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This paper presents an observatory for registering applications that use participatory sensing to collect data. Cataloging these applications will aid the scientific community to exchange more information, facilitating the comparison between different initiatives. Through an initial research, the applications are categorized in areas usually considered in the literature. We propose a survey to validate the platform and also discuss the taxonomies created as a result of this survey. The main contributions of this paper include the classification of crowdsensing applications in different ontological categories, as well as the proposal of a technology platform that enables the distributed and collaborative cataloging of crowdsensing initiatives. Keywords — crowdsourcing; participatory sensing; crowdsensing; taxonomy; observatory; survey; literature review
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Abstract—This paper presents an observatory for
registering applications that use participatory sensing to
collect data. Cataloging these applications will aid the
scientific community to exchange more information,
facilitating the comparison between different initiatives.
Through an initial research, the applications are categorized
in areas usually considered in the literature. We propose a
survey to validate the platform and also discuss the
taxonomies created as a result of this survey. The main
contributions of this paper include the classification of
crowdsensing applications in different ontological categories,
as well as the proposal of a technology platform that enables
the distributed and collaborative cataloging of crowdsensing
Keywords crowdsourcing; participatory sensing;
crowdsensing; taxonomy; observatory; survey; literature
The purpose of this work is to describe an observatory
created specifically for catalogging mobile participatory
sensing applications. In order to do this, a small version of a
survey was carried out, where dimensions that classify these
The knowledge on the subject has allowed the
identification of important aspects to classify applications
into categories, and in the scope of this work, the categories
are: application domain, sensors, incentive techniques, user
engagement levels, access types and shared data protection
It will also be possible to address new developments, of
software and hardware, with the goal of benefiting from the
Some key research questions we aim to answer in this
work include: what is the distribution of the applications in
the investigated dimensions, what information was
unavailable or difficult to obtain and what are the key
academic contributions in the field of participatory sensing.
This paper is not a definitive work: it consists of a previous
study of taxonomies, supported by an observatory built for
There is evidence of research published in Participatory
Sensing area, and one of the most important papers was
published in 2010 [1]. Since then, there has been significant
progress in the development of mobile applications and
devices, which nowadays have a greater number of sensors,
improved hardware and other aspects such as battery
performance and large scale use of 3G and 4G networks.
This has allowed more people to contribute with data and it
has also created an innovative development scenario.
Therefore, it is necessary to advance research in this area,
dealing with issues that must be considered when performing
the development of an application that has its content
generated by crowds. Moreover, there is a characteristic of
innovation that the development of this technology may
bring to the area, potentially impacting the lives of ordinary
citizens. Creating a taxonomy can help researchers to have
an overview of the area and to have visibility of the many
initiatives that explore the topic. This review will allow
researchers to position themselves and their work within the
field, increasing the synergy between researchers. Thus it is
possible that research challenges are addressed more quickly
through greater interaction and collaboration between groups
as new researchers can achieve their research goals more
rapidly and benefit from the results achieved previously by
The rest of the paper is organized as follows. Section II
explores the related work; Section III presents the proposed
observatory and discusses its architecture, Section IV
presents the methodology addressed, Section V discuss
results found of the survey. Finally, Section VI presents some
In recent years, with the development of mobile sensing
technologies, the scope of crowd computing systems [54]
using mobile devices has been broadened. Participatory
sensing is described as a sensing paradigm largely based on
the power of devices used by people, including mobile
phones, smart cars, portable devices, and others [2]. There is
also a definition proposed by Burke [3] which encompasses
the range of sensing, as it describes participatory sensing
networks as a mechanism that allows the general public and
professionals to collect, analyze and share local knowledge.
