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Keynote 1. Task Execution in Collaborative Edge Computing Environments
Prof. Jiannong Cao
Hong Kong Polytechnic University

Prof. Jiannong Cao is currently a Chair Professor of Department of Computing at The Hong Kong Polytechnic University, Hong Kong. He is also the director of the Internet and Mobile Computing Lab in the department and the director of University’s Research Facility in Big Data Analytics. His research interests include parallel and distributed computing, wireless sensing and networks, pervasive and mobile computing, and big data and cloud computing. He has co-authored 5 books, co-edited 9 books, and published over 500 papers in major international journals and conference proceedings. He received Best Paper Awards from conferences including DSAA’2017, IEEE SMARTCOMP 2016, ISPA 2013, IEEE WCNC 2011, etc.
He served the Chair of the Technical Committee on Distributed Computing of IEEE Computer Society 2012-2014, a member of IEEE Fellows Evaluation Committee of the Computer Society and the Reliability Society, a member of IEEE Computer Society Education Awards Selection Committee, a member of IEEE Communications Society Awards Committee, and a member of Steering Committee of IEEE Transactions on Mobile Computing. Dr. Cao has served as chairs and members of organizing and technical committees of many international conferences, and as associate editor and member of the editorial boards of many international journals. Dr. Cao is a fellow of IEEE and ACM distinguished member. In 2017, he received the Overseas Outstanding Contribution Award from China Computer Federation.

Advances in edge computing will boost more smart applications including AI models, AR, video analytics, and Industrial IoT applications. In an advanced edge computing environment, edge nodes of multiple stockholders are interconnected to facilitate the share of data and computation resources, and to collaborate on joint task execution using the shared resources. A fundamental issue is how to optimize the performance of collaborative task execution in terms of various metrics. In this talk, I will first introduce the major approaches to collaborative task execution, i.e., task partitioning, task allocation and task migration. In particular, I will present our recent work on multi-user multi-resource task partitioning, which is a new challenge and was not solved before. I will also highlight our works in data aware task allocation and task migration addressing the challenges arising from collaborative edge computing. Finally, I will conclude the talk by pointing out some future directions in this topic area.

Keynote 2. Airborne Communication Networks: Challenges and Approaches
Prof. Dapeng Oliver Wu
University of Florida, USA

Dr. Dapeng Oliver Wu received Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University, Pittsburgh, PA, in 2003. Since 2003, he has been on the faculty of Electrical and Computer Engineering Department at University of Florida, Gainesville, FL, where he is currently Professor. His research interests are in the areas of networking, communications, video coding, image processing, computer vision, signal processing, and machine learning.
He received University of Florida Term Professorship Award in 2017, University of Florida Research Foundation Professorship Award in 2009, AFOSR Young Investigator Program (YIP) Award in 2009, ONR Young Investigator Program (YIP) Award in 2008, NSF CAREER award in 2007, the IEEE Circuits and Systems for Video Technology (CSVT) Transactions Best Paper Award for Year 2001, the Best Paper Award in GLOBECOM 2011, and the Best Paper Award in QShine 2006.

Owing to the explosive growth of requirements of rapid emergency communication response and accurate observation services, airborne communication networks (ACNs) have received much attention from both industry and academia. ACNs are subject to heterogeneous networks that are engineered to utilize satellites, high-altitude platforms (HAPs), and low-altitude platforms (LAPs) to build communication access platforms. Compared to terrestrial wireless networks, ACNs are characterized by frequently changed network topologies and more vulnerable communication connections. Furthermore, ACNs have the demand of the seamless integration of heterogeneous networks such that the network quality-of-service (QoS) can be improved. Thus, designing mechanisms and protocols for ACNs poses many challenges. In this talk, I will present mechanisms and protocols for ACNs and point out future research directions.

