VITAL VIRTUALIZED PROGRAMMABLE INTERFACES FOR INNOVATIVE COSTEFFECTIVE IOT

VITAL VIRTUALIZED PROGRAMMABLE INTERFACES FOR INNOVATIVE COST-EFFECTIVE IOT DEPLOYMENTS IN SMART CITIES Io. T Analytics with the VITAL Smart Cities Platform Prof. John Soldatos Athens Information Technology, Greece – VITAL (Acknowledgement: Prof. Ioannis Christou) Io. T Week, Belgrade, June 2, 2016 © Copyright 2016 VITAL Consortium

Value Potential of Io. T (Mc. Kinsey, June 2015) § Interoperability between Io. T systems is critically important to capturing maximum value: o On average, interoperability is required for 40 percent of potential value across Io. T applications and by nearly 60 percent in some settings § Most Io. T 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 Technical Silos Io. T for Smart Industries Platform & Applications Io. T for Smart Buildings Platform & Applications Io. T for Urban Transport Platform & Applications Io. T for Law Enforcement Platform & Applications Fragmented ICOs Access, Fragmented Intelligence, Fragmented Security, Limited Data Sharing, Limited Integration 3 © 2016 VITAL Consortium

VITAL Architecture Loosely Coupled Modules (REST, JSON-LD) Io. T Systems are accessed via a Virtualized Abstract PPI (Platform Provider Interface) Io. T data are modeled according to a common (VITAL) ontology (extending W 3 C 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

Io. T Analytics Disciplines (incl. Big. Data) Statistics & Machine Learning Visualization & HMI Io. T Analytics & Data Mining and Knowledge Discovery Io. T Data Collection & Intoperability Databases (SQL, no. SQL, 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

Common Semantics JSON-LD Contexts Linked Data 10 Io. T Platform Agnostic Analytics VITAL PPI JSON VITAL PADA Module Dynamic Data Discovery Semantic Unification & Interoperability Data (Streams) Collection VITAL Io. T 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 Io. T to connect supply chain to smart retail, smart services, smart logistics London Istanbul Smart Traffic Management Improve Environmental Performance and optimize the efficiency of the transport network -Urban Regeneration - -Smarter Services- 12 © 2016 VITAL Consortium

Camden Data Collection § Data. Set: 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 3 hour accumulation of people over

People Coming-In vs. Hour Per Day § Relation between number of people coming-in (in an hour), and the actual Hour-of -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 Location. Number, Site. Number Hour-Of-Day, Day-Of-Week, Accepted. In, Accepted. Out, AIn. Minus. Out, 3 Hour. Cum. AIn. Minus. Out, Hourly. AIn. Perc. Chg, Daily. AIn. Perc. Chg, Hourly. AOut. Perc. Chg, Daily. AOut. Perc. Chg § 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 Io. T Analytics applications in smart cities o Emphasis on Semantic Interoperability § Added-value intermediary (proxy) between all different Io. T 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 § Io. T Analytics provide a huge potential for extracting knowledge and deriving insights about humans’ behaviour and the physical environment o Io. T Data remain largely unexploited o Interoperability is an issue § Io. T Analytics present their own challenges: o Velocity: Streams with high ingestion rates o Semantic Interoperability: Alleviate the fragmentation of Io. T systems o Lack of Tools: Io. T 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! Io. T Analytics with the VITAL Smart Cities Platform Prof. John Soldatos Athens Information Technology, Greece Belgrade, June 2, 2016 © 2016 VITAL Consortium