| Personalized Driving Behavior Monitoring and Analysis for Emerging Hybrid Vehicles | | BIBA | Full-Text | 1-19 | |
| Kun Li; Man Lu; Fenglong Lu; Qin Lv; Li Shang; Dragan Maksimovic | |||
| Emerging electric-drive vehicles, such as hybrid electric vehicles (HEVs)
and plug-in HEVs (PHEVs), hold the potential for substantial reduction of fuel
consumption and greenhouse gas emissions. User driving behavior, which varies
from person to person, can significantly affect (P)HEV operation and the
corresponding energy and environmental impacts. Although some studies exist
that investigate vehicle performance under different driving behaviors, either
directed by vehicle manufacturers or via on-board diagnostic (OBD) devices,
they are typically vehicle-specific and require extra device/effort. Moreover,
there is no or very limited feedback to an individual driver regarding how
his/her personalized driving behavior affects (P)HEV performance.
This paper presents a personalized driving behavior monitoring and analysis system for emerging hybrid vehicles. Our design is fully automated and non-intrusive. We propose phone-based multi-modality sensing that captures precise driver-vehicle information through de-noise, calibration, synchronization, and disorientation compensation. We also provide quantitative driver-specific (P)HEV analysis through operation mode classification, energy use and fuel use modeling. The proposed system has been deployed and evaluated with real-world user studies. System evaluation demonstrates highly-accurate (0.88-0.996 correlation and low error) driving behavior sensing, mode classification, energy use and fuel use modeling. | |||
| Mimic Sensors: Battery-Shaped Sensor Node for Detecting Electrical Events of Handheld Devices | | BIBAK | Full-Text | 20-38 | |
| Takuya Maekawa; Yasue Kishino; Yutaka Yanagisawa; Yasushi Sakurai | |||
| In this paper we propose and implement a battery-shaped sensor node that can
monitor the use of an electrical device into which it is inserted by sensing
the electrical current passing through the device. We live surrounded by large
numbers of electrical devices and frequently use them in our daily lives, and
so we can estimate high-level daily activities by recognizing their use.
Therefore, many ubiquitous and wearable sensing studies have attempted to
recognize the use of electrical devices by attaching sensor nodes to the
devices directly or by attaching multiple sensors to a user. With our node, we
can easily monitor the use of an electrical device simply by inserting the node
into the battery case of the device. We also propose a method that
automatically identifies into which electrical device the sensor node is
inserted and recognizes electrical events related to the device by analyzing
the current sensor data. We evaluated our method by using sensor data obtained
from three real houses and achieved very high identification and recognition
accuracies. Keywords: Sensors; Electrical devices; Battery | |||
| Leveraging Children's Behavioral Distribution and Singularities in New Interactive Environments: Study in Kindergarten Field Trips | | BIBAK | Full-Text | 39-56 | |
| Inseok Hwang; Hyukjae Jang; Taiwoo Park; Aram Choi; Youngki Lee; Chanyou Hwang; Yanggui Choi; Lama Nachman; Junehwa Song | |||
| The behavior observations on young children in new, first-in-the-life
environments have significant implications. We can often uniquely observe a
child's unforeseen interaction with the environment and peer-children. It would
be not only a piece of discovery but a beginning of an open quest worth
exploring. Out-of-classroom activities like kindergarten's field trips are
perfect opportunities, but those are quite different from regular classroom
activities where the teachers' conventional observation methods are hardly
practical. This paper proposes a novel approach to extend the teachers'
awareness on the children's field trip behaviors by means of mobile and sensor
technology. We adopt the notion of behavioral distribution and singularities.
