=Paper=
{{Paper
|id=None
|storemode=property
|title=Short Paper: Addressing the Challenges of Semantic Citizen-Sensing
|pdfUrl=https://ceur-ws.org/Vol-839/corsar.pdf
|volume=Vol-839
|dblpUrl=https://dblp.org/rec/conf/semweb/CorsarEVNP11
}}
==Short Paper: Addressing the Challenges of Semantic Citizen-Sensing==
https://ceur-ws.org/Vol-839/corsar.pdf
Short Paper: Addressing the Challenges of
Semantic Citizen-Sensing
David Corsar, Peter Edwards, Nagendra Velaga, John Nelson, and Jeff Pan
dot.rural Digital Economy Hub,
University of Aberdeen,
Aberdeen, UK.
dcorsar,p.edwards,n.r.velaga,j.d.nelson,jeff.z.pan}@abdn.ac.uk
http://www.dotrural.ac.uk
Abstract. The challenges of the sensor web have been well documented,
and the use of appropriate semantic web technologies promises to offer
potential solutions to some of these challenges (for example, how to repre-
sent sensor data, integrate it with other data sets, publish it, and reason
with the data streams). To date a large amount of work in this area
has focused on sensor networks based on“traditional” hardware sensors.
In recent years, citizen sensing has became a relatively well-established
approach for incorporating humans as sensors within a system. Often
facilitated via some mobile platform, citizen sensing may incorporate ob-
servational data generated by hardware (e.g. a GPS device) or directly
by the human observer. Such human observations can easily be imperfect
(e.g. erroneous or fake), and sensor properties that would typically be
used 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 work
as part of the Informed Rural Passenger project, in which the passengers
themselves are our main source for transport related sensing (such as ve-
hicle occupancy levels, available facilities). We discuss the challenges of
incorporating and using such observational data in a real world system,
and describe how we are using semantic web technologies, combined with
models of provenance to address them.
Keywords: Citizen-Sensing, Semantic Sensing, Semantic Citizen-Sensing,
Provenance, Semantic Web
1 Introduction
The challenges of the sensor web have been well documented in, for example, [21],
[22] and [8]. Documented challenges include: modeling, querying, and reasoning
with large scale sensor data [8, 11, 17, 15]; identification of, and integration with
other relevant data sets, at scale [8, 11, 18, 24, 7]; characterizing and managing
sensor data quality [8]; and supporting rapid application development [8].
The use of semantic web technologies offer potential solutions to some of
these challenges. Ontologies, such as the W3C SSN XG ontology1 provide mod-
1
http://www.w3.org/2005/Incubator/ssn/XGR-ssn
els for sensors, sensor networks, and observations; and linked data [5] enables
integration 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 as
RDF 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 offer support for rapid application
development.
To date a large amount of work in this area has focused on sensor networks
based on“traditional” hardware sensors. In recent years, citizen sensing [19] has
became a relatively well-established approach for incorporating humans as sen-
sors within a system. Often facilitated via applications (apps) running on a
mobile phone, citizen sensing may generate observational data by hardware (e.g.
a GPS device) or directly by the human observer. Such human observations
can easily be imperfect (e.g. erroneous, incomplete, or fraudulent), and so, as
with 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 generated
directly by the human observer, sensor properties that would typically be used
to detect and reason about imperfect data, for example measurements of accu-
racy and sampling rate, do not exist. Similar problems exist with observations
generated by the mobile phone’s hardware: often the mobile APIs provide few
details 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 difficult to perform the necessary assess-
ments of observations produced using citizen sensing. Semantic web technologies
potentially 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 the
challenges faced by this system (section 3); and describe how we are addressing
those challenges (section 4).
2 Example System
As part of the Informed Rural Passenger (IRP) project4 , we are investigating the
challenges of developing a trusted passenger information system (PIS) for rural
areas. In our system the passengers themselves are our main source of transport
related sensing, performed using a mobile app. The app enables passengers to
contribute observations about their journey on public transport, including ob-
servations generated directly by the phone (e.g. location, presence of Wi-Fi) and
by 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/irp
Using linked data principles, this data is then integrated with other relevant
data 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 to
adversely effect its outputs, reducing user trust in the system.
