The International Conference on Internet of Things and Big Data – IoTBD 2016

Internet of Things (IoT) is a platform and a phenomenon that allows everything to process information, communicate data, analyze context collaboratively and in the service or individuals, organizations and businesses. In the process of doing so, a large amount of data with different formats and content has to be processed efficiently, quickly and intelligently through advanced algorithms, techniques, models and tools. This new paradigm is enabled by the maturity of several different technologies, including the internet, wireless communication, cloud computing, sensors, big data analytics and machine learning algorithms. Conference Areas: Each of these topic areas is expanded below but the sub-topics list is not exhaustive. Papers may address one or more of the listed sub-topics, although authors should not feel limited by them. Unlisted but related sub-topics are also acceptable, provided they fit in one of the following main topic areas: 1. INTERNET OF THINGS (IOT) 2. INTER... Read more

Internet of Things (IoT) is a platform and a phenomenon that allows everything to process information, communicate data, analyze context collaboratively and in the service or individuals, organizations and businesses. In the process of doing so, a large amount of data with different formats and content has to be processed efficiently, quickly and intelligently through advanced algorithms, techniques, models and tools. This new paradigm is enabled by the maturity of several different technologies, including the internet, wireless communication, cloud computing, sensors, big data analytics and machine learning algorithms. Conference Areas: Each of these topic areas is expanded below but the sub-topics list is not exhaustive. Papers may address one or more of the listed sub-topics, although authors should not feel limited by them. Unlisted but related sub-topics are also acceptable, provided they fit in one of the following main topic areas: 1. INTERNET OF THINGS (IOT) 2. INTERNET OF THINGS (IOT) SMART CITY AND INTELLIGENT SYSTEMS 3. BIG DATA RESEARCH 4. BIG DATA AND COMPLEX BIOLOGICAL SCIENCE 5. EMERGING SERVICES AND ANALYTICS AREA 1: INTERNET OF THINGS (IOT) • Network Design and Architecture • Software Architecture and Middleware • Mobile Services • Data and Knowledge Management • Context-awareness and Location-awareness • Security, Privacy and Trust • Performance Evaluation and Modeling • Networking and Communication Protocols • Machine to Machine Communications • Intelligent Systems for IoT and Services Computing • Energy Efficiency • Social Implications for IoT • Future of IoT and Big Data AREA 2: INTERNET OF THINGS (IOT) SMART CITY AND INTELLIGENT SYSTEMS • Technological focus for Smart Environments • Next Generation Networks • Smart City Examples and Case Studies • Data Analysis and Visualization for Smart City, Green Systems and Transport Systems • Architecture for secure and interactive IoT • Intelligent Infrastructure and Guidance Systems • Traffic Theory, Modeling and Simulation • Sensor Networks, Remote Diagnosis and Development • Transportation Management • Pattern Recognition and Behavioral Investigations for Vehicles, Green Systems and Smart City AREA 3: BIG DATA RESEARCH • Big Data fundamentals – Services Computing, Techniques, Recommendations and Frameworks • Modeling, Experiments, Sharing Technologies & Platforms • SQL/NoSQL databases, Data Processing Techniques, Visualization and Modern Technologies • Analytics, Intelligence and Knowledge Engineering • Data Center Enabled Technologies • Sensor, Wireless Technologies, APIs • Networking and Social Networks • Data Management for Large Data • Security, Privacy and Risk • Software Frameworks (MapReduce, Spark etc) and Simulations • Modern Architecture • Volume, Velocity, Variety, Veracity and Value • Social Science and Implications for Big Data AREA 4: BIG DATA AND COMPLEX BIOLOGICAL SCIENCE • Smart City and Transportation • Education and Learning • Business, Finance and Management • Large-scale Information Systems and Applications • Energy, Environment and Natural Science Applications • Social Networks Analysis, Media and e-Government • Proofs-of-concepts and Large-scale Experiments • Risk Modeling, Simulation, Legal Challenges • Open data: Issues, Services and Solutions • Earth Science Simulation and Processing • GPUs and Visualization • Case Studies of Real Adoption • Biomedical Experiments and Simulations • Healthcare Services • Health Informatics • Cancer and Tumor Studies with Big Data • DNA Sequencing • Brain, Heart and Organ Simulations and Processing • Volume, Velocity, Variety and Veracity for Biological Science AREA 5: EMERGING SERVICES AND ANALYTICS • Health Informatics as a Service (HIaaS) for any Type of Health Informatics, Computation and Services • Big Data as a Service (BDaaS) including Frameworks, Empirical Approaches and Data Processing Techniques • Big Data Algorithm, Methodology, Business Models and Challenges • Security as a Service including any Algorithms, Methodology and Software Proof-of-concepts • Financial Software as a Service (FSaaS) including Risk and Pricing Analysis; Predictive Modeling • Education as a Service (EaaS) including e-Learning and Educational Applications • Business Process as a Service (BPaaS) including Workflows and Supply Chain in the Cloud • Software Engineering Approaches, including Formal Methods, Agile Methods and Theoretical Algorithms for Cloud • Natural Science as a Service (NSaaS) including Weather Forecasting and Weather Data Visualization • System Design and Architecture • Mobile APIs, Apps, Systems and Prototype • Gaming as a Service (GaaS) • Framework (conceptual, logical or software) • Analytics as a Service (AaaS) for any Types of Analytics • Electronic, Logic, Robotic and Electrical Infrastructure, Platforms and Applications • Energy-saving and Green IT Systems or Applications • Middleware and Agents for Cloud, Grid and Cluster Computing • Integration as a Service (data; service; business; federated clouds) • Scheduling, Service Duplication, Fairness, Load Balance for SaaS and Analytics • Tenant Application Development including Customization, Verification, Simulation, and testing on SaaS and Analytics • IaaS, PaaS and SaaS quality of service (QoS), security, reliability, availability, service bus mechanisms • Social Networks and Analytics • User Evaluations and Case Studies • IaaS, PaaS and SaaS, Big Data and Analytics demonstrations and Research Discussions from Computing Scientists, Business IS Academics and Industrial Consultants • Wireless Systems and Applications • e-Government, e-Commerce, e-Science and Creative Technologies for IoT and Big Data • Any emerging services KEYNOTE LECTURES Ling Liu, Georgia Institute of Technology, United States Peter Sloot, University of Amsterdam, Complexity Institute Singapore, ITMO St. Petersburg, Singapore, Russia, Netherlands IoTBD CONFERENCE CHAIR Victor Chang, Leeds Beckett University, United Kingdom PROGRAM CO-CHAIRS Muthu Ramachandran, Leeds Beckett University, United Kingdom Gary Wills, University of Southampton, United Kingdom Robert Walters, University of Southampton, United Kingdom Víctor Méndez Muñoz, Universitat Autònoma de Barcelona, UAB, Spain PROGRAM COMMITTEE http://www.iotbd.org/ProgramCommittee.aspx