Keynote 3. Battery-free Sensing and Computing in Age of Everything Connected
Prof. Keqiu Li
Tianjin University, China

Keqiu Li is currently a full professor, the dean of the College of Intelligence and Computing, Tianjin University, China. He is the recipient of National Science Foundation for Distinguished Young Scholars of China. He received his bachelor’s and master’s degrees from the Department of Applied Mathematics at the Dalian University of Technology in 1994 and 1997, respectively. He received the Ph.D. degree from the Graduate School of Information Science, Japan Advanced Institute of Science and Technology in 2005. He keeps working on the topics of mobile computing, datacenter, and cloud computing. He has more than 150 papers published on prestigious journals or conferences such as TON, TPDS, TC, TMC, INFOCOM, ICNP, etc.


Internet of Things (IoT) brings us to a totally new age of everything connected. Built on billions of smart IoT devices, people, things, processes, and data can be deeply fused to create a safe, efficient, and intelligent world for us and unprecedented opportunities can also be expected. Among various IoT devices, Radio Frequency Identification (RFID) technology has attracted much attention from both academia and industry communities because of its battery-free sensing and computing capabilities. In this talk, I will discuss three RFID application problems including large-scale inventory, indoor localization based on mobile RF-robot, and human-object interaction, which are practically important for future smart logistics and warehousing scenarios. For each topic, I will first trace its development history and then point out the key challenging issues. After that, the related works made by my group to address these problems will be presented. Finally, future works and possible research directions will be also discussed.

Keynote 4. Cyber-Physical-Social Intelligence: System Design and Data Analytics
Prof. Laurence T. Yang
St Francis Xavier University, Canada

Laurence T. Yang got his BE in Computer Science and Technology and BSc in Applied Physics both from Tsinghua University, China and Ph.D in Computer Science from University of Victoria, Canada. He is a professor and W.F. James Research Chair at St. Francis Xavier University, Canada. His research includes parallel and distributed computing, embedded and ubiquitous/pervasive computing, and big data. He has published around 400 international journal papers in the above areas, of which half on top IEEE/ACM Transactions and Journals, others mainly on Elsevier, Springer and Wiley Journals. In recent several years, 5 and 24 papers have been listed as top 0.1% and top 1% highly-cited ESI papers, respectively.
He has been involved actively act as a steering chair for 6+ IEEE international conferences. He served as the vice-chair of IEEE CS Technical Committee of Supercomputing Applications (2001-2004), the chair of IEEE CS Technical Committee of Scalable Computing (2008-2011). He was the vice-chair (2014) and the chair (2015) of IEEE Canada Atlantic Section. Now he is the chair of IEEE CS Technical Committee of Scalable Computing (2018-), the co-chair of IEEE SMC Technical Committee on Cybermatics (2016-) and the vice-chair of IEEE CIS Technical Committee on Smart World (2016-2018).In addition, he was the editors-in-chief of several international journals. Now he is serving as an editor for many international journals (such as IEEE Systems Journal, IEEE Access, Future Generation of Computer Systems (Elsevier), Information Sciences (Elsevier), Information Fusion (Elsevier), Big Data Research (Elsevier), etc). He has been acting as an author/co-author or an editor/co-editor of more than 25 books from well-known publishers. He has been invited to give around 40 keynote talks at various international conferences and symposia. His recent honours and awards include Clarivate Analytics Highly Cited Researcher (2019), Fellow of Engineering Institute of Canada (2019), AMiner Most Influential Scholar Award for Internet of Things (2018), IEEE TCCPS Distinguished Leadership Award on Cyber-Physical Systems (2018), IEEE SCSTC Life-Career Achievement Award on Smart Computing (2018), Fellow of Canadian Academy of Engineering (2017), IEEE System Journal Best Paper Award (2017), IEEE TCSC Award for Excellence in Scalable Computing (2017), and the PROSE Award on Engineering and Technology (2010).

