Civic Crowdsensing through Location-aware
Virtual Monsters
Takuro Yonezawa1 , Mina Sakamura2 , Nobuo Kawaguchi1 , and Jin Nakazawa2
1

Nagoya University, Nagoya, Aichi, Japan{takuro,kawaguti}@nagoya-u.jp
2
Keio University, Fujisawa, Kanagawa, Japan{mina,jin}@ht.sfc.keio.ac.jp

Abstract. We present a new model for encouraging people to get involved with monitoring and taking part in the life of cities. Cities could
be smarter if IoT and people could serve as engaged and pro-active data
resources (i.e., crowd sensing). This study tackles two challenges: methods by which the privacy of people who act as sensors/actuators canbe guaranteed and methods to create a unified programming model for
crowd sensors alongside other IoT functions. To achieve these goals, we
introduce a new concept called Lokemon (Location Monster). Each sensing space is characterized as a personified target. Lokemon asks users
to imagine themselves to be monsters associated with target spots whenachieving sensing tasks. Lokemon is also expressed as a PubSub node sothat the data from Lokemon can be easily accessed in the same way as
data from IoT is assessed. The article explains the concept of Lokemon
and its programming model. We report our evaluation of the eﬀectiveness of Lokemon in a campus experiment that was performed for fourweeks.
Keywords: Crowdsensing · Urban Computing.

1

Introduction

Computing is now widely applied in the management of urban activities andresources. The internet of things (IoT) will be ubiquitously embedded in equipment and infrastructure throughout cities. Real-time sensor data from IoT underpins the monitoring of a variety of city contexts for tasks such as day-to-day
activities, which include city management, disaster management, enhancing thequality of life and encouraging economic growth. Another important resourcein the cities is people. By using smartphones and wearable devices, people canact as sensors (e.g., reporting city happenings by capturing photos or counting
the number of people in a line) and even actuators (e.g., picking up garbage
or erasing graﬃti). Since both IoT and people each have advantages, utilizingthem in a complementary and transparent manner is the next paradigm for a
programmable world.
We tackle following two challenges: 1) how can we persuade people to act
as active sensors/actuators by reducing concerns that they will thereby sacrifice2

F. Author et al.

their privacy and 2) what might be suitable as a programming model/platformthat would combine data from both IoT and crowd sensing. First, as several
papers have already shown, crowd sensing requires incentives to motivate people
to participate in sensing tasks. Also, people who report sensor data at a certain
location are sometimes faced with a problem of loss of privacy - in location-aware
reporting the position of the user can be identified when the report is made. It is,therefore, necessary to reduce privacy concerns to involve more people in crowd
sensing. Secondly, though many platforms have been proposed and developed
for IoT and crowd sensing, they are mainly designed for supporting either IoT
or crowd sensing. To access data transparently and write programs eﬀectively,
any sensing method should be managed and applied in a unified way.
To solve these issues and provide a unified method for computing with bothIoT and people, we introduce Lokemon, a new crowd sensing concept and platform that will embody the concept. Lokemon is short for location monster, an
example of which is to be virtually placed at each point of interest (PoI) in cities.
Lokemon asks users to imagine themselves to be the monsters as they performsensing tasks. Any users currently located near the PoI identify with the Lokemon and complete tasks or answer questions from other remote users. To reduce
the loss of privacy, people’s actions are assigned to the Lokemon rather than being labeled with the user name or even an anonymous name. Moreover, Lokemon
provides a new experience whereby the user can collect data while mimicking asa Lokemon. It is implemented by using a universal sensor network system basedon the PubSub protocol [9], so that Lokemon provides an open API, which issame to that for IoT. This article presents details of the Lokemon and reports our
first campus-wide experiment to evaluate its potential. In summary, the article
establishes the following three developments:– Introducing a new concept called Lokemon that achieves crowd sensing by
mimicking monsters that are installed virtually at PoIs
– Providing a system architecture that integrates IoT and crowd sensing andadapting it for Lokemon– Presenting an initial evaluation of Lokemon in a four-week experiment on
campus2

