Realizing the full potential of the Internet of Things (IoT) requires solving technical and business challenges including the identification of things, the organization, integration and management of big data, and the effective use of knowledge-based decision systems. These challenges, and more, are the focus for the International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) international conference series.
IIKI 2018, the seventh conference in the series, provides a dedicated forum for international experts to discuss current trends, challenges, and state-of-the-art solutions in the Internet of Things.
Extended versions of invited papers from the conference will be published in major international peer-reviewed journals.
The Internet of Things, and of services and people, coupled with social networks means a huge increase in data. Analysing Big Data will become a key focus of research, competition, and innovation in the IoT. Processing of Big Data will in the cloud, and data mining will use background knowledge of societal, cultural, and personal trends. Knowledge engineering for better data mining, new approaches to cloud computing for big data, and new paradigms for Big Data processing are key topics. The topics in this track includes but are not limited to
In recent years, advances in wireless and mobile technologies have dramatically changed our personal and working lives. Many challenges exist in wireless and mobile applications, particularly, security ensuring and privacy.. The goal of this track is to explore cutting-edge research in this area. Topics include but are not limited to
With the rapid development and wide adoption of the Internet of Things (IoT), traditional device-centric IoT is moving into a new era where ubiquitous IoT resources are encapsulated and represented in terms of smart IoT services. In this setting, IoT resources at a certain network region are dynamically integrated through innovative IoT services for the realization of the value of interconnected IoT resources and the satisfaction of (near) real-time, intelligent and local user demands, and consequently, for the promotion of IoT intelligence at the edge of the network. To address this challenge, this research track calls for submissions on the topics including, but are not limited to, the following:
Cyber-physical systems (CPS) are engineered systems that are built from, and depend upon, the seamless integration of computational algorithms and physical components. Advances in CPS will enable capability, adaptability, scalability, resiliency, safety, security, and usability that will far exceed the simple embedded systems of today. CPS technology will transform the way people interact with engineered systems — just as the Internet has transformed the way people interact with information. New smart CPS will drive innovation and competition in sectors such as agriculture, energy, transportation, building design and automation, healthcare, and manufacturing. Work focused on theory, algorithms, implementation and field deployments will be of great interest to the track. Areas of interests include (but are not limited to)
Blockchain, a form of Distributed Ledger Technology, has been gaining enormous attention in areas beyond its cryptocurrency roots since more or less 2014: blockchain and IoT (the Internet of Things), Blockchain And Intellectual Property, blockchain and security, blockchain and finance, blockchain and logistics. In this track, we are looking forward novel researches in blockchain and its related work in IoT. Some possible topics are listed below and not limited to the following items:
Mobile opportunistic networks are delay tolerant networks where mobile carriers communicate with each other via their short-distance and low-cost devices to share data objects among mobile users. Unlike mobile ad hoc networks (MANETs) that require end-to-end communication paths for message exchange, the communications in mobile opportunistic networks take place on the establishment of opportunistic contacts among mobile nodes, without availability of end-to-end message routing paths. Such networks face numerous challenges due to the frequent disruptions and delays, and intermittent connectivity environment. Areas of interest in this track include (but are not limited to)
“Big data” is endowing the traditional healthcare with mobility, intelligence and convenience, which has given birth to “E-Health & Mobile Health”. In such a mobile health environment, tasks like health monitoring of patients, information exchange between doctors and patients, intelligent diagnosis and information push, etc., can be automatically and rapidly accomplished by analyzing a large number of data collected from various mobile devices. However, such mobile applications face numerous challenges due to the voluminous data and complex procedures. Topics in this track include but are not limited to:
As The Internet of Things (IoT) continues to be envisioned as the most popular technology, the research of IoT has turned to how to drive value from IoT. While people have enjoyed a treasure trove of big data from IoT, the sheer volume of data being created by the IoT creates a big problem to analyze the deluge of data and information. Recently, the rapid development of artificial intelligence technology encounters great challenges as well as opportunities for IoT.
The proposed track provides the ground for emerging research ideas on how Artificial Intelligence (AI) can make a valuable contribution to solving problems that the Internet of Things. Contributions may come from diverse fields, including artificial intelligence; dependable computing; the Internet of Things; cyber-physical systems; mobile, wearable, and ubiquitous computing; ambient intelligence; architecture.
