Program

• Program Schedule of AICON 2019 (May.25-27, Sofitel Hotel, Harbin)

Day 1 (May. 25): Keynotes and Technical Sessions

Day 2 (May. 26): Keynotes and Technical Sessions

• Invited Paper
  

Sensor-based Human Activity Recognition for Smart Healthcare: A Semi-supervised Machine Learning

Abrar Zahin, TanThanh Le, Rose Qingyang Hu

Human action recognition is an integral part of smart health monitoring, where intelligence behind the services is obtained and improves through sensor information. we propose a semi-supervised classifier for time-series data. The proposed framework is the joint design of variational auto-encoder (VAE) and convolutional neural network (CNN). In particular, the VAE intends to extract the salient characteristics of human activity data and to provide the useful criteria for the compressed sensing reconstruction, while the CNN aims for extracting the discriminative features and for producing the low-dimension latent codes. Given a combination of labeled and raw time-series data, our architecture utilizes compressed samples from the latent vector in a deconvolutional decoder to reconstruct the input time-series. We intend to train the classifier to detect human actions for smart health systems.

Edge Computing based Traffic Analysis System Using Broad Learning

Xiting Peng, Kaoru Ota, Mianxiong Dong

Edge computing is a more promising way to save the bandwidth resources and improve users’ privacy by offloading these tasks to the edge node. In this paper, we propose a novel edge computing based traffic analysis system using broad learning. For one thing, edge computing can provide a distributed architecture for saving the bandwidth resources and protecting users’ privacy. For another, we use broad learning to incrementally train the traffic data, which is more suitable for edge computing because it can support incremental updates of models on the edge nodes without retraining all data. We implement our system on the Raspberry Pi, and experimental results show that we have 98% probability to accurately identify these traffic data. Moreover, our method has the faster training speed compared with Convolutional Neural Network (CNN).

Transmission Quality Improvement Algorithms for Multicast Terrestrial-Satellite Cooperation System

Yuandong Zhang, Liuguo Yin

In this paper, we investigate a terrestrial-satellite multicast beamforming cooperative system to optimize the problem of low expenses and high capacity requirements of ground users. We first formulate a joint optimization problem to maximize the system capacity performance, and we design the beamforming vectors of the base stations and the satellite cooperatively on the basis of user group scheduling and power constraints. Then we extend the problem into a more realistic scene that the link delay of satellite is larger than it of base stations, this may influence the joint optimization timeliness of condition changes. So we propose a two phases algorithm that we optimize terrestrial-satellite system jointly in the first phase and optimize terrestrial part independently in the second phase. The simulation results show that, the proposed algorithm gains

Simultaneous Wireless Information and Power Transfer Protocol Under the Presence of Node Hardware Impairments

Yanlin Liu, Juan Li, Fengye Hu, Qiao Qiao

Hardware impact to the wireless sensor network node can reduce the life cycle and communication quality of the energy harvesting network. In this paper, we use a three-node network communication model to derive the exact closed form and asymptotic expression of the outage probability and through the Rayleigh fading channel. Then, we study the outage probability and throughput of the wireless sensor network from the hardware impact of the source node and the destination node. In addition, we provide numerical results to demonstrate the correctness of the simulation. The hardware impact of the node physical transceiver is unavoidable, but we can minimize the impact on the network by selecting configuration parameters, which is of great significance to engineering practice.

Non-orthogonal Multiple Access enabled Power Allocation for Cooperative Jamming in Wireless Networks

Yuan Wu, Weicong Wu, Daohang Wang, Kejie Ni, Liping Qian, Weidang Lu, Limin Meng

In this work, we investigate the non-orthogonal multiple access (NOMA) enabled power allocation for cooperative jamming under a two-user downlink scenario. In particular, we consider that there exists a malicious eavesdropper overhearing the data transmission of the mobile user (MU) with a stronger channel power gain. In particular, we formulate a power allocation problem to maximize the secure throughput of the strong MU while satisfying the throughput requirement of the weak MU. Despite the non-convexity of the formulated problem, we provide an efficient algorithm to compute the optimal solution (i.e., the power allocations for the two users). Numerical results have been provided to validate the effectiveness of our proposed algorithm and the performance of our optimal power allocation scheme.