We estimate the children's representative behavioral state in a given context,
and study the effect of focusing on the behaviors which are unlikely in this
context. We discuss our 14-month collaborative study and various qualitative
benefits through multiple deployments on actual kindergarten field trips. Keywords: Behavior; distribution; singularity; children; kindergarten; field trip;
smartphone; sensor | |||
| Urban Traffic Modelling and Prediction Using Large Scale Taxi GPS Traces | | BIBA | Full-Text | 57-72 | |
| Pablo Samuel Castro; Daqing Zhang; Shijian Li | |||
| Monitoring, predicting and understanding traffic conditions in a city is an important problem for city planning and environmental monitoring. GPS-equipped taxis can be viewed as pervasive sensors and the large-scale digital traces produced allow us to have a unique view of the underlying dynamics of a city's road network. In this paper, we propose a method to construct a model of traffic density based on large scale taxi traces. This model can be used to predict future traffic conditions and estimate the effect of emissions on the city's air quality. We argue that considering traffic density on its own is insufficient for a deep understanding of the underlying traffic dynamics, and hence propose a novel method for automatically determining the capacity of each road segment. We evaluate our methods on a large scale database of taxi GPS logs and demonstrate their outstanding performance. | |||
| A Unified Framework for Modeling and Predicting Going-Out Behavior | | BIBAK | Full-Text | 73-90 | |
| Shoji Tominaga; Masamichi Shimosaka; Rui Fukui; Tomomasa Sato | |||
| Living in society, to go out is almost inevitable for healthy life. There is
increasing attention to it in many fields, including pervasive computing,
medical science, etc. There are various factors affecting the daily going-out
behavior such as the day of the week, the condition of one's health, and
weather. We assume that a person has one's own rhythm or patterns of going out
as a result of the factors. In this paper, we propose a non-parametric
clustering method to extract one's rhythm of the daily going-out behavior and a
prediction method of one's future presence using the extracted models. We
collect time histories of going out/coming home (6 subjects, total 827 days).
Experimental results show that our method copes with the complexity of patterns
and flexibly adapts to unknown observation. Keywords: Methodology; Activity recognition; Location representation | |||
| The Hidden Image of the City: Sensing Community Well-Being from Urban Mobility | | BIBAK | Full-Text | 91-98 | |
| Neal Lathia; Daniele Quercia; Jon Crowcroft | |||
| A key facet of urban design, planning, and monitoring is measuring
communities' well-being. Historically, researchers have established a link
between well-being and visibility of city neighbourhoods and have measured
visibility via quantitative studies with willing participants, a process that
is invariably manual and cumbersome. However, the influx of the world's
population into urban centres now calls for methods that can easily be
implemented, scaled, and analysed. We propose that one such method is offered
by pervasive technology: we test whether urban mobility -- as measured by
public transport fare collection sensors -- is a viable proxy for the
visibility of a city's communities. We validate this hypothesis by examining
the correlation between London urban flow of public transport and census-based
indices of the well-being of London's census areas. We find that not only are
the two correlated, but a number of insights into the flow between areas of
varying social standing can be uncovered with readily available transport data.
For example, we find that deprived areas tend to preferentially attract people
living in other deprived areas, suggesting a segregation effect. Keywords: Mobility; Urban Analysis; Sensors; Well-Being | |||
| Scalable Mining of Common Routes in Mobile Communication Network Traffic Data | | BIBAK | Full-Text | 99-106 | |
| Olof Görnerup | |||
| A probabilistic method for inferring common routes from mobile communication
network traffic data is presented. Besides providing mobility information,
valuable in a multitude of application areas, the method has the dual purpose
of enabling efficient coarse-graining as well as anonymisation by mapping
individual sequences onto common routes. The approach is to represent spatial
trajectories by Cell ID sequences that are grouped into routes using
locality-sensitive hashing and graph clustering. The method is demonstrated to
be scalable, and to accurately group sequences using an evaluation set of GPS
tagged data. Keywords: Cellular networks; mobility; data mining; location privacy | |||
| Accounting for Energy-Reliant Services within Everyday Life at Home | | BIBA | Full-Text | 107-124 | |
| Oliver Bates; Adrian K. Clear; Adrian Friday; Mike Hazas; Janine Morley | |||
