Affiliation:
1. Department of Electronics and Communication Engineering, National Institute of Technology Raipur, Raipur, India
Abstract
The abrupt changes in the sensor measurements indicating the occurrence of an event are the major factors in some monitoring applications of IoT networks. The prediction-based approach for data aggregation in wireless sensor networks plays a significant role in detecting such events. This paper introduces a prediction-based aggregation model for sensor selection named the Grey prediction model and the Kalman filter-based data aggregation model with rank-based mutual information (GMKFDA-MI) that has a dual synchronization mechanism for aggregating the data and selecting the nodes based on prediction and cumulative error thresholds. Furthermore, the nodes after deployment are clustered using K-medoids clustering along with the Salp swarm optimization algorithm to obtain an optimized aggregator position concerning the base station. An efficient clustering promises energy efficiency and better connectivity. The experiments are accomplished on real-time datasets of air pollution monitoring applications and the results for the proposed method are compared with other similar state-of-the-art techniques. The proposed method promises high prediction accuracy, low energy consumption and enhances the throughput of the network. The energy-saving is recorded to be more than 10 to 30% for the proposed model when compared with other similar approaches. Also, the proposed method achieves 97.8% accuracy as compared to other methods. The method proves its best working efficiency in the applications like event reporting, target detection, and event monitoring.
Subject
Artificial Intelligence,General Engineering,Statistics and Probability
Reference36 articles.
1. Approximate Data Aggregation in Sensor Equipped IoT Networks;Li;Tsinghua Science and Technology,2019
2. Learning-Based IoT Data Aggregation for Disaster Scenarios;Peng;IEEE Access.,2020
3. Al-Karaki I.N. , UI-Mustafa R. and Kamal A.E. , Data aggregation in wireless sensor networks-exact and approximate algorithms. In2004Workshop on High Performance Switching and Routing, 2004. HPSR (2004), 241–245. IEEE.
4. Data prediction model in wireless sensor networks based on bidirectional LSTM;Cheng;EURASIP Journal on Wireless Communications and Networking,2019
5. Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter;Wei;Computer Communications.,2011
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献