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VITAL VIRTUALIZED PROGRAMMABLE INTERFACES FOR INNOVATIVE COST-EFFECTIVE IOT DEPLOYMENTS IN SMART CITIES IoT Analytics with the VITAL Smart Cities Platform Prof. John Soldatos Athens Information Technology, Greece – VITAL (Acknowledgement: Prof. Ioannis Christou) IoT Week, Belgrade, June 2, 2016 © Copyright 2016 VITAL Consortium Value Potential of IoT (McKinsey, June 2015) Interoperability between IoT systems is critically important to capturing maximum value: o On average, interoperability is required for 40 percent of potential value across IoT applications and by nearly 60 percent in some settings Most IoT data are not used currently: o Only 1 % of data from an oil rig with 30,000 sensors is examined o Data that are used today are mostly for anomaly detection and control, not optimization and prediction, which provide the greatest value 2 © 2015 VITAL Consortium VITAL Motivation & Challenge Integrating Silos & Reducing Fragmentation Process Integration, Integrated Security, Enhanced Intelligence, City Operations Optimization Organizational Silos Sustainable Development Connected Governance Natural Resources Management VITAL Virtualization Layer – Integrated Development Application Silos Information Silos & Fragmentation IoT for Smart Industries IoT for Smart Buildings IoT for Urban Transport Platform & Applications Platform & Applications Platform & Applications IoT for Law Enforcement Platform & Applications Technical Silos Fragmented ICOs Access, Fragmented Intelligence, Fragmented Security, Limited Data Sharing, Limited Integration 3 © 2016 VITAL Consortium VITAL Architecture Loosely Coupled Modules (REST, JSON-LD) IoT Systems are accessed via a Virtualized Abstract PPI (Platform Provider Interface) IoT data are modeled according to a common (VITAL) ontology (extending W3C SSN) Added Value Functionalities (CEP, Discovery, Filtering) are provided via Virtualized Interfaces (VUAIs), but (also) through PPIs for specific platforms VITAL Provides a range of development & management tools 4 © 2016 VITAL Consortium VITAL City Management Platform (1) 5 © 2016 VITAL Consortium VITAL City Management Platform (2) 6 © 2016 VITAL Consortium VITAL City Applications Development Tool Contains all VITALrelated nodes There is one node for each piece of functionality that each one of the integrated components provides R Node provides integration with R project 7 © 2015 VITAL Consortium IoT Analytics Disciplines (incl. BigData) Statistics & Machine Learning Visualization & HMI IoT Analytics & Data Mining and Knowledge Discovery IoT Data Collection & Intoperability Databases (SQL, noSQL, HDFS, Cloud,..) 8 © 2016 VITAL Consortium Data Processing and Analytics Workflow in Smart Cities Source: Scottish Cities Alliance, “Smart Cities Maturity Model and Self-‐Assessment Tool Guidance Note for completion Of Self-‐Assessment Tool”, January 2015. 9 © 2016 VITAL Consortium JSON VITAL PADA Module Dynamic Data Discovery Common Semantics JSON-LD Contexts Linked Data 10 IoT Platform Agnostic Analytics VITAL PPI Semantic Unification & Interoperability Data (Streams) Collection VITAL IoT Analytics Pipeline VITAL Development Tool R Node for Analytics Functions © 2016 VITAL Consortium Use of Cross Industry Standard Process for Data Mining (CRISP-DM) VITAL Data Scientists use CRISP-DM like model Sample / Prepare / Model Data Test / Validate and Evaluate DM Mechanisms (off-line) Deployment using VITAL Platform (on-line) Shearer C., The CRISP-DM model: the new blueprint for data mining, J Data Warehousing (2000); 5:13—22. 11 © 2016 VITAL Consortium VITAL Validating Use Cases Scenario Scope City Smart Working Patterns Use IoT to connect supply chain to smart retail, smart services, smart logistics London Improve Environmental Performance and optimize the efficiency of the transport network Istanbul -Urban Regeneration - Smart Traffic Management -Smarter Services- 12 © 2016 VITAL Consortium Camden Data Collection DataSet: o Camden Footfall Data from all locations for 10 days (23/10/2015-02/11/2015) Preprocessing: o Measurements from different sites and locations have been merged together o Analyses applies to the entire dataset, rather than the individual datasets coming from the individual sources Problem: o What is the peak hour for Camden market? 