The goal of participatory sensing is to enable ordinary
citizens to share data they know, whether from personal
experiences or the environment in which they find
themselves, through mobile devices, allowing that data can
be analyzed, treated and also distributed according to the
According to Vital [4] NASA (National Aeronautics and
Space Administration), a North American agency, has
defined a classification taxonomy for corporate portals that
arose from the need to integrate experts from the same area,
thus facilitating communication between these experts. A
work conducted by [4] introduced the division of this
practice in two phases, alpha and beta, the former made by a
large group of users and the latter made by experts in the
field. As a result, different levels of relationships between the
This creation of taxonomies used in this work has support
in Computer Science [5], and allow multiple users to use the
proposed observatory detailed in Section 3.2 of this work
to create new entries as well as new items for the
taxonomies, which would be the classification alpha. The
first classification made within the scope of this work was
the beta rating since it was done by specialists in the
participatory sensing area. The aim was to provide the
platform for the general public, which in possession of the
guidelines described in Section 3.3 of this paper, will be able
to classify applications areas, thus performing the phase
As the use of spreadsheets was not appropriate to carry
out this work, due to the need of maintaining multiple
hierarchical structures in the inventory that are not supported
by the twodimensional structure, the development of an
observatory or platform that enables the creation of an
application inventory was addressed. This platform is
important because it allows people to collaboratively register
the existing applications while defining new taxonomies and
creating a central repository of data on mobile participatory
sensing applications. The collaboration aspect was also
considered, as it was important that the platform had a fluid
interface for inventory and visualization of data collected. As
the requirements described here were considered to be quite
specific, the authors found no known solution to accomplish
The solution chosen by the authors was to develop a
specific platform for cataloging items that could enable the
construction of hierarchical structures on demand. The
platform was developed with the technologies of Wordpress,
Figure 1 depicts some of the main fields that make up the
registration of an application. As can be seen, the screen is
very intuitive and allows the user (enroller) to see at the
same time all the metadata available to be filled or selected.
The application architecture allows the researcher to create
hierarchical ontologies related to the studied entity, which in
our case is the application. In this way, it is possible to
associate the application with one or more categories, one or
more sensors, levels of engagement, incentive techniques,
For each ontology, the researcher can choose one or more
items already available in the list of options, or add a new
option on the same screen where the application is cataloged,
with no need to interrupt the cognitive process of registration
accessing other screens to register domain data. For each of
the ontologies it is possible to consult (during the cataloging
process) the most commonly used options, contributing to
The main screen of the observatory can be visualized in
Figure 2. In this screen it is possible to observe a search
field, a list of applications and facets calculated according to
the defined ontologies. The search field helps the researcher
to find an application by keywords. This feature also allows
the researcher to search for an application before registering
it, thus avoiding duplication of records. In the left part of the
screen, the applications resulting from a selection made by
the user are listed, either through a textual search or by
choosing one of the options defined by the ontologies. On
the right side are displayed facets, which express very rich
information from the observatory. Through the facets it is
possible to filter applications according to a specific type of
one of the ontologies, as well as to visualize the number of
The platform provides customization facilities and
facilitates the visualization of data collected during the
research. The retrieval of the registered data can be made at
the page. The application of
registration is made only by authenticated users, which may
be authorized by an administrator. Data inserted on the tool
This paper proposes a classification for mobile
participatory sensing applications supported by the
aforementioned observatory. The starting point to create this
main classification that describes the purpose of the
applications was inspired by the work of Khan [6]. The terms
peoplecentric and urbansensing for participatory sensing
have been widely used so far, so we found it appropriate to
continue using them. From this starting point it was possible
to build leads as a result of the participation of other
researchers and these leads could be validated through the
application of dozens of frameworks that could be
inventoried and classified according to the proposed
To perform a survey to validate the proposed platform,
initial terms were used and search strings were formulated
and submitted to the following digital libraries: IEEE,
Journal of Systems and Software, ACM Digital Library and
Scopus. The following terms were used to search the initial
papers on the main subject: ( “crowd sensing” OR
“crowdsensing” OR “participatory sensing”) AND app.
From the list of papers published in journals and
conferences, it was possible to extract a list of applications
that make use of participatory sensing technologies, such as
CenceMe and NoiseSpy [7], UbiFit Garden and PEIR [8],
LiveCompare, MoVi and DietSense [9]. We set at 40 the
number of applications that would be classified, and with this
list in hand, papers describing these apps were then searched
on Google Scholar database. All applications were registered
and classified in the proposed observatory. By using this
tool, it was possible to view information registered about
In order to get the best use of the registration platform of
participatory sensing applications, a stepbystep catalog is
1) Identify an application that has its content created by
2) Checkifitwasnotalreadyregistered.
