Crowdsensing Solutions in Smart Cities towards a Networked Society

Abstract

The goal of the paper is to give an overview of the most relevant aspects of mobile crowdsensing that are already utilized by the society. The paper focuses on best practices applied in smart cities today, how these applications can be motivated (incentives), and how they rely on technology enablers of today’s vertical silos and future’s horizontal approaches. We introduce a path for transforming the vertical silos of today containing separated solutions in various domains into a horizontal, unified ecosystem, giving a way to novel technology and business opportunities.

Full text

EAI Endorsed Transactions
on Internet of Things Research Article
1
Crowdsensing Solutions in Smart Cities towards a
Networked Society
Á. Petkovics1
*
, V. Simon2, I. Gódor3 and B. Böröcz4
1 PhD student, Budapest University of Technology and Economics
2 Associate professor, Budapest University of Technology and Economics
3 Ericsson Research
4 MSc student, Budapest University of Technology and Economics
Abstract
The goal of the paper is to give an overview of the most relevant aspects of mobile crowdsensing that are already utilized
by the society. The paper focuses on best practices applied in smart cities today, how these applications can be motivated
(incentives), and how they rely on technology enablers of today’s vertical silos and future’s horizontal approaches. We
introduce a path for transforming the vertical silos of today containing separated solutions in various domains into a
horizontal, unified ecosystem, giving a way to novel technology and business opportunities.
Keywords: crowdsourcing, mobile crowdsensing, smart city, networked society
Received on 22 September 2015, accepted on 16 October2015, published on 26 October 2015
Copyright © Petkovics et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons
Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any
medium so long as the original work is properly cited.
doi: 10.4108/eai.26-10-2015.150600
1. Introduction
At the early stage of the evolution, mankind discovered
that cooperation can help people solving more complex
tasks than one can do alone. They hunted, constructed
buildings, carried goods together with success. Co-
working remained significant, nowadays more complex
tasks are solved based on this principle. IT-related tasks
with enormous computational power needs are often
outsourced to many users with different hardware and
computational power. The performance of collaborators in
solving a complex problem often exceeds the power of
today’s supercomputers.
Crowdsourcing is a form of cooperation of a big group of
users (so called crowd) where single users are solving
small subtasks of a greater job, so complex problems can
be handled more efficiently with many co-working users
involved (e.g. Seti@home [1]). Cooperation is also useful
when the user can add something beside the
computational power. With modern mobile phones
equipped with various sensors (accelerometer, GPS,
gyroscope, etc.) the cooperation between the members of
the crowd is possible in large sensing tasks, as well.
Mobile phones can not only provide access to their
sensors, but there is also a possibility to manually send
information about the owner’s surroundings through a
mobile application. This way, people provide information
which is already processed by their mind, so it is more
valuable for the community sensing service.
Crowdsensing is a subtype of crowdsourcing where the
actual outsourced job is a complex sensing task. One
example is the operation of thematic web logs (blogs),
where users provide their sensed information of the same
phenomena. This particular example belongs to social
crowdsensing applications, where participants share their
produced data through a central server with each other.
The database created this way provides a better
understanding of problems and helps to make decisions
and prepare community-based solutions together which
are not only better grounded, but will satisfy more
individuals. Microblogs [2] also belong here, which is a
universal platform for cooperation between users. People
can share information on touristic areas, not only different
experiences but also real-time questions, at a certain
venue within the location-based application. Another aim
**Corresponding author. Email:petkovics@hit.bme.hu
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of the platform is to help spreading news and other
interesting information in a short timescale among the
users. Another example of social crowdsensing
applications is Flysensing [3], which relies on flight
passengers carrying electronic devices and presents a
diverse collection of use cases including health
monitoring, safety and surveillance, scientific discovery
and business analytics.
To map a phenomenon or to discover a territory, sensors
in different places in or around the area of interest should
send their data so the crowdsensing application could
have a clear vision of what is happening there. These
sensors can be immovable ones, deployed in advance, like
in wireless sensor networks (WSNs) or moving ones.
Static sensor placement has disadvantages of insufficient
coverage of the area of interest, high costs of deployment
and maintenance and it is not scalable as well. A novel
approach is the usage of sensors that are carried by people
on their daily routes. They represent a variant of mobile
sensor networks which rely on people’s smartphones
utilizing the sensors integrated in these devices.
Smartphones are uncontrolled mobile sensors as their
mobility is not restricted as of those sensors, which are
deployed in advance (e.g. on public vehicles). They move
along with their owner and collect the information about
speed, acceleration, connected cell towers, Wi-Fi hotspots
in sight, etc.
Utilizing moving sensors in crowdsourcing is called
mobile crowdsensing (MCS). That is, MCS differs from
the deployed sensor networks in involving people who are
moving, and accordingly, collecting data from different
places and routes. People are not only carrying sensors
integrated to their mobile devices like smartphones, but
are able to provide information about the surroundings
manually, as well. Advantages of MCS over other types
of sensor networks include a) high computing capacity of
smartphones for pre-processing of data, b) connectivity to
cloud supporting data processing and c) individuals are
able to provide useful information about the surroundings
that is hard to monitor by sensors [4]. Humans can
contribute deep, qualitative knowledge; they can analyze
fuzzy or incomplete data; and they can act in ways that
digital systems often cannot [5]. However, the evaluation
of sensed data can be biased by the variance of available
sensors and their accuracy in different smartphones.
Reliability of the sensed information is also a question as
it depends on the number of sensing received about a
certain phenomena. Phone owners cannot be forced to run
the crowdsourcing application or to provide manual
sensing, so the critical size of the crowd has to be
maintained with various types of incentive mechanisms.
They help the system in getting just the right quantity and
quality of information needed for the actual sensing use-
case.
The way how members of the crowd provide information
can be divided into two categories, participatory [6] or
opportunistic, whether the owner of the sensing device
plays an active role in sensing or not. In participatory
sensing the user helps the sensing process with manual
intervention, for example indicating when he/she gets on a
public vehicle, naming the traffic line and the stop where
the sensing event happened. In automated or opportunistic
sensing the user plays a passive role in the process as the
sensing tasks are carried out automatically, e.g. by an
application running in the background. Although
participatory sensing can lead to more exact data,
opportunistic sensing can be more reliable, because it can
provide continuous and large scale data feed, which can
eliminate the bias of human factor.
