The term "Mobile Web" is typically used to describe the situation
where users employ their mobile device (e.g., smart phone, PDA, portable
game console) and a dedicated mobile browser (e.g., Skyfire, Opera Mini)
to access the Web. Although the Mobile Web thus stands for making the
Web accessible anywhere and anytime, the limitations of mobile devices
(e.g., small screen, limited input capabilities, processing power and
bandwidth) still hinder the widespread mobile use of the Web. Although
some Web applications have versions that are specifically targeted towards
mobile devices (e.g. Google Mobile, Yahoo Mobile), most Websites do not
take the limitations of the client device into account. Furthermore, in
a mobile setting (e.g., driving, walking, sightseeing), users are often
unable or reluctant to spend large amounts of time browsing and locating
the information and services that they need at that particular moment and
time. For instance, when you are walking around in your free time and wish
to view information on a monument or find out what items a specific shop
sells, you do not want to constantly "google" for the necessary information.
Therefore, it is important that users can get the information or services
at the time and place that they need it, with minimal effort. Modern mobile
devices often offer the possibility to determine the user's current location
(e.g., using GPS coordinates), and by using wireless identification technologies
(e.g., RFID, NFC), a mobile device can detect entities and other (mobile)
users around him. By harnessing these capabilities, the user's environment can be
mapped in detail. Furthermore, information on these physical entities is already available
on the Web: for instance, in Web pages or in Semantic Web sources. If a reference to this
information can be obtained from the detected entity (e.g., by reading a URL from an RFID tag),
extensive knowledge on the userís environment can be obtained. Furthermore, by also including
some basic knowledge on the mobile user (i.e., his
background, preferences, etc.), it is possible to offer a completely
personalized mobile experience, where relevant information and services
are offered not only depending on the mobile user's current environment,
but also his particular interests and preferences.
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SCOUT is a mobile application development framework for the development of mobile applications
that are aware of the user's context (e.g., profile, device characteristics, ..), his current (physical) environment,
and the people, objects and places in it. By exploiting this knowledge on the user and his environment, such
applications are able to provide personalized information and services to the user. To realize this support,
SCOUT provides mobile applications with an integrated and extensive view on the user's environment, by acquiring and
integrating data from different, decentralized sources. SCOUT supports different sensing technologies to become aware
of the user's surrounding environment (and the physical entities in it), and is primarily based on Web technologies
for communication and data acquisition, and Semantic Web technologies for integrating and enriching the knowledge present
in the decentralized data sources. The SCOUT framework consists of a layered architecture: each layer (from the bottom up)
is shortly explained below.
Fig. 1. Scout architecture overview.
The Detection Layer is responsible for detecting identifiable physical entities in the vicinity of the user,
by employing sensing technologies which are increasingly available in modern mobile devices (e.g., RFID, Bluetooth,
QR code, Microsoft Tag). Existing online services such as LinkedGeoData can also be employed, which return references
to data associated with physical entities nearby the user (based on his current GPS position). As these detection techniques
implement a common interface, they may be used interchangeably or in parallel.
The Location Management Layer receives raw detection information from the Detection Layer, and determines when the
detected entities are (no longer) nearby the user or other detected entities. To determine proximity, this layer employs
so-called nearness and remoteness strategies (collectively called proximity strategies). These strategies exploit detection
information from the previous layer to determine the current proximity of detected entities. For instance, a straightforward
nearnes strategy may be used in case detection techniques with limited range are employed (e.g., RFID readers), where nearness
to the user is directly inferred when the entity is detected. For services such as LinkedGeoData, latitude and longitude are
compared with the userís (GPS) position. A possible remoteness strategy consists of comparing the userís
current position with the detected entityís (exact or inferred) position at set time intervals, to determine when the distance
exceeds a predefined nearness distance. This layer notifies the Environment Layer of nearness and remoteness events, passing along
references to the entities' associated online datasources and their (approximate) absolute locations.
The Environment Layer provides mobile applications with an integrated view on the user, his environment and the physical entities
in it, called the Environment Model. This layer maintains two data models that provide the information for this integrated view.
In the User Model, the userís personal information (e.g., characteristics, preferences) is stored using existing ontologies (e.g., CC/PP or FOAF).
This first model allows personalizing content and functionality to the user. Positional information on the userís environment, as obtained
from the previous layer, is encoded in the Proximity Model. It keeps time-stamped positional relations between the user and the physical
entities, together with references to the entitiesí associated online data sources. Such a positional relation represents the fact that
an entity is (or has been) nearby the user or another entity. This second model emphasizes the location-based nature of SCOUT, as it keeps
relative positional information in an abstract, high-level format. The Proximity Model Management component keeps the data in this model
up-to-date, based on nearness and remoteness notifications from the Location Management Layer.
Finally, the Environment Model encompasses the User and Proximity Model, and extends them with information obtained from the physical entitiesí
online semantic sources. Fig. 2 shows an abstract representation of a sample Environment Model, where the user node is linked (via positional relations from the Proximity Model)
to other physical entity nodes in the user's environment. For each positional relation, nearness and remoteness timestamps are also kept. In this example, detection techniques such as QR codes, Bluetooth and RFID are employed to detect
physical entities, and to obtain a reference to their online associated data.
Fig. 2. Sample Environment Model.
The Query Service and Notification Service provide applications with push- and pull-based access (respectively) to the data in the Environment Model. The Notification Service allows applications to register conditions (in the form of SPARQL queries), where the application
is notified whenever the user's context and environment satisfies the given condition.
The final layer is the Application Layer, consisting of the applications built on top of SCOUT. Applications
that are developed using the SCOUT framework can benefit from the functionality described in the previous layers.
Two examples of such applications are:
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For more information on the SCOUT framework, please contact William Van Woensel or Sven Casteleyn.
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