Original technical submissions on, but not limited to, the following topics are invited:
With the development of wireless communications and networking, various communication models, interference models and channel models appeared, which lead to the effect that algorithmic design becomes more and more important and challenging. On the other hand, for the well-known reasons, centralized algorithms may not be the best choice to implement in large-scale wireless and heterogeneous networks, especially for the IoT, and distributed solutions are more desirable and appealing. Therefore, it is necessary to pay more efforts on designing distributed algorithms and protocols for solving problems from all layers of wireless networks. Areas of interest in this track include all the following aspects in wireless networks (but are not limited to)
With the rapid development of high tech such as Artificial Intelligence, the Internet of Things (IoT) and Big data, both higher and basic education also experiences an unprecedented revolution. For example, Artificial Intelligence helps to design cognitive tools for learning, distributed learning environments, educational robotics etc. IoT improves the education itself and brings advanced value to the physical environment and structures, such as smart devices, computational IoT nervous system for schools and safer campus designs. Digital learning technologies such as online learning systems like MOOC collect vast amounts of data. This kind of incremental information can give a more complete picture of the learning process. Educational data mining and learning analytics apply methods from statistics, computer science, and machine learning to identify patterns and make predictions. It can also help educators and researchers gain valuable insight into how to improve and personalize learning for students. The research track welcomes basic and applied papers describing mature work involving AI, IoT and Big data applications in education. Specifically, it welcomes high-quality original work including but not limited to the following topics:
As the wide application of information technology in the military and civil fields, information superiority has become the key factor to determine the outcome of all kinds of competition. Electromagnetic and optical sensing of target and environment, and the corresponding processing and analysis techniques are playing an increasingly important role. Therefore, the data acquisition of electromagnetic and optical characteristics, data analysis and database construction will become the key topics. The topics in this track includes but not limited to:
Ubiquitous Sensing and Intelligent Media, including environment sensing, Internet of Things (IoT), data or multimedia acquisition, intelligent media processing, harmonious human-computer interaction and pervasive computing have attracted huge interests from researchers. Accordingly, there are a variety of potential application in Ubiquitous Sensing and Intelligent Media. The aim of this track is to survey a state of art of methodologies, algorithms and systems in advanced research into Ubiquitous Sensing and Intelligent Media, which may involve any types of media data such as visual (including 2D, 3D and RGB data), audial, Electroencephalography (EEG)/MRI/CT and touch sensory data etc.
- Paper Submission Deadline: August 24, 2018
- Paper Acceptance Notification: October 7, 2018
– Expected publication date: (tentative)
– Conference Date: OCTOBER 19-21 2018
We encourage submission of full papers and position papers presenting novel ideas that may lead to insightful technical discussions.
Papers should contain original contributions that have not been published or submitted elsewhere, and references to related state-of-the-art research.
Please submit your papers at
Please contact Yunchuan Sun (firstname.lastname@example.org) for further enquiries.
Dr. Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), a Not-for-Profit Scientific Network for Innovation and Research Excellence connecting Industry and Academia. The Network with Head quarters in Seattle, USA has currently more than 1,000 scientific members from over 100 countries. As an Investigator / Co-Investigator, he has won research grants worth over 100+ Million US$ from Australia, USA, EU, Italy, Czech Republic, France, Malaysia and China.
Dr. Abraham works in a multi-disciplinary environment involving machine intelligence, cyber-physical systems, Internet of things, network security, sensor networks, Web intelligence, Web services, data mining and applied to various real world problems. In these areas he has authored / coauthored more than 1,300+ research publications out of which there are 100+ books covering various aspects of Computer Science. One of his books was translated to Japanese and few other articles were translated to Russian and Chinese. About 1000+ publications are indexed by Scopus and over 800 are indexed by Thomson ISI Web of Science. Some of the articles are available in the ScienceDirect Top 25 hottest articles
Topic:Industry 4.0: Challenges from a Data Analysis Perspective
We are blessed with the sophisticated technological artifacts that are enriching our daily lives and the society. It is believed that the future Internet is going to provide us the framework to integrate, control or operate virtually any device, appliance, monitoring systems, infrastructures etc. Industry 4.0 is the current trend of automation and data exchange in manufacturing technologies, which also includes a close integration of cyber-physical systems, the Internet of things and cloud computing. In this talk, the concept of Industry 4.0 will be presented and then various research challenges from several application perspective will be illustrated. Some real world applications involving the analysis of complex data / applications would be the key focus.
