A Cloud-based Approach for Context Information Provisioning

TL;DR

This paper introduces a cloud-based framework for context information provisioning that uses context brokers and a multi-attributes decision algorithm to efficiently match and scale context services according to user needs.

Contribution

It presents a novel cloud-centric architecture with a context broker and a multi-attributes decision algorithm for scalable, efficient context service selection.

Findings

The framework enables dynamic scaling of context providers based on demand.

The decision algorithm effectively ranks context services by QoS and QoC.

The approach improves resource utilization and context information delivery.

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.