Day 1 22/11/2019
Room #1

Registration 08:30 - 09:00

Opening Session 09:00 - 09:15

Keynote Speaker - Prof. Baochun Li 09:15 - 10:00

Coffee Break 10:00 - 10:30

Paper Session 1 (Chair: Ruiting Zhou) 10:30 - 11:30

10:50 - 10:50
Search Planning and Analysis for Mobile Targets with Robots

With robotics technologies advancing rapidly, there are many new robotics applications such as surveillance, mining tasks, search and rescue, and autonomous armies. In this work, we focus on use of robots for target searching. For example, a collection of Unmanned Aerial Vehicle (UAV) could be sent to search for survivor targets in disaster rescue missions. We assume that there are multiple targets. The moving speeds and directions of the targets are unknown. Our objective is to minimize the searching latency which is critical in search and rescue applications. Our basic idea is to partition the search area into grid cells and apply the divide-and-conquer approach. We propose two searching strategies, namely, the circuit strategy and the rebound strategy. The robots search the cells in a Hamiltonian circuit in the circuit strategy while they backtrack in the rebound strategy. We prove that the expected searching latency of the circuit strategy for a moving target is upper bounded by $\frac{3n^2-4n+3}{2n}$ where $n$ is the number of grid cells of the search region. In case of a static or suerfast target, we derive the expected searching latency of the two strategies. Simulations are conducted and the results show that the circuit strategy outperforms the rebound strategy.
Authors: Shujin Ye (Department of Computing, The Hang Seng University of Hong Kong, Hong Kong), Wai Kit Wong (Department of Computing, The Hang Seng University of Hong Kong, Hong Kong), Hai Liu (Department of Computing, The Hang Seng University of Hong Kong, Hong Kong),
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10:50 - 11:10
Stability of Positive Systems in WSN Gateway for IoT&IIoT

Modern sensor networks work on the basis of intelligent sensors and actuators, their connection is carried out using conventional or specifically dedicated networks. The efficiency and smooth transmission of such a network is of great importance for the accuracy of measurements, sensor energy savings, or transmission speed. Ethernet in many networks is typically based on the TCP / IP protocol suite. TCP ensures transmission reliability through retransmissions, congestion control and flow control. The most important here is the transmission speed achieved by shortening the header or the lack of an acknowledgment mechanism. Assuming the network is an automatic control system, it has interconnected elements that interact with each other to perform some specific tasks such as speed control, reliability and security of transmission. Such a system returns to equilibrium after being unbalanced. There are many definitions of stability, e.g. Laplace or Lupanov. To check the stability of the sensor network connected to the Internet, different stability criteria should be used. We are going to analyze the stability of a computer network as a dynamic linear system, described by the equations known in the literature. In this paper, we propose the method of testing stability for positive systems using the Metzlner matrix in sensor networks such as IoT or IIoT. We will carry out tests in a place where wide area networks connect to sensor networks, that is in gates.
Authors: Jolanta Mizera-Pietraszko (Opole University), Jolanta Tancula (Opole University),
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11:10 - 11:30
A-GNN: Anchors-aware Graph Neural Networks for Node Embedding

In network application,an un-solved primary challenge is to find a way to represent the network structure to ef-ficiently compute, process and analyze network tasks. Graph Neural Network (GNN) based node representation learning is an emerging learning paradigm that embeds network nodes into a low dimensional vector space through preserving the network topology as possible. However, existing GNN architectures have limitation in distinguishing the position of nodes with the similar topology, which is crucial for many network prediction and classification tasks. Anchors are de-fined as special nodes which are in the important positions, and carries a lot of in-teractive information with other normal nodes. In this paper, we propose An-chors-aware Graph Neural Networks (A-GNN), which can make the vectors of node embedding contain location information by introducing anchors. A-GNN first selects the set of anchors, computes the distance of any given target node to each anchor, and afterwards learns a non-linear distance-weighted aggregation scheme over the anchors. Therefore A-GNN can obtain global position infor-mation of nodes regarding the anchors. A-GNN are applied to multiple prediction tasks including link prediction and node classification. Experimental results show that our model is superior to other GNN architectures on six datasets, in terms of the ROC, AUC accuracy score.
Authors: Chao Liu (China University of Geosciences), Xinchuan Li (China University of Geosciences), Dongyang Zhao (China University of Geosciences), Shaolong Guo (Sinopec Exploration Company, Chengdu, Sichuan), Xiaojun Kang (China University of Geosciences), Lijun Dong (China University of Geosciences), Hong Yao (China University of Geosciences),
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Invited Speaker - Prof. Yanjiao Chen 13:30 - 13:50

