Important Dates

April 8, 2018

Workshop Proposal Due

May 30, 2018

Special Session Proposal Due

May 15, 2018

Regular Paper Submission Deadline

May 30, 2018

Industry Session Paper Submission Due

May 30, 2018

WiP Paper Submission Due

May 30, 2018

Poster/Demo Submission Due

July 8, 2018

Authors Notification

August 8, 2018

Camera-ready & Registration


Panel 2018

Panel Sessions

You are Welcome to Attend 3 Panel Sessions Shared by IEEE Smart World Congress 2018 and Co-Located IEEE UIC 2018 & IEEE ATC 2018 & IEEE ScalCom 2018 & IEEE CBDCom 2018 & IEEE IoP 2018 & IEEE SCI 2018 Conferences!

Part I: Plenary Panel Session

Part II: Panel Session-2

Part III: Panel Session-3

Part I: Plenary Panel Session

Title: Future Computing in Smart World

9:10 a.m. to 10:30 a.m., October 10, 2018

. Due to the information intensive nature of many services required in the smart world, information technology, especially those aspects heavily relying on computing, will continue to be vital in building the smart world. Recent advances in areas, such as big data, multimedia, machine learning, language processing, as well as powerful and intelligent computing systems, ranging from large-scale cloud systems to mobile intelligent devices, and Internet of Things, have shown significant progress in meeting the challenges of providing the desirable services in the smart world. Furthermore, quality of services, such as security, privacy, dependability and adaptability, need to be incorporated in the services with human-centric characteristics in the smart world environment. In doing so, some existing research areas, such as applications of neuron networks and blockchains, have emerged as promising approaches to addressing certain challenging problems in the smart world due to the recent major progress made in other areas. such as big data, algorithms, computing systems, networking, and sensing. In this panel session, we have four distinguished panelists with different research and practical background in computing will discuss the future research directions of computing in the smart world in their opening remarks, followed by open discussions from the floor.

Stephen S. Yau, Arizona State University, USA
Zhi Jin, Peking University, China
Dame Wendy Hall, University of Southampton, UK
Xin Yao, University of Birmingham, UK and
Southern University of Science and Technology, China
Mazin Yousif, Digital Transformation at T-Systems, International, USA

Prof. Stephen S, Yau
Arizona State University, USA

Biography. Stephen S, Yau is Professor of Computer Science and Engineering at Arizona State University (ASU), Tempe, Arizona, USA. He served as the chair of the Department of Computer Science and Engineering, and later as the director of Information Assurance Center at ASU. Previously, he was on the faculties of Northwestern University, Evanston, Illinois, and University of Florida, Gainesville.

He served as the president of the Computer Society of the Institute of Electrical and Electronics Engineers (IEEE) and the editor-in-chief of IEEE COMPUTER magazine. He organized many major conferences, including the 1989 World Computer Congress sponsored by the International Federation for Information Processing (IFIP), and the 2018 IEEE World Congress on Services in July, 2018.

His current research includes cloud and services computing, cyber security, software engineering, ubiquitous computing and Internet-of-Things. He has received many awards and recognitions, including the Tsutomu Kanai Award and Richard E. Merwin Award of the IEEE Computer Society, and the Outstanding Contributions Award of the Chinese Computer Federation. He is a Life Fellow of the IEEE and a Fellow of the American Association for the Advancement of Science. He received the Ph.D. degree from the University of Illinois, Urbana in electrical engineering.

Prof. Zhi Jin
Peking University, China

Biography. Dr. Zhi Jin is currently a professor of Computer Science at Peking University. She is deputy director of Key Lab of High Confidence Software Technologies (Ministry of Education) at Peking University. She worked at the Academy of Mathematics and System Sciences, Chinese Academy of Science from 1994 to 2009. Dr. Jin's research interests include software engineering, requirements Engineering, knowledge engineering, and machine learning. She is/was principle investigator of over 10 national competitive grants, including the chief scientist of a national basic research project (973 project) of the Ministry of Science and Technology of China.

Prof. Dame Wendy Hall
University of Southampton, UK

Biography. Dame Wendy Hall, DBE, FRS, FREng is Regius Professor of Computer Science, Pro Vice-Chancellor (International Engagement) at the University of Southampton, and is the Executive Director of the Web Science Institute.

With Sir Tim Berners-Lee and Sir Nigel Shadbolt she co-founded the Web Science Research Initiative in 2006 and is the Managing Director of the Web Science Trust, which has a global mission to support the development of research, education and thought leadership in Web Science.

She became a Dame Commander of the British Empire in the 2009 UK New Year's Honours list, and is a Fellow of the Royal Society.

