APEC Conferences

© Chengdu 2016 APEC Smart City Innovation & Technology Cooperation Forum,all rights reversed.

APEC Keynote Speakers

Realizing programmable matter: the next steps
Prof. Julien Bourgeois
Head, Department of computer science

Programmable matter (PM) has different meanings but they can be sorted depending on four properties: Evolutivity, Programmability, Autonomy and Interactivity. In my talk, I will present our research in the Claytronics project which is an instance of PM, evolutive, programmable, autonmous and interactive. In Claytronics, PM is defined as a huge modular self-reconfigurable robot. To manage the complexity of this kind of environment, we propose a complete environment including programmable hardware, a programming langage, a compiler, a simulator, a debugger and distributed algorithms.


Julien Bourgeois is a professor of computer science at the University of Bourgogne Franche-Comté (UBFC) in France. He is leading the computer science department at the FEMTO-ST institute (UMR CNRS 6174). His research interests include distributed intelligent MEMS (DiMEMS), Programmable Matter, P2P networks and security management for complex networks. He has worked for more than 15 years on these topics and has co-authored more than 140 international publications.He was an invited professor at Carnegie Mellon University (US) from September 2012 to August 2013, at Emory University (US) in 2011 and at Hong Kong Polytechnic University in 2010, 2011 and 2015. He led different funded research projects (Smart Surface, Smart Blocks, Computation and coordination for DiMEMS). He is currently leading the programmable matter project funded by the ANR and the topic “System architecture, communication, networking” in the LABEX ACTION, a 10 M€ funded program whose aims at building integrated smart systems. He has also worked in the Centre for Parallel Computing at the University of Wetsminster (UK) and in the Consiglio Nazionale delle Richerche (CNR) in Genova. He collaborated with several other institutions (Lawrence Livermore National Lab, Oak Ridge National Lab, etc.). He organized and chaired many conferences (dMEMS 2010, 2012, HotP2P/IPDPS 2010, Euromicro PDP 2008 and 2010, IEEE GreenCom 2012, IEEE iThings 2012, IEEE CPSCom 2012, GPC 2012, IEEE HPCC 2014, IEEE ICESS 2014, CSS 2014, IEEE CSE 2016, IEEE EUC 2015, IEEE ATC 2017, IEEE CBDCom 2017). He is also acting as a consultant for the French government and for companies.

Searchable Symmetric Encryption: Potential Attacks, Practical Constructions and Extensions
Prof. Jinjun Chen
Director, Lab for Data Systems and Visual Analytics Global Big Data Technologies Centre
Faculty of Engineering and Information Technology, University of Technology Sydney

Data outsourcing has become one of the most successful applications of cloud computing, as it significantly reduces data owners' costs on data storage and management. To prevent privacy disclosure, sensitive data has to be encrypted before outsourcing. Traditional encryption tools such as AES, however, destroy the data searchability because keyword searches cannot be performed over encrypted data. Though the above issue has been addressed by an advanced cryptographic primitive, called searchable symmetric encryption (SSE), we observe that existing SSE schemes still suffer security, efficiency or functionality flaws. In this research, we further study SSE on three aspects. Firstly, we address the search pattern leakage issue. We demonstrate that potential attacks are exist as long as an adversary with some background knowledge learns users' search pattern. We then develop a general countermeasure to transform an existing SSE scheme to a new scheme where the search pattern is hidden. Secondly, motivated by the practical phenomenon in data outsourcing scenarios that user data is distributed over multiple data sources, we propose efficient SSE constructions which allow each data source to build a local index individually and enable the storage provider to merge all local indexes into a global one. Thirdly, we extend SSE into graph encryption with support for specific graph queries. E.g., we investigate how to perform shortest distance queries on an encrypted graph.


Dr Jinjun Chen is a Professor from Faculty of Engineering and IT, University of Technology Sydney (UTS), Australia. He is the Director of Lab for Data Systems and Visual Analytics in the Global Big Data Technologies Centre at UTS. He holds a PhD in Information Technology from Swinburne University of Technology, Australia. His research interests include scalability, big data, data science, data intensive systems, cloud computing, workflow management, privacy and security, and related various research topics. His research results have been published in more than 130 papers in international journals and conferences, including ACM Transactions on Software Engineering and Methodology (TOSEM), IEEE Transactions on Software Engineering (TSE), IEEE Transactions on Parallel and Distributed Systems (TPDS), IEEE Transactions on Cloud Computing, IEEE Transactions on Computers (TC), IEEE Transactions on Service Computing, and IEEE TKDE.