| Researchers in pervasive and ubiquitous computing have produced much work on new sensing technologies for disaggregating domestic resource consumption, and on designs for energy-centric interventions at home. In a departure from this, we employ a service-oriented approach, where we account for not only the amount of resources that specific appliances draw upon, but also how the associated services may be characterised in the context of everyday life. We undertook a formative study in four student flats over a twenty-day period, collecting data using interviews with eleven participants and over two hundred in-home sensors. Following an in-depth description of observations and findings from our study, we argue that our approach provides a more inclusive range of understandings of resources and everyday life than has been shown from energy-centric approaches. | |||
| Smart Blueprints: Automatically Generated Maps of Homes and the Devices Within Them | | BIBA | Full-Text | 125-142 | |
| Jiakang Lu; Kamin Whitehouse | |||
| Off-the-shelf home automation technology is making it easier than ever for people to convert their own homes into "smart homes". However, manual configuration is tedious and error-prone. In this paper, we present a system that automatically generates a map of the home and the devices within it. It requires no specialized deployment tools, 3D scanners, or localization hardware, and infers the floor plan directly from the smart home sensors themselves, e.g. light and motion sensors. The system can be used to automatically configure home automation systems or to automatically produce an intuitive map-like interface for visualizing sensor data and interacting with controllers. We call this system Smart Blueprints because it is automatically customized to the unique configuration of each home. We evaluate this system by deploying in four different houses. Our results indicate that, for three out of the four home deployments, our system can automatically narrow the layout down to 2-4 candidates per home using only one week of collected data. | |||
| Hacking the Natural Habitat: An In-the-Wild Study of Smart Homes, Their Development, and the People Who Live in Them | | BIBAK | Full-Text | 143-160 | |
| Sarah Mennicken; Elaine M. Huang | |||
| Commercial home automation systems are becoming increasingly common,
affording the opportunity to study technology-augmented homes in real world
contexts. In order to understand how these technologies are being integrated
into homes and their effects on inhabitants, we conducted a qualitative study
involving smart home professionals who provide such technology, people
currently in the process of planning or building smart homes, and people
currently living in smart homes. We identified motivations for bringing smart
technology into homes, and the phases involved in making a home smart. We also
explored the varied roles of the smart home inhabitants that emerged during
these phases, and several of the challenges and benefits that arise while
living in a smart home. Based on these findings we propose open areas and new
directions for smart home research. Keywords: Home automation; smart homes; domestic technologies | |||
| The Design of a Segway AR-Tactile Navigation System | | BIBAK | Full-Text | 161-178 | |
| Ming Li; Lars Mahnkopf; Leif Kobbelt | |||
| A Segway is often used to transport a user across mid range distances in
urban environments. It has more degrees of freedom than car/bike and is faster
than pedestrian. However a navigation system designed for it has not been
researched. The existing navigation systems are adapted for car drivers or
pedestrians. Using such systems on the Segway can increase the driver's
cognitive workload and generate safety risks. In this paper, we present a
Segway AR-Tactile navigation system, in which we visualize the route through an
Augmented Reality interface displayed by a mobile phone. The turning
instructions are presented to the driver via vibro-tactile actuators attached
to the handlebar. Multiple vibro-tactile patterns provide navigation
instructions. We evaluate the system in real traffic and an artificial
environment. Our results show the AR interface reduces users' subjective
workload significantly. The vibro-tactile patterns can be perceived correctly
and greatly improve the driving performance. Keywords: Segway; Navigation; Augmented Reality; Vibro-Tactile; Feedback modalities;
Real Traffic; Evaluation | |||
| Route Guidance Modality for Elder Driver Navigation | | BIBAK | Full-Text | 179-196 | |
| Seungjun Kim; Jin-Hyuk Hong; Kevin A. Li; Jodi Forlizzi; Anind K. Dey | |||
| Differences in perceptual and cognitive abilities between the young and
elderly have implications for in-car tasks. As a primary example, although
in-car navigation systems enhance situational awareness, this comes at the cost
of increasing visual distraction and cognitive load. To address these
shortcomings, this paper explores the efficacy of multi-modal cues for
providing route guidance information. We present the results of a study
evaluating the impact of multi-modal feedback on driving performance and
cognitive load. We found that the full combination of visual, auditory, and
haptic feedback was generally most useful to reduce way-finding errors.