LokemonThis section begins by describing the problems involved in crowd sensing andthen introduces the concept of Lokemon as a solution.
2.1

Problems of crowdsensing

Recent progress with mobile devices such as smartphones allows people to refine and integrate their perceptive faculties as a part of sensing framework. Thissensing framework, so-called crowd sensing (or participatory sensing) [1], distributes various sensing tasks (such as reporting the weather, waiting time in a

Civic Crowdsensing through Location-aware Virtual Monsters

3

queue and traﬃc conditions) to potential participants. By people sending a text,
photo, sound data and so on, we can get subjective and qualitative data thatit has been hard for physical sensors to collect hitherto, such as the mood of aplace. It would make wide and high density sensing possible. However, there aresome problems with existing crowd sensing systems, as follows:
1. Privacy
Users who are participating in sensing send information to each PoI. As aresult, the system knows where they are at particular times.
2. Motivation
It is important to give an incentive because getting involved in crowd sensing
is a burden on users. Without adequate incentive mechanisms, most users
may not want to participate.
3. Quality of information
It is possible to allow users to contribute information anonymously to protect
their privacy. However, such information may lead to diﬃculties such asdeterioration of quality (false information), ravage and allegations of libeland slander.
Recent researches have described many existing eﬀorts to solve these problems [2–4]. For example, Groat et al.[2] proposed a way of protecting privacy
by reconstructing reports from users using a Negative Survey’ technique. In
terms of incentive for participation, Sasank et al.[4] explored what kind of monetary rewards work eﬀectively in crowd sensing. However, several limitations
remain. For example, the privacy protection method works only for multiplechoice questions. Using external factors such as monetary rewards is insuﬃcientfor enhancing motivation compared to internal factors. Previous research has
been tackled the problems by using username or anonymous user as
the alias for the information sender. We, on the other hand, explore the possibility of solving these problems by providing an alternative: namely, mimicking
location-aware virtual monsters.
2.2

Concept of Lokemon

We developed the concept of Lokemon to solve the three aforementioned issues
simultaneously. The basic idea of the Lokemon is very simple: users can achieve
crowd sensing as monsters, which are virtually located at each PoI. Figure 1
shows a comparison between the typical crowd sensing model and the Lokemonmodel. In the typical crowd sensing model, users usually use their user names(real names or pseudonyms) to send sensing data. In Lokemon model, by contrast, users send sensor data in the name of a nearby Lokemon virtually locatedat each sensing. We explain the detail of the Lokemon in the following scenario.
Scenario: Today is the first day of Bob’s trip to Kyoto. Because of a bad caseof jet lag, he wakes up at his hotel at noon and goes outside to have lunch withhis smartphone in which the Lokemon application is installed. He is a great loverof ramen noodles, so he heads to a famous ramen restaurant by bus. While he4

F. Author et al.
Typical Crowd Sensing Model
4 people in line

LokeMon’s Crowd Sensing Model
4 people in line
Lokemonize(mimic)LokeMonat but stop

Fig. 1. Comparison of current participatory sensing model and Lokemon’s sensing
model. In typical crowd sensing model, a user sends sensor data by using his/her username. In Lokemon model, a user sends sensor data by using monster name which isvirtually located at a sensing spot.

