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.