Android Titles & abstracts 2013-2014

IEEE- Project Title 2013
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1. GPS/HPS-and Wi-Fi Fingerprint-Based Location Recognition for
Check-In Applications Over Smartphones in Cloud-Based LBSs
ABSTRACT:
This paper proposes a new location recognition algorithm for automatic
check-in applications (LRACI), suited to be implemented within Smartphones,
integrated in the Cloud platform and representing a service for Cloud end users.
The algorithm, the performance of which is independent of the employed device,
uses both global and hybrid positioning systems (GPS/HPS) and, in an
opportunistic way, the presence of Wi-Fi access points (APs), through a new
definition of Wi-Fi FingerPrint (FP), which is proposed in this paper. This FP
definition considers the order relation among the received signal strength (RSS)
rather than the absolute values. This is one of the main contributions of this paper.
LRACI is designed to be employed where traditional approaches, usually based
only on GPS/HPS, fail, and is aimed at finding user location, with a room-level
resolution, in order to estimate the overall time spent in the location, called
Permanence, instead of the simple presence. LRACI allows automatic check-in in a
given location only if the users' Permanence is larger than a minimum amount of
time, called Stay Length (SL), and may be exploited in the Cloud. For example, if
many people check-in in a particular location (e.g., a supermarket or a post office),
it means that the location is crowded. Using LRACI-based data, collected by
smartphones in the Cloud and made available in the Cloud itself, end users can
manage their daily activities (e.g., buying food or paying a bill) in a more efficient
way. The proposal, practically implemented over Android operating system-based

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Smartphones, has been extensively tested. Experimental results have shown a
location recognition accuracy of about 90%, opening the door to real LRACI
employments. In this sense, a preliminary study of its application in the Cloud,
obtained through simulation, has been provided to highlight the advantages of the
LRACI features.
2. CloudMoV: Cloud-Based Mobile Social TV
ABSTRACT:
The rapidly increasing power of personal mobile devices (smartphones,
tablets, etc.) is providing much richer contents and social interactions to users on
the move. This trend however is throttled by the limited battery lifetime of mobile
devices and unstable wireless connectivity, making the highest possible quality of
service experienced by mobile users not feasible. The recent cloud computing
technology, with its rich resources to compensate for the limitations of mobile
devices and connections, can potentially provide an ideal platform to support the
desired mobile services. Tough challenges arise on how to effectively exploit cloud
resources to facilitate mobile services, especially those with stringent interaction
delay requirements. In this paper, we propose the design of a Cloud-based, novel
Mobile sOcial tV system (CloudMoV). The system effectively utilizes both PaaS
(Platform-as-a-Service) and IaaS (Infrastructure-as-a-Service) cloud services to
offer the living-room experience of video watching to a group of disparate mobile
users who can interact socially while sharing the video. To guarantee good
streaming quality as experienced by the mobile users with time-varying wireless
connectivity, we employ a surrogate for each user in the IaaS cloud for video

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downloading and social exchanges on behalf of the user. The surrogate performs
efficient stream transcoding that matches the current connectivity quality of the
mobile user. Given the battery life as a key performance bottleneck, we advocate
the use of burst transmission from the surrogates to the mobile users, and carefully
decide the burst size which can lead to high energy efficiency and streaming
quality. Social interactions among the users, in terms of spontaneous textual
exchanges, are effectively achieved by efficient designs of data storage with
BigTable and dynamic handling of large volumes of concurrent messages in a
typical PaaS cloud. These various designs for flexible transcoding capabilities-
battery efficiency of mobile devices and spontaneous social interactivity together
provide an ideal platform for mobile social TV services. We have implemented
CloudMoV on Amazon EC2 and Google App Engine and verified its superior
performance based on real-world experiments.
3. Content Sharing over Smartphone-Based Delay-Tolerant Networks
ABSTRACT:
With the growing number of smartphone users, peer-to-peer ad hoc content
sharing is expected to occur more often. Thus, new content sharing mechanisms
should be developed as traditional data delivery schemes are not efficient for
content sharing due to the sporadic connectivity between smartphones. To
accomplish data delivery in such challenging environments, researchers have
proposed the use of store-carry-forward protocols, in which a node stores a
message and carries it until a forwarding opportunity arises through an encounter
with other nodes. Most previous works in this field have focused on the prediction