3) If the application has been identified in a paper, check
4) Search on Google Scholar database the papers that
Based on the information read in the paper, fill out the
registration platform, Figure 1. In addition to these catalog
information, each application must be classified according to
defined taxonomies, as described in the next section of this
This section discusses the results achieved with the
registration of 40 selected applications in the developed
platform. Six different dimensions in which the cataloged
1) Application working area: The apps were classified in a
hierarchical tree structure, so that they are always associated
with a leaf node of the tree. This leaf node objectively
describes the business domain of the application, ie its most
basic purpose. It should be a description in a few words and
simple enough so that other applications can also fit into this
classification in case they have the same purpose. The
description of the most internal nodes can be seen in Table 1
2) Sensors: In the scope of this work, sensors are devices that
can be embedded or connected in mobile devices. When built
in, the device already has the sensors independently of
whether they are used by applications sensors. There are also
derived sensors, which are those that have a particular
purpose, but if adjusted to the context, can be used for other
purposes, such as using the flash and the camera lens
together to detect the heartbeat by lightning the fingertip and
detecting the blood flow movement. The sensors may also be
coupled to the mobile device, such as a drone coupled to the
device or connected via a network with other devices and
lastly, may be human sensors that are actually feelings and
sensations that cannot be mapped by sensors, for example,
mood or the safety feeling in a particular place. The sensors
used by the application are usually explicitly described in the
3) Incentive techniques: Describes which techniques are
used to encourage users to share information via
participatory sensing applications. They are classified as
financial incentives when participants have the value of
their work paid in cash or coupons [10] and
nonfinancial, the latter having more than one
classification level, which are shown on Figure 3 and
A. Civility: The user contributes in order to make where
B. Collective: The user contributes towards a common
good, so that his information when analyzed together
with the information provided by others will generate
C. Fun: The user considers the act of contributing a fun
D. Gamification: Uses hedonistic components (such as
E. Give and take: The user must supply information to the
application in order to receive information from other
F. Selfcentered: the user contributes for his own benefit,
to follow a routine, or watch closely his personal
evolution. The data may or may not be shared with
G. Social interaction: The user contributes to interact with
In regard to the incentive techniques that were chosen to
guarantee the user interaction with the application, the
4) User engagement levels: The proposed classification
shows how the user data is collected by the application, and
it can be participatory, opportunistic or hybrid. If the user
requires explicit interaction (participatory) or if the data
collection is done automatically when the sensors themselves
realize the collecting opportunistically. Some applications
also use a hybrid form of user engagement [13] [28] [29]
[30] [45]. The hybrid form uses the two levels at different
times, or collect the data opportunistically and even request
further user confirmation. In the case of the DietSense
application [28], the user’s diet is recorded through
photographs in an opportunistic manner by a device worn on
the neck, but there is also the need for the user to inform
participatively feedback on the captured images. In
participatory format there is an active user cooperation, and
in opportunistic format, the user is not involved in
contributing his data to the system, ie, in data sharing [6].
With regard to the degree of user participation, the
opportunistic data collection one that happens without the
user being aware of its occurrence addresses different
technological challenges, since there is no human interaction.
Therefore, the development of tools and the use of sensors
should be directed to this end, seeking ways to detect the best
time in which data collection must begin and end. This active
monitoring of the behavior of people through mobile devices
that they carry with them has the potential to optimize the
society as a whole. Examples would be the possibility to
check the distribution of people in order to conduct urban
planning and the perception of the presence of a person in
shopping centers to generate marketing campaigns [48].
When the collection is explicit [49], ie the user interacts
explicitly, there are other challenges to be addressed, such as
notifications to the user to alert about the possibility to
interact with the application and ways to create a real interest
in the user to share good quality data, ie, true and also real
5) Access methods: This paper describes the following
application access methods: authenticated, anonymous and
hybrid. The user may or may not be logged in to share data,
that means the user must create an account in the application,
fill the username and password. Applications can also work
in hybrid form, for a particular need the user have to be
authenticated, and for others there may occur an anonymous
interaction. The authenticated form creates privacy
challenges for the application, as users’ personal data used in
the registration will be linked to the data that will be shared.
6) Data protection types: used to perform protection of data
A. Anonymity: guaranteed by the application, forcing the
B. Authorize content sharing: The user explicitly authorizes
C. Exclusion and selective retention: The user chooses to
delete data collected before being shared with the
application server or keep on his local device specific
data [6]. It is different than to authorize the sharing of
content, because this type does not allow the user to
D. Data Aggregation: Data aggregation is any process in
which information is gathered and expressed in a
summary form, for purposes such as statistical analysis.