She has previously been President of the ACM, Senior Vice President of the Royal Academy of Engineering, a member of the UK Prime Minister’s Council for Science and Technology, was a founding member of the European Research Council and Chair of the European Commission’s ISTAG 2010-2012, was a member of the Global Commission on Internet Governance, and until June 2018, was a member of the World Economic Forum’s Global Futures Council on the Digital Economy.

Dame Wendy was co-Chair of the UK government’s AI Review, which was published in October 2017, and has recently been announced by the UK government as the first Skills Champion for AI in the UK.

Prof. Xin Yao
University of Birmingham, UK and Southern University of Science and Technology, China

Biography. Xin Yao is a Chair Professor of Computer Science at the Southern University of Science and Technology, Shenzhen, China, and a part-time Chair Professor at School of Computer Science, the University of Birmingham, UK. His major research interests include evolutionary computation, ensemble learning and search-based software engineering. His work won the 2001 IEEE Donald G. Fink Prize Paper Award, 2010, 2016 and 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Awards, 2010 BT Gordon Radley Award for Best Author of Innovation (Finalist), 2011 IEEE Transactions on Neural Networks Outstanding Paper Award, and many other best paper awards. He received the prestigious Royal Society Wolfson Research Merit Award in 2012 and the IEEE CIS Evolutionary Computation Pioneer Award in 2013.

Prof. Mazin Yousif
Digital Transformation at T-Systems, International, USA

Biography. Mazin Yousif is the Vice President of Digital Transformation at T-Systems, International. Before that, he with IBM Canada, Avirtec, Intel and IBM. He is the founding editor-in-chief of the IEEE Cloud Computing Magazine. He chaired the Advisory Board of the European Research Consortium for Informatics and Mathematics (ERCIM), and founded the NSF Industry/University Cooperative Research Center on Automatic Computing with three Universities (Florida, Arizona & Rutgers).
He was an adjunct professor in several universities, including Duke and North Carolina State Universities. He has served as the General Chair or Program Chair for many conferences and serves on the editorial board of many journals. He is a frequent speaker at academic and industry conferences on topics related to cloud computing, the Internet of Things, big data and digital transformation. He finished his Master and PhD degrees from the Pennsylvania State University in 1987 and 1992, respectively.


Part II: Panel Session-2

Title: Big Data, AI and Applications

15:50 p.m. to 18:30 p.m., October 9, 2018

. Big data is a term used to refer to the study and applications of data sets that are so big and complex that traditional data-processing application software are inadequate to deal with them. AI plays key role in the big data processing, transmission, and applications. Big data and AI are also big challenges in computer sciences. In this panel, we are delighted to have distinguished planelists share their versions on the research directions and challenges in big data, AI, and applications followed by open discussions.

Jiannong Cao, The Hong Kong Polytechnic University, Hong Kong
Jie Li, Shanghai Jiaotong University, China
Weijia Jia, University of Macau, Macau
Xiaohua Jia, City University of Hong Kong, Hong Kong
Qun Jin, Waseda University, Japan
Qing Li, City University of Hong Kong, Hong Kong
Junzhou Luo, Southeast University, China
Geyong Min, University of Exeter, UK
Chengzhong Xu, Shenzhen Institutes of Advanced Technology of Chinese Academy of Sciences, China

Prof. Jiannong Cao, The Hong Kong Polytechnic University, Hong Kong
Title: Cross-Domain Big Data Fusion and Analytics
Abstract: Big data analytics using cross-domain multi-source datasets allow us to study the phenomena of our interest by fusing views from multiple angles, facilitating us to identify meaningful problems and discover new insights. However, we need methods and techniques to solve the challenges like heterogeneity, uncertainty and high dimensionality in analyzing cross-domain datasets. In this talk, I will describe a general framework of cross-domain big data analytics and share our work of fusing and analyzing datasets from multiple domains to uncover the underlying patterns, correlations and interactions. Example applications include human and urban dynamics like predicting traffic congestions, optimize demand dispatching in emerging on-demand services, and designing wireless networks.

Prof. Weijia Jia, University of Macau, Macau
Title: Smart City Research at The University of Macau
Abstract: We would like to introduce the research status on smart city at the University of Macau. Especially the newly set up of state key lab and their research activity.