Privacy–Preserving Auction for Dynamic Spectrum Access

Invited Speaker - Prof. Kaishun Wu 13:50 - 14:10

Challenges and Opportunities of HCI on Novel Smart Devices

Invited Speaker - Prof. Ju Ren 14:10 - 14:30

Block-streaming App Execution for Lightweight IoT Devices

Invited speaker - Prof. Fangming Liu 14:30 - 14:50

Shaping the Cloud & Edge from a new QoS/QoE (Quality-Open-Smart-grEen) Perspecitve

Coffee Break 14:50 - 15:20

Paper Session 2 (Chair: Fangming Liu) 15:20 - 16:40

15:20 - 15:40
A Reinforcement Learning based Placement Strategy in Datacenter Networks

As the core infrastructure of cloud computing, the datacenter network is supposed to provide high efficient storage and low delay communication. Most of the existing data placement solutions cannot better adaptive to the network dynamic; moreover, they have not considered the selection of routing path toward the storage node. Since reinforcement learning (RL) has been developing as a promising solution to address dynamic network issues; in this paper, we integrate RL into datacenter network to deal with the data placement issue. Considering the dynamic of resource, we propose a Q-learning based data placement scheme for datacenter network. By leveraging Q-learning, each node can adaptively select next-hop based on the network information collected from downstream, and forwards the data toward the storage node with adequate capacity along the path with high available bandwidth. We evaluate our proposal on NS-3 simulator in terms of average delay, throughput, and load balance. Simulation results show that Q-learning placement scheme can effectively reduce network delay and increase average throughout while achieving load balance among servers.
Authors: Weihong Yang (School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)), Yang Qin (School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)), Zhaozheng Yang (School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)),
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15:40 - 16:00
Scheduling Virtual Machine Migration during Datacenter Upgrades with Reinforcement Learning

Physical machines in modern datacenters are routinely upgraded due to their maintenance requirements, which involves migrating all the virtual machines they currently host to alternative physical machines. For this kind of datacenter upgrades, it is critical to minimize the time it takes to upgrade all the physical machines in the datacenter, so as to reduce disruptions to cloud services. To minimize the upgrade time, it is essential to carefully schedule the migration of virtual machines on each physical machine during its upgrade, without violating any constraints imposed by virtual machines that are currently running. Rather than resorting to heuristic algorithms, we propose a new scheduler, Raven, that uses an experience-driven approach with deep reinforcement learning to schedule the virtual machine migration process. With our design of the state space, action space and reward function, Raven trains a fully-connected neural network using the cross-entropy method to approximate the policy of choosing destination physical machines for each migrating virtual machine. We compare Raven with state-of-the-art heuristic algorithms in the literature, and our results show that Raven effectively leads to shorter times to complete the datacenter upgrade process.
Authors: Chen Ying (University of Toronto), Baochun Li (University of Toronto), Xiaodi Ke (Huawei Canada), Lei Guo (Huawei Canada),
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16:00 - 16:20
Batch Auction Design For Cloud Container Services

Cloud containers represent a new, light-weight alternative to virtual machines in cloud computing. A user job may be described by a container graph that specifies the resource profile of each container and container dependence relations. This work is the first in the cloud computing literature that designs efficient market mechanisms for container based cloud jobs. Our design targets simultaneously incentive compatibility, computational efficiency, and economic efficiency. It further adapts the idea of batch online optimization into the paradigm of mechanism design, leveraging agile creation of cloud containers and exploiting delay tolerance of elastic cloud jobs. The new and classic techniques we employ include: (i) compact exponential optimization for expressing and handling non-traditional constraints that arise from container dependence and job deadlines; (ii) the primal-dual schema for designing efficient approximation algorithms for social welfare maximization; and (iii) posted price mechanisms for batch decision making and truthful payment design. Theoretical analysis and trace-driven empirical evaluation verify the efficacy of our container auction algorithms.
Authors: Yu He (Wuhan University), Lin Ma (Wuhan University), Ruiting Zhou (Wuhan University), Chuanhe Huang (Wuhan University),
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16:20 - 16:40
Goldilocks: Learning Pattern-based Task Assignment in Mobile Crowdsensing

