Clustering the sensor nodes is one of the most popular and effective approaches for applications that must support hundreds or thousands of nodes. The conventional algorithms consider various parameters to evenly distribute the energy load. However, energy consumption problem of the cluster head still remains. In this paper, we propose a novel clustering approach that periodically elects cluster heads with assistant nodes. The assistant nodes substitute for each cluster head to transmit sensor readings to the base station. Performance evaluations show that our proposed clustering algorithm achieves about 10-40% better performance than the existing clustering algorithms in terms of lifetime.

In this paper, a novel cache conscious indexing technique based on space partitioning trees is proposed. Many researchers investigated efficient cache conscious indexing techniques which improve retrieval performance of in-memory database management system recently. However, most studies considered data partitioning and targeted fast information retrieval. Existing data partitioning-based index structures significantly degrade performance due to the redundant accesses of overlapped spaces. Specially, R-tree-based index structures suffer from the propagation of MBR (Minimum Bounding Rectangle) information by updating data frequently. In this paper, we propose an in-memory space partitioning index structure for optimal cache utilization. The proposed index structure is compared with the existing index structures in terms of update performance, insertion performance and cache-utilization rate in a variety of environments. The results demonstrate that the proposed index structure offers better performance than existing index structures.