Intership Report on Mobile Crowd Sourcing

7/27/2019 Intership Report on Mobile Crowd Sourcing

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Center for Research and Telecommunication

Experimentation for Networked Communities(CREATE-NET)

Networking & security solutions for pervasive computingsystems (iNSPIRE)

Internship Report

Implementation of a Mobile

Crowd Sensing System

by

Emmanuel Robert Ssebaggala

Msc Telecommunication Engineering

Supervisors:

Iacopo Carreras,PhD

Pervasive and Secure Computing Environment Mobile

Computing Technical Group Leader

Nicola Conci, PhD

Ass. Prof, University of Trento

Trento, 2012


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To ...


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Contents

Contents i

1 Introduction 3

1.1 CREATE-NET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2 Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 Literature Review 5

2.1 Mobile Crowd Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.3 Tools used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3 System Development 9

3.1 Problem formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.2 Sampling Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.3 Energy Efficient Context Sampling . . . . . . . . . . . . . . . . . . . . 11

3.3.1 The adaptive sampling algorithm. . . . . . . . . . . . . . . . . 11

3.3.2 Simulation Study . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.4 System Implementation and Experimentation . . . . . . . . . . . . . . 15

3.4.1 Prototype Implementation . . . . . . . . . . . . . . . . . . . . 15

3.5 Experimentation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Bibliography 21

List of Symbols and Abbreviations 23

List of Figures 24

Index 25

i


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Acknowledgements

I want to express my sincere appreciation to CREATE-NET for hosting me as an intern

for a period of three months.

I want to thank Dr. Iacopo Carrerras for his vast reserve of patience and knowledge

that was very helpful in carrying out the tasks assigned. His ideas and practical guidance

in researching were an invaluable lesson.

I want to thank Dr. Andrei Tamilin whose help, stimulating suggestions, knowledge,

and experience were very helpful in implementing most of the tasks assigned.

Also, very special thanks to my Multimedia Networking course lecturers, Ass. Prof.

Nicola Conci and Danielle Miorandi,PhD, whose lecture sessions sparked my interest in

multimedia networks.

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Chapter 1

Introduction

This report gives an account of the internship activities carried out during the period of

11th July to 11th October within CREATE-NETs iNSPIRE research group. During this

period, the author contributed to the implementation of a mobile crowd sensing system;

specifically, the development of an Android mobile application.

1.1 CREATE-NET

The Center for Research And Telecommunication Experimentation for Networked com-

munities (CREATE-NET) is an non-profit organization created to achieve excellence in

research, promote technology transfer toward the industry through the engineering of

technologies and solutions, promote innovation , and focus on key application areas and

services with an impact on quality of life.[2].

Within CREATE-NET, the author worked in the iNSPIRE research team which pur-

sues a multidisciplinary approach to devise innovative networking and security solutions

for pervasive computing systems. The group activities are inspired by a hybrid approach,

whereby research is deeply intertwined with experimentations, leading to solutions able

to turn novel scientific paradigms into working prototypes.The group works in tight collaboration with the ENGINE Team for the realization of

prototypes and proof-of-concept implementation of innovative solutions. Key aspects of

the group research agenda include: the focus on data as the basic brick of pervasive com-

munication systems, the introduction of security considerations as constituent properties

of the system, the design of distributed, autonomic and green algorithms/protocols,

the development of methods for evaluating performance, the focus on robustness and

resilience as key performance. The iNSPIRE team pursues research on pervasive com-

puting and communications environments [2].

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4 CHAPTER 1. INTRODUCTION

1.2 Project

The internship activities were focused on the mobile crowd sensing. Crowd sensing

involves the allocation of tasks to the crowd(people), or some other way of engagingthe crowd in task execution. Mobile crowd sensing uses smart phones as a medium for

engaging the crowd to respond or participate in tasks. The internship activities were cen-

tered around the development of a mobile crowd sourcing system code named Matador.

The system was composed of a server sub-system and a client application which was

an Android mobile application. The author focused on the development of the Android

application.

The reminder of this report is broken down into three major chapters. The literature

review section which gives a detailed background on mobile crowd sourcing, notable

work already done, and present endeavors in the area. In the same section, the tools used

are presented and the motivation behind their choice over other alternatives is explained.Chapter three covers the actual work done. It introduces the system with a full

description of the architecture and its design. It discusses the algorithm implemented for

sampling and the prototype developed for testing.

Lastly, a concluding chapter stating the key highlights of the internship and re-

iterating the benefits and promise this research area holds.