A common aggregation purpose is to get more
information about particular groups based on specific
E. Data Encryption: The data is encrypted by the
F. Data Perturbation: intentionally perturbs the sensor
samples by adding artificial noise to the data at the
G. Spatial cloaking: a technique that blurs a user’s exact
location into a spatial region in order to preserve his
location privacy. The blurred spatial region must satisfy
the user’s specified privacy requirement. The most
widely used privacy requirements are kanonymity and
H. Hiding sensitive data: sensitive data can be selected by
the participants and protected using selective location.
This option is better than the disturbance data and
I. Keep data: Keep the data stored inside the mobile and
do not transfer it to any server or provide it to any other
J. Pseudonymity: Pseudonymity is the use of pseudonyms.
Instead of transmitting names in plain text, all
interaction with the application is performed under an
K. Turn off the device: The user can turn off the device that
collects the data whenever he wants the data not to be
L. Not mentioned: When the aspect of privacy is not
M. Not yet treated: The privacy aspect is mentioned in the
paper, but as a future work plan or not as a real problem
This section will report on what was found as the survey
result, after reading the papers that described the
applications. It was possible to divide the findings into
1) Privacy: Despite being an important issue to consider,
some applications do not mention the privacy of data
collected, as HeartPhones [47], Transafe [46], Balance [45],
HazeWatch [35], SkiScape [33], Jog Falls [34], Sensay [30],
HealthSense [29], FloraCaching [27], MobiShop [24], eBird
[23], Galaxy Zoo [22] and UbiFit Garden [20]. Privacy is an
important aspect since if there is the intention, any
application can perform an opportunistic data collection, that
2) Development status: Not all applications were in fact
developed; some are only proposals and were not released.
Therefore, some tools were not available for download and
others do not even have a Web page describing them. It is
possible to find information about them in scientific papers,
3) Coverage: Some applications have been developed in
order to conduct research at local communities, to
understand and improve the daytoday for small groups,
such as Transafe [46], which was developed for the city of
4) Hybridity of classifications: The same application, such as
eBird [23], may have two classifications for incentive
techniques. In the work of Jaimes [7] the incentive described
for this app is fun, and in the application paper from Wiggins
[23] it is claimed that the incentive techniques is for
collective good, because the app can be used as a knowledge
5) User participation: There is some debate on how not to
depend only on the user to gather information, but it strongly
depends on the application characteristics and the types of
sensors on which it is based. The opportunistic collection
generates a strong concern for user privacy. The most
modern mobile devices have sensors capable of performing
accurate data collection on sensitive information such as
user’s location at any given time and even over a long
period. The sound that can be collected through the
microphone and the images from the camera have a high
definition quality. This collection combined with the use of
mobile internet can allow user data to be transmitted even in
real time. There is a need to create mechanisms to ensure
users the purpose of the use of their data or warn users that
their data is being collected, in order not to generate a sense
of distrust, or there may be barriers, created by users, to
6) Monetization: No application investigated in the first
selected application group uses financial incentives to
encourage users to contribute. All the investigated tools work
with nonfinancial incentives, by using other techniques that
The evolution of hardware and software of mobile devices
and the development of new sensors in these devices not
only allows but also facilitates the sharing of personal
information, since these devices are used massively by
people all over the world. The mobile phone is now an
essential item for communication, and it has become more
accessible and cheaper over time. The collection of
information about people enables a completely customized
experience, and the advancement of machine learning also
contributes to the development of new applications. Thus,
search is no longer adapted to what is offered by the market,
but the market creates solutions that automatically fits to its
New mobile devices are also being created based on these
assumptions, such as the SmartWatches [50], SmartGlasses
[51] and items for smart homes, like the Amazon Echo [52]
and the Google Home [53]. These devices also work with the
concept of engaging applications to their system, as well as
mobile phones, and will rely heavily on participatory sensing
to provide service to its users. Moreover, it is possible to
adapt external sensors to mobile devices and to make
communication between them via bluetooth or wireless, as
This work enables researchers to position themselves in the
present state of the art in mobile participatory sensing design
space, and contributes to the development of taxonomies
related to participatory sensing in mobile devices, which will
guide interested researchers on this field and also application
developers for mobile devices. It also describes a tool created
to be an observatory of applications on participatory sensing,
As a future work, we propose to extend the research to
consider more articles, such as [54] and others, as well as
opening the platform for general researchers, so that they can
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[2] B.Guo,Z.Yu,X.Zhou,andD.Zhang,“Fromparticipatory
[3] J.A.Burkeetal.,“Participatorysensing,”Centerfor
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[5] L.P.VitalandL.M.A.Café,“Práticasdeelaboraçãode
[6] W.Z.Khan,Y.Xiang,M.Y.Aalsalem,andQ.Arshad,
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[8] M.Munetal.,“PEIR,thepersonalenvironmentalimpact
[9] D.Christin,A.Reinhardt,S.S.Kanhere,andM.Hollick,“A
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[11] L.F.Oliveira,D.Schneider,J.MoreiradeSouza,andS.A.