Prof. Xiaohua Jia, City University of Hong Kong, Hong Kong
Title: Privacy-Preserving Service Matching Platforms on Public Clouds
Abstract: As smart phones and other mobile devices are popularly used nowadays, users can use mobile phone to subscribe services, as well as publish services they would like to offer. There are many platforms to facilitate users to publish or subscribe their services over the Internet. Most of such platforms are hosted on public clouds where security and privacy is a major concern of users. It’s essential to make the trading platforms secure and privacy-preserving for users. In this talk, we first present pMatch: a privacy-preserving task matching scheme for crowdsourcing systems. With pMatch, task requesters encrypt the information of tasks and publish the encrypted task to the platform, and workers encrypt their interests and submit them to the platform for task subscription. The platform can match the tasks to the most suitable workers over the encrypted data. Thus, no private information about either the tasks or the workers is leaked to the cloud server. Then, we present pRide: a privacy-preserving ride-hailing system. With pRide, both riders and drivers submit their encrypted location information to the platform, and the platform is able to match a rider with its closest driver without learning any location information of either the rider or the drivers.

Prof. Qun Jin, Waseda University, Japan
Title: Personal analytics and individual modeling for data-driven and AI-enhanced healthcare
Abstract: In this talk, after briefly introducing the basic concept of data-driven and AI-enhanced healthcare, our vision and work on personal analytics and individual modeling for smart health to enhance quality of life (QoL) and promote well-being for all of the people will be described and explained. Furthermore, quality control and sustainable use of health data as well as opportunities and issues of data-driven smart health will be addressed and discussed.

Prof. Qing Li, City University of Hong Kong, Hong Kong
Title: Event Management and Multi-dimensional Analysis through EC Mechanism
Abstract: The last 3 decades have witnessed the big changes of data types, scales, and links with neighboring areas, from simple data with closed-world assumption to more complex objects with semi-closed/open assumption, from MB/GB/PB scale to PB/TB/EB/ZB scale, and from loose coupling to tight coupling with areas like Programming, Cloud Computing, IoT, and AI (machine learning in particular). In this talk, I will discuss several aspects of data management from a historical perspective, and through a joint collaboration we initiated, elaborate on the recent and complex types of data like (multi-modal) events for management. In particular, I will start with overviewing techniques of discovering events from multi-modal big data, and elaborate on building an event cube (EC) model to support event queries and analysis. Based on the essential event elements of 5W1H, the discovered events can be organized w.r.t. the dimensions and operated at various levels of granularity through the EC model. In addition, this model greatly facilitates analyzing and mining hidden/inherent relationships among the events, thereby enabling the system to answer the challenging questions of "how" and "why", thereby facilitating the analysis and mining of hidden/inherent relationships among the events effectively.

Prof. Junzhou Luo, Southeast University, China
Title: Industrial Transparent Computing: Architecture and Key Technologies
Abstract: The industrial internet is a highly cooperative global network, which connects machines and humans together for smart manufacturing. While smart manufacturing heavily relies on intelligent, speedy and secure control, novel computing paradigms that support real-time decision with secure execution have garnered much attention from the industrial internet research community. This talk introduces industrial transparent computing (ITC), a promising paradigm for the industrial internet. With ITC, industrial control programs are stored by ITC servers but executed by industrial terminals, hence it can be viewed as a special application scenario of transparent computing. ITC offers a high level of security and reduces complexity and cost of terminals. Moreover, ITC smoothly coordinates the computation and communications between ITC servers and industrial terminals through intelligent optimization, in order to achieve real-time decision and control. In this talk, I will introduce ITC's architecture, elaborate the opportunities and challenges of transparent computing in the industrial environments, and at last, identify future research directions.

Prof. Geyong Min, University of Exeter, UK
Title: Network Big Data Analysis for Future Intelligent Internet
Abstract: Research and development of Next-Generation Internet (NGI) have become a global endeavour. With an overwhelming amount of data pouring into the Internet, network domains are embracing an unprecedented wave of traffic flows and are stepping into the era of network big data. To achieve high performance and high availability of NGI, our vision is to conduct efficient data analysis in order to dig valuable insights and knowledge hidden in network big data for improving the design, operation, and management of NGI. We will present the innovative big data processing technologies, real-time incremental data analysis tools, and cost-effective distributed platform we have recently developed to support better decision-making for network design, anomaly detection, fault localization, resource management and optimization.

Prof. Chengzhong Xu, Shenzhen Institutes of Advanced Technology of Chinese Academy of Sciences, China
Title: Intelligent Scheduling and QoS-aware Resource Management based on Machine Learning
Abstract: Scheduling and resource management is a key to success of cloud datacenter. Today’s cloud DC is characteristic of deep heterogeneity in nodal architecture and group dynamics in workloads. Applications tend to be deployed in a mixed way between online and offline jobs. Traditional scheduling and resource management become hard to meet the requirements for efficiency, job completion time, and fairness in a fine-grained manner. Machine learning is by nature able to deal with uncertainty. The non-deterministic setting of Cloud DCs renders ML plausible. The vast log data accumulated in the management of cloud DCs paves a way for deep learning approaches. This talk will discuss the opportunities and challenges of ML for the management of cloud DCs. Our past experience will be presented as well.