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Chapter 2

Literature Review

2.1 Mobile Crowd Sensing

The ubiquitous availability of internet-connected, media-equipped and sensor-equipped

portable devices is enabling a new class of applications which are exploiting the power

of crowds to perform sensing of real world tasks. Such a paradigm is typically referred

to as crowd-sensing[3], and lies at the intersection of crowd-sourcing and participatory

sensing [1]. Crowd-sourcing allows the issuing of tasks to users requiring human inter-

vention, while participatory sensing exploits smart phone sensing capabilities to record,

analyze, and discover patterns that are of importance in peoples lives.Crowd-sensingallows issuing sensing tasks to users without necessarily requiring direct human inter-

vention by exploiting the inherent sensing capabilities of their mobile devices.

Crowd-sensing has a wide range of potential applications, including direct involve-

ment of citizens in public decision making, such as urban planning and quality assess-

ment campaigns of public services. In this case, it provides a means to inquire directly

from citizens (or indirectly from citizen-created information sources) about their opin-

ions, emotional tonalities regarding certain arguments, and problems, as well as to seam-

lessly involve citizens in decision making. This differs from a pure bottom-up interac-

tion, which involves citizens-to-administration communication, resulting in a participa-

tory civic reporting system where citizens with mobile phones can eventually submit the-matic multimedia reports on civic issues observed in their neighborhoods. Conversely,

the top-down interaction modality enables administration-to-citizens communication, re-

sulting in a mobile civic crowd-sensing system, in which the administration can launch

surveys or, more generally, post queries directly inquiring from citizens.

This internship focused on the development of a crowd-sensing framework which

combines the power of crowd-sourcing with the instantaneity and situation-awareness

of mobile technologies. With respect to state of the art solutions [9,8,6], the system

allows to specify the context in which a given sensing task should be executed by the

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6 CHAPTER 2. LITERATURE REVIEW

user or by the users device. The resulting system is able to deliver the right tasks to the

right people in the right circumstances. The notion of context-awareness in the delivery

and execution of tasks characterizes our approach and allows to (i) maximize conditions

for user participation by presenting only tasks relevant to the user, with minimal userintervention (ii) minimize the consumption of mobile device resources, specifically the

battery, thus preserving normal operation.

2.2 Related work

Participatory sensing is increasingly becoming a sensing paradigm to include people-in-

the-loop for performing data gathering tasks. In a typical scenario, people are motivated

by their personal pay-off to participate in collective data gathering, and the assurance to

anonymously share the data they provide. As an example, PEIR [6] provides a way for

users to measure their personal exposure to pollution, while contributing to the creation

of a fine-grained map of the air quality in an urban setting. Similarly, NoiseMap[10]

tackles the problem of noise pollution through a participatory approach.

In a similar fashion, crowd-sourcing exploits the wisdom of the crowds in order to

perform specific tasks. This has been widely explored in the web, Amazon Mechanical

Turk being the most prominent example.

At the intersection of Participatory sensing and Crowd-sourcing lies mobile crowd-

sensing [3], which is a mobile sensing paradigm different from participatory sensing

in that user involvement is minimal and sensing occurs autonomously. In such a setting,

tasks are typically delivered to users and the results aggregated and processed on a server

[9,8].

In the area of mobile sensing, several works addressed the issue of energy-aware

localization over smart-phones. In [4] , authors propose an energy-aware localization

method, which trades off localization accuracy for energy, depending on the specific

context.

Our approach extends the state-of-the art by specifically focusing on crowd-sensing

application scenarios and by designing a framework to dynamically adapt the sensing ofthe context to the specific tasks to be performed by users. Further, in order to preserve

the battery of the mobile device, the proposed algorithm combines both howthe sensing

is performed, andwhenit is performed.

2.3 Tools used

The Android platform was used for the development of the mobile application. The

choice to develop for the Android platform was due to the fact that it is open source and


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2.3. TOOLS USED 7

its development can be done on virtually any operating system which has the Java Run-

time(JRE) and Java Development Kit(JDK) Installed. Practically, every OS has support

for the Java language;this makes the development platform independent.

Android comes with an SDK and along with other helper tools and utilities to facili-tate the development process. However, keeping in mind the need to port the application

in the future to other mobile platforms, such as iOS and Windows Phone, Appcelera-

tor Titaniums cross platform development studio was used. Appcelerator Titanium is a

platform for developing mobile, tablet and desktop applications using web technologies.

It allows developers to use the familiar JavaScript syntax which is easy to learn, and then

deploy the application for any of the currently support mobile platforms.

