• Sriharsha Gudi,

Industrial Internet of Things (IIoT) have witnessed tremendous growth with mass deployments in energy, logistics, and manufacturing industries. As a result, huge range of sensory data has been introduced for real-time processing to ensure cost-effectiveness, safety, and consistency in operations. Traditional batch processing methods are not effective to handle failures, anomalies and cyber threats that require a quick response. This study uses a systematic literature review to identify a complete mechanism on how to create low latency and scalable pipelines for real time data optimization of the detection of anomalies in sensors from the IIoT. In this study, too, the problems of sensor mix, high data rates, edge to cloud bandwidth limitations and architectural limitations are discussed. Tools used for data ingestion (MQTT, Apache Kafka, etc.), edge computing, modular pipeline, and real-time analytics, are also investigated. Special emphasis is given on anomaly detection models that can manage high-dimensional data, such as, statistical models for process control, autoencoders, and hybrid methods related to machine learning. This study investigates various methodologies of optimizing a data pipeline for fault tolerance, load balancing and real-time alerts in the lifecycle by systematically reviewing the literature available to date. In this study, several other studies were synthesized and presented smart manufacturing scenario to analyse use case and obtained very low latency and reduced downtime. In IIoT applications, such as quality assurance, security, and predictive maintenance, it is noted that real-time and scalable pipelines are key and achievable.

IIoT, data pipeline, machine learning, MQTT, Apache Kafka, cloud environment, edge environment

"Anomaly Detection in Industrial IoT Sensor Streams: A Systematic Review of Data Engineering Pipelines and Detection Mechanisms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 9, page no.a279-a293, September-2026, Available :http://www.jetir.org/papers/JETIR2609032.pdf

"Anomaly Detection in Industrial IoT Sensor Streams: A Systematic Review of Data Engineering Pipelines and Detection Mechanisms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 9, page no. ppa279-a293, September-2026, Available at : http://www.jetir.org/papers/JETIR2609032.pdf