In general, the created sensor data goes through the
following typical processing steps in case of mobile
crowdsensing: data collection and mediation, data storage
and distribution, data analytics and any service specific
use case logic, service exposure. The mediation layer is
responsible for collecting the data from the individual
data sources and then forward it to the data layer, tagged
with appropriate meta-information. These information
may originate from the active help of participatory crowd
or automatically learned from similar measures received
in the past. The data layer has the functionality of storing
information in an appropriate storage model and of
distributing the data among consumers. These consumers
can be the service specific use cases hosted by the service
layer and creating value from the sensed data. The service
layer is also responsible for exposing the results either via
creating new data / knowledge or by offering services.
The target of the exposure can be both the source of the
sensor data (even the individual) and the connected
society.
The goal of the paper is to give an overview of the most
relevant aspects of mobile crowdsensing that are utilized
already by the society. The paper focuses on best
practices applied in smart cities today, how these
applications can be motivated and rely on technology
enablers of today’s vertical silos and futures horizontal
approaches. The rest of the paper is organized as follows.
First, the practical applications of crowdsensing in smart
cities are detailed in Section 2. These applications focus
on urban transportation, public safety and environmental
applications. In order to facilitate data creation, it is
important to actively involve people both in participatory
and opportunistic crowdsensing: incentive mechanisms
are discussed in Section 3. Finally, Section 4 provides an
outlook for next generation crowdsensing supported by
technology evolution via more generic horizontal
solutions.
2.Crowdsensing in Smart Cities
Researchers of the United Nations reported in 2014 [7]
that more than half of the human population lived in the
urban/city areas and this number is already about 80% in
North America and Latin America, and this proportion is
on the rise. This fact calls for smart operation of these
cities by applying all the new technologies that recent
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development of information and communication
technologies can bring us. Among similar concepts of
digital cities, intelligent and ubiquitous cities, so called
smart cities can adopt the collaborative aspect of
operation between different role-players of the town,
including citizens. This initiative is actually the
recognition of the importance of using digital
technologies for a sustainable future [8]. All smart
applications rely on collecting available information from
sensor networks in and around the city and make the
operation of public services (like lighting, heating,
garbage collection, etc.) intelligent. As discussed
crowdsensing is a novel way for data collection,
particularly in densely populated areas where insuring the
appropriate number of sensing users is easier. Therefore
its application in smart cities is beyond dispute.
Crowdsensing has use-cases in smart city concepts alone
and alongside with sensor networks as an additional
technology that involves moving (carried) sensors and
human intelligence into the sensing process. Many
crowdsensing applications address tasks related to urban
transportation systems, which include the tracking of
public vehicles (buses, trams, subways and rentable bikes)
or others like mapping bumps on the road to quickly
inform authorities where to intervene. Public safety is
another category of applications where the power of the
crowd is used to indicate unusual/abnormal behavior of
people, extreme situations like riots, demonstrations and
similar. Tracking the urban environment is also of interest
and of help in maintaining and improving the quality of
life in cities. Although it is a typical use-case for fixed
sensors, the development of new smartphone sensors
makes them able to participate in monitoring and the
manual sensing can indicate phenomena that are not
discernible by fixed sensors.
Further smart city scenarios are emerging every day, the
CityPulse [9] project has identified 101 smart city
scenarios and related use cases, developing an evaluation
metric to measure the requirements for a smart city
framework. The scenarios include examples for
facilitating transportation such as a real time travel
planner or a service predicting public parking space
availability, and other applications belong to public safety
for example e-Neighborhood and Smart events.
2.1. Crowd-assisted urban transportation
2.1.1 Bike Sharing Systems
As the population in urban areas is growing significantly,
new solutions are needed to cope with growing traffic and
air pollution. The problem of growing traffic can be
solved by diverging people among different means of
transportation. To cut air pollution down, we can think
about using alternative fuel in engines or maybe we can
simply replace engines with our physical abilities. Using
bicycles in short distances could significantly decrease air
pollution caused by cars, meanwhile the impact on daily
commuting time is rather positive. To motivate more
people to use bicycles in cities, establishing bike sharing
systems seems to be a good solution. These systems
already exist in many cities around the world, where the
main idea is that people can rent a bicycle in the streets at
certain stations and after the ride leaving them at another
station which is close to their destination.
Beyond finding the optimal location of the stations (the
distance is normally not bigger than 500 meters between
two arbitrary stations), the number of docks in each
station and the number of bicycles in the whole system
need to be planned carefully. When the system starts
operating the main challenges become the maintenance of
the equipment and the redistribution of bicycles among
the stations.
In all of these bike sharing systems, the bicycles have a
built-in GPS sensor which enables them to be tracked.
Knowing the location of bicycles helps not only in
preventing theft, but it can also be used for route planning
or facilitating redistribution through crowdsensing.
For being capable to run such BSS systems, there are
several problems to be solved. The optimized number of
bicycles should been determined in the system, examining
the effect of redistribution vs. the performance of the
whole system from the business point of view. The
redistribution is crucial in bike sharing systems, therefore
in order to improve it, users can be motivated and
rewarded to alter the actually unfavorable bicycle
distribution. For example, two stations are offered, one of
them is clearly closer to the destination. The other is
slightly farther, which results in a longer walk from the
end station to the destination, but it helps the
redistribution from the system point of view. To motivate
people helping redistribution in this way pricing
incentives are used, i.e. choosing the less ideal station in
terms of additional walking distance will cost less for the
individual than the other option. For example the above
mentioned problems were addressed in Singapore’s bike
sharing system, where they want to replace short train
routes (maximum three stops in the popular train network
of the city) [10] and in [11], where the authors propose
that if an individual starts a ride in the system he/she is
asked to give his/her destination. The incentive schemes
will be introduced more thoroughly in Section 3.
Major challenge is to involve public and personal bikes of
daily commuters into public transportation. For example,
suburban buses and trains have very limited capacity for
carrying bikes. Official bike locking stations would let
accept people that their bikes are safely stored. Here the
free space availability and tracking are essential basic
features. Moreover, capacities should be harmonized with
weekly needs of people. Examples of Dutch trains stations
with massive and large scale bike storages are a first idea.
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2.1.2 Transport tracking
Mobile Crowdsensing applications in transportation
include measurement of traffic conditions on roads (e. g.
traffic congestion), mapping parking place availabilities in
garages and in the streets, furthermore collecting
maintenance spots on different facilities causing outages
and traffic diversions.
