The theft sensor device for  participatory sensing

City Risks is now completing the design of the theft detection sensor to be employed in a participatory sensing approach. The sensor is a coin-sized low energy module (only 18.5 mm x 21.0 mm), equipped with a radio based transceiver (Bluethooth-BLE) and a battery to power the circuit for a “long period”. The user is provided with a sensor (unique ID) and registers it into the portal through the app or the web interface. The sensor is attached to a personal item (e.g. bike or bag) and by default is set in “beacon” mode, so that it can be scanned by other devices. Optionally, the user can set the sensor into the “stealth” mode to avoid scanning, but this is more power consuming.
In case of theft, the user reports item as stolen through the app and the Operation Centre sends the information to the City Risk detecting network, including gateways (fixed position, large and static coverage) and users’ smartphones (mobile position, smaller coverage). In case of stealth mode, the City.Risks gateways and activist smartphones broadcast an encrypted activation signal, the radio sensor is activated and the BLE beacon mode is enabled. In the beacon mode, the BLE sensor can broadcast its signal to nearby devices (City Risks users’ smartphones and gateways), providing ID and “stolen” status. The nearby citizens’ mobile apps will capture the BLE alarm signal and the app will report the sensor ID, status, GPS location, timestamp and other details to the Operation centre, who will visualize the item position on a map and will activate the recovery procedure.

Designing the City.Risks core platform

The core platform designed by City.Risks comprises an infrastructure for managing user communities and policies, that will allow citizen users to specify their profile and preferences: e.g. to configure how to view information and when/what notifications and alerts should be received. It also provides a fine-grained mechanism for allowing citizen users to create (possibly nested) communities, like family, friends, colleagues, neighbours and to specify rules and policies for sharing information and experiences among them, e.g. which users can see one’s location, or to/from which users to send/receive alerts. The City.Risks platform provides the open APIs and its set of interfaces plus a SDK, i.e. an implementation tooling that allows third parties to build custom-made applications and to integrate them into the City.Risks platform. It also includes components for low level system monitoring and event logging. [More info in D2.5]

Designing the mobile app and the risk management system

City.Risks is designing and implementing a mobile app to empower citizens to interact with authorities for criminality prevention, detection and evidence gathering and risk reporting (citizen as reporter). Community-based functions are one of the major cornerstones of the City.Risks mobile app. Currently, new functionality is integrated that enables users to ask whether somebody witnessed a crime. Community members that could have witnessed the incident, are notified and can directly contact the requesting user.
The City.Risks app interacts with the RMRS (incident reporting) system featuring incident management, automatic alerting and decision management for operation center. Once the user has sent a report through the app (text and media), the system will translate the report to associate it with a given risk category, will decide whether it is worth publishing, will update it on the basis of new incoming reports and eventually will make it accessible to the City.Risks user community.

Designing the Operation Centre functionalities

The City.Risks approach foresees a command and control centre that will allow the authorities to operate and manage the whole system, by monitoring ongoing activities and events, run (emulate) scenarios, fire specific actions (eg massively notify users within a geographic bounding box), determine evacuation strategies on the basis of local and historical information, select and trigger response actions, determine the “Gateways” to be informed for a stolen item, allow for receiving and acting upon streaming (video) input from citizens acting as reporters.
Main functionalities of the Operation Centre will be tracking and retrieval of multiple stolen items simultaneously, monitoring and responding to several incident reports, dispatching real-time alerts to citizens, data aggregation, visualization and filtering to support decision making, conducting simulations for training and preparedness.

Data management & analysis

City.Risks provides a crime data repository for historical crime incident records provided in an anonymized form by police authorities. These are processed, together with area demographics and geographic information to analyze and model the geospatial distribution of crime in large urban environments. The aim of the analysis is to identify crime hotspots for various types of crime and to reveal insights about potential environmental factors of crime.
Demographic data, including population distribution and characteristics, income levels, educational background, type of occupation, etc., are collected from official sources. Geographic information, including various types of Points of Interest, roads and public transportation networks, land use, etc., are collected from publicly available data sources and Web APIs.
Using these data, the system learns models regarding the geospatial distribution of crime, and generates crime hotspot maps and other statistics which are accessible through the City.Risks Web Portal.

Designing the City.Risks web portal

City.Risks provides a crime data repository for historical crime incident records provided in an anonymized form by police authorities. These are processed, together with area demographics and geographic information to analyze and model the geospatial distribution of crime in large urban environments. The aim of the analysis is to identify crime hotspots for various types of crime and to reveal insights about potential environmental factors of crime.
Demographic data, including population distribution and characteristics, income levels, educational background, type of occupation, etc., are collected from official sources. Geographic information, including various types of Points of Interest, roads and public transportation networks, land use, etc., are collected from publicly available data sources and Web APIs.
Using these data, the system learns models regarding the geospatial distribution of crime, and generates crime hotspot maps and other statistics which are accessible through the City.Risks Web Portal.

Identified use cases for pilot activity

City.Risks has elaborated six scenarios where the adoption of modern technologies can assist in addressing security threats. Emphasis has been laid on using citizen engagement in criminality detection and response, building communities to improve citizens’ perception of security and to reduce their fear of crime, and using citizens’ mobile devices and generated content as tools for gaining insights into security threats and for addressing them more effectively.
The following scenarios have been identified: theft of personal belongings, vehicle theft, information gathering and dissemination for ongoing events, tourists’ and women’s safety, citizen engagement, neighbourhood safety. Each of them have been analysed to produce a comprehensive set of technical requirements, both functional and non-functional, necessary for the development of the City.Risks technical solutions.[More info in D2.4]