Testing and debugging of the application was done using the Android emulator that

comes with the Android SDK. Also, two Nexus S phones with Android 2.3.7(Ginger-

bread) and a Samsung Galaxy Tab 10.1 with Android 4.0.4(Ice Cream Sandwich) were

used for testing and running field tests.


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Chapter 3

System Development

This is a description of how the application works. When the Matador application is

installed, it runs in the background and periodically updates the list of tasks from those

on the server. Each task is associated with an action depending on the parameters. A

task can require the user to take a photo our his/her surrounding, record a video or sound

clip, or participate in a survey by filling out a questionnaire. In addition to the actions, a

tasks is characterized by itscontext; this specifies the conditions that should be satisfied

for it to be triggered to the user. The context is defined along geography, time, user

profile(age and gender), and user activity(movement or stationary). A task can require

explicit intervention by the user , or it can be implicitly handled by the phone.

3.1 Problem formulation

Amobile crowd-sourcing taskt is a a tuplet = ct, at, wherect is atask contextscop-

ing its applicability, andat is anactionthe task consists of.

For the sake of clarity, the simplest structure of a task context is considered. The task

context is defined by a geographical dimension that is circular and temporal dimension.

ct =latt,lont,radt, [startt,endt] (3.1)

where latt,lont are latitude and longitude coordinates of the circle center, radt is its

radius, andstartt,endt are start and end timestamps.

A system typically consists of multiple tasks represented by a tasks listt = {tj}of

ntaskstj , wheretj =ctj , a

tj,0 j n.

A user context is an essential element of the mobile crowdsourcing system; it de-

termines if the user is in conditions relevant to some tasks. Similarly to tasks, a user

contextcan be structured along multiple dimensions, which can be determined by sens-

ing capacities of the user device. Hereafter, we assume the simplest structure of a user

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10 CHAPTER 3. SYSTEM DEVELOPMENT

contextcu defined by a geographical dimension, represented by the current estimate of

users location, and a temporal dimension, represented by the current time:

c

u

=latu

,lon

u

,acc

u

, ts

u

(3.2)

wherelatu,lonu are latitude and longitude coordinates of the user location,accu is the

accuracy, with which the location has been obtained, and tsu is the timestamp.

The list of chronologically ordered user context instances is a user context history

cu ={cui}i0.

We define the distance du,t between a user context cu and a task context ct as a

function:

du,t =fdist(cu, ct) (3.3)

which depends on the dimensions constituting the contexts.

Given a user with the context cu

, the task t is said to be detectedby the user ifdistancedu,t , where is a threshold depending on a specific application scenario.

A user context sampling is the process of obtaining a user context by the mobile

device. This process can be controlled by the following two parameters:

sampling accuracy : this parameter adjusts the required distance accuracy for

the context sampling. The higher its value, the more precise the sampled context

is.This parameter is particularly relevant for estimation of user location, because

the application of different localization sensors gives different estimation accura-

cies (e.g., GPS vs. Cell Towers);

sampling rate : this parameter dynamically adjusts the time between any twoconsecutive context samplings. The higher its value, the more frequent the context

is sampled.

In practice, the sampling rate can be directly manipulated in the mobile device, although

there are no practical ways of explicitly controlling the sampling accuracy, e.g., location

cannot be forced to be acquired with a given accuracy. A work around for controlling the

accuracy is selecting the sensing method with an a priori known error rate;the resulting

accuracy values are dependent on the method used, e.g., localization using cell towers is

known to be roughly in hundreds of meters to kilometers. However, the estimated accu-

racy in the current position is known only after localization has occurred. For simplicityof the notation, hereafter we will interchangeably use for sampling method and the

sampling accuracy value.

Auser context samplingis a function that, given a user context history and a list of

tasks, estimates the appropriate conditions for the next user context sampling:

, = fsampling(c, t) (3.4)

Clearly, the variation of the aforementioned parameters of the user context sampling

process is an indication of that the amount of resources consumed by the mobile device,


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3.2. SAMPLING ALGORITHM 11

in particular the energy consumed by sensors involved in the sampling. In order to quan-

titatively describe the consumption of resources in the proposed crowd-sensing system,

we adopt the following energy model: each of the sampling methods supported by the

mobile device is associated with aresource cost required for its invocation for contextsampling and is determined by the following function:

= fcost() (3.5)

In the present work we consider the simplest resource cost function determining the

amount of energy consumed by the mobile device.