Distinguished Professor Jie Lu is an internationally renowned scientist in the areas of computational intelligence, specifically in decision support systems, fuzzy transfer learning, concept drift, and recommender systems. She is the Associate Dean in Research Excellence in the Faculty of Engineering and Information Technology at University of Technology Sydney (UTS) and the Director of Centre for Artificial Intelligence (CAI) at UTS. She has published six research books and 400 papers in Artificial Intelligence, IEEE transactions on Fuzzy Systems and other refereed journals and conference proceedings. She has won 20 Australian Research Council (ARC) discovery grants and other research grants. She serves as Editor-In-Chief for Knowledge-Based Systems (Elsevier) and Editor-In-Chief for International Journal on Computational Intelligence Systems (Atlantis), has delivered over 20 keynote speeches at international conferences and chaired 10 international conferences. She has received a number of Best paper awards, University research medal and other awards. She is a Fellow of IEEE and Fellow of IFSA.
Topic:Cross-domain knowledge transfer for data-driven decision making
This presentation highlights knowledge transfer learning methods and related algorithms for handling complex prediction and decision-making problems in rapidly-changing data distribution and data-shortage situations. It provides a framework for utilizing previously-acquired knowledge to predict new but similar problems quickly and effectively by using fuzzy set techniques. It systematically presents developments in knowledge transfer learning methods for prediction and decision making, including fuzzy transfer learning-based prediction framework, fuzzy domain adaptation, fuzzy cross-domain adaptation, and in particular, cross-domain adaptive fuzzy inference system, and their respective applications in prediction and decision support. This presentation demonstrates the successful use of fuzzy techniques in facilitating the incorporation of approximation and expressiveness of data uncertainties within knowledge transfer, machine learning and data-driven decision support systems.
Omer Rana is Professor of Performance Engineering in the School of Computer Science and Informatics at Cardiff University. He leads the Complex Systems Research Group which also hosts an Internet of Things laboratory and the Airbus Centre in Cybersecurity Analytics. He is on the Executive Committee of Cardiff University’s multi-disciplinary “Data Innovation” Research Institute, and he also contributes as an advisory board member of the “Energy Systems” Research Institute. Omer Rana holds a PhD in Neural Networks and Parallel Architectures from Imperial College (University of London, UK).
Topic:Integrating IoT/Edge Devices with Cloud Systems for Real Time Sensing, Data Analytics
Increasing availability of sensing infrastructure, through Internet of Things (IoT) and Edge devices, requires access to computing environments that are able to process such data within time, cost and efficiency constraints. Due to the limited capacity (compute power, storage, transmission range, battery life) of such devices, it is often necessary to off-load computation to another system. A number of applications can now make use of this sensing infrastructure, such as city-wide surveillance, transport management and environment monitoring. Understanding how such infrastructure can be used in an effective manner remains a challenge for software application developers. The availability of various device specific libraries, variable frequency of firmware upgrades and data formats adds additional complexity to this. An approach for integrating IoT-based sensing with real time data analysis is proposed, which attempts to address some of the challenges identified above.
Dr. Ruidong Zhang is a professor in computer information systems at University of Wisconsin – Eau Claire in US. He also currently serves as the founding director of Blockchain Labs at the Academy of Internet Finance at Zhejiang University. He was the president of the International Chinese Information Systems Association in US from 2014 to 2015. He was a visiting professor in the School of Business at Renmin University of China in the fall of 2016. His research areas include cloud computing architecture and optimization, blockchain technology applications, digital currency policy and regulations, m-commerce and the development and application of next generation Internet. His research is published in international academic journals and conference proceedings. He also has a book about Internet Finance published in 2017. He received his B.S from University of Posts and Telecommunications in telecommunications engineering management, M.A. from Renmin University of China in Business Administration and Ph.D. from University of Nebraska at Lincoln doctorate in information systems management.
Topic: Service Oriented Blockchain Technical Architecture: Design and Issues
It is generally believed that, beyond the current cryptocurrency applications, a large-scale application use case of blockchain technology will be in the B2B category, such as money remittance among banks, security ownership clearing, and digital asset management. This calls for the development of service oriented enterprise blockchain applications. The core is the design of service oriented architecture of blockchain enterprise application. In this speech, we will discuss how service or business oriented use case or decision-making scenarios can be resolved with the blockchain lower level consensus protocols or smart contracts. One way to deliver this resolution is to introduce a layered architecture similar to TCP/IP model, and separate business or service layers from technical layers to form such as a 3-layer architecture, which includes an application layer, blockchain OS layer and blockchain core protocol layer. Design issues and challenges will be discussed as well.
Beijing Normal University is a public research university located in Beijing, China, with a strong emphasis on basic disciplines of the humanities and sciences. It is one of the oldest and most prestigious universities in China.
The term “normal school” refers to an institution that aimed to train schoolteachers in the early twentieth century. This terminology is preserved in the official names of such institutions in China even after these schools gained university status and expanded to offer courses beyond education-related fields. This term reflects BNU’s heritage as a former unit of the Imperial University of Peking dedicated to training schoolteachers.
