Quality Control for Real-time Ubiquitous Crowdsourcing
Afra J.Mashhadi Dept. of Computer Science University College London London WC1E 6BT, UK email@example.com
Licia Capra Dept. of Computer Science University College London London WC1E 6BT, UK firstname.lastname@example.org
as required. If such information could be gathered in real-time, users could be given useful updates while their journeys execute, so to dynamically and eﬀectively adapt their travel plans [3, 4]. In such scenarios, the real-time information contributed by participants can be invaluable, and thus the more participants are engaged in providing this information, the better such applications can work . However, the very same openness characteristic of such applications can threaten their success and impact the correctness of the results, as they allow anyone to contribute information. Indeed, a ﬁeld trial study of ubiquitous crowdsourcing application has shown that users are concerned with the credibility of data provided by the other participants . Therefore, quality control in crowdsourcing applications is an important issue which cannot be neglected. In the web domain, this challenge has been highlighted and eﬀectively tackled by using various approaches such as aggregation and reporting. However, ubiquitous crowdsourcing exhibits unique properties that lead to diﬀerent requirements for controlling the quality of contributions. These properties are as follows: • Real-time Events: in ubiquitous crowdsourcing the task of collecting information is often tightly linked to events which are highly dynamic. Furthermore, the collected information needs to be analysed, and the results provided to users, in real-time. For instance, in the above scenario, participants can upload information about the status of the bus journey they are taking, and the collected information has to be processed in real-time to give an estimate of buses real arrival time to awaiting users. This requirement of processing contributions in real-time diﬀers from what is observed in web-based crowdsourcing, where the applications can achieve quality assurance by relying on users (or authorised users) to ﬂag and report poor quality content with some time delay. • Dynamic Crowds: as opposed to web-based crowdsourcing, in ubiquitous crowdsourcing the crowd set (i.e., participants) keeps changing all the time. Let us refer back to the public transport scenario, where the crowd that can contribute travel information is formed by public transport users, undertaking their daily journeys. Such crowd varies throughout the day (e.g., the travellers who can report disruptions on bus route 24 will vary about every 15 minutes), and it may not always reach the critical mass required for
Crowdsourcing has become a successful paradigm in the past decade, as Web 2.0 users have taken a more active role in producing content as well as consuming it. Recently this paradigm has broadened to incorporate ubiquitous applications, in which the smart-phone users contribute information about their surrounding, thus providing a collective knowledge about the physical world. However the acceptance and openness of such applications has made it easy to contribute poor quality content. Various solutions have been proposed for the Web-based domain, to assist with monitoring and ﬁltering poor quality content, but these methods fall short when applied to ubiquitous crowdsourcing, where the task of collecting information has to be performed continuously and in real-time, by an always changing crowd. In this paper we discuss the challenges for quality control in ubiquitous crowdsorucing and propose a novel technique that reasons on users mobility patterns and quality of their past contributions to estimate user’s credibility.
Thanks to the widespread adoption of powerful and networked (i.e., Internet-enabled) handheld devices, consumers of digital content are now taking a more active role in producing content on the go. This trend has allowed a new category of applications to surface, in which data is collected by participants and is collectively used to oﬀer services to citizens [1, 2]. In this paper we focus on a new stream of research known as ubiquitous crowdsourcing, in which the contributed information is not limited to passively-generated sensor-readings from the device, but also includes proactively-generated user’s opinions and perspectives, that are processed to oﬀer real-time services to participants. For example, in an urban city as London, where there exists a complex transport network with unavoidable disruptions, users engagement in travel updates can be very valuable. By introducing a ubiquitous crowdsourcing application for public transportation, participants could actively contribute real-time information related to their journey. These contributions could include, for instance, information about accidents, unplanned road closures, congestions, and other highly dynamic events that aﬀect user’s journeys, but that transport authorities do not have the capacity to process as promptly
such applications to function (e.g., night-bus riders may be just a handful). Sparsity of contributions by small crowds is a well-known challenge in webbased crowdsourcing systems too, with severe impact on content quality . While web-based systems can aﬀord to tackle the issue by means of explicit users’ ratings and data aggregation, these techniques cannot directly be applied in our domain, because of the the real-time and highly dynamic nature of the applications at hand. To address the above challenges, we propose a technique which estimates the quality of contributions based on the contributor’s mobility, as well as their trustworthiness score based on their past contributions. In particular, our model leverages two sets of information: ﬁrst, user’s mobility is explored and a regularity value computed to oﬀer information about user’s faimilarity with certain locations and at a given time. This information is gathered implicitly, for example, by monitoring the user’s device GPS signal. Second, user’s trustworthiness is computed based on his past interactions with the ubiquitous crowdsourcing application, thus reﬂecting the usefulness of his past contributions as seen by other travellers. We combine these two sets of information to estimate a credibility weight for each contributor, allowing us to compute the results based on a weighted average of all the uploaded contributions. We continue this paper with an overview of the current state-of-the-art in quality control within the crowdsourcing paradigm; we then proceed to our novel quality control technique and lay out our evaluation plan.