A total costof the crowd-sensing system functioning is determined by costs of the

individual user context samplings and is represented as the sum of their individual costs:

= 0i|cu|

i (3.6)

Given a task list t, the goal of the mobile crowd-sensing system is to define the

functionfsampling that maximizes the number of tasks properly detected and presented

to the user, and minimizes the total costof consumed resources.

3.2 Sampling Algorithm

3.3 Energy Efficient Context Sampling

One of the key challenges in implementing the sampling function fsampling(cu, t) lies

in the minimization of the battery consumption in order to preserve the normal operation

of the users mobile device. In this work, we focus on the energy consumption due to

the user localization, which is considered to be critical. In fact, when obtaining the user

location, battery consumption is a trade off for the accuracy of the location estimation,

depending on the type of sensor applied for the localization [4], and the frequency at

which a given sensor is sampled. We consider the two most common smart-phone lo-

calization methods: GPS-based and cellular network-based. The typical GPS antenna of

a modern phone gives an accurate location estimation, with an error value in the range

of a few meters (or in the worst case a couple of dozen meters). At the same time, GPS

is also known to be energy intensive and leads to significant battery drains [7, 11,4].

Conversely, the cellular networks method estimates the location with a high error of

hundreds of meters (or in the worst case several kilometers), but with negligible battery

consumption [5,4].

3.3.1 The adaptive sampling algorithm

The intuitive idea behind the adaptive energy-efficient user context sampling is to dy-

namically adapt (i) the way the context is sampled, choosing between GPS and network

localization and (ii) the time between two consecutive context samples. As illustrated in


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Fig.3.1,the aim is to utilize the cellular network localization method when approaching

the closest task. In this case, the energy consumption should be quite limited. When the

uncertainty on the user location due to the coarse accuracy of the network localization

overlaps with the spatial validity of the closest task, we should switch to GPS local-ization. In this latter case, the sampling should adapted over time on the basis on the

approaching rate of the user to the closest task.

!"#$%&'(

*($+,-. /,'%/"0%1,&

234 /,'%/"0%1,&5%#.

Figure 3.1: Adaptive user context sampling concept.

In order to capture this overlap we define a distance function (3.3) as follows:

fdist=

hvrs(cu, ct)accu radt iftsu [startt,endt]

otherwise

where hvrs is the haversine formula for calculating the spatial distance between thelatitude-longitude coordinates of the user and task location.

The sampling function fsampling is implemented by the algorithm Alg. 1 and is

graphically illustrated in Fig.3.2.

When the context distancedu,t, computed byfdist, becomes less than twice the user

location accuracy the mobile application localization switches to GPS, instead of net-

work localization. With such an approach network localization is utilized as a rough

probe for checking if there are tasks in range, while the precise probing of near tasks is

performed using GPS. In order to generate the next sampling time (tsui+1) the algorithm

computes the average speedof approach to the closest task based on the context history

and sets the value to the time anticipating the overlapping between user context and taskcontext. It is worth noting that differs from the user velocity in that it measures how

fast the user is getting close to the nearest task, taking into account also his direction. As

an example, if the user is moving around a certain task, although his velocity will never

be null, the approach speed will be 0.

The sampling interval is parameterized in such a way so that it varies between a min-

imum value of 10s and a maximum regular interval of 120s. These bounds have been

experimentaly proved to produce optimum sensitivity to the change in user direction and

speed. The


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3.3. ENERGY EFFICIENT CONTEXT SAMPLING 13

Algorithm 1User context sampling function fsampling

1: functionN EX TCONTEXTSAMPLING( cu, t)

2: cui getCurrentUserContext(cu);

3: cui1 getPreceedingUserContext(c

u

);4: tj getClosestT ask(c

ui, t); Find the closest tasktj the user is heading to

5: i hvrs(cu

i,ct

j)hvrs(cu

i1,ctj)

tsitsi1; Approach rate to closest tasktj

6: j=i3

j=i vi

3 Averate speed over 3 samples

7: du,t fdist(cui, c

tj );

8: =1.5km;9: tmin= 10s; Minimum sampling interval

10: to= 120s; Maximum regular sampling11: if(du,t )then12: i+1 GPS; Use GPS next

13: else14: i+1 NETWORK; Use Network next15: end if

16: t du,t

; Next sampling interval

17: tmax= to(t)2

18:

=

1 iftmin


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Figure 3.2: Adaptive user context sampling illustration.

time), (2) the accuracy value of a sampling method is constant through the simulation

(we used GP S = 20 meters and NETWORK = 1000 meters), (3) average cost, in

terms of battery consumption, for invoking a sampling is constant through the simula-

tion (we usedGP S =???and NETWORK =???).