Mobile crowdsensing (MCS) depends on mobile users to collect sensing data (e.g., clear video, right photos), whose quality highly depends on the expertise/experience of the users. It is always critical for MCS to identify right persons for a given sensing task. A commonly-used strategy is to ``teach-before-use", i.e., training users with a set of questions and selecting a subset of users who have answered the questions correctly the most of times. This method has large room for improvement if we consider users' learning curve during the training process. As such, we propose an interactive learning pattern recognition framework, Goldilocks, that can filter users based on their learning patterns. Goldilocks uses an adaptive teaching method tailored for each user to maximize her learning performance. A user can thus be safely excluded from similar MCS tasks later on if her performance still does not match the desired learning pattern after the training period. Real-world experiments show that compared to the baseline methods, Goldilocks can identify suitable users to obtain more accurate and more stable results for multi-categories classification problems.
Authors: Kui Wu (University of Victoria), Jinghan Jiang (University of Victoria), Yiqin Dai (National University of Defense Technology), Rong Zheng (McMaster University, Canada),
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Banquet 18:00 - 20:00

Day 2 23/11/2019
Room #1

Paper Session 3 (Chair: Xiaowen Chu) 09:00 - 10:00

09:00 - 09:20
Accelerating Face Detection Algorithm on the FPGA Using SDAccel

In recent years, with the rapid growth of big data and computation, high-performance computing and heterogeneous computing have been widely concerned. In object detection algorithms, people tend to pay less attention to training time, but more attention to algorithm running time, energy efficiency ratio and processing delay. FPGA can achieve data parallel operation, low power, low latency and reprogramming, providing powerful computing power and enough flexibility. In this paper, SDAccel tool of Xilinx is used to implement a heterogeneous computing platform for face detection based on CPU+FPGA, in which FPGA is used as a coprocessor to accelerate face detection algorithm. A high-level synthesis (HLS) approach allows developers to focus more on the architecture of the design and lowers the development threshold for software developers. The implementation of Viola Jones face detection algorithm on FPGA is taken as an example to demonstrate the development process of SDAccel, and explore the potential parallelism of the algorithm, as well as how to optimize the hardware circuit with high-level language. Our final design was 70 times faster than a single-threaded CPU.
Authors: Jie WANG (School of Software Technology, Dalian University of Technology, Dalian 116620, China), Wei Leng (Dalian University of Technology, Dalian 116620, CHINA),
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09:20 - 09:40
Hybrid NOMA/OMA with Buffer-Aided Relaying for Cooperative Uplink System

In this paper, we consider a cooperative uplink network consisting of two users, a half-duplex decode-and-forward (DF) relay and abase station (BS). In the relaying network, the two users transmit packetsto the buffer-aided relay using non-orthogonal multiple access (NOMA) or orthogonal multiple access (OMA) technology. We proposed a hybrid NOMA/OMA based mode selection (MS) scheme, which adaptivelyswitches between the NOMA and OMA transmission modes according tothe instantaneous strength of wireless links and the buffer state. Then,the state transmission matrix probabilities of the corresponding Markovchain is analyzed, and the performance in terms of sum throughput,outage probability, average packet delay and diversity gain are evaluatedwith closed form expressions. Numerical results are provided to demonstrate that hybrid NOMA/OMA achieves significant performance gainscompared to conventional NOMA and OMA in most scenarios.
Authors: Jianping Quan (Chongqing University of Posts and Telecommunications), Peng Xu (Chongqing University of Posts and Telecommunications), Yunwu Wang (Chongqing University of Posts and Telecommunications), Zheng Yang (Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal Univ., Fuzhou 350007),
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09:40 - 10:00
Divide and Conquer: Efficient Multi-Path Validation with ProMPV

Path validation has long be explored toward forwarding reliability of Internet traffic. Adding cryptographic primitives in packet headers, path validation enables routers to enforce which path a packet should follow and to verify whether the packet has followed the path. How to implement path validation for multi-path routing is yet to be investigated. We find that it leads to an impractically low efficiency when simply applying existing single-path validation to multi-path routing. In this paper, we present ProMPV as an initiative to explore efficient multi-path validation for multi-path routing. We segment the forwarding path into segments of three routers following a sliding window with size one. Based on this observation, we design ProMPV as a proactive multi-path validation protocol in that it requires a router to proactively leave to its second next hop with proofs that cannot be tampered by its next hop. In multi-path routing, this greatly optimizes the computation and packet size. A packet no longer needs to carry all proofs of routers along all paths. Instead, it iteratively updates its carried proofs that correspond to only three hops. We validate the security and performance of ProMPV through security analysis and experiment results, respectively.
Authors: Anxiao He (Zhejiang University), Yubai Xie (Zhejiang University), Wensen Mao (Zhejiang University), Tienpei Yeh (Zhejiang University),
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Coffee Break 10:00 - 10:30