waits for a bus at a bus stop, he gets notification that a new Lokemon is locatedat the bus stop. He opens the Lokemon application and adds the new Lokemon
named Busbie to his Lokemon collection. At the same time, he finds that a user
named Alice is asking Busbie how many people there are in the line. Being at thebus stop, he is able to give the information and he reports the number of people
in the line identifying himself as Busbie. After a 15 minute bus ride, he finallyarrives at the ramen restaurant. But the restaurant is full of people; more than 20
people are waiting outside. He gives up to eat ramen noodle, however, he collects
a new Lokemon called ramen-man in the restaurant. The next day, he considers
giving the ramen restaurant another try. He opens the Lokemon application andasks ramen-man about current congestion at the ramen restaurant. Ramen-man
reports that there are only a few people in the restaurant, and he decides to go
there to eat.
As shown in the scenario, Lokemon enables users to perform the following
actions: 1) collect a Lokemon by visiting a place where it virtually lives, 2)
use a Lokemon as an alias to report information near to its site, and 3) ask
a question remotely of a collected Lokemon. The scenario suggests solving thethree aforementioned issues as follows:
1. Privacy
Since the users can send data in the name of Lokemon, other users cannot
know who is actually reporting the data. This reduces the chance of loss ofprivacy.

Civic Crowdsensing through Location-aware Virtual Monsters

5

2. Motivation
Lokemon also uses gamification techniques such as collection, ranking orcooperation functions to motivate the users. The gamification techniquesmotivate the users’ participation. In Lokemon, people can help others without revealing their identity. This can satisfy people’s voluntary kindness forothers without them being regarded as ‘meddlers’. Lastly, some people getpleasure from acting as a Lokemon. These strategies may enhance the spontaneous motivation of the users to participate in crowd sensing.
3. Quality of information
Using Lokemon, people can concentrate on achieving the task of reporting
information from that particular location. Also, since each Lokemon has aneach character visual design, reported information and/or people’s behavior
might be controlled with the design.
2.3

Sociological theory behind Lokemon

Lokemon’s potential for solving the issues can also be explained by introducing several sociological theories. First, dramaturgy theory[5] suggests that thedesign of Lokemon might enhance information quality. Dramaturgy, originallydeveloped by Erving Goﬀman, argues that human interactions are always influenced by time, place, and audience. It suggests that a person’s identity is not astable and independent psychological entity, but rather gets remade as the person interacts with others. In other words, people always have to aware of whether
they are playing an expected role, or change their behaviors to manage the impression they make on others. Since Lokemon forces users to act as the Lokemon
rather than themselves, they can focus on playing a more stable role/characteras the Lokemon, rather than varying their behavior to make a particular impression on others. Thus, Lokemon provides a stable front stage and guide ofperformance for the users. In addition, the concept of positive/negative face orface-threatening acts in politeness theory[6] also suggests that Lokemon provides
safe opportunities to help others while avoiding excessive mutual interference. In
the real world or when using a user name that identifies each user, it is diﬃcult
for users to balance positive face and negative face. By mediating their communications through a virtual monster that is characterized by a common identity
for the PoI, users can meld into a common identity shared by other people. This
could help to enhance users’ motivation to participate in crowd sensing.

3

Programming Model

We propose a programming model for computing people (leveraging people as apart of sensing/actuating in program) through Lokemon. Instead of requesting users directly to undertake various tasks such as sensing or acting, ourmodel places requests to users indirectly by doing it through Lokemon. Moreover, our model enables developers to access data both from IoT and people(Lokemon) via unified APIs. We adapted the Publish-Subscribe model to accessF. Author et al.

Cyber World

6

PubSub Node

Subscribe
Subscribe
Subscribe
Subscribe

PublishPublish

Pub
l

i sh

Subscribe

Physical World

PublishApplication
Application
Application

IoT
IoT
LokemonApplication

LokemonApplication

KappanBusbieBookmanFig. 2. IoT sensors are placed to phyiscal world. Lokemons are placed virtually to eachlocations. Applications can access them via PubSub nodes, which are associated toeach IoT and Lokemon, by subscribing or publishing the nodes.