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of whether two nodes would encounter each other, without considering the place
and time of the encounter. In this paper, we propose discover-predict-deliver as an
efficient content sharing scheme for delay-tolerant smartphone networks. In our
proposed scheme, contents are shared using the mobility information of
individuals. Specifically, our approach employs a mobility learning algorithm to
identify places indoors and outdoors. A hidden Markov model is used to predict an
individual's future mobility information. Evaluation based on real traces indicates
that with the proposed approach, 87 percent of contents can be correctly discovered
and delivered within 2 hours when the content is available only in 30 percent of
nodes in the network. We implement a sample application on commercial
smartphones, and we validate its efficiency to analyze the practical feasibility of
the content sharing application. Our system approximately results in a 2 percent
CPU overhead and reduces the battery lifetime of a smartphone by 15 percent at
most.
4. Uncovering Measurements of Social and Demographic Behavior From
Smartphone Location Data
ABSTRACT:
Human behavior, and in particular location behavior, is highly routine based.
Modern mobile phones, through global position system (GPS) technology and cell
tower and WiFi location identification, enable us to trace human location behavior
at scales that were previously unattainable. The goal of this paper is to examine
human location behavior, through mobile phone data, and investigate if links can
be made between location behavior patterns and particular demographic and social

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characteristics about an individual. We hypothesize that an individual's daily
predictability can be key to linking their behavior to certain characteristics, and we
propose predictability and geographic areas of interest models to analyze this
hypothesis. Experiments reveal that measurements, which are based on our
proposed location predictability models, can correctly infer 17 different
characteristics about an individual with an average accuracy of 85.5%.
5. On the Investigation of Cloud-Based Mobile Media Environments With
Service-Populating and QoS-Aware Mechanisms
ABTRACT:
Recent advances in mobile devices and network technologies have set new
trends in the way we use computers and access networks. Cloud Computing, where
processing and storage resources are residing on the network is one of these trends.
The other is Mobile Computing, where mobile devices such as smartphones and
tablets are believed to replace personal computers by combining network
connectivity, mobility, and software functionality. In the future, these devices are
expected to seamlessly switch between different network providers using vertical
handover mechanisms in order to maintain network connectivity at all times. This
will enable mobile devices to access Cloud Services without interruption as users
move around. Using current service delivery models, mobile devices moving from
one geographical location to another will keep accessing those services from the
local Cloud of their previous network, which might lead to moving a large volume
of data over the Internet backbone over long distances. This scenario highlights the
fact that user mobility will result in more congestion on the Internet. This will

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degrade the Quality of Service and by extension, the Quality of Experience offered
by the services in the Cloud and especially multimedia services that have very tight
temporal constraints in terms of bandwidth and jitter. We believe that a different
approach is required to manage resources more efficiently, while improving the
Quality of Service and Quality of Experience of mobile media services. This paper
introduces a novel concept of Cloud-Based Mobile Media Service Delivery in
which services run on localized public Clouds and are capable of populating other
public Clouds in different geographical locations depending on service demands
and network status. Using an analytical framework, this paper argues that as the
demand for specific services increases in a location, it might be more efficient to
move those services closer to that location. This will prevent th- Internet backbone
from experiencing high traffic loads due to multimedia streams and will offer
service providers an automated resource allocation and management mechanism
for their services.
6. Distributed Cooperative Caching in Social Wireless Networks
ABSTRACT:
This paper introduces cooperative caching policies for minimizing electronic
content provisioning cost in Social Wireless Networks (SWNET). SWNETs are
formed by mobile devices, such as data enabled phones, electronic book readers
etc., sharing common interests in electronic content, and physically gathering
together in public places. Electronic object caching in such SWNETs are shown to
be able to reduce the content provisioning cost which depends heavily on the
service and pricing dependences among various stakeholders including content

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providers (CP), network service providers, and End Consumers (EC). Drawing
motivation from Amazon's Kindle electronic book delivery business, this paper
develops practical network, service, and pricing models which are then used for
creating two object caching strategies for minimizing content provisioning costs in
networks with homogenous and heterogeneous object demands. The paper
constructs analytical and simulation models for analyzing the proposed caching
strategies in the presence of selfish users that deviate from network-wide cost-
optimal policies. It also reports results from an Android phone-based prototype
SWNET, validating the presented analytical and simulation results.
7. Validating the Impact on Reducing Fuel Consumption by Using an
EcoDriving Assistant Based on Traffic Sign Detection and Optimal
Deceleration Patterns
ABSTRACT:
This paper implements and validates an expert system that, based on the
detection or previous knowledge of certain types of traffic signals, proposes a
method to reduce fuel consumption by calculating optimal deceleration patterns,
minimizing the use of braking. The expert system uses a mobile device's embedded
camera to monitor the environment and to recognize certain types of static traffic
signals that force or can force a vehicle to stop. The system uses an adaptation of
the algorithm proposed by Viola and Jones for the recognition of faces in real time,
adapted to the detection of traffic signals. Detected signals are also incorporated
into a central database for future use. When the vehicle approaches an upcoming
traffic signal, the algorithm estimates the distance required to stop the vehicle

