Advanced-Data Processing in IoT using MQTT and OPTICS with Spiking Neural Networks and Mist Computing for Real-Time Analytics - Chauhan, Guman Singh; Awotunde, Joseph Bamidele (Journal of Ubiquitous Computing and Communication Technologies, 2025)
The proposed combination of Message Queuing Telemetry Transport (MQTT), Ordering points to identify the clustering structure (OPTICS), Spiking Neural Networks (SNNs), and Mist computing improves real-time processing of IoT data by tackling issues with event-driven analytics, communication, and clustering. Effective clustering and anomaly detection in big, dynamic datasets are made possible by OPTICS, while MQTT guarantees effective, low-latency communication. Biological neuron-inspired SNNs offer energy-efficient real-time event detection, while Mist Computing decentralizes computing to lower latency and bandwidth consumption. 90% energy efficiency, 92% data throughput, 95% latency reduction, and 97% anomaly detection accuracy are among the notable performance gains the system makes. Smart cities, industrial IoT, and healthcare systems are just a few examples of the sophisticated IoT applications that benefit from this all-inclusive framework’s great scalability and efficiency. Through the integration of sophisticated communication protocols, clustering techniques, and real-time processing capabilities, it guarantees accurate, scalable, and energy-efficient answers to contemporary IoT problems.