sensors/actuators including IoT and Lokemon. Each IoT/Lokemon is managed
as a PubSub node, and applications can leverages data of the node by subscribing the node or publishing data to the node. This allows developers to makeapplications easily because both IoT and Lokemon can be treated in the same
way as real-time data streams. Fig 2 represents how data from IoT and Lokemon
are flowed in the same manner. In the figure, two IoT sensors are deployed in thephysical world. They periodically publish sensor data to the PubSub node corresponding to the each IoT sensor. In addition, three Lokemons named Kappan,
Busbie, and Bookman are deployed virtually at several locations, and people whoare located closely to each Lokemon publish sensor data as Lokemon by using the
Lokemon application. Third party applications can retrieve data from both IoT
and Lokemon in a unified way by subscribing to PubSub nodes corresponding
to each IoT and Lokemon. Thus, information from Lokemon can be regarded as
open sensor data streams just like public IoT sensors. One of advantages of the
programming model is to provide simplicity; developers do not have to specify
exact users to interact with. Developers can make use of sensor data from people
who are in PoI by accessing virtual sensors as Lokemon. For the PubSub-based
platform, we extended the sensor-over-xmpp protocol [7] [8] to manage not only
IoT sensors/actuators but also crowd sensing nodes with Lokemon. In addition,
we also made use of our XMPP server implementation, SOXFire [9], which isdesigned for distributed and federated infrastructure for data sharing among
various users/organizations in a way that is scalable, extensible, and easy to use
and secure.

Civic Crowdsensing through Location-aware Virtual Monsters

4

Experiment

4.1

Purpose of the experiment7

To evaluate the eﬀectiveness of a Lokemon model, we conducted a experimentthat compared two diﬀerent applications-a crowd sensing application with a
Lokemon mode (called the Lokemon application) and another lacking Lokemon(called the Lokerepo application). We recruited 34 students aged 19-30. We offered them a reward of 3,800 yen at the end of experiment, and divided theminto two groups (17 each) randomly. One group installed only Lokemon, andthe other group installed only Lokerepo on their smartphones. The experiment
took place between January 24th and February 28th. Because the experimental
period coincided with the spring vacation, we recruited students who planned tovisit the campus for more than seven days during the period. At the beginningof the experiment, we taught each group how to use Lokemon or Lokerepo as
appropriate. We did not force the users to use Lokemon/Lokerepo. We required
only that the users always have Bluetooth turned on, allowing notification fromthe application. The experiment allowed us to compare the diﬀerence between of
the number of reports and communications among participants using Lokemonand those equipped with Lokerepo. Finally, we conducted a questionnaire survey
to help us understand the participants’ impression of the applications.
4.2

Prototype applications and experiment setting

We implemented two iOS applications - Lokemon and Lokerepo. In both applications, the users’ basic task is defined as to reporting current information fromeach location. For each PoI, we prepared a virtual noticeboard at the locations
where participants could report current information. Messages in noticeboard
can be also accessed remotely so that remote users can ask questions to possible
users who are/will be at the locations. In other words, prototype applications
were designed to work as a location-oriented Q&A service. In Lokemon, participants can report sensing information by mimicking monsters, which are virtuallyplaced in each PoI (though those asking remotely were supposed to use their username). In Lokerepo, on the other hand, participants have to use their registeredusername (real name or a pseudonym) every time they used the application.
We defined nine locations in our campus as PoI - Theta building, the bus
stop, the Subway restaurant, building Iota, Kappa building , the Yukichi statue,
the Gulliver pond, the co-op store and Omega building. Figure 3 shows the nine
locations and the Lokemons designed by the authors and placed in each PoI. We
also installed iBeacon nodes in each PoI to specify the users’ location. We set
iBeacon’s signal strength as far mode, which can be detected within a distanceof about 10 meters. When the users enter each PoI (i.e.,. the iBeacon signal
area), a notification is sent to the user’s smartphone to make them aware of theexistence of the PoI. Users with the Lokemon application can then pretend to be
the Lokemon associated to the PoI to report information. GPS can also useful for
specifying the users’ location, for implementing Lokemon, but in this experiment

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F. Author et al.

we used iBeacon to specify location more precisely. Figure 4 shows screenshotsof implemented applications. We designed and implemented iOS applications ofLokemon and Lokerepo to provide very similar usage for the users. The only
diﬀerence was that the Lokemon application expresses each PoI as its Lokemon,
enabling the users to pretend to be a Lokemon. Other functions such as receiving
notifications when users are within PoIs, when any reports/questions are posted,
the interface design for map and posting messages take the same form in the two
applications (see Fig 4).