The booming growth and rapid development in embedded systems, wireless communications, sensing techniques and emerging support for cloud computing and social networks have enabled researchers and practitioners to create a wide variety of Cyber-Physical-Social Systems (CPSS) that reason intelligently, act autonomously, and respond to the users’ needs in a context and situation-aware manner. The CPSS are the integration of computation, communication and control with the physical world, human knowledge and sociocultural elements. It is a novel emerging computing paradigm and has attracted wide concerns from both industry and academia in recent years. Currently, CPSS are still in their infancy stage. Our first ongoing research is to study effective and efficient approaches for CPSS modeling and general system design automation methods, as well as methods analyzing and/or improving their power and energy, security, trust and reliability features. Once the CPSS have been designed, they collect massive data (Volume) from the physical world by various physical perception devices (Variety) in structured/semi-structured/unstructured format and respond the users’ requirements immediately (Velocity) and provide the proactive services (Veracity) for them in physical space or social space. These collected big data are normally high dimensional, redundant and noisy, and many beyond the processing capacity of the computer systems. Our second ongoing research is focused on the Data-as-a-Service framework, which includes data representation, dimensionality reduction, incremental and distributed processing (securely on cloud), deep learning, clustering, prediction and proactive services, aiming at representing and processing big data generated from CPSS, providing more valued smart services for human and refining the previously designed CPSS.
This talk will present our latest research on these two directions. Corresponding case studies in some applications such as smart home and traffics will be shown to demonstrate the feasibility and flexibility of the proposed system design methodology and analytic framework.

Keynote 5. Aero, Terra, Human: Next Generation Disaster Response Platform
Prof. Mianxiong Dong
Muroran Institute of Technology, Japan

Mianxiong Dong received B.S., M.S. and Ph.D. in Computer Science and Engineering from The University of Aizu, Japan. He became the youngest ever Professor of Muroran Institute of Technology, Japan where he currently serves Advisor to Executive Director, and Vice Director of Office of Institutional Research. He was a JSPS Research Fellow with School of Computer Science and Engineering, The University of Aizu, Japan and was a visiting scholar with BBCR group at University of Waterloo, Canada supported by JSPS Excellent Young Researcher Overseas Visit Program from April 2010 to August 2011. Dr. Dong was selected as a Foreigner Research Fellow (a total of 3 recipients all over Japan) by NEC C&C Foundation in 2011. His research interests include Wireless Networks, Cloud Computing, and Cyber-physical Systems. He has received multiple best paper awards including 2017 IET Communications Premium Award and IEEE ComSoc CSIM Best Conference Paper Award 2018. Dr. Dong serves as an Editor for IEEE Transactions on Green Communications and Networking (TGCN), IEEE Communications Surveys and Tutorials, IEEE Network, IEEE Wireless Communications Letters, IEEE Cloud Computing, IEEE Access. He has been serving as the Vice Chair of IEEE Communications Society Asia/Pacific Region Information Services Committee and Meetings and Conference Committee, Leading Symposium Chair of IEEE ICC 2019, Student Travel Grants Chair of IEEE GLOBECOM 2019. He is the recipient of IEEE TCSC Early Career Award 2016, IEEE SCSTC Outstanding Young Researcher Award 2017, The 12th IEEE ComSoc Asia-Pacific Young Researcher Award 2017, Funai Research Award 2018 and NISTEP Researcher 2018 (one of only 11 people in Japan) in recognition of significant contributions in science and technology. He is currently the Member of Board of Governors and Chair of Student Fellowship Committee of IEEE Vehicular Technology Society, and Treasurer of IEEE ComSoc Japan Joint Sections Chapter. He is Clarivate Analytics 2019 Highly Cited Researcher (Web of Science).