Mobility-Aware Task Parallel Offloading for Vehicle Fog Computing

Jindou Xie, Yunjian Jia, Zhengchuan Chen, Liang Liang

When applying fog computing paradigm into Internet of vehicle, vehicles are regarded as intelligent devices with computation and communication capability. These moving intelligent devices are often employed to assist various computation-intensive task offloading in vehicle fog computing, which brings real-time responses. However, vehicles mobility and network dynamics make it challenging to offload tasks to ideal target nodes for user-vehicle. In this paper, leveraging the result of vehicle mobility-awareness, we investigate the task offloading problem in vehicle fog computing aiming to minimize service time. Specifically, we consider that a task can be decomposed into subtasks in any proportion and offloaded from user-vehicle to multi service vehicles in parallel via vehicle-to-vehicle (V2V) links. Mobility information of vehicles collected by RSU is modeled to predicted the states of V2V links based on hidden Markov model (HMM). Then, we refine a rule to select target service-vehicles and the size of each subtask according to predicted results. Comparing with random and single-point task offloading, the proposed approach indicates a better performance on amount of finished task and service time in vehicle dense area.

Research on the Security of Personal Information in the Era of Big Data

Cheng Chi, Tengyu Liu, Xiaochen Yu, Shuo Zhang

The advent of the era of big data has made people’s lives more intelligent and convenient, and has brought enormous value to human beings. At the same time, it has brought about a lot of challenges. Personal information security is one of them. In the big data era, massive amounts of personal information are continuously input, simultaneously, the means of illegally acquiring, disseminating, and applying personal information are emerging in endlessly, Raising human thinking about information security. Therefore, the personal information security problem caused by big data should not be underestimated. Firstly, it introduces the related concepts of personal information, discusses the sources of risks faced by personal information in the process of implantation, dissemination and application. Secondly, it discusses the existing problems and solutions in China. Finally, the application difficulties and coping strategies of information security technology are analyzed, which provides theoretical support for personal information security in the era of big data.

• Invited Talk

Key Technologies for Vehicle Behavior-Driven Intelligent Vehicular Networks

Haixia Zhang received the B.E.degree from the Department of Communication and Information Engineering, Guilin University of Electronic Technology, China, in 2001, and received the M.Eng. and Ph.D. degrees in communication and information systems from the School of Information Science and Engineering, Shandong University, China, Currently, she works as full professor at Shandong University. She has been actively participating in many academic events, serving as TPC members, session chairs, and giving invited talks for conferences, and serving as reviewers for numerous journals. She is the associate editor for the International Journal of Communication Systems. Her current research interests include cognitive radio systems, cooperative (relay) communications, resource management, space time process techniques, mobile edge computing and smart communication technologies.

Big Data Privacy: from Networking and Artificial Intelligence Perspectives

Shui Yu is a Professor of School of Software, University of Technology Sydney, Australia. Dr Yu’s research interest includes Security and Privacy, Networking, Big Data, and Mathematical Modelling. He has published two monographs and edited two books, more than 200 technical papers, including top journals and top conferences, such as IEEE TPDS, TC, TIFS, TMC, TKDE, TETC, ToN, and INFOCOM. Dr Yu initiated the research field of networking for big data in 2013. His h-index is 35. Dr Yu actively serves his research communities in various roles. He is currently serving a number of prestigious editorial boards, including IEEE Communications Surveys and Tutorials (Area Editor), IEEE Communications Magazine (Series Editor). He has served many international conferences as a member of organizing committee. He is a Senior Member of IEEE, a member of AAAS and ACM, and a Distinguished Lecturer of IEEE Communication Society.

• Sessions

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