In large cities traffic can often be unpredictable
(especially during rush hours), therefore most traffic-
influenced public transit lines can bear shorter or longer
delays. Weather conditions like a rainstorm or an extreme
snowfall can seriously influence the traveling speed of all
the vehicles in the city. To public transport passengers it
is important not to wait too long at the stations in extreme
weather conditions. This challenge was addressed in [12],
where a naming scheme for a content-centric
crowdsourcing network as an effective way of describing
the entities of a crowdsourced public transit network
(locations, and vehicles) was presented. It is a hierarchical
nomenclature which can easily express even some
properties of a station inside a city, in an area. The
interconnections of route parts can be expressed how they
are connected in real life or by a particular transit line
(including the places of stops, traffic lights, etc.). The
solution firstly records GPS and accelerometer data. Field
tests showed losing the GPS signal on average 18.4% of
the travel time, thus the conclusion is that GPS cannot be
relied entirely. Network localization (GSM) were foreseen
as Wi-Fi hotspots are not so widespread in India, where
the tests took place, so they cannot be used for
localization. They always collect historical data so if the
CS system could not provide sensed data then it can be
substituted with older sensing. Fuzzy-intersection and
fuzzy-union are used to find bus stops and traffic lights
automatically from sensor readings. It turns out this is a
tough problem to solve even if they raise the number of
considered journeys.
To overcome this challenge, [13] proposed a system,
which monitors public transport vehicles with a
crowdsensing application running on traveling users’
mobile phones and detects the stopping places of the
vehicles. As stopping places are timestamped, they can be
compared with the fixed timetable. This means that
possible delays can be reported to the members of the
crowd-community, enabling the development of geo-
dependent online services that can check the updated
arrival times at the current stop of every transit line
belonging there. In this way waiting too long for public
vehicles without notifications can be prevented. The
method relies on accelerometer readings of the
smartphones and it uses a progressive localization
technique comparing Wi-Fi SSIDs sensed at different
stopping places.
Public transport companies often seek the opportunity to
synchronize the public transport network of a big city
with the habits and lifestyles of the inhabitants to reach
higher efficiency. Another challenge is to satisfy customer
(passenger) needs and desires when using their service.
Crowdsensing can provide help by collecting data through
many sources including mobile phones and smartphones.
The anonymous data helps tracking the movements of
thousands of people from place to place and correlating
this information with time and the speed of travel. The
system understands the mode of transportation people are
using and knows where they are traveling to and from.
This all facilitates optimizing public transport routes and
reduction of costs and pollution which overall results in a
healthier environment (example for this is the project
started in 2011 called ‘Istanbul in motion’ [14]). Apart
from automated, opportunistic sensing, other applications
which concentrate more on increasing customer
experience usually require participatory sensing as the
collected data is often subject to personal opinions.
Tranquilien [15] alleviates train transportation by not only
helping to reduce delays, but also giving commuters more
comfort, easing peak hours by spreading passenger load
across more trains, and generally increasing the overall
efficiency of the network. The optimization algorithm is
able to predict how many people will be boarding and
disembarking from the trains at each station throughout
the day up to a week in advance. The model aggregates
many available data resources besides user data such as
app check-in information and search queries.
Crowdedness of vehicles determine general comfort level,
but passengers are often interested in many other qualities
of vehicles such as cleanliness, availability of air
conditioning, power outlets and accessibility with
wheelchairs. Applications like Moovit [16] and Tiramisu
[17] support these challenges; however, their basic
function is also to facilitate route planning with public
transportation in big cities around the world by
incorporating possible GPS data of the vehicles.
Not only passenger behavior but also road conditions and
congestions have severe impact on the efficiency of a
transportation network. Real-time traffic-condition
monitoring solutions already exist, which use only GPS
traces and nothing else, requiring low bandwidth when
transferring recorded traces via wireless networks. Since
only a single sensor is activated and a small amount of
data is transmitted, energy efficiency is a key advantage
of the application. Saving energy is essential when users
are to be encouraged to use such an application on their
smartphones with quickly draining batteries. After data
collection and traffic visualization, road segments and
similar characteristics on different roads can be defined.
The computed results are distributed amongst the
contributors, taking into consideration the real-time traffic
conditions when planning their route. A fine example for
this is the Surface application [18]. Cities of the
developing world have to cope with some additional
challenges. Besides route planning, rich sensing
application helps detecting traffic conditions utilizing the
accelerometer and the microphone of the smartphones.
Rich sensing is critical in the context of cities concerned,
because road conditions tend to be variable (e.g. a lot of
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potholes), vehicle types are heterogeneous, and the flow
of traffic is chaotic (e.g. a lot of braking and honking).
For instance, the accelerometer is used to detect potholes
and the microphone to detect honking. Energy efficiency
should be also addressed, by using triggered sensing,
wherein a sensor that is relatively inexpensive from the
energy viewpoint is used to trigger the operation of a
more expensive sensor when needed. For example, the
accelerometer is used to detect a high incidence of
braking, which then triggers microphone-based sensing to
check for honking. Another challenge arises in the context
of an accelerometer that is disoriented because the phone
is at an arbitrary orientation with respect to the vehicle.
The orientation of the disoriented accelerometer should be
determined, so that the measured accelerations along its x,
y and z axes could be mapped to accelerations along the
true X, Y and Z axes [19].
Users are easier to be motivated to use an application
when they can have direct advantage as they use the
collected data. One of the key advantages can be saving
fuel, which not only cause them saving money but it also
contributes to our environment by decreasing pollution.
E.g., using community-shared fuel prices helps to
navigate to the cheapest petrol station on the way to save
money [20]. A solution could be to concentrate on traffic
hotspots (high volume traffic with lower average speed)
detection, after aggregating data from the users to offer
such routes for them, which avoid hotspots not only to
decrease travel time, but also to reduce fuel consumption
[21]. When finding a fuel-efficient route for the users, the
mobile phones can connect to a standard interface in cars
that provide data related to fuel consumption.
Aggregating data from many vehicles on roads allow the
system to suggest suitable directions for the drivers.
However, it does not take the driving style and real-time
traffic conditions into account [22].
