal of Computer Research and Development    2020, 57 (9): 1800-1809.   DOI: 10.7544/issn1000-1239.2020.20200253
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    In recent years, smart speakers and robotic vacuum cleaners have played important roles in many peoples daily life. With the development in technology, more and more intelligent devices will become parts of home infrastructure, making life more convenient and comfortable for residents. When different types of specialized intelligent devices are connected and operated over the Internet, how to minimize network latency and guarantee data privacy are open issues. In order to solve these problems, edge computing in smart homes becomes the future trend. In this article, we present our research work along this direction, covering the topics on edge sensing, communication and computation. As for sensing, we focus on the pervasive sensing capability of the edge node and present our work on contactless breath monitoring; as for communication, we work on the joint design of sensing and communication, so that sensing and communication systems can work harmoniously on limited spectrum resources; as for computation, we devote our efforts to personalized machine learning at the edge, building personalized model for each individual while guaranteeing their data privacy.
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    CATS: Cost Aware Task Scheduling in Multi-Tier Computing Networks
    Liu Zening, Li Kai, Wu Liantao, Wang Zhi, Yang Yang
    Journal of Computer Research and Development    2020, 57 (9): 1810-1822.   DOI: 10.7544/issn1000-1239.2020.20200198
    Abstract1700)   HTML30)    PDF (2103KB)(920)       Save
    Due to more data and more powerful computing power and algorithms, IoT (Internet of things) applications are becoming increasingly intelligent, which are shifting from simple data sensing, collection, and representation tasks towards complex information extraction and analysis. The continuing trend requires multi-tier computing resources and networks. Multi-tier computing networks involve collaborations between cloud computing, fog computing, edge computing, and sea computing technologies, which have been developed for regional, local, and device levels, respectively. However, due to different features of computing technologies and diverse requirements of tasks, how to effectively schedule tasks is a key challenge in multi-tier computing networks. Besides, how to motivate multi-tier computing resources is also a key problem, which is the premise of the formation of multi-tier computing networks. To solve these challenges, in this paper, we propose a multi-tier computing network and a computation offloading system with hybrid cloud and fog, define a weighted cost function consisting of delay, energy, and payment, and formulate a cost aware task scheduling (CATS) problem. Furthermore, we propose a computation load based payment model to motivate cloud and fog, and include the payment related cost into the overall cost. To be specific, based on different features and requirements of cloud and fog, we propose a static payment model and a dynamic payment model for cloud and fog, respectively, which constitute the hybrid payment model. To solve CATS problem, we propose a potential game based analytic framework and develop a distributed task scheduling algorithm called CATS algorithm. Numerical simulation results show that CATS algorithm can offer the near-optimal performance in system average cost, and achieve more number of beneficial UEs (user equipment), compared with the centralized optimal method. Besides, it shows that the dynamic payment model may help fog obtain more income, compared with the static payment model.
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    Dynamic Task Offloading for Mobile Edge Computing with Green Energy
    Ma Huirong, Chen Xu, Zhou Zhi, Yu Shuai
    Journal of Computer Research and Development    2020, 57 (9): 1823-1838.   DOI: 10.7544/issn1000-1239.2020.20200184
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    Mobile edge computing (MEC) has recently emerged to fulfill the computation demands of richer applications, and provide better experience for resource-hungry Internet-of-Things (IoT) devices at the edge of mobile networks. It is readily acknowledged that edge infrastructures are less capable of improving power usage efficiency (PUE) and integrating renewable energy. Besides, due to the limited battery capacities of IoT devices, the task execution would be interrupted when the battery runs out. Therefore, it is crucial to use green energy to prolong the battery life-time. Moreover, IoT devices can share computation and communication resources dynamically and beneficially among each other. Therefore, we develop an efficient task offloading strategy in order to improve PUE of edge server as well as achieving green computing. We also propose a green task offloading framework which leverages energy harvesting (EH) and device-to-device communication (D2D). Our framework aims at minimizing the long-term grid power energy consumption of edge server and cloud resource rental costs for task executions of all EH IoT devices. Meanwhile, the incentive constraints of preventing the over-exploiting behaviors should be considered, since they harm devices’ motivation for collaboration. To address the uncertain future system information, such as the availability of renewable energy, we resort to Lyapunov optimization technique to propose an online task offloading algorithm, in which the decisions only depend on system current state information. The implementation of this algorithm only requires to solve a deterministic problem in each time slot, for which the core idea is to transform the task offloading problem of each time slot into a graph matching problem and get the approximate optimal solution by calling Edmonds’s Blossom algorithm. Rigorous theoretical analysis and extensive evaluations demonstrate the superior performance of the proposed scheme.
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    TensorFlow Lite: On-Device Machine Learning Framework
    Li Shuangfeng
    Journal of Computer Research and Development    2020, 57 (9): 1839-1853.   DOI: 10.7544/issn1000-1239.2020.20200291
    Abstract3271)   HTML145)    PDF (1882KB)(3415)       Save
    TensorFlow Lite (TFLite) is a lightweight, fast and cross-platform open source machine learning framework specifically designed for mobile and IoT. It’s part of TensorFlow and supports multiple platforms such as Android, iOS, embedded Linux, and MCU etc. It greatly reduces the barrier for developers, accelerates the development of on-device machine learning (ODML), and makes ML run everywhere. This article introduces the trend, challenges and typical applications of ODML; the origin and system architecture of TFLite; best practices and tool chains suitable for ML beginners; and the roadmap of TFLite.
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    Robot 4.0: Continual Learning and Spatial-Temporal Intelligence Through Edge
    Wang Zhigang, Wang Haitao, She Qi, Shi Xuesong, Zhang Yimin
    Journal of Computer Research and Development    2020, 57 (9): 1854-1863.   DOI: 10.7544/issn1000-1239.2020.20200254
    Abstract1332)   HTML38)    PDF (2293KB)(729)       Save
    With the expansion of the global robot market, robotics is moving from the robot 3.0 era to the robot 4.0 era. In robot 4.0 era, robots should not only have the capability of perception and collaboration, but also have the capability of understanding the environment and making decisions by themselves just like human being. Then they can provide service to people autonomously. Although there have been many breakthroughs in deep learning, it is still a very challenging goal to make robots understand environment and make decisions like humans being. This paper explores three key technologies that are expected to solve these problems: continual learning, spatial-temporal intelligence, and edge computing. Continual learning enables robots to migrate the knowledge of old tasks to the knowledge of new tasks quickly without catastrophic forgotten problems; spatial-temporal intelligence enables robots to establish a bottom-up knowledge representation of the environment and to share and solve problems at different levels. Through edge computing, robots can get more cost-effective computation resource and integrate a variety of intelligence and knowledge easily. It is very useful for the large-scale deployment. These technologies are on the rise, and this paper is just a preliminary analysis.
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