In the state-of-the-art Flow sensor networks are becoming an integral part of ubiquitous computing. Context information is ubiquitous due to the deployment of flow sensors in Internet infrastructure and availability to services. This corresponds to the phenomena where any situation can be send and analyzed anywhere. Services can access heterogeneous context information anywhere through the distributed acquisition and dissemination of Flow sensor data assembled from physical objects. A novel idea of clustering Flow sensors based on context similarity it will be investigate in this project. 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. In This approach topological flow sensor networks with scalability, robustness, mobility, heterogeneity support, adaptability to different contexts will be investigate . A performance study via simulation of different scenarios to demonstrates feasibility and scalability, adaptability, heterogeneity, and robustness of the proposed approach.