However, our study highlighted a number of differences between elder and
younger drivers for their safer navigation. Adding more modalities strained the
already high workload of elder drivers. In contrast, adding haptic feedback to
traditional audio and visual feedback led to more attentive driving by younger
drivers. Therefore, for elder drivers, navigation systems need to be
personalized to enhance the benefit of auditory feedback without increasing the
number of sensory feedbacks. For younger drivers, it is necessary to
incorporate new non-visual feedback to minimize distractions caused by visual
feedback. We demonstrate these results through task performance-based measures,
subjective workload measures and through objective workload measures that use
psychophysiological responses of participants to predict a driver's cognitive
load in near real-time. Keywords: Elderly driver; Car navigation; Cognitive load; Divided attention; Haptics;
Psycho-physiological measurement | |||
| Interactive Environment-Aware Handheld Projectors for Pervasive Computing Spaces | | BIBAK | Full-Text | 197-215 | |
| David Molyneaux; Shahram Izadi; David Kim; Otmar Hilliges; Steve Hodges; Xiang Cao; Alex Butler; Hans Gellersen | |||
| This paper presents two novel handheld projector systems for indoor
pervasive computing spaces. These projection-based devices are "aware" of their
environment in ways not demonstrated previously. They offer both spatial
awareness, where the system infers location and orientation of the device in 3D
space, and geometry awareness, where the system constructs the 3D structure of
the world around it, which can encompass the user as well as other physical
objects, such as furniture and walls. Previous work in this area has
predominantly focused on infrastructure-based spatial-aware handheld projection
and interaction. Our prototypes offer greater levels of environment awareness,
but achieve this using two opposing approaches; the first infrastructure-based
and the other infrastructure-less sensing. We highlight a series of
interactions including direct touch, as well as in-air gestures, which leverage
the shadow of the user for interaction. We describe the technical challenges in
realizing these novel systems; and compare them directly by quantifying their
location tracking and input sensing capabilities. Keywords: Handheld projection; geometry and spatial awareness; interaction | |||
| .NET Gadgeteer: A Platform for Custom Devices | | BIBA | Full-Text | 216-233 | |
| Nicolas Villar; James Scott; Steve Hodges; Kerry Hammil; Colin Miller | |||
| .NET Gadgeteer is a new platform conceived to make it easier to design and build custom electronic devices and systems for a range of ubiquitous and mobile computing scenarios. It consists of three main elements: solder-less modular electronic hardware; object-oriented managed software libraries accessed using a high-level programming language and established development environment; and 3D design and construction tools designed to facilitate a great deal of control over the form factor of the resulting electronic devices. Each of these elements is designed to be accessible to a wide range of people with varying backgrounds and levels of experience and at the same time provide enough flexibility to allow experts to build relatively sophisticated devices and complex systems in less time than they are used to. In this paper we describe the .NET Gadgeteer system in detail for the first time, explaining a number of key design decisions and reporting on its use by new users and experts alike. | |||
| Recognizing Handheld Electrical Device Usage with Hand-Worn Coil of Wire | | BIBAK | Full-Text | 234-252 | |
| Takuya Maekawa; Yasue Kishino; Yutaka Yanagisawa; Yasushi Sakurai | |||
| This paper describes the development of a new finger-ring shaped sensor
device with a coil of wire for recognizing the use of handheld electrical
devices such as digital cameras, cellphones, electric toothbrushes, and hair
dryers by sensing time-varying magnetic fields emitted by the devices.
Recently, sensing the usage of home electrical devices has emerged as a
promising area for activity recognition studies because we can estimate
high-level daily activities by recognizing the use of electrical devices that
exist ubiquitously in our daily lives. A feature of our approach is that we can
recognize the use of electrical devices that are not connected to the home
infrastructure without the need to equip them with sensors. We evaluated the
performance of our approach by using sensor data obtained from real houses. We
also investigated the portability of training data between different users. Keywords: Activity sensing; Electrical devices; Wearable sensors | |||
| Self-calibration of RFID Reader Probabilities in a Smart Real-Time Factory | | BIBAK | Full-Text | 253-270 | |
| Bilal Hameed; Farhan Rashid; Frank Dürr; Kurt Rothermel | |||
| RFID technology is now widely used to identify, locate, track and monitor
physical objects. However, the use of RFID technology in modern manufacturing
has been limited because of the unreliability of RFID devices. In addition to
this, where it is used, the technology is mostly deployed to be a substitute
for manual inventory management. In this paper we present the Smart Factory, a
modern factory infrastructure capable of monitoring each and every product part
that moves across the factory during the entire production process. In order to