The above mentioned challenges are combined in a
crowdsensing platform called Waze [20], which helps
drivers to get real-time information on road conditions,
namely accidents, potholes, police safety cameras,
breakdowns and many more relevant details. Being a
versatile platform explains its popularity. After it had
been incorporated into Google Maps, Waze became the
world's largest community-based traffic-outsmarting
navigation app. Data is collected opportunistically and
manually as well to warn drivers when they approach
police, accidents, road hazards or traffic jams. To improve
routing around the world, users can report/edit changes in
maps to keep them up-to-date. For this purpose many
national Waze-communities were formed (the very first
one in Hungary) to deal with local map issues: closures,
diversions, speed cameras by connecting the
announcements of the national police and road
maintenance services.
All the above solutions are focusing to one or several use-
cases. However there is a wider initiative for these
challenges which is called Cooperative Intelligent
Transportation System (C-ITS) [23]. To support special
applications like crowdsensing in the ITS domain,
advanced mobile networking schemes and optimization
techniques (e.g. [24]) are becoming more and more
essential also in vehicular communication architectures.
2.1.3 Urban mapping
The power of the crowd is discovered by governments all
over the world and involving citizens to map their
surroundings and provide these information to the
authorities is more and more common. This kind of data
harvesting can rely besides the sensor reading also on the
intelligence of the sensing users which is called manual
sensing. However automated sensing (or so called pull-
based sensing, as the server/app pulls the data from the
sensors) has the advantage of continuous data supply from
the sensed area which is often valuable than using the
often less reliable manual sensing. Street Bump [25]
application is one of those, which are automatically
collecting the data from the phone sensors to map
potholes in the streets of Boston. It was developed by the
Boston authorities and in 2013 it received The Digital
Government Achievement Awards (DGAA). It detects
bumps on the roads while the users are travelling with the
application running on their mobile phones. As the phone
analyzes accelerometer data and detects possible places of
potholes. Findings are reported to the city authorities so
the road can be repaired before it causes serious damage
in the cars.
Urban mapping crowdsensing applications can not only
be used for problem-solving but also providing up-to-date
information on maps not only for drivers but also for
pedestrians. Map++ addresses this challenge, which helps
digital maps to improve with automated sensing [26]. It
uses standard smartphone sensors to automatically enrich
digital maps with different road semantics like tunnels,
bumps, footbridges, crosswalks and road capacity. The
application emphasizes energy-efficiency (uses only
common sensors with low energy consumptions, which
are anyway active normally on a smartphone while in
use), but still provides high accuracy both for in-vehicle
and pedestrian traces. The data is captured by a cloud-
based architecture which feeds useful conclusions back to
high number of users after data processing.
Most urban mapping applications rely on manual sensed
data or push-based sensing, as users note their
observations when they want to by pushing the
information to the central server. Users’ sensory organs
provide much richer data source then sensors: people can
easily conclude from their surroundings if there are issues
on the road or if they are in a dangerous situation. The
following applications are working on the same principle:
CitySourced [27] is a civic engagement platform offering
the possibility for residents to report quality of life, public
safety and environmental issues directly to the local
government. The application is operational in most of the
neighborhoods around the United States. The type of data
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collected is referred to as volunteered geographic
information (VGI) as users upload geotagged photos of
the discovered issues noting the category of the problem.
A geographic information system server, ArcGIS is
receiving the issues so the governments of the cities can
determine trends and typical hotspots (graffities
concentrated to abandoned neighborhoods) of the issues
so they can react with action plans [28]. Another reporting
interface, called FixMyStreet [29] is available for the
citizens of the United Kingdom. There is an interactive
map that allows reporting potholes, streetlight outages,
and other street-related problems. These problems are
brought to the appropriate council which is responsible
for maintenance and repair. As the individuals’ reports
appear on a shared public map it aggregates valuable
information on the state of the streets. One of the newest
examples from September 2014 is the application of
Budapest’s XII district’s Government with similar goals:
to collect, map and flick off landfills, potholes and
abandoned cars in their neighborhood [30]. Another co-
working, data harvesting application, targeting groups of
people with special interests is Cyclopath [31]. It creates
an interactive map for cyclists in the Minneapolis area
that enables users to find bicycle-friendly routes in the
region. It not only contains road surface conditions, off-
road paths but also location of coffee shops. Compared to
the generic Google Maps bicycle road planning service
Cyclopath relies on a place-based community to
contribute local knowledge and its existence and use is a
point of pride for the local bicycling community.
A challenging application for future would be a map
service covering special limitations. E.g., routes without
stairs for disabled people and mothers with small
children; or even providing a route for kids and young
pupils where all the crossroads have lamps.
2.2 Crowdsensing for public safety
In growing cities and mass events like festivals,
peaceful or violent demonstrations, authorities have to
find new data sources to track masses of people and
happenings in the society. Crowdsourcing can not only
provide real-time information from the streets through
comments, pictures or videos taken at the spot by the
crowd-members but can also offer the feeling of being
safe and listened to. Public safety supervision requires
user and sensor data for early fire-, earthquake- and other
natural disaster-alerts, for maintaining public utility
services, for planning safe routes for pedestrians across
the city and for many other use-cases. Several
applications target to solve these problems, others are still
unaddressed.
Crowdsensing applications can help e.g. rescue teams
with real-time information in the event of an earthquake
to estimate the situation and reduce the time needed to
prepare for an intervention [32].
A mobile application uRep is developed for users to
see how utility companies are proceeding with solving
outages in their systems (electricity, water, etc.). The
companies and users can both provide location based
information on the level of parts of cities, sections of
roads. Developers foresee extension of their app for
offering assistance to people in need and gathering data to
help prevent damages in future events.
Route planning inside a city is not a new task and is
solved by numerous apps/services but taking into account
the notorious city districts, bad-security streets is
something new to deal with. A solution is offered by
SafetyRouter [33], which is a map-based route planner
and it offers the safest path between two points of the city,
according to crowdsensed live data stream, and stored
historical crime entries. Shortest and safest paths can be
combined to give the "best path" for the users. This is
described by a simple summarizing-minimization
formula. Shortest path is determined with Dijkstra or A*
search method. Crime analytics is also given by this app
by determining crime hotspots and clusters in a city which
can be of serious help to the authorities. A density-based
method is used to generate crime heatmap. Crime clusters
are determined by k-means and DBScan clustering
algorithms. These two outcomes of the app are
demonstrated and visualized by a mobile platform for
crowdsourcing of crime incidents. In the app, data from
crowdsourcing is ranked along three models: 1) vector
model for relevance-investigation between textual queries
and search results, 2) spatial ranking based on Euclidean
distance, 3) timeliness: decreasing a sensing's weight as
time passes by. A weighted linear combination of the 3
rankings is provided as the outcome.