Prof. Jiannong Cao
The Hong Kong Polytechnic University, Hong Kong

Biography. Dr. 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.
Dr. Cao’s 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.
Dr. Cao 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, including IEEE TPDS, IEEE TCC, IEEE TC, IEEE Network, ACM TOSN, Elsevier Pervasive and Mobile Computing Journal (PMCJ), and Springer Peer-to-Peer Networking and Applications.
Dr. Cao is a fellow of IEEE and ACM distinguished member. In 2017, he received the Overseas Outstanding Contribution Award from China Computer Federation.

Prof. Jie Li
Shanghai Jiaotong University, China

Biography. Jie Li is a Chair Professor of Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai, China where he is a chair professor. His current research interests are in big data, cloud computing, mobile distributed computing and networking, network security, OS, modeling and performance evaluation of information systems. He was a full professor in Department of Computer Science, University of Tsukuba, Japan. He was a visiting Professor in Yale University, USA, Inria Sophia Antipolis and Inria Grenoble-Rhone-Aples, France. He is the co-chair of IEEE Big Data Technical Community and the founding Chair of IEEE ComSoc Technical Committee on Big Data and the Co-Chair of IEEE Big Data Community. He serves as an associated editor for many IEEE journals and transactions. He has also served on the program committees for several international conferences.
He received the B.E. degree in computer science from Zhejiang University, Hangzhou, China, the M.E. degree in electronic engineering and communication systems from China Academy of Posts and Telecommunications, Beijing, China. He received the Dr. Eng. degree from the University of Electro-Communications, Tokyo, Japan.

Prof. Weijia Jia
University of Macau, Macau

Biography. Weijia Jia is currently a Chair Professor at University of Macau while he is taking no-pay leave from the position of Zhiyuan Chair Prof from Shanghai Jiaotong University, China (he received 2013 China 1000 Talent Award). He received BSc/MSc from Center South University, China in 82/84 and Master of Applied Sci./PhD from Polytechnic Faculty of Mons, Belgium in 92/93, respectively, all in computer science. For 93-95, he joined German National Research Center for Information Science (GMD) in Bonn (St. Augustine) as research fellow. From 95-13, he worked in City University of Hong Kong as a full professor in Computer Science Dept. His research interests include smart city; next generation IoT, knowledge graph constructions; multicast and anycast QoS routing protocols, wireless sensor networks and distributed systems. In these fields, he has over 400 publications in the prestige international journals/conferences and research books and book chapters (H-inedx 46 as at 2018). His past research contributions can be summaried from the aspects of vertex cover and efficient anycast for optimal placement and routing of severs/sensors in many applications of mobile/sensor/wireless networks and the Internet. He received Best Product Awards from the Internatonal Science&Tech. Expos (Shenzhen) in 2011/2012 and 1st- Prize of Scientific Research Awards from Ministry of Education of PR China in 2017 (list 2). He has served as area editor for various prestige international journals, chair and PC member/keynote speaker for many prestige international conferences. He is the Senior Member of IEEE and the Member of ACM.

Prof. Xiaohua Jia
City University of Hong Kong, Hong Kong

Biography. Xiaohua Jia received his BSc (1984) and MEng (1987) from University of Science and Technology of China, and DSc (1991) in Information Science from University of Tokyo. He is currently Chair Professor with Dept of Computer Science at City University of Hong Kong. His research interests include cloud computing and distributed systems, data security and privacy, computer networks and mobile computing. Prof. Jia is an editor of IEEE Internet of Things, IEEE Trans. on Parallel and Distributed Systems (2006-2009), Wireless Networks, Journal of World Wide Web, Journal of Combinatorial Optimization, etc. He is the General Chair of ACM MobiHoc 2008, TPC Co-Chair of IEEE GlobeCom 2010 – Ad Hoc and Sensor Networking Symp, Area-Chair of IEEE INFOCOM 2010, 2015-2017. He is Fellow of IEEE.