13 © 2016 VITAL Consortium 3-Hour Accumulation of People Visualization of 3hour accumulation of people over the Hour-of-Day No discernible pattern of correlation between the two variables. 14 © 2016 VITAL Consortium People Coming-In vs. Hour Per Day Relation between number of people coming-in (in an hour), and the actual Hourof-Day There is a clear peak in certain hours, but there are many instances during the same hours in which the number of incoming people is very low 15 © 2016 VITAL Consortium Highlight of VITAL Machine Learning Toolbox Quantitative Association Rule Mining (QARM) algorithms going beyond all currently known QAR techniques, specifically customized. Utilizes domain knowledge and is highly parallelizable and distributable in nature. State–of –the-art fusion of user-based, item- based, and content-based algorithms o Achieving 64 times faster response times than SOTA (e.g., Apache Mahout) while improving results by a whopping 100%. 16 © 2016 VITAL Consortium Applying QARM Mining all quantitative rules from the dataset comprising the following features: o LocationNumber, SiteNumber Hour-Of-Day, Day-Of-Week, AcceptedIn, AcceptedOut, AInMinusOut, 3HourCumAInMinusOut, HourlyAInPercChg, DailyAInPercChg, HourlyAOutPercChg, DailyAOutPercChg Algorithm produced all valid non-dominated rules whose antecedent is the 3-hour accumulation of people o Requirement that this quantity must exceed the value 10, having minimum support of 10% and minimum confidence of 80% in the dataset. More than 1000 rules produced – top 20 can serve as basis for planning: o Production of rules for certain locations vs. time of day 17 © 2016 VITAL Consortium Smart Traffic Management Scenario # 1 • Incident Detection: - When a sharp decrease in average speeds is detected for a road segment, a notification is generated to inform about a potential incident 18 © 2016 VITAL Consortium Smart Traffic Management Scenario # 2 • Sensor Failure Detection: -By comparing speeds collected from sensors & floating cars, automatic notification is generated when a contradiction is detected, i.e. mismatch in road segment colors 19 © 2016 VITAL Consortium Smart Traffic Management Scenario # 3 • Traffic Prediction: -Traffic estimation up to an hour is generated by using Modified Linear Regression algorithm & calculations are made using stored data considering external conditions such as national holidays & other events. Data Flow Diagram Historical & Live Traffic Data w/ External Conditions Processing & Calculations Predicted Traffic Status (up to an hour) Instant Condition 20 © 2016 VITAL Consortium VITAL Added-Value for Analytics Integrated cross-platform and cross-context approach to the development & deployment of IoT Analytics applications in smart cities o Emphasis on Semantic Interoperability Added-value intermediary (proxy) between all different IoT deployments and systems in the smart city Provides access to cached, aggregated, integrated data (Data-as-a-Service) Open Source Solution targeting Smaller Cities (e.g., ~ up to 200.000 inhabitants) which cannot afford the Cost of Enterprise Scale Solutions from giant vendors 21 © 2016 VITAL Consortium Conclusions IoT Analytics provide a huge potential for extracting knowledge and deriving insights about humans’ behaviour and the physical environment o IoT Data remain largely unexploited o Interoperability is an issue IoT Analytics present their own challenges: o Velocity: Streams with high ingestion rates o Semantic Interoperability: Alleviate the fragmentation of IoT systems o Lack of Tools: IoT Development tools are not enough VITAL is providing the means to confront some of these challenges 22 © 2016 VITAL Consortium VITAL VIRTUALIZED PROGRAMMABLE INTERFACES FOR INNOVATIVE COST-EFFECTIVE IOT DEPLOYMENTS IN SMART CITIES Thank You! IoT Analytics with the VITAL Smart Cities Platform Prof. John Soldatos Athens Information Technology, Greece Belgrade, June 2, 2016 © 2016 VITAL Consortium