An efficient prediction based data collection method for wireless sensor networks using hybrid fuzzy clustering and optimized deep maxout neural networks.

In the world of the Internet of Things (IoT), wireless sensor networks (WSNs) play a vital role in enabling seamless communication between connected devices. In a WSN data prediction system based, clustering and routing are crucial for efficiently collecting and transmitting sensor data, significantly improving energy efficiency by minimizing unnecessary transmissions, optimizing network lifetime, and enabling accurate data aggregation, which is critical for reliable prediction models due to the large volume of data generated by sensor nodes. A significant challenge in traditional WSN is their suboptimal energy management and data routing, which can lead to decreased network longevity. To address this issue, the proposed system utilizes fuzzy clustering for adaptive cluster formation, Red Piranha Optimization for efficient cluster head selection, and Leopard Seal Optimization for energy-efficient routing, along with deep maxout neural networks (DMNN) for precise data prediction, thereby significantly improving energy efficiency and enhancing network performance. Integrating fuzzy clustering, Red Piranha Optimization, Leopard Seal Optimization, and DMNN into a single system enhances both data management and predictive accuracy. Fuzzy clustering accommodates uncertainty and overlaps in data, leading to more precise and flexible cluster formation. Red Piranha Optimization refines the selection of cluster heads, optimizing energy usage and load distribution. Leopard Seal Optimization improves routing efficiency by finding optimal paths for data transmission, reducing delays and conserving energy. Finally, DMNN leverages advanced neural network capabilities to deliver accurate predictions, enhancing overall system performance and decision-making. Together, these techniques offer a robust solution for effective data handling, transmission, and forecasting.The performance findings for QoS parameters include transmission suppression (TS) of 98.02% and 98.92%, energy consumption of 3.9 J and 2.43 J, data quality of 0.025% and 0.035% for temperature and humidity respectively.The proposed approach achieves transmission overhead of 2.05%, positive prediction of 450.92 bytes, packet transmission of 1.25 bytes. Consequently, there is a notable effectiveness gain in comparison to the existing techniques are gated recurrent unit (GRU), hybrid convolutional neural network with bidirectional long short-term memory (CNN-Bi LSTM), energy efficient weighted clustering (EEWC), energy efficient clustering based on artificial fish swarm algorithm (EAFSCP), and multi-strategy fusion swarm optimization (MSSO). The versatility and appropriateness of the suggested DMNN enable it to find enhancements for a particular failure pattern in a shorter amount of computing period.