Context‐Based Logical Clustering of Flow‐Sensors ‐ Exploiting HyperFlow and Hierarchical DHTs
Rahim Rahmani, Hasibur Rahman, Theo Kanter · 2013
In the state-of-the-art sensor networks are becoming an integral part of ubiquitous computing. Context information is ubiquitous due to the deployment of sensors in Internet infrastructure and availability to services. This corresponds to the phenomena where any situation can be sensed and analyzed anywhere. Services can access heterogeneous context information anywhere through the distributed acquisition and dissemination of sensor data assembled from physical objects. A novel idea of clustering sensors based on context similarity is presented in this paper. The sensors are physically distributed but logically clustered based on similar context. This will enable resources (data, services) to be shared. The network is a two-tier hierarchical distributed hash tables (DHTs) system based on the HyperFlow platform. The approach provides topological sensor networks with scalability, robustness, mobility, heterogeneity support, adaptability to different contexts, etc. A performance study demonstrates feasibility and scalability, adaptability, heterogeneity, and robustness of the proposed approach.