Advanced-Data Processing in IoT using MQTT and OPTICS with Spiking Neural Networks and Mist Computing for Real-Time Analytics
Year: 2025
Type: article
Abstract: 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.
Authors: Guman Singh Chauhan, Joseph Bamidele Awotunde
Institutions: Tesla (United States), University of Ilorin
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4407116464