!"##$

!" $%

&" $%

'" $%

$&!'()*#

$&!'()*#+,-.$!#)!&)/ '(!0!

Figure 3.3: Variable speed experiment.


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3.4. SYSTEM IMPLEMENTATION AND EXPERIMENTATION 15

!



3.4 System Implementation and Experimentation

3.4.1 Prototype Implementation

The Matador context-aware mobile crowd-sensing system has been fully implemented

into a working prototype consisting of a server-side web application and a mobile appli-

cation for smartphones.

The server-side part has been realized as a web application providing an interface to

to create tasks and monitor the process of their execution. For each task, it is possible to

configure: (i) its context, which in the current prototype implementation consists of the

spatio/temporal region in which the the task should be triggered to users; (ii) the action

requested to be performed by users. Currently, an action can be implicitor explicit. In

the former case, no user intervention is required for triggering the action, a part for hav-ing the mobile application running in the background. In this case, the smartphone starts

to collect data autonomously, and sends it back to the server for later processing. Exam-

ples of such data includes accelerometer data, anonymous GPS logging. Conversely, in

the latter case, a direct human intervention is required in order to complete the task. In

the current implementation, explicit tasks consists of a combination of questionnaires,

multimedia reports. The server component receives users responses for each task, com-

putes response statistics and provides a dashboard for visualizing the analytics of each

task. Fig.3.6depicts the user interface of the server-side component.


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Figure 3.6: TheMatadorserver-side user interface.

In addition to the user interface, a RESTful API has been implemented on the server

to communicate with mobile clients in order to (i) synchronize tasks and (ii) receive task

responses. A specific ontology has been created in order to define a common language

between the server and mobile clients, and to properly represent and interpret tasks and

tasks responses. Each task is modeled as in the following XML sample:

< t a s k i d = t a s k I D >

< t i t l e>T as k a t V i l l a z z a no

Take p ho to o f t h e b us s t op i n V i l la z z an o

4 6 . 0 4 55 2 1 1 . 1 38 5 2

50

0 1 . 0 7 . 2 0 1 2

3 1 . 0 7 . 2 0 1 2

Mo T u We Th F r

0917


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3.5. EXPERIMENTATION 17

P l ea s e t a ke a p ho to o f t h e b us s t op .

Listing 3.1: XML representation of a crowd-sensing task

The mobile application has been implemented for Android-based smart-phones us-

ing Titanium Appcelerator, a framework for mobile applications development. The

application downloads tasks from the server, schedules energy-efficient acquisition of

the phones context and notifies the user of relevant tasks around him - inviting him/her

to participate. The context acquisition component uses the GPS-based and network-

based localization capabilities of Android smart-phones. The implemented multimedia

capture component accesses the microphone and camera of the smart-phone allowing

the execution of multimedia actions assigned to tasks, e.g., taking a photo, recording a

video, or recording a sound clip. Responses to tasks and acquired multimedia content

are communicated to the server for elaboration.

Fig.3.7presents the user interface of the implemented mobile application.

3.5 Experimentation

In order to validate the proposed system we have considered two different user trajec-

tories, one to be covered by car and one to be covered by foot. The first one consisted

of a 20 Km path over a sub-urban road, featuring two roundabouts, a 10 Km high speed

segment and various accelerations/decelerations. 10 tasks were randomly distributed

along such path. Each task was characterized by its spatio-temporal contextual validity.

In particular,each task had a circular geographical validity, with radius 150 m and the

center along the path. The latter case, was a 1 Km path in the Trento city center. Also

in this case, 10 tasks were randomly placed along such path, with a radius of 25 m. and

center along the path.

Since the primary aim of the experimental validation was to evaluate the adaptive sam-

pling mechanisms, we did not assume any specific constraint on the temporal context of

tasks. Which means tasks were always active, independently from the specific time.

In order to evaluate the performance of the system, we considered the tradeoff be-

tween the consumed energy, measured as a function of the context samples performed

by the users smart-phone, and the accuracy, measured as the number properly detected

tasks, over the total number of tasks present over the path covered by the user. We have

compared such results with the case of a GPS logging mechanism, and compared the

results in terms of energy savings.

http://www.appcelerator.com
http://www.appcelerator.com/http://www.appcelerator.com/


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(a) Tasks list. (b) Map of the closest tasks.

Figure 3.7: TheMatadormobile application user interface.