[12] L.F.Oliveira,D.Schneider,F.Oliveira,J.M.deSouza,andS.
[13] N.Maisonneuve,M.Stevens,M.E.Niessen,andL.Steels,
[14] X.BaoandR.RoyChoudhury,“Movi:mobilephonebased
[15] E.Kanjo,J.Bacon,D.Roberts,andP.Landshoff,“MobSens:
[16] R.K.Rana,C.T.Chou,S.S.Kanhere,N.Bulusu,andW.Hu,
[17] “WeatherSignal,TheCrowdSourcedWeatherMap,[online]
[18] E.Miluzzoetal.,“Sensingmeetsmobilesocialnetworks:the
[19] S.Gaonkar,J.Li,R.R.Choudhury,L.Cox,andA.Schmidt,
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[21] D.EstrinandJ.Burke,“ParticipatoryCampaignsfor
[22] K.L.Mastersetal.,“GalaxyZoo:barsindisc
[23] A.Wiggins,“eBirding:technologyadoptionandthe
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[27] “Floracaching,[online]Available:
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[50] “AppleWatch.Available:”.
[51] “Google.Googleglasses.Available:”.
[52] “Amazon.AmazonEcho.Available:”.
[53] “Google.GoogleHome.Available:”
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... We illustrate the validation of the proposed taxonomy over 50 distinct IoE applications in the following three domains: crowdsourcing applications [134], IoT/IoE applications with analytics [114], and cyber-physical systems [135]. We selected crowdsourcing applications due to the integration of crowd knowledge and participatory sensing, IoT/IoE applications with analytics to validate the applications with an implicit knowledge and big data sensing and cyber-physical systems due to the pervasive environment of thing-to-thing collaborations. ...
... Regarding the crowdsourcing application domain and using the proposed taxonomy, we analyzed 11 applications observed by Melo et al. [134] in a Crowd Application Database ( According to the authors of this study, there is a need to create mechanisms to warn users about using their data. ...
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The paradigm of the Internet of everything (IoE) is advancing toward enriching people’s lives by adding value to the Internet of things (IoT), with connections among people, processes, data, and things. This paper provides a survey of the literature on IoE research, highlighting concerns in terms of intelligence services and knowledge creation. The significant contributions of this study are as follows: (1) a systematic literature review of IoE taxonomies (including IoT); (2) development of a taxonomy to guide the identification of critical knowledge in IoE applications, an in-depth classification of IoE enablers (sensors and actuators); (3) validation of the defined taxonomy with 50 IoE applications; and (4) identification of issues and challenges in existing IoE applications (using the defined taxonomy) with regard to insights about knowledge processes. To the best of our knowledge, and taking into consideration the 76 other taxonomies compared, this present work represents the most comprehensive taxonomy that provides the orchestration of intelligence in network connections concerning knowledge processes, type of IoE enablers, observation characteristics, and technological capabilities in IoE applications.
... This mediates the collection of relevant data, which are consequently used to assess, measure, and map various phenomena through a crowd-sourced participatory manner [111]. These use case scenarios include, among others, monitoring urban noise and pollution levels, monitoring urban cleanliness levels, and monitoring urban road and traffic conditions [117]. ...