The impact of crowdsourcing through social networks
(which are also operated by a crowd) like Usahidi,
Facebook, Twitter and similar in disaster-management
[34] helped state services to find troubled people in Haiti
earthquake, Japanese Tsunami and other disasters as well
by providing geotagged pictures and localized emergency
calls. Everyday security is targeted with the AlertID [35]
application in the US, which is a secure social network for
neighborhoods providing local crime information, alerts
in case of extreme weather and of course the possibility to
the citizens to help each other by providing useful
information about their surroundings. In Hungary a new
smartphone application is aiming to offer a danger
reporting system that unites all the public authorities
(ambulance, police, and firefighters) who are listening to
the calls of help through this application [36]. Users can
also see their family members’ calls and can help in
emergency situations happened near to them as the
application can navigate them to the spot.
Machine learning-based decision-making with artificial
intelligence can be also used to increase public safety in
crisis situations. In SmartRescue application [37], a
framework is defined for data collection, decision and
communication to users who are provided with necessary
instructions regarding evacuation in case of an emergency
situation. The system uses a publish-subscribe mechanism
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which enables flexible data collection and transmission
amongst real-time users.
Among several research projects, the European Union
also recognized the importance to address problems
related to data harvesting, social sensing, managing
crowds of people not only for collecting data but to
maintain safety. An EU FP7 running project [38] is
focusing on management of complex evacuation
operations according to current conditions: number and
placement of people to evacuate. Aims also include to
help the civil protection authorities, but they are basically
trying to rely mostly on CCTV cameras, Wireless Sensor
Networks and so called “people-sensors” utilizing the
well-known data harvesting method, crowdsensing.
INSIGHT [39] is an EU-financed project ending in 2015,
which utilizes diverse deployed sensors, social networks
and smartphones to map the surroundings and,
accordingly, to offer a better social management of
disaster monitoring involving citizens. They can
participate in data-collection and also in decision making.
They aim to develop a system which can handle real-time
processing of the incoming datasets coming from sensors
and the crowd. SafeCity [40] is a public-safety enhancer
project of the European Commission which involves all
possible data sources to detect events happening in the
cities in real-time, makes smart decisions automatically to
reduce the reaction time of the first responders in
hazardous situations, relying on citizens, but also using
CCTV cameras and sensor networks of different types.
2.3 Environmental monitoring
Environmental applications concentrate on measuring and
mapping large-scale phenomena happening in the natural
environment around us, such as natural disasters or
pollution level. People nowadays are keen to store data
(mainly photos and videos) on their smartphones which
reflect their personal experience and memories. The data
produced like this can be easily transferred to mobile
crowdsensing applications that can, for example help in
maintaining the air quality in big cities where it can be a
serious question. Activating particular sensors of
smartphones will not require such manual activities of
users to upload data for the applications as they can be
produced automatically with the consent of the user.
These sensor-based applications can provide location-
based information on air pollution level and on weather
situations including temperature, humidity and light
conditions. Processing information retrieved from these
applications can support our decision-making process
when choosing appropriate clothing for a day or when
planning our route in a city (especially when using
outdoor transport facilities or bicycles and the quality of
the air is a concern). The iMAP [41] is a cellphone-based
indirect sensing application which estimates the pollution
level in streets. Compared to other crowdsensing
problems where a dense set of sensors is available this
problem addresses a sparsely sensed phenomenon,
concentrating on air-pollution. Deploying fewer sensors
provides a more feasible approach compared to direct
sensing, but challenges data processing. The paper
identifies Land Use Regression (LUR) as a suitable
modelling solution. When the sensed data is captured,
local traffic, population, and weather characteristics
measured at regional air monitoring stations are taken into
consideration to provide estimated pollutant
concentrations for each user.
According to a recent discovery, ‘space weather’ is a
useful sensor to predict many types of Earth- and space-
based phenomena such as earthquakes and tsunamis.
Disaster prevention is recognized to rise public safety in
urban areas. The Mahali project [42] facilitates this goal
by monitoring ionospheric electron density with the help
of mobile crowdsensing that helps to increase the number
of sensors and to expand data transport capabilities
through participating devices acting as relays. Mahali uses
GPS signals that penetrate the ionosphere not for
positioning but for science this time. First the data is
collected by GPS receivers which have a line of sight to
several GPS satellites. This information is fed in a cloud-
based environment by internet-connected mobile phones
(solving the last mile connectivity problem) to make
further calculations.
3. Incentive mechanisms
Mobile crowdsensing can outsource sensing tasks to
mobile phone users, who are willing to collaborate.
However people and their willingness to take part in
crowdsensing are not reliable enough for most of the
applications that aim to map phenomena of high interest.
Some of the sensing tasks are interesting and sometimes a
good way to spend time for the users who are travelling
on public transport and have nothing to do in the
meantime. But if they have, the crowdensing platform can
easily be left without enough number of sensings from the
users. In this case users should be motivated in some way
to continue providing information and these methods are
called the incentive mechanisms. So crowd-cooperation
could and should be rewarded in one or several ways,
possibly including monetary payments, and gamification
or by offering the sense of security: as a reward of their
work the actual city will be a safer place to live.
Monetary rewards are the most common way of incenting.
Users can be rewarded according to the number of
smartphone sensors they enable for the platform to use
user for crowdsourcing purposes. Different sensors can
result in different payout depending on their energy
consumption and the type of the sensing task they are
taking part in. An example for this is Apisense, a mobile
sensing platform that uses a multi-cloud architecture with
a trusted central node, enabling scientists to run sensing
tasks on a widespread crowdsensing system [43].
Monetary rewarding is not an option for single
researchers thus authors strongly believe in gamification-
type incentives.
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The article of [44] describes and tests two different
incentive methods: micro payments (MP) (5 dollars for
five small sensing tasks) and weighted lottery (WL),
where for the same amount of tasks completed,
participating users receive a ticket and at the end of day
tickets are drawn to decide who gets the 50 dollar reward.
