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arXiv 2020年

作者： Hua, Meng Yang, Luxi Wu, Qingqing Pan, Cunhua Li, Chunguo Swindlehurst, A. Lee School of Information Science and Engineering Southeast University Nanjing210096 China State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering University of Macau Macao999078 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Center for Pervasive Communications and Computing University of California at Irvine IrvineCA92697 United States

This paper investigates a symbiotic unmanned aerial vehicle (UAV)-assisted intelligent reflecting surface (IRS) radio system, where the UAV is leveraged to help the IRS reflect its own signals to the base station, and meanwhile enhance the UAV transmission by passive beamforming at the IRS. First, we consider the weighted sum-rate maximization problem among all IRSs by jointly optimizing the UAV trajectory, IRS phase shift matrix, and IRS scheduling, subject to the minimum primary rate requirements. To tackle this complicated problem, a relaxation-based algorithm is proposed. We prove that the converged relaxation scheduling variables are binary, which means that no reconstruct strategy is needed, and thus the UAV rate constraints are automatically satisfied. Second, we consider the problem of maximizing the minimum rate of the IRSs. We find that the relaxation-based method cannot solve this max-min problem since the minimum primary rate requirements may not be satisfied by the binary reconstruction operation. To address this issue, we first transform the binary constraints into a series of equivalent equality constraints. Then, a penalty-based algorithm is proposed to obtain a suboptimal solution. Numerical results are provided to evaluate the performance of the proposed designs under different setups, as compared with benchmarks. Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Distributed resource scheduling for large-scale MeC systems: A multi-agent ensemble deep reinforcement learning with imitation acceleration

arXiv

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arXiv 2020年

作者： Jiang, Feibo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the internet of things devices (IoTDs) in a large-scale mobile edge computing (MeC) system. To address this problem, we propose a distributed intelligent resource scheduling (DIRS) framework, which includes centralized training relying on the global information and distributed decision making by each agent deployed in each MeC server. More specifically, we first introduce a novel multi-agent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Secondly, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel Lvy flight search. Finally, an imitation acceleration scheme is presented to pre-train all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. extensive simulations are conducted to demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Stacked auto encoder based Deep Reinforcement Learning for online resource scheduling in large-scale MeC networks

arXiv

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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the mobile users, by optimizing offloading decision, transmission power, and resource allocation in the mobile edge computing (MeC) system. Towards this end, a deep reinforcement learning (DRL) method is proposed to obtain an online resource scheduling policy. Firstly, a related and regularized stacked auto encoder (2r-SAe) with unsupervised learning is proposed to perform data compression and representation for high dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Secondly, we present an adaptive simulated annealing based approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Thirdly, a preserved and prioritized experience replay (2p-eR) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. Numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks. It also shows that the proposed framework is suitable for resource scheduling problem in large-scale MeC networks, especially in the dynamic environment. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Ai driven heterogeneous MeC system for dynamic environment - Challenges and solutions

arXiv

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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Xu, Wei Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom NCRL Southeast University Nanjing China School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

—By taking full advantage of computing, Communication and Caching (3C) resources at the network edge, Mobile edge computing (MeC) is envisioned as one of the key enablers for the next generation network and services. However, current fixed-location MeC architecture may not be able to make real-time decision in dynamic environment, especially in large-scale scenarios. To address this issue, in this paper, a Heterogeneous MeC (H-MeC) architecture is proposed, which is composed of fixed unit, i.e., Ground Stations (GSs) as well as moving nodes, i.e., Ground Vehicles (GVs) and Unmanned Aerial Vehicles (UAVs), all with 3C resource enabled. The key challenges in H-MeC, e.g., mobile edge node management, real-time decision making, user association and resource allocation along with the possible Artificial Intelligence (AI)-based solutions are discussed in this paper. In addition, an AI-based joint Resource scheduling (ARe) framework is proposed, with simulation results shown to verify the efficiency of our proposed framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Observable liveness of Petri nets with controllable and observable transitions

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Science China(Information Sciences) 2017年第11期60卷 256-264页

作者： Guanjun LIU Changjun JIANG Department of Computer Science Tongji University Key Laboratory of Embedded System and Service Computing of Ministry of Education Tongji University E-commerce Transactions and Information Services Collaborative Innovation Center of Shanghai University Knowledge Service Platform Tongji University

Observability and/or controllability are paid much attention to by researchers not only from the control science field[1–3]but also from the computer science field[4–9].Recently,Desel and Kilinc[4]proposed the notion of observable liveness for Petri nets with(un-)observable and(un-)controllable *** purpose of defining observable liveness is to represent that"a user can always enforce the occurrence of any observable transition only by stimulating the net by choosing appropriate enabled controllable transition"[4].An important assumption of

关键词： net cot PN Observable liveness of Petri nets with controllable and observable transitions

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Does web news media have opinions? evidence from real estate market prediction 18

Does web news media have opinions? Evidence from real estate...