He received UTS Vice-Chancellor's Awards for Research Excellence Highly Commended (2014), UTS Vice-Chancellor's Awards for Research Excellence Finalist (2013), Swinburne Vice-Chancellor’s Research Award (ECR) (2008), IEEE Computer Society Outstanding Leadership Award (2008-2009) and (2010-2011), IEEE Computer Society Service Award (2007), Swinburne Faculty of ICT Research Thesis Excellence Award (2007). He is an Associate Editor for ACM Computing Surveys, IEEE Transactions on Big Data, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Cloud Computing, as well as other journals such as Journal of Computer and System Sciences, JNCA. He is the Chair of IEEE Computer Society’s Technical Committee on Scalable Computing (TCSC), Vice Chair of Steering Committee of Australasian Symposium on Parallel and Distributed Computing, Founder and Coordinator of IEEE TCSC Technical Area on Big Data and MapReduce, Founder and Steering Committee Co-Chair of IEEE International Conference on Big Data and Cloud Computing, IEEE International Conference on Big Data Science and Engineering, and IEEE International Conference on Data Science and Systems.

Unlock the Deployment of Cyber Physical Systems by Real-Time Wireless Communications
Dr. Zhibo Pang, Senior Scientist
ABB AB, Corporate Research

The big efforts from industries towards the Internet of Things, Services and People (IoTSP) and Industry4.0 are driving the evolution of design pidgin form the IoT and automation pyramid to the so-called Cyber Physical Systems. Today, we have seen some pre-cursor CPS examples in the areas like space, avionics, automotive, chemical processes, civil infrastructure, energy, healthcare, manufacturing, transportation, entertainment, and consumer appliances. Despite the diversity of specific solutions, the primary shared feature is the tight combination of computation, networking, and physical processes. In CPY, computational elements monitor and control the physical elements through high performance networks with feedback loops where physical processes affect computations and vice versa.

To delivery enough business values, the CPS solutions have to provide real-time, reliable and deterministic computation and communication to effectively control the physical loops with short time constant. Additionally, we have to apply more complicated AI (artificial intelligence) algorithms for sufficient smartness, deploy the solution through cloud to cover highly distributed facilities, and adopt wireless communications to reach mobile objects and harsh environments. All these requirements are pulling the evolution from conventional CPY to the Real Time CPS (RT-CPS), which is motivating the emerging technologies like edge computing, 1-ms internet, hard real time wireless communications, centimeter level indoor localization, etc. In this presentation, I will overview the detailed requirements and challenges by practical use cases of RT-CPS in process industry, factory automation, cloud robotics, smart buildings, and power systems. The latest progresses on wireless communications with ultra-high performances e.g. Gbps level data rate and sub-us level latency, will be introduced as well.


Dr. Zhibo Pang (Senior Member IEEE) received B.Eng. in Electronic Engineering from Zhejiang University, Hangzhou, China in 2002, MBA in Innovation and Growth from University of Turku, Turku, Finland in 2012, and PhD in Electronic and Computer Systems from the Royal Institute of Technology (KTH), Stockholm, Sweden in 2013. He is currently a Senior Scientist and Project Manager on Industrial IoT and Buildings at ABB Corporate Research, Västerås, Sweden, leading research projects on real-time industrial wireless communications, high accuracy localization, IP-based convergence of communications, and vertical solutions for smart homes and buildings, factory and manufacturing, and power systems. He is also serving as Adjunct Professor or similar roles at universities such as Royal Institute of Technology (KTH), Sweden, Tsinghua University, China, and Beijing University of Post and Telecommunication (BUPT), China. He is a Senior Member of IEEE and serves as Chair of Sub TC in the Technical Committee on Industrial Informatics, and Vice Chair of Sub TC in the Technical Committee on Cloud and Wireless Systems for Industrial Applications, Industrial Electronics Society of IEEE. He is serving in the editorial boards of the Journal of Management Analytics (Taylor & Francis), and the Journal of Industrial Information Integration (Elsevier). His current research interests include the real-time cyber physical systems, Internet-of-Things, wireless sensor network, industrial communication, real time embedded system, enterprise information systems, automation networks, multicore system-on-chip and network-on-chip. He also works on the business-technology joint research such as strategy, business model, value chain, and entrepreneurship.

Dr. Zhibo Pang led the productization of the world first single chip DVB-S receiver SoC in 2005, conceptualized and demonstrated for the first time the Intelligent Medicine Box for in-home healthcare in 2009, developed one of the earliest functional implementations of WirelessHART stack for industrial wireless sensor networks in 2012, and demonstrated centimeter level accuracy in-door localization as industrial infrastructure with the world-wide leading performances in 2014. He has 40+ patents and 15+ refereed journal papers and 40+ conference papers. He won the National Great Invention Award of China in 2005, the First Place Prize of the RFID Nordic EXPO in 2008, and the Outstanding Paper Awards in ICACT2013.

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