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The continuously increasing number of mobile devices actively being used in the world amounted to approximately 6.8 billion by 2022. Consequently, this implies a substantial increase in the amount of personal data collected, transported, processed, and stored. The authors of this paper designed and implemented an integrated personal health data management system, which considers data-driven software and hardware sensors, comprehensive data privacy techniques, and machine-learning-based algorithmic models. It was determined that there are very few relevant and complete surveys concerning this specific problem. Therefore, the current scientific research was considered, and this paper comprehensively analyzes the importance of deep learning techniques that are applied to the overall management of data collected by data-driven soft sensors. This survey considers aspects that are related to demographics, health and body parameters, and human activity and behaviour pattern detection. Additionally, the relatively complex problem of designing and implementing data privacy mechanisms, while ensuring efficient data access, is also discussed, and the relevant metrics are presented. The paper concludes by presenting the most important open research questions and challenges. The paper provides a comprehensive and thorough scientific literature survey, which is useful for any researcher or practitioner in the scope of data-driven soft sensors and privacy techniques, in relation to the relevant machine-learning-based models.
... Individuals with sensing and computing devices volunteer to collectively share data to measure and map phenomena of common interest, in a crowd-sourced fashion [66]. Applications where mobile participatory sensing has been used include noise pollution monitoring, litter monitoring, monitoring of traffic and road conditions, among others [104]. ...
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The number of mobile devices, such as smartphones and smartwatches, is relentlessly increasing to almost 6.8 billion by 2022, and along with it, the amount of personal and sensitive data captured by them. This survey overviews the state of the art of what personal and sensitive user attributes can be extracted from mobile device sensors, emphasising critical aspects such as demographics, health and body features, activity and behaviour recognition, etc. In addition, we review popular metrics in the literature to quantify the degree of privacy, and discuss powerful privacy methods to protect the sensitive data while preserving data utility for analysis. Finally, open research questions are presented for further advancements in the field.
... Individuals with sensing and computing devices volunteer to collectively share data to measure and map phenomena of common interest, in a crowd-sourced fashion [66]. Applications where mobile participatory sensing has been used include noise pollution monitoring, litter monitoring, monitoring of traffic and road conditions, among others [104]. ...
Full-text available
The number of mobile devices, such as smartphones and smartwatches, is relentlessly increasing to almost 6.8 billion by 2022, and along with it, the amount of personal and sensitive data captured by them. This survey overviews the state of the art of what personal and sensitive user attributes can be extracted from mobile device sensors, emphasising critical aspects such as demographics, health and body features, activity and behaviour recognition, etc. In addition, we review popular metrics in the literature to quantify the degree of privacy, and discuss powerful privacy methods to protect the sensitive data while preserving data utility for analysis. Finally, open research questions a represented for further advancements in the field.
... Over the past few years, the CSCW and HCI communities have been mobilizing to meet the growing demand for research in social and crowd computing, with an expectation that these technologies can positively impact the lives of ordinary people [16]. However, research on social computing systems faces its challenges and dilemmas. ...
Conference Paper
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Citizen engagement in building user-curated narratives of complex or long-lasting news stories has been the key foundation of the design and implementation of the Acropolis virtual environment. Previous user studies have shown, by positive evidence, that this goal can be pragmatically achieved, but the challenge now lies in assessing: a) the extent to which an environment like Acropolis can be used to empower citizens; and b) whether and how the tool could be used to support the work of professional curators. Findings from a focus group study highlighted the tool's potential to engage citizens with news, the usefulness of the environment to build virtual memories, and the convenience of using Acropolis to support professional journalistic work.
... In this paper, we lay out some ideas for a research agenda, examining whether the same success that microtask CS has achieved in other domains can be obtained in the field of social news curation. Models and techniques to exploit the power of the crowd in solving problems of various types have been proposed and evaluated in recent years in areas ranging from strategic planning [5], to participatory sensing [6] and crowdsourcing in the news value chain [7]. In previous work [8,9] we described the design, implementation and evaluation of two speculative prototypes of Acropolis, a social computing platform designed to engage citizens in building and sharing their own narratives about complex or long-term news stories. ...
Conference Paper
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Over the past decade, online crowdsourcing has established itself as an emerging paradigm that industry and government have been using to harness the cognitive abilities of a multitude of users distributed around the world. In this context, microtask crowdsourcing has become the method of choice for addressing a wide range of diverse problems. Microtasks typically require a minimum of time and cognitive effort, but combined individual efforts have made it possible to accomplish great achievements. The goal of this paper is to contribute to the ongoing effort of understanding whether the same success that microtask crowdsourcing has achieved in other domains can be obtained in the field of social news curation. In particular, we ask whether it is possible to turn online news curation, typically a social and collaborative activity on the Web, into a model in which curatorial activities are mapped into microtasks to be performed by a crowd of online users.