"How to face the threat of natural disasters" is always an important research topic. Currently, the mainstream research of disaster management is how to accurately and promptly forecast and notify such as earthquake early warning, but since complete disaster prevention is impossible, we still have to focus on the rapid response after disasters. In addition, to gain insight into the real-time situation of the affected area, we need the two-way communication between affected area and outside world. However, once the network infrastructure suffers from disasters, connections can be interrupted and support cannot reach affected area. Moreover, it is difficult to reconstruct the communication line from scratch. In order to achieve a set of post-disaster two-way communication solutions not relying on traditional network infrastructure, we design a next generation disaster response platform. There are mainly three problems to solve in this platform. First, the connections among users near to each other. Second, the connections between users and access points (APs). Third, the connections between APs and access network to outside world. To figure out first problem, we are going to make use of Device-to-Device (D2D) emergency communication in gathering users within the range of 100+m. Then for the second one, we take advantage of the high mobility of UAVs in fast building the emergency network with the range of 1000+m. For the third one, to realize the connection to outside world, we apply lower power wide area network (LPWAN) to expand the range to 10000+m.

Keynote 6. Towards Sustainable Smart Society: Big Data Driven Approaches
Prof. Liangxiu Han
Manchester Metropolitan University, UK

Prof. Liangxiu Han has a PhD in Computer Science from Fudan University, Shanghai, P.R.China (2002). She is currently a full Professor of Computer Science at the Department Computing and Mathematics, Manchester Metropolitan University. Her research areas mainly lie in the development of novel big data analytics/Machine Learning/AI, and development of novel intelligent architectures that facilitates big data analytics (e.g., parallel and distributed computing, Cloud/Service-oriented computing/data intensive computing) as as well as applications in different domains (e.g. Health, Precision Agriculture, Smart Cities, Cyber Security, Energy, etc.) using various large scale datasets such as images, sensor data, network traffic, web/texts and geo-spatial data. As a Principal Investigator (PI) or Co-PI, Prof. Han has been conducting research in relation to big data processing and data mining, cloud computing/parallel and distributed computing (funded by EPSRC, BBSRC, Innovate UK, Horizon 2020, British Council, Royal Society, Industry, Charity, respectively, etc.).
Prof. Han is a member of EPSRC Peer Review College, an independent expert for Horizon 2020 proposal evaluation/mid-term project review, and British Council Peer Review Panel. She is served as an associate editor/a guest editor for a number of reputable international journals (e.g. IEEE Access, PPNA, Journal of Medical Systems, etc.) and a chair (or Co-Chair) for organisation of a number of international conferences/workshops in the field. She has been invited to give a number of keynotes and talks on different occasions (including international conferences, national and international institutions/organisations).

By 2020, the total size of digital data generated by social networks, sensors, biomedical imaging and simulation devices, will reach an estimated 44 Zettabytes (e.g. 44 trillion gigabytes) according to IDC report. This type of 'big data', together with the advances in information and communication technologies such as Internet of things (IoT), connected smart objects, wearable technology, ubiquitous computing, is transforming every aspect of modern life and bringing great challenges and spectacular opportunities to fulfill our dream of a sustainable smart society.
This talk will focus on new developments and methods based on big data driven approaches to address society challenges. The talk will also present real case studies to demonstrate how we apply big data approaches in various application domains such as Health, Food, Smart Cities, etc. to realize the smart society.

Keynote 7. A Data-Driven Model for Managing Information Services in the Internet of Things
Prof. Lu Liu
University of Leicester, UK

Prof. Lu Liu is the Head of School of Informatics at the University of Leicester, UK. Professor Liu received his PhD degree from Surrey Space Centre at the University of Surrey, UK. Professor Liu's research interests are in the areas of data analytics, service computing, cloud computing and the Internet of Things. He has over 200 scientific publications in reputable journals, academic books and international conferences. Professor Liu has secured many research projects which are supported by research councils, BIS, Innovate UK, British Council and leading industries. He received the Vice-Chancellor’s Awards for Excellence in Doctoral Supervision in 2018, BCL Faculty Research Award in 2012 and the Promising Researcher Award in 2011. He has been the recipient of 5 Best Paper Awards from international conferences and was invited to deliver 6 keynote speeches at international conferences. Professor Liu is a Fellow of BCS (British Computer Society) and currently serve as an Editorial Board member of 6 international journals and the Guest Editor for 15 international journals. He has chaired over 30 international conference and workshops, and presently or formerly serves as the program committee member for over 60 international conferences and workshops.