Prof. Qun Jin
Waseda University, Japan

Biography. Qun Jin is a professor at the Networked Information Systems Laboratory, Department of Human Informatics and Cognitive Sciences, Faculty of Human Sciences, Waseda University, Japan. Dr. Jin has been extensively engaged in research works in the fields of computer science, information systems, and social and human informatics. He seeks to exploit the rich interdependence between theory and practice in his work with interdisciplinary and integrated approaches. His recent research interests cover human-centric ubiquitous computing, behavior and cognitive informatics, big data, data quality assurance and sustainable use, personal analytics and individual modeling, intelligence computing, blockchain, cyber security, cyber-enabled applications in healthcare, and computing for well-being. Dr. Jin is a senior member of Association of Computing Machinery (ACM), Institute of Electrical and Electronics Engineers (IEEE), and Information Processing Society of Japan (IPSJ).

Prof. Qing Li
City University of Hong Kong, Hong Kong

Biography. Qing Li is a Professor at the Department of Computer Science, and the Director of the Engineering Research Centre on Multimedia Software at the City University of Hong Kong, where he joined as a faculty member since Sept 1998. He received his B.Eng. from Hunan University (Changsha), and M.Sc. and Ph.D. degrees from the University of Southern California (Los Angeles), all in computer science. His research interests include multi-modal data management, conceptual data modeling, social media and Web services, and e-learning systems. He has authored/co-authored over 300 publications in these areas. He is actively involved in the research community and has served as an associate editor of a number of major technical journals including IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Transactions on Internet Technology (TOIT), Data and Knowledge Engineering (DKE), World Wide Web (WWW), and Journal of Web Engineering, in addition to being a Conference and Program Chair/Co-Chair of numerous major international conferences. He also sits in the Steering Committees of DASFAA, ACM RecSys, IEEE U-MEDIA, ER, and ICWL. Prof. Li is a Fellow of IET (UK), a senior member of IEEE (US) and a distinguished member of CCF (China).

Prof. Junzhou Luo
Southeast University, China

Biography. Dr. Junzhou Luo received his B.S. degree in applied mathematics from Southeast University in 1982, and then got his M.S. and Ph.D. degree in computer networks from Southeast University in 1992 and in 2000 respectively. From 1982 he has been a faculty member at the School of Computer Science and Engineering, Southeast University. His research interests include network architecture, protocol engineering, network security, cloud computing and big data. In the past 30 years, he finished 35 research projects supported by Natural Science Foundation of China and the other China national or Jiangsu Provincial science and technology programs, and published over 450 journal and conference papers on computer networks. He has participated in the AMS physics experiment 14 years led by Professor Samuel C. C. Ting, the Nobel Prize Laureate in physics, and has set up AMS Science Operation Center at Southeast University for the AMS data processing. Now he is a professor and the dean of the School of Computer Science and Engineering, Southeast University. He is IEEE member and ACM member, and he is Co-Chair of IEEE SMC Technical Committee on Computer Supported Cooperative Work in Design and the Chair of ACM SIGCOMM China.

Prof. Geyong Min
University of Exeter, UK

Biography. Professor Geyong Min is a Chair in High Performance Computing and Networking and the academic lead of Computer Science in the College of Engineering, Mathematics and Physical Sciences at the University of Exeter, UK. His recent research has been supported by European Horizon-2020, FP6/FP7, UK EPSRC, Royal Society, Royal Academy of Engineering, and industrial partners including British Telecom, IBM, Huawei Technologies, INMARSAT, Motorola, and InforSense Ltd. His research interests include Future Internet, Wireless Networks, Mobile and Ubiquitous Computing, Cloud Computing, High Performance Computing, and Big Data. He has published more than 200 research papers in leading international journals including IEEE/ACM Transactions on Networking, IEEE Journal on Selected Areas in Communications, IEEE Transactions on Communications, IEEE Transactions on Wireless Communications, IEEE Transactions on Multimedia, IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, and at reputable international conferences, such as SIGCOMM-IMC, ICDCS, IPDPS, GLOBECOM, and ICC. He is an Associated Editor of several international journals, e.g., IEEE Transactions on Computers, and IEEE Transactions on Cloud Computing. He served as the General Chair/Program Chair of a number of international conferences in the area of Information and Communications Technologies.

Prof. Chengzhong Xu
Shenzhen Institutes of Advanced Technology of Chinese Academy of Sciences, China

