World of Computer Science and Information Technology Journal (WCSIT)
ISSN: 2221-0741
Vol. 1, No. 3, 63-70, 2011
63
A Cloud-based Approach for Context Information
Provisioning
Elarbi Badidi
Faculty of Information Technology
United Arab Emirates University
Al-Ain, United Arab Emirates
ebadidi@uaeu.ac.ae
Larbi Esmahi
School for Computing & Information Systems
Athabasca University, University Drive
Athabasca, Alberta, Canada
larbie@athabascau.ca
Abstract— As a result of the phenomenal proliferation of modern mobile Internet-enabled devices and the widespread utilization of
wireless and cellular data networks, mobile users are increasingly requiring services tailored to their current context. High-level
context information is typically obtained from context services that aggregate raw context information sensed by various sensors
and mobile devices. Given the massive amount of sensed data, traditional context services are lacking the necessary resources to
store and process these data, as well as to disseminate high-level context information to a variety of potential context consumers.
In this paper, we propose a novel framework for context information provisioning, which relies on deploying context services on
the cloud and using context brokers to mediate between context consumers and context services using a publish/subscribe model.
Moreover, we describe a multi-attributes decision algorithm for the selection of potential context services that can fulfill context
consumers’ requests for context information. The algorithm calculates the score of each context service, per context information
type, based on the quality-of-service (QoS) and quality-of-context information (QoC) requirements expressed by the context
consumer.
One of the benefits of the approach is that context providers can scale up and down, in terms of cloud resources they use, depending
on current demand for context information. Besides, the selection algorithm allows ranking context services by matching their QoS
and QoC offers against the QoS and QoC requirements of the context consumer.
Keywords- mobile users; context-aware web services; context services; cloud services; quality-of-context; quality-of-service;
service selection.
I. INTRODUCTION
The proliferation of wireless and cellular networks over the
last few years has led to a remarkable rise in the number of
users who are using a variety of modern mobile Internet-
enabled devices --such as iPhones, iPads, and Android-based
smartphones-- to consume online services. Mobile users are
increasingly requiring services tailored to their context as they
are on the move. Therefore, enterprise services should be
context-aware to deal with the changing environment of the
user. Several definitions of the notion of context have been
provided in the literature. According to Dey [1], “Context is
any information that can be used to characterize the situation
of an entity. An entity is a person, place, or object that is
considered relevant to the interaction between a user and an
application, including the user and applications themselves.”
According to this definition, the amount of information that
can be categorized as context information is extremely wide.
Location, time, temperature, humidity, pressure, and mobile
user activity are the most widely used context indicators by
applications. Specialized services, that we call context services,
capture, store, analyze and aggregate data to provide high-level
context information to consumer application services as
needed. Context services and context consumers are often
physically distributed. Besides, it is likely that these context
sources provide the same context information but with different
QoC [2][3]. The QoC concept is explained in Section 3.
Context-awareness raises challenges like aggregation of
context information in a structured format, discovery, and
selection of appropriate context services for context delivery to
context consumers.
To cope with the issues of context delivery and context
service selection, we propose a novel framework for context
provisioning, which is relying on using components called
context brokers, and deploying context services on the cloud.
Context brokers mediate between context consumers and
context services using a publish/subscribe model. To the best of
our knowledge there was no previous work on deploying