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Xiangjie Kong

Bio: Xiangjie Kong is an academic researcher from Zhejiang University of Technology. The author has contributed to research in topics: The Internet & Computer science. The author has an hindex of 37, co-authored 152 publications receiving 3929 citations. Previous affiliations of Xiangjie Kong include Dalian University of Technology & Zhejiang University.


Papers
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Journal ArticleDOI
TL;DR: An iterative heuristic MEC resource allocation algorithm to make the offloading decision dynamically and results demonstrate that the algorithm outperforms the existing schemes in terms of execution latency and offloading efficiency.
Abstract: With the evolutionary development of latency sensitive applications, delay restriction is becoming an obstacle to run sophisticated applications on mobile devices. Partial computation offloading is promising to enable these applications to execute on mobile user equipments with low latency. However, most of the existing researches focus on either cloud computing or mobile edge computing (MEC) to offload tasks. In this paper, we comprehensively consider both of them and it is an early effort to study the cooperation of cloud computing and MEC in Internet of Things. We start from the single user computation offloading problem, where the MEC resources are not constrained. It can be solved by the branch and bound algorithm. Later on, the multiuser computation offloading problem is formulated as a mixed integer linear programming problem by considering resource competition among mobile users, which is NP-hard. Due to the computation complexity of the formulated problem, we design an iterative heuristic MEC resource allocation algorithm to make the offloading decision dynamically. Simulation results demonstrate that our algorithm outperforms the existing schemes in terms of execution latency and offloading efficiency.

383 citations

Journal ArticleDOI
TL;DR: An application scenario on trajectory data-analysis-based traffic anomaly detection for VSNs and several research challenges and open issues are highlighted and discussed.
Abstract: Vehicular transportation is an essential part of modern cities. However, the ever increasing number of road accidents, traffic congestion, and other such issues become obstacles for the realization of smart cities. As the integration of the Internet of Vehicles and social networks, vehicular social networks (VSNs) are promising to solve the above-mentioned problems by enabling smart mobility in modern cities, which are likely to pave the way for sustainable development by promoting transportation efficiency. In this article, the definition of and a brief introduction to VSNs are presented first. Existing supporting communication technologies are then summarized. Furthermore, we introduce an application scenario on trajectory data-analysis-based traffic anomaly detection for VSNs. Finally, several research challenges and open issues are highlighted and discussed.

286 citations

Journal ArticleDOI
TL;DR: A novel approach to estimate and predict the urban traffic congestion using floating car trajectory data efficiently using a new fuzzy comprehensive evaluation method in which the weights of multi-indexes are assigned according to the traffic flows.

202 citations

Journal ArticleDOI
TL;DR: The recent hybrid CF-based recommendation techniques fusing social networks to solve data sparsity and high dimensionality are introduced and provide a novel point of view to improve the performance of RS, thereby presenting a useful resource in the state-of-the-art research result for future researchers.
Abstract: In the era of big data, recommender system (RS) has become an effective information filtering tool that alleviates information overload for Web users. Collaborative filtering (CF), as one of the most successful recommendation techniques, has been widely studied by various research institutions and industries and has been applied in practice. CF makes recommendations for the current active user using lots of users’ historical rating information without analyzing the content of the information resource. However, in recent years, data sparsity and high dimensionality brought by big data have negatively affected the efficiency of the traditional CF-based recommendation approaches. In CF, the context information, such as time information and trust relationships among the friends, is introduced into RS to construct a training model to further improve the recommendation accuracy and user’s satisfaction, and therefore, a variety of hybrid CF-based recommendation algorithms have emerged. In this paper, we mainly review and summarize the traditional CF-based approaches and techniques used in RS and study some recent hybrid CF-based recommendation approaches and techniques, including the latest hybrid memory-based and model-based CF recommendation algorithms. Finally, we discuss the potential impact that may improve the RS and future direction. In this paper, we aim at introducing the recent hybrid CF-based recommendation techniques fusing social networks to solve data sparsity and high dimensionality and provide a novel point of view to improve the performance of RS, thereby presenting a useful resource in the state-of-the-art research result for future researchers.

177 citations

Journal ArticleDOI
TL;DR: A green and sustainable virtual network embedding framework for cooperative edge computing in wireless-optical broadband access networks is put forward, which leverages a reliability function to confirm the number of backup edge devices, and embed virtual networks onto the suitable edge devices in CoTs.
Abstract: The proliferation of IoTs beside the emergence of various cloud services push the horizon of edge computing. By offering cloud capabilities at the network edge closer to mobile devices, edge computing is a promising paradigm to resolve several vital challenges in IoTs, such as bandwidth saturation, energy constraints, low latency transmission, and data security and privacy. To provide a comprehensive understanding of edge computing supported by the integration of IoTs and cloud computing, that is, CoTs, this article first discusses some distinct research directions in CoTs with respect to edge computing. Given the significance of energy efficiency and sustainability of edge deployment in CoTs, we put forward a green and sustainable virtual network embedding framework for cooperative edge computing in wireless-optical broadband access networks. Specifically, we leverage a reliability function to confirm the number of backup edge devices, and embed virtual networks onto the suitable edge devices in CoTs. Finally, several research challenges and open issues are discussed.

150 citations


Cited by
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01 Jan 2012

3,692 citations

01 Jan 1995
TL;DR: In this paper, the authors propose a method to improve the quality of the data collected by the data collection system. But it is difficult to implement and time consuming and computationally expensive.
Abstract: 本文对国际科学计量学杂志《Scientometrics》1979-1991年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析,揭示出科学计量学研究的重点、活动的中心及发展趋势,说明了学科带头人在发展科学计量学这门新兴学科中的作用。

1,636 citations

Journal ArticleDOI
22 Jun 2015
TL;DR: In this article, the authors considered an MIMO multicell system where multiple mobile users (MUs) ask for computation offloading to a common cloud server and formulated the offloading problem as the joint optimization of the radio resources and the computational resources to minimize the overall users' energy consumption, while meeting latency constraints.
Abstract: Migrating computational intensive tasks from mobile devices to more resourceful cloud servers is a promising technique to increase the computational capacity of mobile devices while saving their battery energy. In this paper, we consider an MIMO multicell system where multiple mobile users (MUs) ask for computation offloading to a common cloud server. We formulate the offloading problem as the joint optimization of the radio resources—the transmit precoding matrices of the MUs—and the computational resources—the CPU cycles/second assigned by the cloud to each MU—in order to minimize the overall users’ energy consumption, while meeting latency constraints. The resulting optimization problem is nonconvex (in the objective function and constraints). Nevertheless, in the single-user case, we are able to compute the global optimal solution in closed form. In the more challenging multiuser scenario, we propose an iterative algorithm, based on a novel successive convex approximation technique, converging to a local optimal solution of the original nonconvex problem. We then show that the proposed algorithmic framework naturally leads to a distributed and parallel implementation across the radio access points, requiring only a limited coordination/signaling with the cloud. Numerical results show that the proposed schemes outperform disjoint optimization algorithms.

715 citations