Ethical Challenges of participatory sensing for crisisinformation managementMassimiliano Tarquini - Maurizio Morgano
Agenda• Introduction to SMART Search Engine a holisticopen source web-scale multimedia searchframework for multimedia data...
SMART FP7 Search Engine
Project Factsheet• Consortium:– Atos– Athens Information Technology– IBM Haifa Research Lab– Imperial College London– Cons...
SMART Search Engine: Motivation
SMART Search Engine: Characteristics
High Level Architectural OverviewSMART (Command-Line)QueriesSMART App#1SMART App#2Terrier Search Engine (SMART Edition)SMA...
SMART Search Engine: Layers
Scope of the SMART Ethical AspectsSMART entails acquisition and processing of content andcontext from the surrounding envi...
SMART Sensing Technologies
SMART Sensing Technologies
Enriching Multimedia Steams
Ethical Issues Associated with the SMARTVisual Sensing technologies
Ethical Issues Associated with theSMART Sensing technologies (Others)
SMART Ethical Management Strategy
Ethical Challenges of Participatory Sensing for Crisis Information Management
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Ethical Challenges of Participatory Sensing for Crisis Information Management


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Massimiliano Tarquini and Maurizio Morgano
on "Ethical Challenges of Participatory Sensing for Crisis Information Management " at ISCRAM 2013 in Baden-Baden.

10th International Conference on Information Systems for Crisis Response and Management
12-15 May 2013, Baden-Baden, Germany

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  • As concerns crisis information management, informed decision is preferable to uninformed decision in particular when facing life and death situation, the protection of vulnerable populations and other critical issues. The SMART Search framework will enable the implementation of search services over large-scale community environmental and participatory sensing infrastructures to get information about the environment providing, in addition to this, an ambient related synthesis of related contents in real time. This data manipulation will contribute to a better situational picture.
  • The Future Internet will include a proliferating number of internet-connected sensors, including cameras and microphone arrays [1]. Based on these sensors, emerging applications will be able to collect, filter, analyze and store large amounts of data captured from the physical world, as well as related metadata captured as part of perceptive multimedia signal processing algorithms. The ability to search this information in a scalable, effective, real-time and intelligent way can empower a wide range of added-value applications in the areas of security/surveillance, smart cities, social networking, e-science and more. Consider, for example, people adopting smartphone equipped with multiple sensors and connected to the internet. Those ubiquitous digital tools are increasingly enabling individuals to collect data about the environment they lives in. Those billions of ever-connected devices transmit user locations, images, motion, many kinds of other inputs. Participatory sensing is the process whereby individuals and communities use evermore-capable mobile phones and cloud services to collect and analyze systematic data for use in discovery. The SMART search framework will enable the implementation of search services over large scale community environmental and participatory sensing infrastructures, which have recently attracted the interest of cities, communities and individuals. In particular, participatory sensing describes the use of individuals and communities to gather information about their environment. It usually leverages the ubiquity of smart phones as sensing devices, of cloud based services for big data analysis, resource discovery and application delivery, while anticipating the trend towards more powerful sensing and processing capabilities of mobile devices and social networking sites. SMART aims to combine sensor networks information with social networks information in order to answer sensor based queries in a more social, useful and accurate way. Indeed, information from social networks can be used to enhance the end-users’ context and overall understand the context of the query in a much better way. Social networks information can be used to adapt a query for environment generated context to the end-user’s daily life. The concept is quite new, but there is a mutual benefit from the convergence of both sensor networks and social networks. Social networks can benefit from the fact that human activity and intent can be directly derived from sensors, which obviates the needs for explicit use input. On the other hand, sensor societies could start their collaboration in a social way (i.e. based on information derived from social networks). However, even though the potential of integrating social networks with sensor networks has been identified, only a few applications exist thus far. The SMART framework aims to provide an infrastructure where multimedia sensing devices in the physical world can be easily used to provide information about the status of their environments and make it available in real-time for search in combination with information from social networks.