In order to give a quantitative characterization of the energy-efficiency of the pre-

sented adaptive sampling function, we conducted a series of programmed experiments

to simulate the functioning of a mobile crowd-sensing system. The implemented simula-

tion environment allows to (i) specify the route to be followed by the user, (ii) configure

random variation of the user speed when moving along a route, (iii) place a set of tasks

along the users route for detection. Three simplifying assumptions are made: (1) a sam-

pling method can be activated immediately without delays (in practice, depending on the

environment conditions, getting a fix on GPS requires some time), (2) the accuracy value

of a sampling method is constant through the simulation (we used GP S = 20 metersandNETWORK = 1000meters), (3) average cost, in terms of battery consumption, for

invoking a sampling is constant through the simulation.

We compared the proposed context-aware sampling algorithm, with the case of a con-

stant rate GPS sampling. In both cases, we measured the detection rate, and we com-

pared the associated energy costs. Fig.3.8shows the results in the case of a user moving

along a 30 Km route at a speed of 50 Km/h. It is possible to observe how the detection

rate varies when the sampling rate is increased, and therefore the associated trade-off

with the energy consumption. In particular, it is evident that the performance deterio-


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3.5. EXPERIMENTATION 19

rate rapidly for a sampling rate greater than 30 sec.. Setting the task detection rate to

80% leads to a required sampling rate of approximately 60s, and a total number of36

GPS samples over a 30 km route. Under the same setting, the Matador context-aware

sampling algorithm provides similar performance at the cost of12 GPS samples and 7network samples. This leads to a significant saving in terms of energy consumption.

In particular, neglecting the energy cost of Network samples [4], the proposed adap-

tive context sampling mechanism can lead to approximately a 60% savings in terms of

battery consumption.

Figure 3.8: Task detection rate in the case of a constant GPS sampling, user moving

along a path at a speed of50 Km/h.

Fig. 3.9 provides a visual comparison of the constant GPS sampling with theMatadadorcontext-aware sampling. When the user is far away from the closest task, only Network

localization is used, with a considerable saving in terms of energy consumption. Instead,

when approaching the task, GPS is used, but with a sampling rate which depends on the

distance from the task.

Figure 3.9: Visual comparison of the constant GPS sampling and theMatadadorcontext-

aware sampling.

Clearly, this simulation study represents an ideal case, but, at the same time, provides

an indication on the potential savings that is possible to obtain by adapting the context

sampling function to the specific crowd-sensing campaign being supported. In Sec. 3.4,

we will provide the results of a small-scale pilot.


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3.6 Conclusions

At the end of this internship, Matadora mobile context-aware crowd-sensing system

which exploits user context in order to optimally deliver tasks to users, while preserving

mobile device resources. The system design was presented, together with the framework

we used to model and evaluate the developed algorithmic solutions. The initial evalua-

tion supports the proposed approach.

More work is needed to extend the dimensions utilized for characterizing the context,

and also to implement and evaluate large-scale experimentation involving a larger user

base.


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29/31

List of Symbols

and Abbreviations

Abbreviation Description Definition

CREATE-NET Center for Research And Telecommunication Experi-

mentation for Networked communities

page3

iNSPIRE Networking & security solutions for pervasive com-

puting systems

page3

ENGINE Engineering and fast prototyping Research Team page3

JRE Java Runtime page7

JDK Java Development Kit page7

SDK Software Development Kit page7

23


30/31

List of Figures

3.1 Adaptive user context sampling concept. . . . . . . . . . . . . . . . . . . . 12

3.2 Adaptive user context sampling illustration. . . . . . . . . . . . . . . . . . 14

3.3 Variable speed experiment. . . . . . . . . . . . . . . . . . . . . . . . . . . 14



3.6 TheMatadorserver-side user interface. . . . . . . . . . . . . . . . . . . . 16

3.7 TheMatadormobile application user interface. . . . . . . . . . . . . . . . 18

3.8 Task detection rate in the case of a constant GPS sampling, user moving

along a path at a speed of50 Km/h.. . . . . . . . . . . . . . . . . . . . . . 19

3.9 Visual comparison of the constant GPS sampling and theMatadadorcontext-

aware sampling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

24


31/31

Index

Appcelerator Titanium,7

context,9

crowdsourcing,9

energy-efficiency, 18

Gingerbread,7

Ice Cream Sandwich,7

iNSPIRE,3

mobile-application,17

RESTful API,16

25

Documents

Intership Report on Mobile Crowd Sourcing