Biography. Chengzhong Xu, IEEE Fellow, is the Director and Chief Scientist of the Institute of Advanced Computing and Digital Engineering, Shenzhen Institutes of Advanced Technology of Chinese Academy of Sciences. He is also the Director of Cloud Security Engineering Center of Guangdong Province. He was in the faculty of Wayne State University. His research interest is mainly in distributed and parallel systems, cloud computing and big data applications, and data-driven intelligence, with an emphasis on resource management for performance, availability, reliability, energy efficiency, and security. He published more than 250 papers in journals and conferences in these areas and received more than 8500 citations with H-index of 46. He was the author of book “Scalable and Secure Internet Services and Architecture” (Chapman & Hall/CRC Press, 2005) and the leading co-author of book “Load Balancing in Parallel Computers: Theory and Practice” (Kluwer Academic/Springer, 1997). He received the Best Paper Nominee Award of HPCA’2012, HPDC’2013, Cluster’2016, and ICPP’2015. He served on a number of journal editorial boards, including IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Cloud Computing, Journal of Parallel and Distributed Computing, and China Science. He is the Chair of IEEE Technical Committee on Distributed Processing. He was a recipient of Career Development Chair Award, and the President’s Award for Excellence in Teaching of WSU. He was also a recipient of the “National Thousands Talent Program” (千人计划) “Outstanding Young Oversea Chinese Scholar”(海外杰青) award. He received his B.S. and M.S. degrees from Nanjing University in 1986 and 1989, respectively, and his Ph.D. degree from the University of Hong Kong in 1993, all in Computer Science. For more information, visit


Part III: Panel Session-3

Title: Cyber-Physical-Social Sensing for the Future Smart World

15:50 p.m. to 18:30 p.m., October 10, 2018.

. Sensing is the ability of any living beings to collect data and perceive the physical world. It is the foundation of knowledge acquisition and formation of intelligence. With the advancement of information and communication technologies, the notion of sensing grows beyond the physical world with data coming from cyber world. Advanced technologies such as social and crowd computing, smart wearables and implants, cognitive computing, and brain informatics, while achieving fruitful results in their own arena, also bring tremendous challenges on the interoperability, scalability, dependability, security and privacy for different Smart World applications. The heterogeneous and transdisciplinary nature of modern Cyber-Physical-Social sensing need to be addressed carefully to facilitate the development of the future Smart World, which aims to provide intelligent, interactive, collaborative and safe services to human beings. In this session, we have six distinguished panellists who will offer their insights on fusing, interpreting, reasoning, security and privacy preservation aspects of the Cyber-Physical-Social sensing for the future Smart World.

Kevin I-Kai Wang, The University of Auckland, New Zealand
Liming Chen, De Montfort University, UK
Paolo Nesi, University of Florence, Italy
Kim-Kwang Raymond Choo, The University of Texas at San Antonio, USA
Richard Hill, University of Huddersfield, UK
Md Zakirul Alam Bhuiyan, Fordham University, USA
Hao Wang, Norwegian University of Science and Technology, Norway

Prof. Liming Chen, De Montfort University, UK
Title: Sensing and Sensibility Making Sense of Sensing for Smart World
Abstract: The physical world is increasingly full of the internet of things, objects and devices, embedded systems which are generating various data. The cyber world is increasingly full of powerful algorithms which can consume large-scale data, and yet the social and human world are still searching intelligent systems which can support a disruptive way we live, do business, communicate and recreate. While new approaches and modalities of sensing is emerging, producing ever-growing data in terms of volume, diversity and speed, making sense of sensing to support the higher level of intelligence of a plethora of smart systems within smart world is still a challenge. In this talk the speaker will examine the unique characteristics of smart world and ensuing demand on sensing and sensibility. Then he will present his thinking and directions to the way forward, which are aimed at stimulating and enlightening new ideas and approaches in the follow-up panel discussions.

Prof. Paolo Nesi, University of Florence, Italy
Title: Complexity of IOT/IOE Architectures for Smart Service Infrastructures
Abstract: The complexity of smart and sentient applications in smart cities in progressively increasing. The early smart services are becoming more complex to reach higher precision. The initial approaches based on time series are overcome by artificial intelligent approaches based on big data techniques taking into account of multiple data kind, their heterogeneity, low quality and discontinuity, etc. A new degree of complexity has reached smart cities with IOT/IOE demand and corresponding technologies. The integration of open data, real time data and private personal data has alto increased complexity of cyber-physical-social aspects, in which the city users are going to have the full control on the rights associated to their content. In Europe, the new GDRP normative has also contributed to regulate the access and control. In the panel Paolo Nesi is contributing by bringing the experience of building a number of smart city solution and platform exploiting the new technologies and copying with GDPR challenge in the area.

Prof. Kim-Kwang Raymond Choo, The University of Texas at San Antonio, USA
Title: The Dark Side of Cyber-Physical-Social Sensing
Abstract: With an ever increasing source of data (e.g. from devices around us, devices on us, and devices in us), what are the potential security and privacy risks? For example, can devices on or in us tell on us, and if so, how so? In this panel discussion, I will briefly discuss some of the potential security and privacy risks of cyber-physical-social sensing.