overcome the reliability issues in RFID devices, we have built up a
probabilistic model to assign probabilities to the RFID readers and to the
product part detections. We also present a probability self-calibration
algorithm that automatically adapts the probabilities of RFID readers to better
reflect their performance at current instance of time. Keywords: RFID; Self-Configuration; Middleware; Smart Factory | |||
| AWESOM: Automatic Discrete Partitioning of Indoor Spaces for WiFi Fingerprinting | | BIBA | Full-Text | 271-288 | |
| Teemu Pulkkinen; Petteri Nurmi | |||
| WiFi fingerprinting is currently one of the most popular techniques for indoor localization as it provides reasonable positioning accuracy while at the same time being able to exploit existing wireless infrastructure. To facilitate calibration efforts and to overcome fluctuations in location measurements, many indoor WiFi positioning systems utilize a discrete partitioning, e.g., a grid or a topological map, of the space where the positioning is being deployed. A major limitation of this approach, however, is that instead of considering spatial similarities in the signal environment, the partitioning is typically based on an uniform division of the space or topological constraints (e.g., rooms and walls). This can significantly decrease positioning accuracy when the signal environment is not sufficiently stable across all partitions. Moreover, current solutions provide no support for identifying partitions that are not compatible with the current wireless deployment. To overcome these limitations, we propose AWESOM (Activations Weighted by the Euclidean-distance using Self-Organizing Maps), a novel measure for automatically creating a discrete partitioning of the space where the WiFi positioning is being deployed. In addition to enabling automatic construction of a discrete partitioning, AWESOM provides a measure for evaluating the goodness of a given partitioning for a particular access point deployment. AWESOM also enables identifying partitions where additional access points should be deployed. We demonstrate the usefulness of AWESOM using data collected from two large scale deployments of a proprietary wireless positioning system in a hypermarket environment. | |||
| Indoor Pedestrian Navigation Based on Hybrid Route Planning and Location Modeling | | BIBA | Full-Text | 289-306 | |
| Kari Rye Schougaard; Kaj Grønbæk; Tejs Scharling | |||
| This paper introduces methods and services called PerPosNav for development of custom indoor pedestrian navigation applications to be deployed on a variety of platforms. PerPosNav is built on top of the PerPos positioning middleware [8] that fusions GPS, WiFi and inertial tracking into indoor positioning with high accuracy in many types of buildings. The challenges of indoor navigation are discussed and the PerPosNav services are introduced. PerPosNav combines symbolic and geometry based modeling of buildings, and in turn combines graph-based and geometric route computation. The paper argues why these hybrid approaches are necessary to handle the challenges of indoor pedestrian navigation. Furthermore, a fluent navigation is maintained via route tracking and navigation services that generate instructions based on how the user moves in relation to the prescribed route. The viability of PerPosNav has been proven by implementation of support for multiple modes of pedestrian indoor navigation: 1) augmented signs, 2) map based navigation on smartphones, 3) auditory navigation on smartphones solely via earbuds, and 4) augmented reality navigation. Experiences from the use of the PerPosNav services are discussed and compared to other indoor pedestrian navigation approaches. | |||
| Estimating Position Relation between Two Pedestrians Using Mobile Phones | | BIBAK | Full-Text | 307-324 | |
| Daisuke Kamisaka; Takafumi Watanabe; Shigeki Muramatsu; Arei Kobayashi; Hiroyuki Yokoyama | |||
| In a complex indoor environment such as a huge station in an urban area,
sometimes the direction and distance relative to another person are more
important for pedestrians than their absolute positions, e.g. to search for a
lost child. We define this information as the position relation. Our goal is to
develop a position relation estimation method on a mobile phone with built-in
motion sensors. In literature, methods of cooperative navigation using two
pedestrians' positions estimated by pedestrian dead reckoning and a range
sensor have been proposed. However, these methods cannot be applied to a mobile
phone because pedestrian dead reckoning does not work well when a mobile phone
is in a bag, and because there is no range sensor in a phone. In fact, no
Bluetooth is reliable as a substitute range sensor. This paper proposes another
approach to estimate the position relation of pedestrians. Our method finds the
timing when two pedestrians are in close proximity to each other and walk
together by using Bluetooth as a proximity sensor and corrects the parameters
of position updates dynamically, even if absolute positions are unknown. The
algorithm and evaluation results are presented in this paper. Keywords: relative position; pedestrian dead reckoning; cooperative navigation; indoor
positioning; gait analysis; accelerometer; magnetometer; mobile phone | |||
| Clearing a Crowd: Context-Supported Neighbor Positioning for People-Centric Navigation | | BIBA | Full-Text | 325-342 | |