RELATED WORK Web-based Crowdsourcing
and WikiMapia). In , the authors investigate the quality of voluntarily tagged Points Of Interests (POIs), and propose an algorithm which aggregates contributed data to retain the POIs that are only consistently repeated. In , Flanagin et al. discuss the issue of credibility of VGI by arguing that credibility is a measure of trustworthiness more than of expertise (i.e., data accuracy). That is, credibility is less about data accuracy and more about which information, or perspective, people believe in. In , a need for a trust model that takes into account subjectivity of geographic information and user’s perspective has been discussed.
A rich body of participatory sensing systems has been proposed in the literature, examples of which include: noise level monitoring [10, 11] and traﬃc monitoring [12, 3, 13]. A common problem faced by most of these applications is known as data pollution. That is, malicious users can compromise the quality of the results by uploading forged data or interfering with the data collection process (e.g., creating deliberate noise to impact the readings from the noise sensing application). In , a novel architecture for participatory sensing, which enables data consumers to assign trust scores to the data they access, is proposed. The authors investigate the challenge of verifying data integrity in terms of verifying participant’s context based on authenticating their location. In , the problem of fabricating the recorded data by malicious users is investigated. The authors assume the existence of a threat model in which malicious users can corrupt the sensors data after the reading. In order to overcome these types of threats, they propose a solution based on a trusted platform module (TPM), which conﬁrms the integrity of sensing devices. Similarly, in  a TPM based model is proposed which enables the service providers to trust the content generated on mobile devices. Other approaches address the problem of verifying the location of participants. In , this is done by relying on the co-located infrastructure to issue a timestamped location certiﬁcate to the participant’s device, which can later be used as the proof of user’s location when the data was collected. Common to all the above approaches is their focus on data integrity; that is, they verify and conﬁrm that the contributed data is indeed from the participant device and was collected at the claimed location. However, unlike participatory sensing, where the data comes from automatic readings from devices, in ubiquitous crowdsourcing contributed data is more subjective and includes users’ opinions. Therefore, there is a need for assessing the quality (i.e., correctness) of contributions in addition to data integrity. The only other work that tackles a similar problem to ours is . In  Huang et al. consider scenarios in which some of the participants may deliberately
Web-based crowdsourcing systems have gained popularity in recent years; in this domain, content quality is assured by means of: manual edition, aggregation and user’s reputation. Within user-generated content (UGC) centric applications, such as Slashdot, Reddit, and Digg, content quality is achieved by relying on users and their social networks to report and ﬁlter inappropriate content from the vast volume of online stories. Other systems such as Wikipedia and IMDB restrict the quality control to only a pre-deﬁned set of editors (i.e., experts), who can delete and correct poor quality articles (e.g., spam, mis-information, abusive language). In these cases, the editors are often chosen based on their proﬁle and historical data on their previous contributions, such as the number of edited articles. In addition to examples of centralised web-based crowdsourcing applications, semi-distributed crowdsourcing systems have also been extensively studied by the research community. An example of this research stream is Voluntary Geographical Information (VGI), in which the data is contributed by participants from the physical world and used to maintain and enhance the overall body of environmental knowledge (e.g., OpenStreeMaps
aﬀect the result of the readings from the sensor, and thus introduce poor quality information. They propose a reputation-based trust model which assigns a trust value to all participants and assesses the overall results of readings by taking into account participants credibility. The trust model is then evaluated using a participatory sensing application designed for assessing the noise level in oﬃce spaces, in which malicious users produce fake data by positioning their devices in various ways that can hinder the readings. Their result shows a clear improvement over the previous non-trust based approach, where quality was assessed using aggregation. We propose to extend this reputation-based line of work by considering scenarios in which users move and produce data within urban spaces such as the described crowdsourcing public transport application. Recently, various works have started studying the correlation between users mobility and their role as contributors in crowdsourcing applications. In particular, research has shown that users are more likely to perform a crowdsourcing task when close to home or a familiar place . Similarly,  has shown that crowdsourcing participants selected based on their mobility pattern can oﬀer much more valuable contributions. In this work, we apply the same reasoning and propose a quality control model for real-time ubiquitous crowdsourced information, based on participants mobility pattern as well as their historical reputation score.
tion of the observation period. For the transport domain, data is separately aggregated for weekdays and weekends, as users tend to travel diﬀerently Monday-toFriday, and Saturday/Sunday. Figure 1 exempliﬁes the frequency with which each tuple is generated during a given observation period, with the x axis representing the diﬀerent time segments of a day, and the y axis corresponding to the frequency with which the associated tuple was observed. In this example, we can see that the user travels from bus stop B on every single morning of the observation period (high regularity), while Tj (“BU S N o.1 − Stop D”, Af ternoon) is produced less regularly. This pattern could reﬂect, for instance, user’s adherence to a strict arrival time at work every morning, and a less strict leave time from the oﬃce.