... There has been an outstanding growth of mobile crowdsensing proposals from both academia and industry in the decade [216]. Crowdsensing has numerous practical applications [141], such as monitoring the public service (e.g., time attendance to services, delivery tracking, traffic conditions, and roads, safety perception), monitoring the environment (e.g., noise and ambiance, atmospheric conditions, garbage, air quality, classify galaxy pictures), and enrichment of social media, just to name a few. Various types of applications have been developed to realize the potential of mobile crowdsensing. ...
Mobile crowdsensing is a powerful mechanism to contribute to the ubiquitous sensing of data at a relatively low cost. With mobile crowdsensing, people provide valuable observations across time and space using sensors embedded in/connected to their smart devices, e.g., smartphones. Particularly, opportunistic crowdsensing empowers citizens to sense objective phenomena at an urban and fine-grained scale, leveraging an application running in the background. Still, crowdsensing faces challenges: The relevance of the provided measurements depends on the adequacy of the sensing context with respect to the phenomenon that is analyzed; The uncontrolled collection of massive data leads to low sensing quality and high resource consumption on devices; Crowdsensing at scale also involves significant communication, computation, and financial costs due to the dependence on the cloud for the post-processing of raw sensing data. This thesis aims to establish opportunistic crowdsensing as a reliable means of environmental monitoring. We advocate enforcing the cost-effective collection of high-quality data and inference of the physical phenomena at the end device. To this end, our research focuses on defining a set of protocols that together implement collaborative crowdsensing at the edge, combining: • Inference of the crowdsensor’s physical context characterizing the gathered data: We assess the context beyond geographical position. We introduce an online learning approach running on the device to overcome the diversity of the classification performance due to the heterogeneity of the crowdsensors. We specifically introduce a hierarchical algorithm for context inference that requires little feedback from users, while increasing the inference accuracy per user. • Context-aware grouping of crowdsensors to share the workload and support selective sensing: We introduce an ad hoc collaboration strategy, which groups co-located crowdsensors together, and assigns them various roles according to their respective contexts. Evaluation results show that the overall resource consumption due to crowdsensing is reduced, and the data quality is enhanced, compared to the cloud-centric architecture. • Data aggregation on the move to enhance the knowledge transferred to the cloud: We introduce a distributed interpolation-mediated aggregation approach running on the end device. We model interpolation as a tensor completion problem and propose tensor-wise aggregation, which is performed when crowdsensors encounter. Evaluation results show significant savings in terms of cellular communication, cloud computing, and, therefore, financial costs, while the overall data accuracy remains comparable to the cloud-centric approach. In summary, the proposed collaborative crowdsensing approach reduces the costs at both the end device and the cloud, while increasing the overall data quality.
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Mobile Participatory Sensing Systems have the potential to improve different services through monitoring of the urban landscape using mobile devices based on the collaboration of thousands of mobile users. Many articles have been recently published related to mobile participatory systems where data collected from thousands of mobile users are analyzed to extract vital community information and a spatiotemporal interpretation of the phenomenon of interest is built. The purpose of this paperis to assess the state-of-the-art mobile participatory sensing systems to classify key practical requirementsof such systems and related challenges. The Kitchenham method has been used to conduct this review. A selection of 24 articles out of 590 articles related to mobile participatory sensing frameworks have been made between the period of 2013 to 2018 from the IEEE Xplore Digital Library and ACM library for assessment. A detailed review has been conducted through the classification of mobile participatory sensing systems and a critical evaluation is carried out. Potential opportunities and challenges for modern mobile participatory sensing systems are also discussed. This paper provides researchers in the field with a comprehensive and up-to-date review of mobile participatory sensing systems.
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With the surging of smartphone sensing, wireless networking, and mobile social networking techniques, Mobile Crowd Sensing and Computing (MCSC) has become a promising paradigm for cross-space and largescale sensing. MCSC extends the vision of participatory sensing by leveraging both participatory sensory data from mobile devices (offline) and user-contributed data from mobile social networking services (online). Further, it explores the complementary roles and presents the fusion/collaboration of machine and human intelligence in the crowd sensing and computing processes. This article characterizes the unique features and novel application areas of MCSC and proposes a reference framework for building human-in-the-loop MCSC systems. We further clarify the complementary nature of human and machine intelligence and envision the potential of deep-fused human-machine systems. We conclude by discussing the limitations, open issues, and research opportunities of MCSC.
