Context-aware Server Research Articles

CAR Approach for the Internet of Things

In this paper, we propose a novel context-aware routing (CAR) approach that uses the cloud as an extra level of data-request processing to improve the network performance in terms of data delivery. Data delivery in the Internet of Things depends heavily on numerous factors, such as the amount of data, end-to-end in-network delay, and setup time. The CAR approach is significantly improving the current request-response model, especially while the exchanged in-network data amount increases and data are sent from source to destination in a peer-to-peer fashion. What we are trying to show in this paper, in particular, is the benefits of having a central context-aware server (in the cloud) in improving the end-user experience. Hence, the proposed CAR approach is a typical candidate for data-intensive cloud-based applications. It considers source and destination requirements in terms of data size, delay, link capacity, and available applications on the operating devices as well. Extensive simulations are performed, and achieved results show the efficiency of our approach against other competitive approaches in terms of in-network delay and packet delivery ratio.

On Recipe Based Service Composition in Ubiquitous Smart Spaces

We present a recipe based service composition scheme in smart spaces. We discuss real time problems of highly synchronized and user customizable processes with the help of an example. On-the-fly Quality of Service (QoS) shows up as a major issue in such environment. Furthermore the QoS of a dynamically composed service depends upon the aggregate QoS of its developing components. The dynamic composition of a service is done through the recipe file which is composed by service provider, it is like the control extension that checks the latest contest of the system from Context Servers and then generates the parameters for required services. Service Composition Manager is the entity that reserves the services from service pool and generates self management services. We setup context servers for context exchange and synchronicity of various processes. The generated service further takes the context from individual devices present in the area of influence and the effective use of context information helps us divide the complex jobs functionality into many simpler jobs on the bases of free time slots available per device. Each device performs its role and reports back to the context aware server and waits for new instruction in the next time slot. The principle objective is the presentation of system requirements of high level of QoS, required in delegating human centric ubiquitous applications. In the end we demonstrate our scheme with the help of an example and discuss its feasibility with the help of our model.