They use three performance metrics to evaluate their
methods: recruitment, the ability to attract participants,
compliance, to which extent participants complete the
task assigned to them and user-effort to measure how hard
sensing users work, because there are big differences
between automated and manual tasks. They carried out a
test at a conference/exhibition in two days, challenging
the two incentive methods mentioned.
They found the following: recruitment - they measured a
16% higher compliance rate with Weighted Lottery than
Micro-Payments; compliance - MP reached higher
compliance rates than WL. There were no correlations
between the popularity of the area where the task should
be carried out and the compliance rate; user effort - MP
significantly outperform WL for participating users with
regards to active session times.
They observed the larger area covered and with greater
density by participants given MP than WL. Authors
concluded that WL attracted more users due to higher
possible payouts, but MP-incented users were more
productive, probably because the guaranteed payment, not
regarding the more than two times higher expected
payment from WL. From the crowdsourcer’s side users
with MP carried out almost the same amount of tasks than
those with WL with a total payout 4 times lower than for
WL-incented users.
Similar payment-based incentives are compared in [45].
The effects of three micro-incentive mechanisms are
examined where subtasks of the crowdsensing process are
rewarded in different ways. First one is the Uniform-
reward method (similar to MP of the previously discussed
work) which is used as baseline to examine the
effectiveness of the other two. In this case sensing users
get the same reward for every accomplished sensing task.
It is used because of easy implementation and because it
is a first step from bulk payments to other, more
complicated micro incentives. The Variable scheme uses
changing monetary incentives for every task which helps
to determine how the changing reward influences the
quality of sensing. It can also remerge the decreased
interest of the sensing user by temporarily raising
rewards. The Hidden scheme is analogous to WL where
the user finds out the value of reward only after
completing the actual sub-task. This is similar to
gambling where the user has the possibility to acquire
high income in a short time. Of course, high rewards are
much rarer than lower ones, but their occasional winning
encourages the users to continue participating. Results
show that the three micro incentive mechanisms have
similar efficiency although it can be concluded that
people favor the more predictable uniform and variable
cases over the gambling-like hidden scheme.
Also an interesting approach is the one presented in [46]
which introduces a new attitude: if there are not enough
users for the particular sensing task then some of the not-
working members of the crowd are rewarded beside the
actively participating ones with a small amount of money
to get them ‘back to business’ in the coming rounds. Of
course the aim is also to sustain diversity of sensing users
so the ‘free’ rewards always go to the groups which are
not well represented in the base. The quality of sensing is
maintained by the SPREAD algorithm [47], by getting
enough sensing users and supervising their diversity. The
algorithm gives a graph representation of the active
sensing users and a geometric coverage algorithm (Set
Cover), which is utilized to select the potential candidates
from the area of interest in the most diverse way. The
second part of the algorithm acquires a sample set in each
sensing round while staying inside a previously set
monetary limit for the job.
The idea of linking the CS application to social networks
can be of help because we are more motivated to help
people who we know but it restricts the number of sensing
users in the app. [2] tries to answer the question why
would somebody answer to any popup question or
complete a task in a crowdsensing application if it takes
his/her time and could deplete the phone’s battery?
Another solution they have proposed is to use a credit
system to avoid that some users only ask for help but they
do not want to provide anything by charging every
question and reward every answer with credits.
The incentives needed for extensive user participation,
quality of sensing and privacy-aware incentives should be
differentiated [48]. The privacy-aware incentive is
actually the degree of security that the crowdsensing
framework can offer the users: at location-based
application users are willing to know that their data is in
absolute anonymity. Number of users can be maintained
by using the Vickrey-Clarke-Grooves bidding mechanism
[49], [50]. It also assures that sensing users are giving
reasonable price-bids as their demand for doing a sensing
task with reverse-auction model defined by the three
scientists. The quality of sensing is often also closely
connected to the number of sensing users. An incentive
platform for parking information systems is presented in
[51] which gets the adequate number and quality of
sensing from the users that are not trustworthy at default.
First they stimulate users’ participation with a credit-
system where every new parking availability information
(PA) is rewarded with a static credit amount and -
depending if the PA led to a successful parking action or
not - a bonus, which can be much higher than the static
reward and can be varied by the TruCentive platform to
keep the quality of sensing high. The PA-buyers are
refunded if the reported parking place has been filled up
in the meantime. Honesty of the role-players of the
crowdsensing platform is maintained by a game theoretic
approach that guarantees that they can obtain the highest
value in the ‘game’ (the TruCentive platform) if they are
honest.
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An online incentive mechanism is presented in [52] where
the task is to select the appropriate users from the crowd
who play their role in a game theoretic way (they are
ready to provide false information about their capabilities
to obtain higher revenues from the crowdsourcer). It is
modelled as an online auction problem, as a realistic case
is considered where the users are arriving one-by-one
continuously in a random order and their availability
changes over the time. In this way it is harder to decide
whether to accept a user’s bid (price) for a task or not,
based on the knowledge to the present moment while
staying inside a budget constraint for all the tasks
together. The method uses a maximization function for
the value of the work done by the sensing users covering
the region of interest all the time, satisfying six properties:
computational efficiency (algorithm runs in real-time),
individual rationality (all users have positive utility),
budget feasibility (staying inside a cost limit), truthfulness
(sensing users have to report their true costs), consumer
sovereignty (users are handled equally, depending only to
their power and costs) and constant competitiveness
which means that the algorithm has to have almost the
same performance like the offline solutions which have
the knowledge of every users’ details before they arrive to
the region of interest. Their mathematical solution to the
problem with the mentioned desired properties is well
evaluated with simulations and it is shown that it can run
in real-time, enabling it to use as an online decision
mechanism which results in value of sensing that highly
outperforms random user-selection methods.
Users are entering an optimal reverse auction each time
the service provider (crowdsourcer) receives a new
sensing job to be done [53]: users report their perceived
costs for a unit amount of sensing work which includes all
the costs starting from the energy cost of sensing,
processing (computational power), battery level and
charges of transmitting data to the provider and also the
discomfort of the user while he/she provides manual
sensing through the smartphone. Their incentive
mechanism aims to minimize the total cost of user
compensation for the delivered sensed data and to
motivate users to participate in sensing jobs. The actual
user-side costs are private data so they would have a
strong motivation to misreport it to obtain higher
payments. To solve this and user-side costs are handled
through Bayesian game among the users, which results in
Bayesian Nash equilibrium which means that users
declare realistic costs (because they are not rewarded
otherwise) and are motivated to participate because their
utility for doing it is always greater than zero.
