Milton Samadder

In today’s data-driven world, the convergence of emerging technologies and innovative approaches has revolutionized the landscape of data analytics, paving the way for transformative solutions in decentralized data sharing, real-time communication, and demand forecasting. This paper explores the dynamic synergy of Distributed Ledger Technologies, particularly IOTA Tangle and Directed Acyclic Graph, with the Message Queuing Telemetry Transport protocol and advanced data analytics techniques for predictive insights. The application of IOTA Tangle and DAG in decentralized data sharing is the subject of our investigation. These technologies provide safe, scalable, and impenetrable platforms that support cross-industry collaboration and data analytics. IOTA Tangle and DAG additionally offer tamper-resistant transactions, preserving data credibility and integrity while promoting peer-to-peer data sharing that boosts effectiveness and ownership. The second area of our investigation focuses on applying the MQTT protocol for real-time communication in cross-industry collaborative supply chains. MQTT enables quick decision-making based on real-time data thanks to features like reduced latency, asynchronous communication, and bidirectional capability. Additionally, the strong security characteristics of MQTT improve data integrity and confidentiality, encouraging cooperation and supply chain effectiveness. Our study includes a comparison of different time series models, showing how Deep Matrix Factorization can significantly improve demand forecasting and prediction in collaborative supply chains across different industries. In this paper, we examine the intriguing interaction between IOTA Tangle, DAG, MQTT, and advanced data analytics techniques, ushering in a period of unmatched insights and efficacy in data analytics across various industries.

The smart manufacturing industry has witnessed a rapid increase in data generation due to the integration of sensors, IoT devices, and other advanced technologies. With this huge amount of data, the need for efficient data processing methods becomes critical for identifying anomalies in real-time. With the rise of Industry 4.0 practices, digitally enabled manufacturing units are shifting their focus towards Smart Manufacturing paradigm for better productivity, throughput and increased business volume. Traditionally digital manufacturing units have considered different AI approaches like Neural Network, Statistical Methods, Deep Learning etc. to detect and predict anomalies in their production lines. But with the Smart Manufacturing ecosystem, a manufacturing unit must integrate manufacturing intelligence in real-time across entire production lines through sensor data of IOT devices. Hence the traditional anomaly detection systems fall short to respond well, under the changed scenario, where large volumes of unstructured and varied types of data are being generated at high velocity, to be processed at (soft) real time. The article reviews the current state-of-the-art in big data processing for anomaly detection in smart manufacturing. The review covers various aspects such as data collection, data processing, anomaly detection, and real-time monitoring. The current paper also proposes a novel stateful data streaming computational model for big data processing in smart manufacturing units which conceptually lays the foundation on top of which any discrete anomaly detection engine would be able to work. The proposed architecture has several benefits, including its ability to handle the large volume, velocity, and variety of data generated in smart manufacturing. The architecture can be applied to various smart manufacturing applications, including predictive maintenance, quality control, and supply chain optimization. It is expected that this proposed architecture will pave the way for the development of more efficient and effective smart manufacturing systems in the future.