| Takamasa Higuchi; Hirozumi Yamaguchi; Teruo Higashino | |||
| This paper presents a positioning system for "people-centric" navigation, which estimates relative positions of surrounding people to help users to find a target person in a crowd of neighbors. Our system, called PCN, employs pedestrian dead reckoning (PDR) and proximity sensing with Bluetooth only using off-the-shelf mobile phones. Utilizing the feature of "group activity" where people naturally form groups moving similarly and together in exhibitions, parties and so on, PCN corrects deviation of distance and direction in PDR. The group information is also helpful to identify the surrounding people in the navigation. A field experiment in a real exhibition with 20 examinees carrying Google Android phones was conducted to show its effectiveness. | |||
| Paying in Kind for Crowdsourced Work in Developing Regions | | BIBAK | Full-Text | 343-360 | |
| Navkar Samdaria; Akhil Mathur; Ravin Balakrishnan | |||
| In developing regions, the reach of crowdsourcing services such as Amazon
Mechanical Turk (mTurk) has been limited by the lack of adequate payment
mechanisms and low visibility amongst the crowd. In this paper, we present a
commodity based model for crowdsourcing where crowd workers get paid in kind in
the form of a commodity instead of money. Our model makes crowdsourcing
services more visible to users in developing regions and also addresses the
issue of payment. We conducted two field studies in urban India to evaluate the
applicability of our proposed model. Our results show that the commodity based
crowdsourcing model reached workers with very different demographics from the
typical mTurk workers. We also found that users preferred to receive a
commodity instead of money as remuneration. Keywords: crowdsourcing; mobile phones; humans as pervasive computing resources;
commodity exchange model; developing regions; Amazon Mechanical Turk; India | |||
| Tangible and Casual NFC-Enabled Mobile Games | | BIBAK | Full-Text | 361-369 | |
| Luis F. G. Sarmenta | |||
| As Near-Field Communication (NFC) becomes a mainstream feature in today's
smartphones, it opens up a new range of applications and games. In this paper,
we introduce and explore the idea of tangible and casual NFC games. First, we
show how we can use NFC not just for its conventional use cases of information
lookup and exchange, but in novel interaction techniques that offer players the
fun of manipulating physical objects as part of the gameplay. Further, we show
how using the unique ID present in most NFC tags and cards enables us to create
games that can be downloaded and played by users "on impulse" -- anytime,
anywhere, without the need to distribute application-specific tags or install
any infrastructure. To demonstrate these techniques, we present several NFC
games we have implemented, including novel variations of popular traditional
games and toys. We also present results from a public beta trial showing that
users around the world are able to successfully play and enjoy our games using
transit cards, ID cards, and other cards they already have. Keywords: HCI; tangible user interfaces; RFID | |||
| Big Brother Knows Your Friends: On Privacy of Social Communities in Pervasive Networks | | BIBA | Full-Text | 370-387 | |
| Igor Bilogrevic; Murtuza Jadliwala; István Lám; Imad Aad; Philip Ginzboorg; Valtteri Niemi; Laurent Bindschaedler; Jean-Pierre Hubaux | |||
| Wireless network operators increasingly deploy WiFi hotspots and low-power, low-range base stations in order to satisfy users' growing demands for context-aware services and performance. In addition to providing better service, such capillary infrastructure deployment threatens users' privacy with respect to their social ties and communities, as it allows infrastructure owners to infer users' daily social encounters with increasing accuracy, much to the detriment of their privacy. Yet, to date, there are no evaluations of the privacy of communities in pervasive wireless networks. In this paper, we address the important issue of privacy in pervasive communities by experimentally evaluating the accuracy of an adversary-owned set of wireless sniffing stations in reconstructing the communities of mobile users. During a four-month trial, 80 participants carried mobile devices and were eavesdropped on by an adversarial wireless mesh network on a university campus. To the best of our knowledge, this is the first study that focuses on the privacy of communities in a deployed pervasive network and provides important empirical evidence on the accuracy and feasibility of community tracking in such networks. | |||
| Map-Aware Position Sharing for Location Privacy in Non-trusted Systems | | BIBAK | Full-Text | 388-405 | |
| Pavel Skvortsov; Frank Dürr; Kurt Rothermel | |||
| Many current location-based applications (LBA) such as friend finder
services use information about the positions of mobile users. So-called
location services (LSs) have been proposed to manage these mobile user
positions efficiently. However, managing user positions raises privacy issues,
in particular, if the providers of LSs are only partially trusted. Therefore,
we presented the concept of private position sharing for partially trusted
systems in a previous paper [1]. The basic idea of position sharing is to split
the precise user position into a set of position shares of well-defined limited
precision and distribute these shares among LSs of different providers.