Yukichi statue

Theta building

Map Interface

PoI description

When the user isinside of PoIs
Lokemon will appear here
if you are close to the Lokemon !

Yukimon

Thetan１

Kappan’s profile

Reporting Interface

The user is within PoI,
so he/she can reportas Lokemon

What’s new here kapa?

Iotie
Iota building

4

6

Be this Lokemon !

BusbieBus stop9

Kappan5

Kappa building

2

3

8
7

SeikyonLokemonOmegirl
Omega building

Place’s information
Let’s talk about this place !
The users always use theirusername

Co-op shop
Report about this spot !

Kamokamo
Galliver pond

Subweei
Subway restaurant

Lokerepo

Fig. 3. Locations of the nine PoIs in the campus, and the identities associated with
the Lokemon at each PoI (left). Screenshots of Lokemon and Lokerepo (right).

4.3

Experimental result

This section describes experiment result and discusses the particular eﬀect ofLokemon. In both applications, participants asked/replied questions about current information near each PoIs such as the current length of waiting line at bus
stop, or whether the shop was open or closed. All communication in both Lokemon and Lokerepo was done in Japanese. We translated the original messages
into English as far as possible in the explanation below while retaining original
nuances of messages.
Diﬀerence of collected information In all, we received 153 messages in
Lokemon, and 114 messages in Lokerepo. Thus, Lokemon collected 134% number of messages compared to Lokerepo. In addition, in the case of Lokemon
application, 72 out of 153 messages (47%) were posted as Lokemon (i.e., posted
within PoI area). On the contrary, in the case of Lokerepo application, only

Civic Crowdsensing through Location-aware Virtual Monsters
Num. of messages about each PoI

Num. of messages in each day

Lokerepo28-Feb

27-Feb

26-Feb

25-Feb

24-Feb

23-Feb

22-Feb

21-Feb

20-Feb

19-Feb18-Feb

17-Feb

16-Feb

15-Feb

14-Feb

13-Feb

12-Feb

8-Feb

11-Feb

7-Feb

10-Feb

6-Feb

9-Feb

5-Feb

4-Feb

3-Feb

2-Feb

1-Feb

31-Jan030-Jan10029-Jan20

528-Jan30

1027-Jan40

1526-Jan20

25-Jan50

24-Jan25

Lokemon9

Theta
build.

Bus stop

Subway Iota build.
restaurant

Kappa
build.

YukichistatueGulliver
pond

Co-op
shop

Lokemon (post as user)

Lokerepo (post from outside of PoI)

Lokemon (post as Lokemon)

Lokerepo (post from inside of PoI)

Omega
build.

Fig. 4. Comparison of the number of messages in Lokemon and Lokerepo in each day(left) and in each PoI (right).