Beijing Normal University was selected to be a Project 211 institution in 1996. In 2002, BNU signed an agreement with the Ministry of Education and Beijing municipal government to become the 10th university participating in Project 985, through which it receives special support from the Chinese government aimed at elevating its reputation to the level of a “world-class” university.
After a special visit from Premier Wen Jiabao to the university on May 4, 2006, the Chinese government implemented a Fee-Waiver Policy for teacher training programs in six normal universities that are supervised by the Ministry of Education, including Beijing Normal University.
The university also has a distinct emphasis on increasing educational equity. Its 2009 demographic composition data shows that 40% of its enrolled students are from western China, almost one third are from rural areas, and a quarter are from low-income families. Ethnic minorities comprise more than 10% of students.
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Beijing Normal University.
Yunchuan Sun, Beijing Normal University, China
Houbing Song, West Virginia University, USA
Huansheng Ning, University of Science & Technology Beijing, China
Antonio J. Jara, University of Applied Sciences Western Switzerland (HES-SO), Switzerland
Jiguo Yu, Qufu Normal University China
Zhipeng Cai, Georgia State University, USA
Fatos Xhafa , Universitat Politècnica de Catalunya, Spain
Yuqi Yang, Beijing Normal University, China
Junyao Wang, Beijing Normal University, China
Qinghe Du, Xi’an Jiaotong University, China
Zhangbing Zhou, China University of Geosciences (Beijing), China & TELECOM SudParis, France
Zhihan Lv, University College London (UCL), UK
Yuan Gao, Tsinghua University, China
Chenglei Yang, firstname.lastname@example.org
Beijing Normal University
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Science and Technology on Optical Radiation Laboratory
Science and Technology on Electromagnetic Scattering Laboratory
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Haider Abbas, Center of Excellence in Information Assurance, King Saud University, KSA
Ebrahim Bagheri, Ryerson University, Canada
Yu Bai, California state university, Fullerton, USA , firstname.lastname@example.org
Didier El Baz, Université de Toulouse, France
José Bravo, Castilla-La Mancha University, Spain
Mario Collotta, Kore University of Enna, Italy
Charalampos Doukas, University of the Aegean, Greece
Suparna De, University of Surrey, UK
Stefan Forsström, Mid Sweden University, Sweden
Walid Gaaloul, TELECOM SudParis, France
Xiaozhi Gao, Helsinki University of Technology, Finland
Huimin Guo, ASTRI, Hong Kong
Junqi Guo, Beijing Normal University, China
Klaus Marius Hansen, University of Copenhagen, Denmark
Weipeng Jing, Northeast Forestry University, China
Rossi Kamal, GerIoT, Bangladesh
Abdelmajid Khelil, HUAWEI TECHNOLOGIES DUESSELDORF GmbH, European Research Cente, European
Andrej Kos, University of Ljubljana, Slovenija
Anton Kos, University of Ljubljana, Slovenija
Charles J. Kim, Howard University, USA
Deying Li, Renmin University of China, China
Kuan-Ching Li, Providence University, Taiwan
Qingsheng Li, Bradford University, UK & Anyang Normal University, China
Song Li, Beijing Normal University, China
Wei Li, Georgia State University, USA
Rui S. Moreira, Associate Professor,Universidade Fernando Pessoa & INESC TEC, Porto, Portugal
Jin Liu, New Jersey Institute of Technology, USA
Xiangfeng Luo, Shanghai University, Shanghai, China
Mohammad S. Obaidat, Monmouth University, USA, Obaidat@monmouth.edu
Chengming Qi, China University of Geosciences (Beijing), China
Houbing Song, West Virginia University, USA
Yukinori Suzuki, Muroran Institute of Technology, Japan
Mikhail Sysoev, University of Ljubljana, Slovenia
Qiu Tie, Dalian university of technology, China
Sana Ullah, CISTER Research Unit at ISEP/IPP
Yongping Xiong, Beijing University of Post and Telecommunications, China
Dongxiao yu, Huazhong University of Science and Technology, China
Jiguo Yu, Qufu Normal University, China
Huihui Wang, Jacksonville University, USA
Junping Wang, email@example.com
Shenling Wang, Beijing Normal University, China
Shengling Wang, Beijing Normal University, China
Junsheng Zhang, Institute of Scientific and Technical Information of China
Yuan Zhang, University of Jinan, China
ZhangBing Zhou, China University of Geosciences (Beijing), China & TELECOM SudParis, France
Jing Zou, State Grid Economic and Technological Research Institute co.. ltd.