The main contributions of this paper are two extended position sharing approaches that improve our previous approach in two ways: Firstly, we reduce the predictability of share generation that allows an attacker to gain further information from a sub-set of shares to further increase the position precision. Secondly, we present a position sharing algorithm for constrained movement scenarios whereas the existing approach was tailored to open space environments. However, open space approaches are vulnerable to map-based attacks. Therefore, we present a share generation algorithm that takes map knowledge into account. Keywords: Location-based service; privacy; obfuscation; sharing; map-awareness | |||
| Sense and Sensibility in a Pervasive World | | BIBA | Full-Text | 406-424 | |
| Christos Efstratiou; Ilias Leontiadis; Marco Picone; Kiran K. Rachuri; Cecilia Mascolo; Jon Crowcroft | |||
| The increasing popularity of location based social services such as Facebook Places, Foursquare and Google Latitude, solicits a new trend in fusing social networking with real-world sensing. The availability of a wide range of sensing technologies in our everyday environment presents an opportunity to further enrich social networking systems with fine-grained real-world sensing. However, the introduction of passive sensing into a social networking application disrupts the traditional, user-initiated input to social services, raising both privacy and acceptability concerns. In this work we present an empirical study of the introduction of a sensor-driven social sharing application within the working environment of a research institution. Our study is based on a real deployment of a system that involves location tracking, conversation monitoring, and interaction with physical objects. By utilizing surveys, interviews and experience sampling techniques, we report on our findings regarding privacy and user experience issues, and significant factors that can affect acceptability of such services by the users. Our results suggest that such systems deliver significant value in the form of self reflection and comparison with others, while privacy concerns are raised primarily by the limited control over the way individuals are projected to their peers. | |||
| From School Food to Skate Parks in a Few Clicks: Using Public Displays to Bootstrap Civic Engagement of the Young | | BIBAK | Full-Text | 425-442 | |
| Simo Hosio; Vassilis Kostakos; Hannu Kukka; Marko Jurmu; Jukka Riekki; Timo Ojala | |||
| We present Ubinion, a service that utilizes large public interactive
displays to enable young people to give personalized feedback on municipal
issues to local youth workers. It also facilitates discussion and sharing the
feedback online using modern social networking services. We present the
motivation and rationale behind Ubinion and analyze the results from three
large-scale user trials conducted in authentic settings. The evaluation shows
that young users are positive about adopting Ubinion, and that they quickly
appropriated its use to provide feedback outside the intended scope of the
system, but still reflecting their concerns. We argue that Ubinion's design as
a fun and informal tool is appropriate for its purpose, and discuss the
versatility of public interactive displays as a municipal feedback medium and
as content sources for online communities in general. Keywords: Social computing; urban computing; public spaces; public displays; social
networking; civic engagement; information interfaces | |||
| Increasing Brand Attractiveness and Sales through Social Media Comments on Public Displays -- Evidence from a Field Experiment in the Retail Industry | | BIBAK | Full-Text | 443-460 | |
| Erica Dubach Spiegler; Christian Hildebrand; Florian Michahelles | |||
| Retailers and brands are just starting to utilize online social media to
support their businesses. Simultaneously, public displays are becoming
ubiquitous in public places, raising the question about how these two
technologies could be used together to attract new and existing customers as
well as strengthen the relationship toward a focal brand. Accordingly, in a
field experiment we displayed brand- and product-related comments from the
social network Facebook as pervasive advertising in small-space retail stores,
known as kiosks. From interviews conducted with real customers during the
experiment and the corresponding analysis of sales data we could conclude three
findings. Showing social media comments resulted in (1) customers perceiving
brands as more innovative and attractive, (2) a measurable, positive effect on
sales on both the brand and the product in question and (3) customers wanting
to see the comments of others, but not their own, creating a give-and-take
paradox for using public displays to show social media comments. Keywords: Public Displays; Digital Signage; Pervasive Advertising; Social Media;
Social Networks; Field Experiment; Mixed Methods; Retail Industry | |||
| Automatic Description of Context-Altering Services through Observational Learning | | BIBA | Full-Text | 461-477 | |
| Katharina Rasch; Fei Li; Sanjin Sehic; Rassul Ayani; Schahram Dustdar | |||
| Understanding the effect of pervasive services on user context is critical to many context-aware applications. Detailed descriptions of context-altering services are necessary, and manually adapting them to the local environment is a tedious and error-prone process. We present a method for automatically providing service descriptions by observing and learning from the behavior of a service with respect to its environment. By applying machine learning techniques on the observed behavior, our algorithms produce high quality localized service descriptions. In a series of experiments we show that our approach, which can be easily plugged into existing architectures, facilitates context-awareness without the need for manually added service descriptions. | |||