Crowd sensing (CS) is an approach to collecting many samples of a phenomena of interest by distributing the sampling across a large number of individuals. While any one individual may not provide sufficient samples, aggregating samples across many individuals provides high-quality, high-coverage measurements of the phenomena. Thus, for participatory sensing to be successful, one must motivate a large number of individuals to participate. In this work, we review a variety of incentive mechanisms that motivate people to contribute to a CS effort. We then establish a set of design constraints or minimum requirements that any incentive mechanism for CS must have. These design constrains are then used as metrics to evaluate those approaches and determine their advantages and disadvantages. We also contribute a taxonomy of CS incentive mechanisms and show how current systems fit within this taxonomy. We conclude with the identification of new types of incentive mechanisms that require further investigation.
Now that billions of people carry sensor-enabled mobile devices (e.g., smartphones), employing powerful capability of such commercial mobile products has become a promising approach for large-scale environmental and human-behavioral sensing. Such a new paradigm of scalable context monitoring is known as opportunistic sensing, and has been successfully applied to a broad range of applications. In this paper, we briefly introduce basic architecture and building blocks on which these emerging systems are based, and then provide a survey of recent progress in the opportunistic sensing technology.
Citizen sensing is a new sensor-based data collection paradigm and is focused on the extraction of data generated by people. Initiatives based on this concept are becoming crucial for designers of intelligent urban infrastructures, since they enable the collection of several types of relevant data that cannot be properly captured by traditional physical sensors. A large number of articles and projects associated with the topic appeared over the last few years, and with them the need for properly classifying and organizing these works. In this paper, we propose a taxonomy of citizen sensing initiatives and illustrate each of its dimensions through a survey of recent articles in the area. The proposed scheme also supports the identification and stimulates the development of projects addressing data collection methodologies that have not been properly explored so far. In addition, we present a platform capable of aggregating, analyzing, and extracting knowledge from data generated by physical and human sensing techniques. Finally, we report a real-world experiment in which we used our platform to map accessibility conditions of streets and sidewalks located in a four square kilometer area in São Paulo, Brazil. Our results show that a full coverage was obtained with the support of eight volunteers after only three hours, hence illustrating the effectiveness of the technology.
They place calls, surf the Internet, and there are close to 4 billion of them in the world. Their built-in microphones, cameras, and location awareness can collect images, sound, and GPS data. Beyond chatting and texting, these features could make phones ubiquitous, familiar tools for quantifying personal patterns and habits. They could also be platforms for thousands to document a neighborhood, gather evidence to make a case, or study mobility and health. This data could help you understand your daily carbon footprint, exposure to air pollution, exercise habits, and frequency of interactions with family and friends.
Personal security is an open problem in large cities. After several attempts to reduce violence and crime, there seems to be an agreement that preventive actions are the best way to address this problem. Trying to help deal with that challenge, this paper proposes a mobile collaborative application, named Personal Guardian, which is used by civilians while walking in urban areas. The application is focused on crime prevention and it implements participatory sensing to help people be aware of the risks that appear to exist in a certain place at a certain time. Based on that information, citizens can take appropriate and on-time preventive actions. The system is supported by a human-centric wireless sensor network, and it is complementary to the security solutions already used by public and private organizations. The system architecture and its main components are described, and the main requirements and design decisions are also discussed. A preliminary evaluation of the solution was conducted to determine its strengths and weaknesses in terms of quality of service. The obtained results indicate that the information feeding process is more relevant for end-users than the unattended delivery of awareness information about their personal security. In addition, this former capability does not require to be adjusted to the end-users' context. Copyright © 2014 John Wiley & Sons, Ltd.
Mobile phone sensing is an emerging area of interest for researchers as smart phones are becoming the core communication device in people's everyday lives. Sensor enabled mobile phones or smart phones are hovering to be at the center of a next revolution in social networks, green applications, global environmental monitoring, personal and community healthcare, sensor augmented gaming, virtual reality and smart transportation systems. More and more organizations and people are discovering how mobile phones can be used for social impact, including how to use mobile technology for environmental protection, sensing, and to leverage just-in-time information to make our movements and actions more environmentally friendly. In this paper we have described comprehensively all those systems which are using smart phones and mobile phone sensors for humans good will and better human phone interaction.