  • Open and Open Source : SMART is designed as an open framework, which is extensible in terms of sensors (e.g., camera, microphone arrays, WSNs), ontologies and semantic structures (e.g., multimedia ontologies, sensor ontologies,), as well as multimedia processing components (notably video and audio processing algorithms). Furthermore, SMART will be to a large extent implemented based on open source technologies and royalty free standards. The main components of the SMART engine will be implemented in the form of open source software over the search engine. SMART will accordingly attempt to create an open source community for sustaining and evolving its results. • Multimedia : The SMART search engine will enable query answering based on the real-time processing of multimedia data stemming from the physical environment (such as audio and video). Cutting edge audio and visual processing components will be researched and adapted, notably in the areas of acoustic event classification and visual scene analysis. These components will be used for the SMART proof-of- concept validation. • Participatory and Reusable : The very same sensor and multimedia processing algorithms will be able to contribute to multiple concurrent queries of the SMART system. Participatory sensing schemes will be researched along with ways of caching data and queries, while also dealing with mobility and sharing application contexts. Furthermore, a number of Web2.0/Web3.0[21] mashups will be implemented to allow reuse of sensor queries across multiple applications and searches. • Smart and anticipatory : Based on machine learning and/or rule-based mechanisms, SMART should be able to anticipate the answers to certain queries, towards proactively responding to them. This will empower a level of intelligence, beyond self-learning and ranking algorithms used by existing search engines. • Social : The SMART search engine will seamless leverage information and search results from (Web2.0) social networks in order to facilitate the interception of social networks with sensor networks, towards social applications and searches of environment generated content. • Scalable and Dynamic : SMART is designed to be scalable at internet scale. Hence, the project will research a scalable architecture for collecting, filtering, processing, caching and combining sensor data in a highly heterogeneous and distributed environment. Furthermore, SMART will dynamically provide up to date information sensed by the underlying sensor networks. To this end, it will deal with the changing context of sensors (e.g., in the scope of mobility scenarios). • Context-aware : SMART enables the context-aware orchestration of sensor data and metadata towards accessing data that pertain to a given context. To this end, metadata associated with time, space, location, goals, tasks and more will be used in order to trade/negotiated the contribution of a sensor to a particular query. To this end, the project researches sensor selection protocols/algorithms, along with collaborative protocols enabling the orchestration of sensors towards a joint task.
  • In figure 1 four layer are identified (from top to bottom). At the top the physical world where sensing devices retrieve data. Data Harvesting and Correlation layer is made of edge nodes. An edge node process raw sensor data to produce metadata about the environment. Those metadata are available, through the knowledge base. The search layer collects the streams from the various edge nodes and indexes them in real-time using an efficient distributed index structure. Finally, the application layer provides a sets of API to build custom SMART based applications. In the next session, we will describe the three major layers: EDGE NODE, SEARCH Edge Node Layer The edge node is the interface of SMART with the physical world. Each edge node can cover sensors from a single geographic area, e.g. a city block or a public square in the city center. At the edge node, the signal streams, either from physical sensors (e.g. audio/visual streams or environmental measurements), or from social networks, are processed to extract events of interest. Edge nodes are built upon the idea of creating distributed architectures where groups of sensors can be associated to create geographically distributed sensor networks. Search Engine Layer The SMART search layer indexes in real-time streams of updates from edge nodes and social networks. The search engine hides the complexity of the edge node network. It is built using the Terrier open source search engine [3] with enhanced real-time indexing and a scalable distributed architecture to handle the large amount of streams. The SMART search layer offers an interface to services and end users to retrieve ‘interesting’ events and associated relevant posts in the social networks for a given query. While an interesting event is a subjective notion that likely depends on the application, the search layer can make inferences on interestingness, based on how unusual an event is, and learning from training examples of interesting events. Application Layer The last layer of the SMART platform (see Figure 1) contains the software applications that can deliver the real benefits of the framework to end users. The application layer mainly supports developers who want to create Web 2.0 services or smart phone applications that exploit the framework capabilities.