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without using the brakes, taking into account the rolling resistance coefficient and
the road slope angle. Appropriate advice and feedback are provided to the driver to
release the accelerator pedal. The expert system is implemented on Android mobile
devices and has been validated using a data set of 180 tests with five different
models of vehicles and nine different drivers. The main contribution of this paper
is the proposal of an assistant that uses information from the environment and from
the vehicle to calculate optimal deceleration patterns when approaching traffic
signals that force or may force the vehicle to stop. In addition, the proposed
solution does not require the installation of infrastructure on the road, and it can be
installed into any vehicle.
8. Adaptive Mobile Cloud Computing to Enable Rich Mobile Multimedia
Applications
ABSTRACT:
With worldwide shipments of smartphones (487.7 million) exceeding PCs
(414.6 million including tablets) in 2011, and in the US alone, more users
predicted to access the Internet from mobile devices than from PCs by 2015,
clearly there is a desire to be able to use mobile devices and networks like we use
PCs and wireline networks today. However, in spite of advances in the capabilities
of mobile devices, a gap will continue to exist, and may even widen, with the
requirements of rich multimedia applications. Mobile cloud computing can help
bridge this gap, providing mobile applications the capabilities of cloud servers and
storage together with the benefits of mobile devices and mobile connectivity,

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possibly enabling a new generation of truly ubiquitous multimedia applications on
mobile devices: Cloud Mobile Media (CMM) applications.
9. Predicting Human Movement Based on Telecom's Handoff in Mobile
Networks
ABSTRACT:
Investigating human movement behavior is important for studying issues
such as prediction of vehicle traffic and spread of contagious diseases. Since
mobile telecom network can efficiently monitor the movement of mobile users, the
telecom's mobility management is an ideal mechanism for studying human
movement issues. The problem can be abstracted as follows: What is the
probability that a person at location A will move to location B after T hours. The
answer cannot be directly obtained because commercial telecom networks do not
exactly trace the movement history of every mobile user. In this paper, we show
how to use the standard outputs (handover rates, call arrival rates, call holding
time, and call traffic) measured in a mobile telecom network to derive the answer
for this problem.
10.A Robust Indoor Pedestrian Tracking System with Sparse
Infrastructure Support
ABSTRACT:
Existing approaches to indoor tracking have various limitations. Location-
fingerprinting approaches are labor intensive and vulnerable to environmental
changes. Trilateration approaches require at least three line-of-sight beacons for

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coverage at any point in the service area, which results in heavy infrastructure cost.
Dead reckoning (DR) approaches rely on knowledge of the initial location and
suffer from tracking error accumulation. Despite this, we adopt DR for location
tracking because of the recent emergence of affordable hand-held devices equipped
with low-cost DR-enabling sensors. In this paper, we propose an indoor pedestrian
tracking system that comprises of a DR subsystem implemented on a mobile phone
and a ranging subsystem with a sparse infrastructure. A particle-filter-based fusion
scheme is applied to bound the accumulated tracking error by fusing DR with
sparse range measurements. Experimental results show that the proposed system is
able to track users much better than DR alone. The system is robust even when: 1)
the initial user location is not available; 2) range updates are noisy; and 3) range
updates are intermittent, both temporally and spatially.
11.Leveraging smartphone cameras for collaborative road advisories
ABSTRACT:
Ubiquitous smartphones are increasingly becoming the dominant platform
for collaborative sensing. Smartphones, with their ever richer set of sensors, are
being used to enable collaborative driver-assistance services like traffic advisory
and road condition monitoring. To enable such services, the smartphones' GPS,
accelerometer, and gyro sensors have been widely used. On the contrary,
smartphone cameras, despite being very powerful sensors, have largely been
neglected. In this paper, we introduce a collaborative sensing platform that exploits
the cameras of windshield-mounted smartphones. To demonstrate the potential of
this platform, we propose several services that it can support, and prototype