Paper Session 4 (Chair: Yang Qin) 10:30 - 11:30

10:30 - 10:50
AHV-RPL: Jammin-Resilient Backup Nodes Selection for RPL-based Routing in Smart Grid AMI Networks

Advanced metering infrastructure (AMI) is the core component of the smart grid. As the wireless connection between smart meters in AMI is featured with high packet loss and low transmission rate, AMI is considered as a representative of the low power and lossy networks (LLNs). In such communication environment, the routing protocol in AMI network is essential to ensure the reliability and real-time of data transmission. The IPv6 routing protocol for low-power and lossy networks (RPL), proposed by IETF ROLL working group, is considered to be the best routing solution for the AMI communication environment. However, the performance of RPL can be seriously degraded due to jamming attack. In this paper, we analyze the performance degradation problem of RPL protocol under jamming attack. We propose a backup node selection mechanism based on the standard RPL protocol. The proposed mechanism chooses a predefined number of backup nodes that maximize the probability of successful transmission. We evaluation the proposed mechanism through MATLAB simulations, results show the proposed mechanism improves the performance of RPL under jamming attack prominently.
Authors: Taimin Zhang (Zhejiang University), Xiaoyu Ji (Zhejiang University), Wenyuan Xu (Zhejiang University),
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10:50 - 11:10
Utility-aware Participant Selection with Budget Constraints for Mobile Crowd Sensing

Mobile Crowd Sensing is an emerging paradigm, which engages ordinary mobile device users to efficiently collect data and share sensed information using mobile applications. The data collection of participants consumes computing, storage and communication resources; thus, it is necessary to give reward to users who contribute their private data for sensing tasks. Fur-thermore, since the budget of sensing task is limited, the Service Provider (SP) needs to select a set of participants such that total utility of their sens-ing data can be maximized, and their bid price for sensing data can be satis-fied without exceeding the total budget. In this paper, firstly, we claim that the total data utility of a set of participants within certain area should be calculated according to the data quality of each participant and the location coverage of the sensing data. Secondly, a participant selection scheme has been proposed, which determines a set of participants with maximum total data utility under the budget constraint, and shows that it is a Quadratic In-teger Programming problem. Simulations have been conducted to solve the selection problem. The Simulation results demonstrate the effectiveness of the proposed scheme.
Authors: Shanila Azhar (Donghua University), Shan Chang (Donghua University), Ye Liu (Donghua University), Yuting Tao (Donghua University), Guohua Liu (Donghua University), Donghong Sun (Tsinghua University),
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11:10 - 11:30
Toward Optimal Resource Allocation for Task Offloading in Mobile Edge Computing

Task offloading emerges as a promising solution in Mobile Edge Computing (MEC) scenarios to not only incorporate more processing capability but also save energy. There however exists a key conflict between the heavy processing workloads of terminals and the limited wireless bandwidth, making it challenging to determine the computing placement at the terminal or the remote server. In this paper, we aim to migrate the most suitable offloading tasks to fully obtain the benefits from the resourceful cloud. We first model this task offloading problem as an optimization problem and prove that the optimal solution is NP-hard. A Genetic Algorithm is then proposed to achieve maximal user selection and the most valuable task offloading. Specifically, the cloud is pondered to provide computing services for as many edge wireless terminals as possible under the limited wireless channels. The BSs serve as edge for task coordination. The tasks are jointly considered to minimize the computing overhead and energy consumption, where the cost model of local devices is used as one of the optimization targets in this wireless mobile selective schedule. We also establish the multi-devices task offloading scenario to further verify the efficiency of the proposed allocating schedule. Our extensive numerical experiments demonstrate that our allocating scheme can effectively take advantage of the cloud server and reduce the cost of end users.
Authors: Wenzao Li (College of Communication Engineering, Chengdu University of Information Technology, China), Yuwen Pan (College of Communication Engineering, Chengdu University of Information Technology, China), Fangxin Wang (School of Computing Science, Simon Fraser University, Canada), Lei Zhang (College of Computer Science and Software Engineering, Shenzhen University,Shenzhen, China), Jiangchuan Liu (School of Computing Science, Simon Fraser University, Canada),
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Invited Speaker - Prof. Xu Chen 13:30 - 13:50