Given the recent proliferation in the number of smart devices connected to the Internet, the era of Internet of Things (IoT) is challenged with massive amounts of data generation and service provision. Efficient management of information services is one of the prevailing challenges in the era of IoT and Big Data. To address this challenge, Professor Liu will introduce his recent research work on data-driven service computing for IoT with the process of how to adaptively index services, how to efficiently discover services, how to securely request services and finally dependably integrate services in a dynamic IoT environment. Professor Liu will further present his work on data-driven service development for engineering data analytics, social data analytics, workload data analytics and commercial data analytics.

Keynote 8. Information Quality Assurance in Mobile Crowdsensing Systems
Prof. Ruiyun Yu
Northeastern University, China

Dr. Ruiyun Yu is currently a professor and vice dean of the Software College at Northeastern University, China. He received his Ph. D. and M.S. degree both in computer science and bachelor degree in Mechanical Engineering from Northeastern University in 2009, 2004, and 1997, respectively. He is one of the Baiqianwan Talents of Liaoning Province, China (Hundred-Talent Level), and one of the Leading Talents of Shenyang, China. He worked as a visiting scholar in the University of California, Irvine (Dec. 2018 – Mar. 2019), and the University of Texas at Arlington (Sep. 2006 – Oct. 2007). His research interests include crowd sensing and computing, computer vision, mixed reality, etc. He has been leading 20 projects funded by the National Natural Science Foundation Committee of China, the Ministry of Education of China, etc. He has published more than 70 papers on high-quality journals and conferences, including ACM TOSN, IEEE TSC, PMC, ICCV, KDD, etc. He received the Best Paper Award from IEEE MSN 2013. He is also a winner of a series of academic awards, such as 2018 National Teaching Achievement Award of China (Second prize) , 2017 China Big Data Academic Innovation Award, 2018 BaoSteel Outstanding Teacher Award, and so on. He has been serving as the TPC chair of MSN 2018, Publicity co-chair of WoWMoM 2019, and TPC members of a number of international conferences. He is now a committee member of the CCF IoT Committee, deputy director of the Expert Committee of National University AI and Big Data Innovation Alliance (China), Deputy Director General of University-Enterprise Alliance for Liaoning Big Data Industry, and vice director of the Joint Lab of Digital Exhibition Design and Technology between LuXun Academy of Fine Arts and Northeastern University.

Mobile Crowd Sensing (MCS) is a promising paradigm for cross-space and large-scale data collection. MCS extends the vision of participatory sensing by leveraging both sensory data from mobile devices offline and interactive data from mobile social networks online. Based on the crowdsourcing model and big data analysis, MCS can complete sensing and computing tasks in the target areas efficiently.
Due to the diversities of participation willingness and personal constraints, the information (including user capacities, sensing data, and so on) updated by users is not always reliable, which will lead to serious Quality of Information (QoI) problems in the MCS system. Therefore, it’s important to analyze the influence factors of QoI and corresponding solutions in specific scenarios. The following three issues about QoI should be investigated: (1) How to define QoI in MCS? (2) How to design an effective MCS framework to enforce QoI? (3) What are the key technologies to guarantee QoI? To answer these questions, this talk firstly summarizes four factors impacting QoI: information incompleteness, information redundancy, information inconsistency, and information incredibility. Then a novel MCS framework is put forward to improve QoI. Finally, this talk presents some of our research on QoI, and raise several open issues in MCS systems.
Important Dates
Workshop Proposal Due:
15 April 2019

Paper Submission Deadline:
30 June 2019

Authors Notification:
25 August 2019

Camera-Ready Paper Due:
21 September 2019

Early Registration Due:
21 September 2019

Conference Date:
21-23 October 2019


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