24 out of 114 messages (21%) were posted from inside of PoIs. Thus, Lokemon
attracted more than twice messages from indside of PoIs compared to Lokerepo.
Right figure in Figure 4 shows the number of messages each day and relating
to each PoI. The number of messages in Lokemon and Lokerepo show a similar
pattern over the period. In terms of messages about each PoI, the total number ofmessages about the bus stop and the co-op shop are larger compared with other
PoIs both in Lokemon and Lokerepo. The bus stop and co-op shop are often
crowded, and we expected that the variation would be reflected in participants’interest in the PoIs. At both the bus stop and co-op shop, Lokemon gets moremessages from the PoI (i.e.,. messages in the name of the Lokemon) comparedwith Lokerepo. By analyzing the content of the Lokerepo messages, we foundthat most of messages were questions from outside the PoI. Messages from insideof the PoI were relatively much fewer than those from than Lokemon users.
Next, in both Lokemon and Lokerepo, buildings Theta, Iota, Kappa, andOmega collected only a few messages. Those buildings are usually used for lectures; however, the experimental period was during spring vacation and no lectures took place in those buildings. That why those buildings did not get messages. In the Subway restaurant and Gulliver pond, Lokemon again collected
more messages than Lokerepo. The diﬀerences were mainly due to the numberof messages from inside the PoIs - about half the messages from the Subway
restaurant and the Gulliver pond in Lokemon were posted using the Lokemon
alias. Even when there were no questions from other users, participants voluntarily pretended to be a Lokemon to post messages about the PoI’s current
situation or their feeling about the mood as the Lokemon such as No one here.
I am very lonely. (posted by user A as Kamokamo), or Today’s special lunch
is Tuna sandwich! (posted by user B as Subweei). In Lokerepo, we could not
see this kind of messages.
Through these observations, we confirmed that Lokemon provided more than
twice the opportunities to collect information from people who are in PoIs. This
result implies that the Lokemon model could motivate people better than the
traditional username model. Moreover, some users voluntary play a role as Lokemon to establish or assume the identity of the Lokemon’s identity when theyreport current information about the PoIs.

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F. Author et al.

Diﬀerences of communication between users We also observed that there
are significant diﬀerences between the types of communication that take place
between the Lokemon and Lokerepo users.
1. Many-to-one communication (Lokemon) vs. many- to-many communication(Lokerepo)
In Lokemon, participants usually asked questions that address the Lokemonsuch as Hey Subweei, what is your recommendation today? (posted by userC) or The wind seems to be strong today, are you OK Busbie? (posted
by user B). On the contrary, in Lokerepo, participants often addressed
questions to someone who could help them such as Does anyone knowwhether the Subway is opened now? (posted by user D). In other words, theusers in Lokerepo were often concerned about possible communication withunknown people. Lokemon provides a more simple communication option
from many users to one Lokemon. Also, many people actually played a roleas the Lokemon when they responded.
2. Casual communication (Lokemon) vs. Polite communication (Lokerepo)
Another interesting observation in Lokemon was that people tended to communicate more casually compared with Lokerepo. When people address questions to a Lokemon, they say things like Hey Busbie,
Are you cold?
or Thanks!. Replies from Lokemon were also expressed in a casual or
monster-like way such as No one here!,
We are closed!, with attaching
onomatopoeia at the end of messages to express the Lokemon’s character,such as (messages) kapa!,(messages) BusBus! or (sentences) yo!.
This onomatopoeia style was developed by the users themselves. On the contrary, in Lokerepo, people tended to ask/answer questions in a more polite
way such as Could someone tell me whether the shop is open or not?,Thank you very much. or Opening time in spring vacation is from 11:00
to 15:00. (in a polite way in Japanese). We consider that Lokemon has thepotential to enhance a friendly mood that contributes to the increase in thenumber of messages.
Questionnaire survey After finishing the experiment, we asked the participants to complete a questionnaire. Table 1 represents statements and averagesof results obtained from the questionnaire. The respective ratings of agreement
are: 1 = strongly disagree, 2 = partly disagree, 3 = neither agree nor disagree,
4 = partly agree, and 5 = strongly agree. In terms of pleasant, easiness, andmotivation, Lokemon users gave better feedbacks than Lokerepo users. In addition, Lokemon users seemed to feel that the risk of loss of privacy is reduced byusing Lokemon as a mediator of crowd sensing. These results show that we wereright to predict that users of Lokemon would use this modality more. On theother hand, users of Lokemon seemed to consider that the reliability of messages
is decreased slightly compared with Lokerepo. We take this to be a result of
its anonymity feature. In future we need to evaluate Lokemon’s reliability, by
looking at the eﬀect of feedback.