  • SMART entails acquisition and processing of content and context from the surrounding environment, including out-doors urban environments. As already outlined, the operation of the SMART search engine requires the deployment of perceptive components. Typically, these components process multimedia signals stemming from the physical environment and extract context. Their development and robust operations hinges in several cases on their training on the basis of data collected at the deployment location. This is also the case with the development of the audio and visual processing components of the SMART project, which will be developed following data collection processes To this end, SMART applications will deploy perceptive algorithms enabling context extractions about the physical environment/world. The development and deployment of such components (in the scope of SMART applications) may entail ethical issues (including privacy issues). For example, although not foreseen in the SMART application scenarios, the project ’ s open architecture could enable the deployment of perceptive algorithms that track people and their behaviour, which could raise privacy concerns.
  • Person Observation These algorithms observe the individual person. In that regard, they raise the most stringent concerns regarding ethical and privacy issues. They are algorithms that are helpful in the security and not in the live news use case. These are algorithms meant to be deployed in restricted/sensitive outdoor areas, not in public or indoors areas where people are warned about the operation of CCTV systems. Person and face tracking These algorithms consider either the whole body or the face of an individual and observe how he/she moves in space as time goes by. Security-related events like loitering detection need such algorithms. Identification Person identification needs an a-priory trained model of the person to be identified. Although of some use to the security use case (e.g. to identify suspect individuals), we do not intend to use any such algorithms within SMART. The obvious reason is their severe privacy implications. The Spanish DPA report has confirmed that identification is indeed the key concern in SMART ’ s video and audio observation. The identification of an individual must be hidden as soon the image is captured, whether it is via a censor or sufficient quality degradation so that anonymity is still respected. Similarly, the project ’ s audio sensors cannot be targeted towards voice recognition. (see Crowd Observation and Acoustic Observation of this section for more details) Crowd Observation These algorithms observe crowds as masses. No individual can be told apart in the crowd. This is ensured by placing the camera sensor far away from the people. They are algorithms that are used extensively in both the security and the live news use cases. Crowd density Here crowds are considered as collections of foreground pixels, which are processed to get an estimate of how crowded the observed space is. Due to the nature of the recording, it is impossible for a human operator to recognise people and for the algorithm to classify foreground as people or vehicles. Hence for these algorithms a ‘’ crowd ” is a mass of moving pixels. If the particular region consists of a pedestrian area, then the density is actually person density. If it is a street, then it is mostly vehicle density but also some pedestrian density. Crowd colours Here the foreground pixel colours are also considered, to get an estimate of the prominent colours worn by crowds in the city, i.e. of the fashion trends. Crowd motion At this level of analysis, blocks of foreground pixels are compared between frames to extract motion information of the masses. We are interested in how much these groups of moving pixels conform to interesting directions of motion. Acoustic Observation The acoustic part of the system processes two types of signals. The first is considered as general audio and the second is speech. The audio processing is relevant to both live news and security scenarios and the speech processing is relevant only to the live news scenario. Acoustic event detection Audio signals are collected in different places (possibly public) using microphones connected to the video capture equipment. Those signals may contain different type of noises such as crowd noises from events or from markets, traffic noises and other city noises. For the security cases we ’ ll consider other types of noises such as door opening and closing, footsteps in isolated areas and screaming. The signals are then processed and relevant event are identified in them. The ethical problems which might arise are related to accidental capture of speech data within the audio signal. Although this speech information will not be processed (e.g. extraction of the information in the speech) speech signals might be identified as a speech event (e.g. pointing that at specific location in the signal it contains speech segments). In order to avoid accidental collection of speech data the microphones will be placed far enough from the people. By placing the microphones away from possible speakers we would reduce the level of the speech below the level of the ambient noises. This will make the speech unintelligible. Speech processing Speech is collected using a smart-phone application which will allow the user to record a voice message and upload it to a server. The following processing will be performed on those speech signals: Speech transcription using automatic transcription engine. The text of the transcription will be used by the SMART system. Speaker verification: users who would like to be identified by the system will have first to perform an enrolment sessions where they will provide several speech samples from which a voice signature will be created. Later, when those users deliver a new message, the voice signature of the message will be compared to the ones from the enrolment in order to verify the identity of the speaker. The speaker verification process therefore requires a biometric voice signature DB for some of the users. This will be done only with users who will consent to this procedure.
