Corsar, David

Edwards, Pete

Nelson, John Donald

Pan, Jeff Z

Velaga, Nagendra Rao

English

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Short Paper: Addressing the Challenges ofSemantic Citizen-SensingDavid Corsar, Peter Edwards, Nagendra Velaga, John Nelson, and Je↵ Pandot.rural Digital Economy Hub,University of Aberdeen,Aberdeen, UK.dcorsar,p.edwards,n.r.velaga,j.d.nelson,jeff.z.pan}@abdn.ac.ukhttp://www.dotrural.ac.ukAbstract. The challenges of the sensor web have been well documented,and the use of appropriate semantic web technologies promises to o↵erpotential solutions to some of these challenges (for example, how to repre-sent sensor data, integrate it with other data sets, publish it, and reasonwith the data streams). To date a large amount of work in this areahas focused on sensor networks based on“traditional” hardware sensors.In recent years, citizen sensing has became a relatively well-establishedapproach for incorporating humans as sensors within a system. Oftenfacilitated via some mobile platform, citizen sensing may incorporate ob-servational data generated by hardware (e.g. a GPS device) or directlyby the human observer. Such human observations can easily be imperfect(e.g. erroneous or fake), and sensor properties that would typically beused to detect and reason about such data, such as measurements of ac-curacy and sampling rate do not exist. In this paper we discuss our workas part of the Informed Rural Passenger project, in which the passengersthemselves are our main source for transport related sensing (such as ve-hicle occupancy levels, available facilities). We discuss the challenges ofincorporating and using such observational data in a real world system,and describe how we are using semantic web technologies, combined withmodels of provenance to address them.Keywords: Citizen-Sensing, Semantic Sensing, Semantic Citizen-Sensing,Provenance, Semantic Web1 IntroductionThe challenges of the sensor web have been well documented in, for example, [21],[22] and [8]. Documented challenges include: modeling, querying, and reasoningwith large scale sensor data [8, 11, 17, 15]; identification of, and integration withother relevant data sets, at scale [8, 11, 18, 24, 7]; characterizing and managingsensor data quality [8]; and supporting rapid application development [8].The use of semantic web technologies o↵er potential solutions to some ofthese challenges. Ontologies, such as the W3C SSN XG ontology1 provide mod-1 http://www.w3.org/2005/Incubator/ssn/XGR-ssnels for sensors, sensor networks, and observations; and linked data [5] enablesintegration of sensor data with other data sets [4, 13, 18]. Sensors typically pro-duce streams of data, and so there is potential for using technologies such asRDF stream querying [6, 3] (as explored in [15]) and RDF stream reasoners (e.g.[2, 23]) to support the use of that data. Further, Application Programming Inter-faces (APIs), such as the Linked Data API2 o↵er support for rapid applicationdevelopment.To date a large amount of work in this area has focused on sensor networksbased on“traditional” hardware sensors. In recent years, citizen sensing [19] hasbecame a relatively well-established approach for incorporating humans as sen-sors within a system. Often facilitated via applications (apps) running on amobile phone, citizen sensing may generate observational data by hardware (e.g.a GPS device) or directly by the human observer. Such human observationscan easily be imperfect (e.g. erroneous, incomplete, or fraudulent), and so, aswith any open system, this raises issues such as information quality (IQ) [10],reliability, trust, and reputation [14].One further challenge of citizen sensing, is that for observations generateddirectly by the human observer, sensor properties that would typically be usedto detect and reason about imperfect data, for example measurements of accu-racy and sampling rate, do not exist. Similar problems exist with observationsgenerated by the mobile phone’s hardware: often the mobile APIs provide fewdetails such as data sheets (describing sensor capabilities), settings used for ob-servations, and, in some cases, which sensor generated an observation3.This lack of information makes it di cult to perform the necessary assess-ments of observations produced using citizen sensing. Semantic web technologiespotentially have a role to play here by, for example, providing additional con-textual information for use in assessment processes.In this paper we describe an example real-world system which combines cit-izen sensing with semantic web technologies (section 2); discuss some of thechallenges faced by this system (section 3); and describe how we are addressingthose challenges (section 4).2 Example SystemAs part of the Informed Rural Passenger (IRP) project4, we are investigating thechallenges of developing a trusted passenger information system (PIS) for ruralareas. In our system the passengers themselves are our main source of transportrelated sensing, performed using a mobile app. The app enables passengers tocontribute