IMPROVING DATA AGGREGATION EFFICIENCY USING MULTI-LAYER APPROACH IN IoT
A. Vanathi, N. Nagamalleswara Rao Dr. · Indian Journal of Computer Science and Engineering · 2021
Recently, tremendous growth and interest in the deployment of tiny sensors in the Internet of Things (IoT) for smart applications improves human lives.With the increasing need for energy-efficient mechanisms in IoT communication, the data aggregation technique for reducing data transmissions is considered a significant research problem.The basic idea in most of the aggregation mechanisms is to build the clustering or aggregation tree in an application layer over IoT, resulting in high complexity.To solve such a problem, the proposed MLDA designs an energy-aware aggregation layer that focuses on utilizing the network layer factors in data aggregation by providing transparency of accessing the topology structure from the network layer.Moreover, the proposed work also focuses on the design of the load-balanced topology structure in the network layer for efficient routing and also takes support from such network structure for energy-efficient data aggregation.The proposed Multi-Layer based Data Aggregation approach (MLDA) avoids the hotspot problem and inefficient data aggregation.The MLDA achieves such goals by improving the network layer protocol, RPL activities and designing the aggregation layer to eliminate redundant transmissions.By using an energy-efficient network structure, the impact of redundant data transmissions on network resources and data aggregation efficiency are eliminated.To support SUM, AVG, MAX, and MIN aggregation functions without redundant data transmissions, the Double Hash Bloom Filter (DHBF), observation scheme, and merge sort are used in the developed aggregation layer.Thus, the proposed MLDA improves the data aggregation efficiency in terms of both energy and accuracy.