Energy-Efficient Data Consistency based Sampling Rate Optimization and Aggregation Method for IoT.

Data consistency is a challenge for designing energy-efficient medium access control protocols used in IoT. The energy-efficient data consistency method makes the protocol suitable for low, medium, and high data rate applications. In this work, the idea of an energy-efficient data consistency protocol is proposed with data aggregation. The proposed protocol efficiently utilizes the data rate as well as saves energy with guaranteed consistency. The idea of an optimal sampling rate selection method is introduced for maintaining the data consistency of continuous and periodic monitoring nodes in an energy-efficient manner. In the starting phase, the nodes will be classified into the event and continuous monitoring nodes. The machine learning-based logistic classification method is used for the classification of nodes. The sampling rate of continuous monitoring nodes is optimized during the setup phase by using the Optimized sampling rate data aggregation algorithm. Furthermore, an energy-efficient time division multiple access (EETDMA) protocol is used for the continuous monitoring of IoT devices, and an energy-efficient bit map assisted (EEBMA) protocol is proposed for the event-driven nodes. The simulation results prove the superiority of the proposed protocol with respect to existing work.