Prof. Richard Hill, University of Huddersfield, UK
Title: Analytics for Smart Cities: some emerging challenges
Abstract: As the potential for inter-connectedness between things and services becomes better understood, there are an increasing number of use cases being developed. All of these use cases require measurement and monitoring so that resources can be deployed effectively and exploited appropriately. Not only can we see that there are challenges for exiting and planned uses of smart city technologies, but researchers are faced with the emerging scenarios that demand not only monitoring, but the use of more sophisticated models that may also incorporate dynamic augmentations to the knowledge bases. This talk examines key challenges for current and future demands of analytics services and proposes some potential research questions for further exploration.

Prof. Md Zakirul Alam Bhuiyan, Fordham University, USA
Title: Dependable Privacy Controls in Data Mining in Cyber-Physical-Social Sensing for the Future Smart World
Abstract: Cyber-Physical-Social Sensing (CPSS) can easily be a point of attraction for the cyber-attackers. Advanced technologies such as social and crowd computing bring tremendous challenges on the dependability in privacy controls in data mining for different Smart World applications. The data used for data mining may have privacy breached even before the data goes through the data mining process, i.e., data privacy may be compromised at the time of data collection, after the collected data forwarding or before the decision making through the data mining, resulting in decision-making on an event of interest in the CPSS undependable. This leads to a question, in terms privacy preservation, what would be the quality of the data mining in the CPSS and the decision made through it. The heterogeneous and transdisciplinary nature of modern CPSS needs to address this carefully in order to preserve privacy in different stage of data mining process. In the panel, I will highlight a set of observations similar to the situation above and discuss potential solutions to deal with the situations of the CPSS for the future Smart World.

Prof. Hao Wang, Norwegian University of Science and Technology, Norway
Title: Security in Crowd Sensing and Industrial IoT
Abstract: Crowdsensing is a technique leveraging the crowd power to accomplish sensing tasks collaboratively at a low cost. There are a lot of benefits in introducing crowdsensing to Industrial IoT (IIoT). With the proliferation of wireless sensor devices, the security of transmitting data in such IIoT based crowdsensing networks deserves much attention, especially for the confidential data related with commercial interest and privacy concern. In this talk, I will briefly discuss our recent work for the security in IIoT based crowdsensing networks.

Prof. Kevin I-Kai Wang
The University of Auckland, New Zealand

Biography. Kevin I-Kai Wang received his BE (Hons) and PhD in Computer Systems and Electrical & Electronics Engineering from the University of Auckland in 2004 and 2009, respectively. He worked as a R&D engineer in different industries for designing intelligent sensing and automation systems before re-join the University in 2012. He is currently a Senior Lecturer at the University of Auckland, New Zealand. His current research focuses on power efficient wireless sensor networks for environmental monitoring, industrial automation, and pervasive healthcare applications; statistical learning and context-aware Internet of Things (IoT) systems; middleware and service-oriented architecture. He has served as a reviewer for several reputable journals including IEEE Transactions on Industrial Informatics, Transactions on Industrial Electronics, Transactions on Computers, Transactions on Service Computing, Sensors; and Elsevier PMC, FGCS and JNCA. He is currently an editorial member for the Journal of Ambient Intelligence and Smart Environments, IET Wireless Sensor Systems and Elsevier Ad Hoc Networks.

Prof. Liming Chen
De Montfort University, UK

Biography. Liming Chen is Professor of Computer Science, Head of the Context, Intelligence and Interaction Research Group and its associated Smart Lab in the School of Computer Science and Informatics, De Montfort University, UK. His current research interests include activity modelling and recognition, computational behaviour analysis, personalisation and adaptation of human-machine systems, decision support, smart environments and their application in smart homes and ambient assisted living. He is currently the coordinator of the EU Horizon2020 ACROSSING project “Advanced Technologies and Platform for Smarter Assisted Living”, and has serves as the principal investigator for the EU AAL PIA project, the MobileSage project and FP7 MICHELANGELO project, and a number of projects funded by industry and third countries. Liming has over 170 peer-reviewed publications in internationally recognised high-profile journals and conferences. He is the general chair or program chair for IEEE UIC2017, IEEE HealthCom2017, SAI Computing 2017, IEEE UIC2016, IntelliSys2016, MoMM2015/2014/2013, SAI2015/2013, IWAAL2014, UCAMI2013, and an organising chair of many workshops such as Romart-City2016 and SAGAware2015/2012, associate editor of IEEE THMS, assistant EIC for IJPCC and guest editors for IEEE THMS, PMC and IJDSN. Liming is a member of IEEE, IEEE SMC and the ETTC Task Force on Smart World, and has delivered many talks, keynote and seminars in various forums, conferences, industry and academic events.