Maintaining the guaranteed quality level of sensing
services is done by neglecting the employment of sensing
users who are consistently providing less accurate data
with the crowdsourcing system.
Table 2 gives a classification of the above presented
incentive mechanisms, considering the type of incentive,
the goal of providing incentives and the reward allocation
method. As we may think that the monetary rewarding is
dominant, however from the table it can be seen that in
several cases other type of incentives are also utilized,
like a service as a reward, in some cases supported by a
game theoretic approach. Rewarding the participation of
the users in the process in not always sufficient, the
quality of the sensing should be also provided by the
incentives system.
Table 3.1. Incentives
Incentive
mechanism
Incentive
type
What is
incented?
Reward
allocation
method / idea
[43]
monetary
participation
number of
sensors dedicated
to CS
[44]
monetary
participation
micro payments
or weighted
lottery
[45]
monetary
participation
per task: uniform
or variable or
hidden scheme
(like gambling)
[46]
monetary
participation
per task + for
non-working as
well if
reinforcement is
needed
[47]
diversified
user
enrollment
quality of
sensing
reverse auction-
based
[2]
credit-
system
(service as
reward)
participation
credit reward for
answering,
credits needed for
asking
[51]
credit-
system
(service as
reward) +
game
theoretic
participation
+ quality of
sensing
credit for
reported parking
place (PA) +
bonus credit for
successful
parking
accordingly
[52]
online
auction
model +
monetary
participation
+ quality of
sensing
auctions result in
prices that are
payed for users
for sensing
[53]
game
theoretic
participation
+ cost
minimization
for the
service
provider
Bayesian Game
for Nash-
equilibrium for
user truthfulness
and willingness
to collaborate
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4. Moving to a horizontal architecture
Many applications for Smart Cities are developed
independently and invites participants on per application
basis. In this section we summarize what are the common
features in these applications.
We show how these applications are built in a vertical
manner today. We also present the most impressive
activities and solutions towards more efficient horizontal
architectures. These applications can share the
participants and the sensed data, as well.
Finally we give an example how current individual
applications could be organized in a “single” horizontal
system.
4.1 Common features in Smart City
applications
Most of smart mobile devices can provide data from a
number of sensors that resemble IMU-like sensors
(mIMUs). Typical available sensors include:
●GPS
●gyroscope
●accelerometer
●magnetometer
●proximity sensor
●temperature sensor
●humidity sensor
●ambient light sensor
●barometer
●gesture sensor
●microphone
●camera (images, video)
Nevertheless, there are other data sources that can be
considered as sensors, like the social feeds [54], [55].
Such feeds like Twitter or Facebook posts are the most
beneficial when many people recognize something
important for the society, especially when correlating with
other data sources. E.g., the change of taste of water and
pressure in the water pipelines indicates that some dust
already entered the water system.
Not all devices have all the sensors, most devices only
contain a subset of the above, while there are some high-
end devices that contain all of them. However, the basic
tuple of GPS + accelerometer + magnetometer are
generally available, thus they can provide basic location
and movement information. All device makers provide
APIs and SDKs through which the sensor data can be read
and gathered programmatically in a custom application.
Several application examples were presented for the
major application areas in smart cities in Section 2.
Table 4.1 collects the main types of these applications
and their impact on people’s life. The solutions were first
classified based on the type of data sources they use, as
this feature heavily affects the energy consumption of the
devices and the completeness of the crowd’s database.
Further classification aspects were the periodicity of data
access and the way how members of the crowd provide
information. The place of the computation is in the
majority of the cases at a central server. There are also
cases when the users preprocess the data, reducing the
data transfer overhead, which is not negligible when
involving large number of users.
In spite of the various available data sources only a
limited set is used today. Beyond manual reports, mostly
accelerometer and GPS data are popular, which are really
the basic information sources. The data report is typically
done via periodic reports or pushed occasionally. Roughly
half of the examples are opportunistic or participatory;
meanwhile the computation is done on the server side;
and the individuals and society benefit from the
information provided by these applications.
4.2 Architectures
Most of the solutions available today are based on the
vertical principle. That is, today’s solutions typically
answer particular questions for one service provider
focusing on a given use case and service upon this use
case. As a result, many aspects of the vertical solution
rely on application-specific or proprietary solutions. This
limits the widespreadness of the services in case of the
tiniest difference in the vertical silo if a service provider
would like to move to a new market. New solutions might
be needed if a new market is targeted or new sensors /
devices are introduced. This vertical concept is illustrated
in Figure 4.1a.
Early platforms like mCrowd [56] already provides
possibility to share questions (tasks) with other user
connected to the app and rely the answers on the
community or even by artificial intelligence connected to
the task distribution proxy of the system. In [57], a
framework is proposed for recruitment in participatory
sensing, especially interesting to organize campaigns,
qualify the collected data/replies and review the progress.
There are platforms giving a complete framework for
crowdsensing applications. Medusa [58] provides
abstractions and programming framework to build
crowdsensing tasks, which are distributed among
smartphones and the cloud. To prove Medusa’s
generality, authors have implemented ten different use-
cases working with cameras, accelerometers, GPS, audio
and network sensors. McSense [59] provides a distributed
architecture complementing Medusa in a sense to exploit
information about the potential users of the app and their
mobile execution context (e.g., processing power, battery,
level, and so on).