observations about their journey on public transport, including ob-servations generated directly by the phone (e.g. location, presence of Wi-Fi) andby the passenger (e.g. occupancy level, and perceived vehicle temperature).2 http://code.google.com/p/linked-data-api/3 For example, the Apple iPhone iOS’s location API uses either the cellular network,Wi-Fi, or GPS sensor to determine location, but does not indicate which was used.4 http://www.dotrural.ac.uk/irpUsing linked data principles, this data is then integrated with other relevantdata sets, and used as the basis of a PIS, which provides passengers with details,including real-time bus locations, delays, and expected arrival times. This there-fore gives the potential for any imperfect data passed as input to the system toadversely e↵ect its outputs, reducing user trust in the system.3 Challenges of Semantic Citizen SensingIn developing the IRP PIS, we have identified a set of issues, which extendthose defined for the sensor web, and, we believe, require to be addressed by anysystem which incorporates humans as a source of sensor data, in order to remaintrusted by its users. These challenges are raised due to the potential generationof imperfect data by humans, and the lack of information for identifying andreasoning about it.Challenge 1, is one of the most pressing: the need to characterise and man-age constructs not just of data quality, but also of, for example, reliability, rep-utation, and trustworthiness, which can use the available types of data.This gives rise to challenge 2: maximising the data available for makingthose assessments. Here, identifying and integrating the sensor data with appro-priate external data sets can help address this challenge. Related to this are:challenge 3, selecting an appropriate model for describing the citizen sensorsand their observations, the possible granularity of which is limited by the lack ofinformation about them; and challenge 4, integrating the qualitative observa-tions generated by humans with the machine generated quantitive observations.In real-time information systems, short response times are vital; however,processes such as data integration and data assessments potentially conflict withthis requirement. Further, the additional data generated by these processes addsto the amount that must be stored and processed. This gives rise to challenge5: designing a system architecture which uses an appropriate combination oftechnologies (e.g. for storing and reasoning about the data), which enable thesystem to perform well while maintaining an acceptable response time.Finally, challenge 6 relates to ensuring user privacy, especially when sensi-tive data such as location is being collected and used as the basis of informationpassed to other users and/or services. Addressing this challenge is made moredi cult by the integration with other data sets, which potentially provide addi-tional data which can be used to violate a user’s privacy.4 Addressing These ChallengesWithin the IRP project we are addressing the above challenges by, firstly explor-ing the data available within the application domain, and secondly investigatinghow it can be integrated to form an information ecosystem supporting a range ofservices which perform PIS functions and data assessments. Whilst the solutionsbelow are outlined within the context of IRP, we believe they are generalisableto other systems incorporating humans as sensors.The information ecosystem that we are developing to address challenge 2integrates the passenger observations with various other types of data, includinguser profiles, social networks, and various types of transport information, such as:operator timetables; NaPTAN and NPTG5 datasets6; road maps; and details ofroadworks and travel disruptions7, which themselves link to NPTG. We will alsoreverse geocode location points obtained from passengers and, where possible,link them to the nearest road/railway line and nearest settlement in the NPTG.Fig. 1 outlines how we are addressing challenges 3 and 4, by basing our modelof citizen sensors and observations on the SSN XG ontology, with domain exten-sions describing: mobile phones as sensor platforms, people as sensors, and theobservations generated via our mobile app (Trips represent timetabled journeys).Within the ecosystem, we also maintain a provenance record of the data.Provenance has previously been identified as essential in supporting reliability,discovery, trust [20], and quality assessment [10] of online information, and somay play an important role in assessments of data in the ecosystem. Fig. 1 showshow we use the Open Provenance Model Vocabulary8 (OPMV) encoding of OPM[12] to provide explicit provenance information about observations. OPM definesprovenance using causal relationships between Artifacts, Processes, and Agents,which we use, for example, to link observations generated by a phone’s hardwareto the passenger controlling the sensing.AgentSensorssn:Property"OccupancyLevel"ssn:Sensorfoaf:Agentopmv:Agentrdfs:subClassOfssn:observesLocationObservation