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Figure 1: Examples of frequency distributions of POI for a given user
We propose to reason upon user’s mobility patterns in order to estimate the quality of their contributed information. Indeed, research has shown that a great degree of information can be learned by monitoring human mobility . For instance, studies demonstrate that urban travellers exhibit a high level of regularity in their daily journey patterns  and this property has been extensively exploited in mobile content dissemination networks . We propose to record a user’s mobility pattern in terms of the locations (POIs) he travels through (e.g., the bus stops the user covers). In addition, for each recorded location, we log the logical time segment of the recording (we are not interested in precise timestamps, but rather application-meaningful times, such as, for the transport domain, early morning, morning, afternoon, evening and night). We then deﬁne, for each user, a set of spatio-temporal tuples, T (locP OIx , ti ), where locP OIx corresponds to a speciﬁc POI that the contribution was recorded from at logical time ti . For instance, in the case of crowdsourcing travel information, the application can allow users to “check in” (` la FourSquare) to public services and a locations, and the tuples can be: T1 (“BUS No.1-Stop B”,
Monday Early Morning), T2 (“BUS No.1-Stop D”, Monday Evening), T3 (“Oxford Street”, Tuesday Afternoon).
Based on this information, we deﬁne a regularity function Reg(Tj ) whose value is determined based on implicit (location) readings from the user’s device. Although at bootstrapping time there is no available record over which to compute regularity patterns, after just a few days of usage of the application the logged records are suﬃcient to compute initial regularities. It is worth noting that, as illustrated in the above example, the regularity value is per user per tuple, that is, the familiarity of a user with a speciﬁc location at a speciﬁc logical time. Based on the regularity function, we can then deﬁne user’s expertise for any tuple as local, familiar, or stranger, and use application-dependent thresholding to delimit each category. In addition to the regularity function, for each user we estimate and maintain a reputation score which corresponds to their trustworthiness based on their past interactions with the ubiquitous crowdsourcing application. In so doing, whenever participants upload a piece of information, the usefulness of their contribution is ranked and the score is fed into a reputation-based trust function, T rust(ui ). In order to estimate the usefulness of UGC, the web-based systems have relied on explicit rating of content by users who vote and thus score the content. However, in ubiquitous crowdsourcing, where the reputation score needs to be calculated in real-time, a more dynamic way of estimating content’s usefulness is needed. For instance, we could compare the uploaded contribution with those provided by local experts (i.e., the highly regular users at that POI). Furthermore, in
As this mobility data is recorded every day, a regularity value, based on the frequency of the repeated locations for each logical time, can be calculated for the dura-
situations where there does not exist a benchmark comparison (i.e., due to sparsity of contributions and lack of contributions by local experts), we can still assess the usefulness of a contribution by proactively asking colocated users to explicitly rate the content in real-time through game strategies . Based on the two sets of information at hand, we can now compute a credibility weight for participant ui using the following equation:
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credibility weight(Tj ) = α·Reg(Tj )+(1−α)·T rust(ui ), where α can be dynamically adjusted to give precedence to either user’s regularity or trustworthiness, and is to be set by the application. For example, when contributions are sparse, the public transport crowdsourcing application might give a higher weight to the regularity function than to the user’s trustworthiness. Once computed, the credibility weight is then used to assess the quality of contributions, and the result of the crowdsourcing task can be provided to the application in the form of weighted average on all uploaded contributions.
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To evaluate the proposed approach, we are building an Android-based public transport crowdsourcing application that enables users to check in to their current journey, rate it, as well as provide comments about it (e.g., status of their bus journey, dynamically occurring events such as traﬃc jams and accidents). The application also includes a game component, in which users are challenged to compete against each other to become local “experts” of their bus routes and for a given time segment (` la FourSqaure, but with the time compoa nent added in). We plan to deploy this application in London, UK, starting in autumn 2011, with a pool of 100 users. To avoid unmanageable data sparsity, users will be selected so that their mobility mainly covers a restricted area in London, with good overlaps (e.g., university staﬀ and students). The quality of their contributions, as dynamically computed by our proposed approach, will then be qualitatively assessed.
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