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SignalGuru, a novel service that leverages windshield-mounted smartphones and
their cameras to collaboratively detect and predict the schedule of traffic signals,
enabling Green Light Optimal Speed Advisory (GLOSA) and other novel
applications. Results from two deployments of SignalGuru, using iPhones in cars
in Cambridge (MA, USA) and Singapore, show that traffic signal schedules can be
predicted accurately. On average, SignalGuru comes within 0.66 s, for pretimed
traffic signals and within 2.45 s, for traffic-adaptive traffic signals. Feeding
SignalGuru's predicted traffic schedule to our GLOSA application, our vehicle fuel
consumption measurements show savings of 20.3 percent, on average.
12.An efficient approach for mobile asset tracking using contexts
ABSTRACT:
Due to the heterogeneity involved in smart interconnected devices, cellular
applications, and surrounding (GPS-aware) environments there is a need to
develop a realistic approach to track mobile assets. Current tracking systems are
costly and inefficient over wireless data transmission systems where cost is based
on the rate of data being sent. Our aim is to develop an efficient and improved
geographical asset tracking solution and conserve valuable mobile resources by
dynamically adapting the tracking scheme by means of context-aware personalized
route learning techniques. We intend to perform this tracking by proactively
monitoring the context information in a distributed, efficient, and scalable fashion.
Context profiles, which indicate the characteristics of a route based on
environmental conditions, are utilized to dynamically represent the values of the
asset's properties. We designed and implemented an adaptive learning based

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scheme that makes an optimized judgment of data transmission. This manuscript is
complemented with theoretical and practical evaluations that prove that significant
costs can be saved and operational efficiency can be achieved.
13.Enhancing privacy and accuracy in probe vehicle-based traffic
Monitoring via virtual trip lines
ABSTRACT:
Traffic monitoring using probe vehicles with GPS receivers promises
significant improvements in cost, coverage, and accuracy over dedicated
infrastructure systems. Current approaches, however, raise privacy concerns
because they require participants to reveal their positions to an external traffic
monitoring server. To address this challenge, we describe a system based on virtual
trip lines and an associated cloaking technique, followed by another system design
in which we relax the privacy requirements to maximize the accuracy of real-time
traffic estimation. We introduce virtual trip lines which are geographic markers
that indicate where vehicles should provide speed updates. These markers are
placed to avoid specific privacy sensitive locations. They also allow aggregating
and cloaking several location updates based on trip line identifiers, without
knowing the actual geographic locations of these trip lines. Thus, they facilitate the
design of a distributed architecture, in which no single entity has a complete
knowledge of probe identities and fine-grained location information. We have
implemented the system with GPS smartphone clients and conducted a controlled

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experiment with 100 phone-equipped drivers circling a highway segment, which
was later extended into a year-long public deployment.
14.Mobile data offloading through opportunistic communications and
Social participation
ABSTRACT:
3G networks are currently overloaded, due to the increasing popularity of
various applications for smartphones. Offloading mobile data traffic through
opportunistic communications is a promising solution to partially solve this
problem, because there is almost no monetary cost for it. We propose to exploit
opportunistic communications to facilitate information dissemination in the
emerging Mobile Social Networks (MoSoNets) and thus reduce the amount of
mobile data traffic. As a case study, we investigate the target-set selection problem
for information delivery. In particular, we study how to select the target set with
only k users, such that we can minimize the mobile data traffic over cellular
networks. We propose three algorithms, called Greedy, Heuristic, and Random, for
this problem and evaluate their performance through an extensive trace-driven
simulation study. Our simulation results verify the efficiency of these algorithms
for both synthetic and real-world mobility traces. For example, the Heuristic
algorithm can offload mobile data traffic by up to 73.66 percent for a real-world
mobility trace. Moreover, to investigate the feasibility of opportunistic
communications for mobile phones, we implement a proof-of-concept prototype,
called Opp-off, on Nokia N900 smartphones, which utilizes their Bluetooth
interface for device/service discovery and content transfer.

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15.Smartphone-based collaborative and autonomous radio Fingerprinting
ABSTRACT:
Although active research has recently been conducted on received signal
strength (RSS) fingerprint-based indoor localization, most of the current systems
hardly overcome the costly and time-consuming offline training phase. In this
paper, we propose an autonomous and collaborative RSS fingerprint collection and
localization system. Mobile users track their position with inertial sensors and
measure RSS from the surrounding access points. In this scenario, anonymous
mobile users automatically collect data in daily life without purposefully surveying
an entire building. The server progressively builds up a precise radio map as more
users interact with their fingerprint data. The time drift error of inertial sensors is
also compromised at run-time with the fingerprint-based localization, which runs
with the collective fingerprints being currently built by the server. The proposed
system has been implemented on a recent Android smartphone. The experiment
results show that reasonable location accuracy is obtained with automatic
fingerprinting in indoor environments.

Android Titles & abstracts 2013-2014

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Android Titles & abstracts 2013-2014