OccupancyLevelObsssn:observedBy onlyssn:observedByiPhoneiPhoneLocationSDLocationSensingLocationSDrdfs:subClassOfssn:Property"Location"ssn:observesTripFOI ssn:isPropertyOf ssn:isPropertyOf opmv:controlledBy somessn:sensingMethodUsed onlyopmv:wasGeneratedBy onlyssn:Platformssn:onPlatform onlyssn:SensingDevicerdfs:subClassOfrdfs:subClassOfssn:Observationopmv:Artifactrdfs:subClassOfssn:Sensingopmv:Processrdfs:subClassOfssn:observedProperty ssn:observedProperty ssn:featureOfInterestssn:FeatureOfInterestrdfs:subClassOfssn:featureOfInterestTrip trip Fig. 1. SSN XG ontology extended for citizen sensing and provenance for IRP.One reason for maintaining this information within the ecosystem is to sup-port various types of data assessment, particularly of IQ and trustworthiness.5 http://data.gov.uk/linked-data6 NaPTAN provides details of all UK access points to public transport; NPTG providesdetails of all UK settlements and roads connected to the public transport network.7 Provided by http://tra cscotland.dataincubator.org/8 http://purl.org/net/opmv/ns-20101006IQ assessments of data typically analyse various dimensions of the data, and sothe additional information should be beneficial; for example, other members ofour research team are currently investigating the role of provenance in IQ as-sessments of linked sensor data [1]. The multi-agent community have extensivelystudied models of trust and reputation [16, 14], which often rely on analysingpast interactions between agents (i.e. analysing the provenance of interactions),while others combine trust, provenance and social networks [9]. As part of ad-dressing challenge 1, we are currently investigating how these models can beapplied within the ecosystem.We will incorporate any data assessments and their results within the ecosys-tem as part of the provenance record (as subclasses of OPMV Process and Arti-fact classes respectively). This will allow services/applications (including thosemaking new assessments) to make use of these assessments if appropriate.The nature of the IRP project requires that it handles large quantities of dataand still functions reliably in real time. To help support this and address chal-lenge 5, passenger contributed observations are currently stored in a database,and exposed as linked data using the D2R server9. This setup takes advantage ofthe strengths of databases (such as scaling to large data sets, and handling mul-tiple concurrent read, update, and delete operations). However, the disadvantageis that it does not exploit many of the advantages of semantic web technologies,such as the ontology based querying and reasoning.5 Conclusions and Future WorkIn this paper we have identified a set of challenges, which we believe, requireto be addressed by any system that incorporates humans as a source of sensordata. We propose the use of semantic web technologies to help address thesechallenges, and illustrate their use in the development of a real-time PIS forrural areas.We currently have three strands of future work addressing challenges 1, 5,and 6: developing a trust model for the ecosystem; evaluating the performance ofdi↵erent options for storing and reasoning about streaming linked sensor data, todetermine if a combination can be found that provides (some of) the advantagesof semantic web technologies without negatively impacting overall performance;and investigating how we can ensure user privacy.Acknowledgements The research described here is supported by the awardmade by the RCUK Digital Economy programme to the dot.rural Digital Econ-omy Hub; award reference: EP/G066051/1References1. Baillie, C., Edwards, P., Pignotti, E.: Assessing Quality in the Web of LinkedSensor Data. In: Proc. of AAAI-11 (2011)9 http://www4.wiwiss.fu-berlin.de/bizer/d2r-server/2. 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Corcho, O., Garc´ıa-Castro, R.: Five challenges for the Semantic Sensor Web. Se-mantic Web Interoperability, Usability, Applicability 1(1), 121–125 (Jan 2010)9. Golbeck, J.: Combining provenance with trust in social networks for semantic webcontent filtering. In: Proc. of IPAW 2006. vol. 4145, pp. 101–108. Springer (2006)10. Hartig, O., Zhao, J.: Using Web Data Provenance for Quality Assessment. In: Proc.of Workshop on Semantic Web and Provenance Management at ISWC (2009)11. Kessler, C., Janowicz, K.: Linking Sensor Data - Why, to What, and How? In:Taylor et al. [22]12. Moreau, L., Cli↵ord, B., Freire, J., Futrelle, J., Gil, Y., Groth, P., Kwasnikowska,N., Miles, S., Missier, P., Myers, J., Plale, B., Simmhan, Y., Stephan, E., den Buss-che, J.V.: The open provenance model core specification (v1.1). Future GenerationComputer Systems (July 2010)13. Page, K., De Roure, D., Martinez, K., Sadler, J., Kit, O.: Linked Sensor Data:RESTfully serving RDF and GML. In: Taylor et al. 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Addressing the Challenges of Semantic Citizen-Sensing

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Addressing the Challenges of Semantic Citizen-Sensing

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