Civic Crowdsensing through Location-aware Virtual Monsters

11

Table 1. Result of questionnaire.
Question
Lokemon Lokerepo
It was pleasant to post message
3.6
3.2
Using Lokemon name (for Lokemon users) or Using username
3.8
2.5(for Lokerepo users) made it easy to post messages
The application enhanced my motivation to participate
3.3
2.5
The application decreased the reliability of messages
2.8
1.9
The application decreased the risk of loss of privacy
3.8
2.5

5

Related work

To motivate people to participate in crowdsensing, a lot of work have been
conducted on incentive study. For example, the eﬀect of monetary incentives has
been investigated in [10, 11]. For non-monetary incentives, researchers mainly
focus on psychological factors. For example, the study of gamification has been
conducted in [12, 13]. The work of [14, 15] studied to use compliance-withoutpressure technology to improve the receptiveness of participants. Our proposal isa new comprehensive approach of non-monetary incentives which uses techniquesfrom sociology, psychology and anthropomorphism which have not been utilized
in crowdsensing by now.
Lots of work have studied the use of avatars in virtual space communication and demonstrated that avatars does place an eﬀect on people’s behavior inboth real and virtual worlds. For example, Yoon et al. [16] investigated whether
certain types of avatars and avatar behaviors could promote pro- or anti-social
actions of humans in daily behavior. Rosenberg et al. [17] illustrated the potential of using experiences in virtual reality technology to increase pro-social
behavior in the physical world. To our best of knowledge, this work is the firstattempt to promoting people’s participation in crowdsensing via avatars. Moreover, in Lokemon, each monster can be mimicked by a group of people while in
the aforementioned study each avatar can only be manipulated by one person.
Anthropomorphic design has also been widely applied to mascot characters,
robotics, and entertainment games. Jetter et al. [18] explored how the physicaldesign of urban sensors can change citizens’ attitudes and perceptions toward being sensed. They found that anthropomorphic design resulted in greater engagement and trust while neutral or less visible designs created rejection and anxiety.
Osawa et al. [19] proposed a direct anthropomorphic method that agentizes anartifact by attaching anthropomorphic parts to it. The study indicated thatthe examinees noticed the target artifact and memorized functions using direct
anthropomorphism method more than doing so using an independent humanoidagent. Compared with these study, our design applies an anthropomorphism of
the property of location rather than physical objects, which has not been studied
before.

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Conclusion

We have presented a new crowd sensing model called Lokemon that mediates
sensing subjects as location-aware virtual monsters. We addressed issues suchas loss of privacy, providing motivation and controlling data quality in crowd
sensing and showed that they are strongly related to aspects of the way of thesensing subject is structured such as whether the user identifies themselves using
individual username or an alias. To address the issue, we introduced the conceptof Lokemon that enables users to mimic location-aware monsters when they participate in crowd sensing. In addition, we also presented a programming model
where a Lokemon can work as a virtual sensor node. Our programming model
enables developers to create an application in a unified way that can handle datafrom both IoT sensors and people via Lokemon sensors. Through a small-scale
four-week comparison experiment, we confirmed that the Lokemon concept was
positively accepted by the users: Lokemon increased the number reporting from
PoIs, and fostered casual communication among users while reducing their concerns about loss of privacy. In future, we will conduct a large-scale experimentto evaluate Lokemon’s eﬀectiveness. In addition, we will focus more on how thedesign of the Lokemon monster will aﬀect the quality of information that people supply. Finally, we will make our Publish-Subscribe APIs public so that the
Lokemon’s crowd sensing data can be easily accessed. This should play an important role in realizing a programmable world that combines information fromboth IoT and people.

Acknowledgment
This research is supported by JSPS KAKENHI Grant Number 19K11945 and
MIC SCOPE project.

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