Prof. Paolo Nesi
University of Florence, Italy

Biography. Paolo Nesi is a full professor at the University of Florence, Department of Information Engineering, chief of the DISIT lab and research group. His research interests include massive parallel and distributed systems, physical models, semantic computing, big data, artificial intelligence, smart city, cloud, IOT. He is and has been the coordinator of several R&D multipartner international R&D projects of the European Commission such as Snap4City, RESOLUTE, ECLAP, AXMEDIS, WEDELMUSIC, MUSICNETWORK, MOODS and he has been involved in many other projects. He is the coordinator of Sii-Mobility, Km4City actions, has been AHG chair in MPEG ISO.

Prof. Kim-Kwang Raymond Choo
The University of Texas at San Antonio, USA

Biography. Kim-Kwang Raymond Choo received the Ph.D. in Information Security in 2006 from Queensland University of Technology, Australia. He currently holds the Cloud Technology Endowed Professorship at The University of Texas at San Antonio (UTSA), and has a courtesy appointment at the University of South Australia. In 2016, he was named the Cybersecurity Educator of the Year - APAC (Cybersecurity Excellence Awards are produced in cooperation with the Information Security Community on LinkedIn), and in 2015 he and his team won the Digital Forensics Research Challenge organized by Germany's University of Erlangen-Nuremberg. He is the recipient of the 2018 UTSA College of Business Col. Jean Piccione and Lt. Col. Philip Piccione Endowed Research Award for Tenured Faculty, IEEE TrustCom 2018 Best Paper Award, ESORICS 2015 Best Research Paper Award, 2014 Highly Commended Award by the Australia New Zealand Policing Advisory Agency, Fulbright Scholarship in 2009, 2008 Australia Day Achievement Medallion, and British Computer Society's Wilkes Award in 2008. He is also a Fellow of the Australian Computer Society, and an IEEE Senior Member.

Prof. Richard Hill
University of Huddersfield, UK

Biography. Professor Richard Hill is Head of the Department of Computer Science, and Director of the Centre for Industrial Analytics, at the University of Huddersfield, UK. Professor Hill has published widely in the areas of Big Data, predictive analytics, the Internet of Things, and Industry 4.0, and has specific interests in digital manufacturing and smart cities.

Prof. Md Zakirul Alam Bhuiyan
Fordham University, USA

Biography. Md Zakirul Alam Bhuiyan, PhD, is currently an Assistant Professor of the Department of Computer and Information Sciences at the Fordham University, NY, USA. He is also a Visiting Professor of Guangzhou University, China. Earlier, he worked as an Assistant Professor at the Temple University. His research focuses on dependability, cyber security, big data, and cyber physical systems. He has over 120 papers published in prestigious venues, including top tier IEEE/ACM transactions/magazines. Two of his papers have been recognized as the ESI Highly Cited Papers in Computer Sciences. He has served as a lead guest/associate editor for IEEE TBD, ACM TCPS, IEEE IoT journal, INS, JNCA, FGCS, and so on. He has also received the IEEE TCSC Early Career Research Award (2016-2017) and the IEEE Outstanding Leadership Awards (2016, 2017, 2018), and so on. He has served as an organizer, general chair, program chair, workshop chair, and TPC member of various international conferences, including IEEE INFOCOM. He is a Senior Member of IEEE and a member of ACM.

Prof. Hao Wang
Norwegian University of Science and Technology, Norway

Biography. Hao Wang is an associate professor and the head of Big Data Lab at the Department of ICT and Natural Sciences in Norwegian University of Science & Technology, Norway. He has worked as a researcher in IBM Canada, McMaster, and St. Francis Xavier University before he moved to Norway. He received a Ph.D. degree in 2006 and a B.Eng. degree in 2000, both in computer science and engineering. His research interests include big data analytics, industrial internet of things, high performance computing, safety-critical systems, and communication security. He has published 80+ papers in international journals and conferences such as IEEE RBME, TVT, JBHI, Design & Test, Elsevier FGCS and Computer Communications. He served as a TPC co-chair for IEEE DataCom 2015, IEEE CIT 2017, ES 2017 and reviewers for journals such as IEEE TKDE, TII, TBD, TETC, T-IFS, IoTJ, and ACM TOMM. He is a member of IEEE IES Technical Committee on Industrial Informatics. His webpage is




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(Updated on September 30, 2017)



Copyright SmartWorld-2018. Created and Maintained by SmartWorld-2018 Web Team.
IP Address
(Updated on September 30, 2017)