Computation Offloading for Edge Intelligence

Invited Speaker - Prof. Jiang Xiao 13:50 - 14:10

Data Management for Blockchain Systems

Invited Speaker - Prof. Hongzi Zhu 14:10 - 14:30

Movement Augmented Sensing on Mobile Devices

Coffee Break 14:30 - 15:00

Paper Session 5 (Chair: Jiang Xiao) 15:00 - 16:00

15:00 - 15:20
UltraComm: High-speed and Inaudible Acoustic Communication

Acoustic communication has become a research focus without requiring extra hardware on the receiver side and facilitates numerous near-field applications such as mobile payment, data sharing. To communicate, existing researches either use audible frequency band or inaudible one. The former gains a high throughput but endures being audible, which can be annoying to users. The latter, although inaudible, falls short in throughput due to the limited available (near) ultrasonic bandwidth (18-22kHz). In this paper, we achieve both high speed and inaudibility for acoustic communication by modulating the coded acoustic signal (0-20kHz) on ultrasonic carrier. By utilizing the nonlinearity effect on microphone, the modulated audible acoustic signal can be demodulated and then decoded. We design and implement UltraComm, an inaudible acoustic communication system with OFDM scheme based on the characteristics of the nonlinear speaker-to-microphone channel. We evaluate UltraComm on different mobile devices and achieve throughput as high as 16.24 kbps, meanwhile, keep inaudibility.
Authors: guoming zhang (zhejiang university), xiaoyu ji (zhejiang university), xinyan zhou (zhejiang university), donglian qi (zhejiang university), wenyuan xu (zhejiang university),
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15:20 - 15:40
A New Coordinated Multi-Points Transmission Scheme for 5G Millimeter-wave Cellular Network

Millimeter-wave network based on beamforming is an interference-limited network. In order to mitigate the interference for the 5G millimeter-wave cellular network, the concept of cooperative multi-beam transmission (Beam-CoMP) is proposed in this paper to improve cell capacity. For users in the beam overlapping zone, there is strong interference between beams, so for such users, overlapping beams provide services to users through cooperation. This method can solve the problems of poor edge coverage and serious interference of overlapping coverage of beams at the same time. The specific process of beam cooperation is given and the Beam-CoMP method proposed is simulated to verify its effectiveness in improving the UE performance. The results show that cell capacity increases with the increase of the number of users in the service beam.
Authors: Xiaoya Zuo (Northwestern Polytechnical University), RUGUI YAO (Northwestern Polytechnical University), Xu Zhang (Xi'an Institute of Space Radio Technology), Jiahong Li (Xi'an Institute of Space Radio Technology), Pan Liu (Xi'an Institute of Space Radio Technology),
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15:40 - 16:00
Privacy Protection Routing and a Self-organized Key Management Scheme in Opportunistic Networks

The opportunistic network adopts the disconnected store-and-forward architecture to provide communication support for the nodes without an infrastructure. As there is no stable communication link between the nodes, so that forwarding messages is via any encountered nodes. Social networks based on such opportunistic networks will have privacy challenges. In this paper, we propose a privacy protection scheme routing based on the utility value. We exploit the Bloom filter to obfuscate the friends lists and the corresponding utility values of nodes in order to make the routing decisions. This is easy to implement with high performance. Considering no infrastructure and stable link in opportunistic networks, this paper presents a self-organized key management system consisting of an identity authentication scheme based on the zero-knowledge proof of the elliptic curve and a key agreement scheme based on the threshold cryptography. The nodes prove their identities by themselves, and each node carries a certificate library to improve the authentication efficiency and success rate. In order to ensure the forward security and improve the session key agreement rate and the success rate, we exploit threshold cryptography to divide the session key, which could reduce the communication consumption of the traditional Diffie Hellman (DH) algorithm. The experimental simulation results show that the proposed schemes are much better than the existing schemes for opportunistic networks.
Authors: Yang Qin (School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)), Tiantian Zhang (School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)), Mengya Li (School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)),
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