Based on the above platforms and the great similarities of
the major smart city use cases summarized in Table 4.1,
one can imagine that a new horizontal principle and
architecture could merge and connect various
crowdsensing jobs at the same time. That is, all job
requests, volunteers to collect data to answer the question
and an interface for the results. That is, transition towards
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Table 4.1: Major characteristics of crowd sensing application areas and reference solutions
Referenced Solution
Type of sensor / data
source
Data access
paradigm
Participatory /
Opportunistic
Place of
computation
Direct
benefit
Bike
Sharing
Systems
BSS Singapore [10]
GPS
periodic
n/a
server
private
company
BSS Redistribution
[11]
GPS
periodic
n/a
server
individual,
private
company
Transport tracking
Improving Public
Transport Through
Crowdsourcing [12]
GPS, accelerometer
periodic
opportunistic
server
individual/
community
Event Detection in Public
Transit Tracking [13]
accelerometer, Wi-Fi
periodic
opportunistic
server
Community
Istanbul in motion [14]
GPS
periodic
opportunistic
server
individual,
society
Tranquilien [15]
n/a
push
participatory
server
Individual
Moovit [16]
GPS
push
participatory
server
individual,
community
Tiramisu [17]
GPS
push
participatory
server
individual,
community
Surface Street Traffic
Estimation [18]
GPS
periodic
opportunistic
server
Individual
Nericell [19]
microphone,
accelerometer
stream
opportunistic
server
Individual
VTrack [21]
GPS, WLAN
periodic
opportunistic
server
Individual
GreenGPS [22]
GPS
periodic
participatory
server
Individual
Waze [20]
GPS
push
participatory
server
individual,
community
Urban mapping
Streetbump [25]
GPS, accelerometer
periodic
opportunistic
server
individual,
community
Map++ [26]
accelerometer, gyroscope,
magnetometer, RSSI
periodic
opportunistic
server
individual,
community
Citysourced [27]
GPS, camera
push
participatory
server
individual,
community
FixMyStreet [29]
reports
push
participatory
server
individual,
community
Hegyvidek [30]
reports
push
participatory
server
community
Cyclopath [31]
reports
push
participatory
server
individual,
community
Public safety
AlertID [35]
reports+ weather services,
crime databases
push
participatory
user + data
providers
community
HelpyNet [36]
report
push
participatory
user itself
small
community
eVACUATE [36]
reports, localization,
accelerometer + sensor
networks
push +
periodic
participatory +
opportunistic
server
community
INSIGHT [39]
diverse sensors
+reports+sensor networks
push +
periodic
participatory +
opportunistic
server
community
SafeCity [40]
reports + sensor networks
push +
periodic
participatory
user itself
community
Environ-
mental
monitoring
iMAP [41]
GPS
periodic
participatory
server
Society
The Mahali project [42]
WLAN
poll
opportunistic
server
society
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a more flexible architecture focuses on a multi-purpose
solution adopting open or standard solutions where it is
not business-critical to use the proprietary solutions.
However, even proprietary solutions might be limited to
the service exposure phase and the rest of the analytics,
distribution, storage and collection of data could be hosted
in a cloud-based architecture as illustrated in Figure 4.1b.
For example, projects like e-LICO [60] have already
proposed solutions supporting analysis workflows in such
a horizontal environment, and provide general-purpose
and application-specific services and related toolkits. One
of the first large-scale real-world experiments covering
the entire horizontal spectrum is the ParticipAct Living
Lab testbed [61]. This is an ongoing experiment at the
University of Bologna involving 300 students for one year
in crowdsensing campaigns that can passively access
smartphone sensors and also require active user
collaboration.
Within such horizontal systems, it would be much easier
for people to join initiatives for the good, social
community or simply business solutions of their personal
interest. The differences between the vertical and
horizontal solutions could be well summarized similarly
to the foreseen evolution from the traditional M2M
principle towards the IoT principle [62] as shown in Table
4.2 [63].
4.3 Transformation of application into
horizontal solutions
In order to illustrate the strength of the horizontal
solutions, let us introduce an example of the transport
tracking application areas containing most of the methods
developed individually. Realization of horizontal
solutions needs more than the above technical
functionalities.
Table 4.2: Vertical vs. horizontal solutions [63]
Aspect
Vertical solutions
Horizontal solutions
Applications
and services
Single application -
single device
Multiple applications -
multiple devices
Communication and
device centric
Information and service
centric
Business
Closed business
operations
Open market place
Use case driven
Participatory community
driven
Technology
Vertical system
solution approach
Horizontal enabler approach
Specialized / generic
devices
Generic devices
De facto and
proprietary
Standards and open source
Closed data formats
Open API
An important additional function is the service broker. It
is responsible to agree with the participants about:
Which sensor data or information is shared
In what format and how often the data is reported
With whom to share the data
What incentive(s) the participants get
a) vertical b) horizontal
Figure 4.1 Vertical and horizontal architectures
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Using the broker, the participants can contribute to a
much larger eco-system or limit their activity to a given
service, which makes much comfortable to join a
crowdsensing community. Meanwhile it provides
complete control of the participants’ data asset. The
compound of the transport tracking use cases is illustrated
in Figure 4.2.
As mentioned above, the most important functionalities in
a horizontal system are the broker and related data
handling functionalities. These are highlighted in Figure
4.2 with light grey boxes. The data handling
functionalities include i) the data storage; and ii) the use
case related correlations & analytics of the different data
sources. The data sources are highlighted with dark grey
boxes in Figure 4.2.
The users/participants of the horizontal system are in
connection with the broker and their corresponding data
sources are reported towards the data handling function.
With the control of the broker function, the data handling
function forwards the processed data towards the
applications presented in the top of Figure 4.2. According
to the functionality, there are two main types of
connections between the elements of the horizontal
system. The dashed lines represent the logical
connections, e.g., about the negotiations between the users
and the broker; and between the broker and the data
handling functions.
Figure 4.2. Illustrating transport tracking use cases in a
single horizontal architecture
The single-ended solid lines represent the sensed data
flows from the users’ sensors towards the data handling
function.
The double-ended solid lines represent the preprocessed
data flows and the mediations of broker functionalities
towards the services on the top of the architecture.
As you can see, in such a horizontal architecture all
presented applications can be efficiently connected
together in a single system. This system can greatly
improve the quality of the individual application via the
involvement of much more wide basis of possible
participants for crowd sensing applications and services.
Similar framework can be defined for the most
applications by the strong cooperation of partners of the
cities including the municipalities, the citizens, the
utilities and private companies, as well. Thereby, smart
operation of cities could be provided for the happiness of
the entire community.
CONCLUSION
In this paper we gave an insight into the applications of
crowdsensing in Smart City-related use-cases like public
transport tracking and urban mapping. Public safety and
environmental monitoring are new areas and also
promising examples of crowdsensing applications. We
proposed a way forward in the field by transforming the
vertical silos of today containing separated solutions in
different domains into a horizontal architecture. The
proposed ecosystem enables fruitful interaction between
crowdsensing entities and supports the networked society.
Acknowledgements.
We are thankful to Ericsson Hungary for funding this
research.
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