  • In the scope of its proof-of-concept applications SMART will also leverage non A/V sensors and sensor networks, including temperature sensors, meteorological sensors, and wireless sensor networks. These sensors will provide information about the environment in the locations where they will be deployed.
  • As we told SMART allows users to enrich knowledge base harvesting data from multiple sources. Multimedia streams can be enriched crawling data from the social network, other sensors. Knowledge can be also generated by the fusion of such data. Geospatial, diachronical, semantical data aggregation E nable the smart chain to generate knowledge using simple data
  • The following table illustrates the main ethical issues associated with the SMART technologies outlined above. Note that the table focuses on the technologies that are being developed in the project (as part of WP3). The take up of the SMART search architecture following the end of the project, may lead to the integration of many other components (i.e. by third-parties, outside the consortium), within the SMART system. Ethical issues raised by such third-party components are expected to be addressed by their providers, as well as by entities in charge of integrating them in search applications.
  • CONTAINMENT APPROACH Directive 95/46/EC of the European Parliament The directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data lays down a series of rights of the data subject. These are: The right of access to own personal data. The rights of erasure, blocking or rectification of the data, which do not comply with the provisions of the Directive, are incomplete or inaccurate. The right to be informed of all relevant details relating to the data processing and the rights granted to self. The right to a judicial remedy for any breach of the above mentioned rights. All these are applicable to SMART. The first three aforementioned rights may be restricted if this is necessary for reasons relating to the protection of the data subject or the rights and freedoms of others or to prevent a criminal offence or for reasons relating to public security. Note that In the EU Data Protection Directive 95/46/EC, personal data are defined as “ any information relating to an identified or identifiable natural person ( “ data subject ” ); an identifiable person is one who can be identified, directly or indirectly, in particular by reference to an identification number or to one or more factors specific to his physical, physiological, mental, economic, cultural or social identity ” . Il diritto di accesso ai propri dati personali. I diritti di cancellazione, il blocco o la rettifica dei dati, che non sono conformi alle disposizioni della direttiva, sono incomplete o inesatte. Il diritto di essere informati di tutti i dettagli importanti riguardo al trattamento dei dati e dei diritti concessi a sé. Il diritto a un ricorso giurisdizionale in caso di violazione dei diritti di cui sopra.
  • Ethical Challenges of Participatory Sensing for Crisis Information Management

    1. 1. Ethical Challenges of participatory sensing for crisisinformation managementMassimiliano Tarquini - Maurizio Morgano
    2. 2. Agenda• Introduction to SMART Search Engine a holisticopen source web-scale multimedia searchframework for multimedia data stemming fromthe physical world;• Introduction to SMART approach to get datafrom the real world (perceptive algorithms);• Ethical aspects of collecting and harvesting suchdata.
    3. 3. SMART FP7 Search Engine
    4. 4. Project Factsheet• Consortium:– Atos– Athens Information Technology– IBM Haifa Research Lab– Imperial College London– Consorzio S3LOG– TELESTO Technologies Ltd. (SME)– University of Glasgow (Research)– Prisa Digital– City-of-Santander• Timeframe: 01/11/2011-31/10/2014• Project Budget: 4.425.000 Euro• EC Contribution: 2.686.292 Euro• Web Site:
    5. 5. SMART Search Engine: Motivation
    6. 6. SMART Search Engine: Characteristics
    7. 7. High Level Architectural OverviewSMART (Command-Line)QueriesSMART App#1SMART App#2Terrier Search Engine (SMART Edition)SMARTEdge Node#1SMARTEdge Node#2SMARTEdge Node#N….SocialNetworksManager
    8. 8. SMART Search Engine: Layers
    9. 9. Scope of the SMART Ethical AspectsSMART entails acquisition and processing of content andcontext from the surrounding environmentSMART will deploy perceptive algorithms enablingcontext extractions about the physicalenvironment/world
    10. 10. SMART Sensing Technologies
    11. 11. SMART Sensing Technologies
    12. 12. Enriching Multimedia Steams
    13. 13. Ethical Issues Associated with the SMARTVisual Sensing technologies
    14. 14. Ethical Issues Associated with theSMART Sensing technologies (Others)
    15. 15. SMART Ethical Management Strategy