引用

18th Pacific Asia Conference on Information Systems, PACIS 2014

作者： Sun, Daoyuan Zhang, Ce Xu, Wei Zuo, Meiyun Zhou, Junjie Du, Yudie Department of Information Systems School of Computing National University of Singapore Singapore Singapore School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China School of Information Beijing China School of E-Commerce and Logistics Management Henan University of Economics and Law Zhengzhou China

In this paper, we propose a novel method for real estate price prediction using web new media sentiments by incorporating human searching behaivor on the web. By combining online daily news' sentiments and Google search engine query data, we construct a web news content and online search behavior-based integrated model for real estate prediction. Besides these factors, real estate price time series data are also considered into the model in order to improve the forecasting performance. Furthermore, we make a comparison between the integrated model and the baseline model without search engine query data. experimental results indicate that the integrated model outperforms the non-integrated model, which suggests that online user searching behavior is of great value in enhancing the prediction performance. These findings imply that the proposed integrated model is effective and feasible for real estate market prediction.

关键词： Search engines

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A reliable multicast protocol for mailbox-based mobile agent communications

A reliable multicast protocol for mailbox-based mobile agent...

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International Symposium on Autonomous Decentralized Systems (ISADAS)

作者： Jiannong Cao Wei Xu A.T.S. Chan Jing Li Internet Computing and E-Commerce Laboratory Department of Computing Hong Kong Polytechnic University Hong Kong China Technology Center of Software Engineering Institute of Software Chinese Academy and Sciences Beijing China

In this paper, we propose a reliable multicast protocol for mobile agent communications, which is a multicast extension to our previous adaptive and reliable protocol (ARP). ARP uses the mailbox-based scheme, where each mobile agent is associated with a mailbox for message delivery. The mailbox can be decoupled from the mobile agent and migrate with lower mobility. By adjusting the system parameters such as synchronization between mobile entities and mailbox migration frequency, ARP achieves adaptability and ensures reliability. In extending ARP to group communications, we group mailboxes residing on a host and belonging to the same multicast group into a cluster, and let all mailbox clusters form a logical ring. We present a token-based algorithm and a mailbox migration algorithm to achieve atomicity and total ordering of multicast messages. Also, we prove the correctness of the protocol and discuss the performance evaluation results obtained from simulations.

关键词： Multicast protocols Mobile agents Mobile communication Multicast algorithms Clustering algorithms Telecommunication network reliability Frequency synchronization Computational modeling Information retrieval Mobile computing

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From BPeL4WS to OWL-S: integrating e-business process descriptions

From BPEL4WS to OWL-S: integrating e-business process descri...

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Ieee International Conference on Services computing (SCC)

作者： J. Shen Y. Yang Chuan Zhu C. Wan Centre for Internet Computing and E-Commerce Faculty of ICT Swinburne University of Technology Melbourne Australia Software Development Laboratory IBM China Beijing China

With the rapid deployment of e-services, many workflow-like e-business process definition languages come into existence. At the same time, ontology Web language for services (OWL-S) aims to build an ontology language to support the integration of various specifications. There has been some work on mapping WSDL (Web services description language) to OWL-S to build a connection between the Web service and service profile. However, in the sense of activity relationships, there has been no effort so far trying to build the OWL-S service model from a workflow process model. Therefore, we design and develop an innovative mapping tool to translate BPeL4WS (business process execution language for Web services) to OWL-S. Through this mapping, semantics in the traditional business process specifications can be enriched significantly to enable more flexible and automatic e-service functions by using existing OWL-S tools such as composition and discovery, especially the execution of workflow-based services.

关键词： Web services Application software internet Ontologies Logic Routing Search engines Australia Programming Laboratories

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VPL: An Online Distance Learning Platform for Virtual Programming laboratory

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International Journal of Computer Processing of Languages 2004年第1期17卷 41-59页

作者： ALVIN T. S. CHAN JIANNONG CAO CHI-KIN LIU WeIDONG CAO Internet and E-Commerce Laboratory Department of Computing The Hong Kong Polytechnic University Hung Hom Kowloon SAR of Hong Kong China Department of Mathematics Jiangsu Institute of Education Nanjing Jiangsu China

The virtual programming lab (VPL) project described in this paper is designed to facilitate internet access to application software. It emulates a real computing laboratory environment that promotes group learning and project management. The laboratory resources are situated at the university and are centrally controlled. Users of the virtual programming laboratory include students, tutors and administrators of the system. Users can be located at different geographical locations to remotely access applications through the internet. The virtual programming lab design is based on a distance education concept. This paper focuses on the design and development of the runtime modules within the VPL framework. These runtime modules provide underlying services that drive the launching of applications, file management and communications services. In addition, this paper presents an evaluation of the performance of the system.

关键词： education Virtual Programming laboratory Distance Learning e-Learning

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Framework of tool integration for internet-based e-commerce

Framework of tool integration for internet-based e-commerce

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2nd International Workshop on Grid and Cooperative computing, GCC 2003

作者： Yong, Jianming Yang, Yun Department of Information Systems University of Southern Queensland ToowoombaQLD4350 Australia CICEC - Centre for Internet Computing and E-Commerce School of Information Technology Swinburne University of Technology PO Box 218 Hawthorn3122 Australia

ISBN: (纸本)3540219935

Tool integration has been an important research area for many years after software engineering became a key player in information industry. With the internet establishing ubiquitous connectivity, more and more tools become reachable through the internet. The internet has massively changed the environment of software engineering. This paper extensively discusses relevant technologies of tool integration for internet-based e-commerce. A new framework of tool integration for internet-based e-commerce is proposed designed and demonstrated. An XML-based message server can effectively serve tool integration for internet-based e-commerce within a business/integration domain. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Software engineering

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