3 Challenges of Semantic Citizen Sensing
In developing the IRP PIS, we have identified a set of issues, which extend
those defined for the sensor web, and, we believe, require to be addressed by any
system which incorporates humans as a source of sensor data, in order to remain
trusted by its users. These challenges are raised due to the potential generation
of imperfect data by humans, and the lack of information for identifying and
reasoning 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 making
those 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 sensors
and their observations, the possible granularity of which is limited by the lack of
information 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 with
this requirement. Further, the additional data generated by these processes adds
to the amount that must be stored and processed. This gives rise to challenge
5: designing a system architecture which uses an appropriate combination of
technologies (e.g. for storing and reasoning about the data), which enable the
system 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 information
passed to other users and/or services. Addressing this challenge is made more
difficult 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 Challenges
Within the IRP project we are addressing the above challenges by, firstly explor-
ing the data available within the application domain, and secondly investigating
how it can be integrated to form an information ecosystem supporting a range of
services which perform PIS functions and data assessments. Whilst the solutions
below are outlined within the context of IRP, we believe they are generalisable
to other systems incorporating humans as sensors.
The information ecosystem that we are developing to address challenge 2
integrates the passenger observations with various other types of data, including
user profiles, social networks, and various types of transport information, such as:
operator timetables; NaPTAN and NPTG5 datasets6 ; road maps; and details of
roadworks and travel disruptions7 , which themselves link to NPTG. We will also
reverse 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 model
of citizen sensors and observations on the SSN XG ontology, with domain exten-
sions describing: mobile phones as sensor platforms, people as sensors, and the
observations 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 so
may play an important role in assessments of data in the ecosystem. Fig. 1 shows
how we use the Open Provenance Model Vocabulary8 (OPMV) encoding of OPM
[12] to provide explicit provenance information about observations. OPM defines
provenance using causal relationships between Artifacts, Processes, and Agents,
which we use, for example, to link observations generated by a phone’s hardware
to the passenger controlling the sensing.
ssn:Platform ssn:SensingDevice ssn:Sensing ssn:Sensor
opmv:Process foaf:Agent
opmv:Agent
rdfs:subClassOf rdfs:subClassOf
rdfs:subClassOf opmv:controlledBy some
iPhone LocationSD LocationSensing rdfs:subClassOf
ssn:Observation
opmv:Artifact AgentSensor
ssn:onPlatform only ssn:sensingMethodUsed only
rdfs:subClassOf opmv:wasGeneratedBy only
rdfs:subClassOf ssn:observedBy only
ssn:observedBy ssn:observes
iPhoneLocationSD
LocationObservation OccupancyLevelObs
ssn:observes ssn:observedProperty
ssn:FeatureOfInterest
ssn:observedProperty
ssn:featureOfInterest ssn:featureOfInterest ssn:Property
rdfs:subClassOf ssn:isPropertyOf "OccupancyLevel"
ssn:Property
"Location" ssn:isPropertyOf TripFOI trip 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-data
6
NaPTAN provides details of all UK access points to public transport; NPTG provides
details of all UK settlements and roads connected to the public transport network.
7
Provided by http://trafficscotland.dataincubator.org/
8
http://purl.org/net/opmv/ns-20101006
IQ assessments of data typically analyse various dimensions of the data, and so
the additional information should be beneficial; for example, other members of
our research team are currently investigating the role of provenance in IQ as-
sessments of linked sensor data [1]. The multi-agent community have extensively
studied models of trust and reputation [16, 14], which often rely on analysing
past 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 be
applied 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 those
making new assessments) to make use of these assessments if appropriate.
The nature of the IRP project requires that it handles large quantities of data
and 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 of
the strengths of databases (such as scaling to large data sets, and handling mul-
tiple concurrent read, update, and delete operations). However, the disadvantage
is 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 Work
In this paper we have identified a set of challenges, which we believe, require
to be addressed by any system that incorporates humans as a source of sensor
data. We propose the use of semantic web technologies to help address these
challenges, and illustrate their use in the development of a real-time PIS for
rural 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 of
different options for storing and reasoning about streaming linked sensor data, to
determine if a combination can be found that provides (some of) the advantages
of 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 award
made by the RCUK Digital Economy programme to the dot.rural Digital Econ-
omy Hub; award reference: EP/G066051/1
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