Real-Time Energy Data Acquisition, Anomaly Detection, and Monitoring System: Implementation of a Secured, Robust, and Integrated Global IIoT Infrastructure with Edge and Cloud AI

Abstract

The industrial internet of things (IIoT), a leading technology to digitize industrial sectors and applications, requires the integration of edge and cloud computing, cyber security, and artificial intelligence to enhance its efficiency, reliability, and sustainability. However, the collection of heterogeneous data from individual sensors as well as monitoring and managing large databases with sufficient security has become a concerning issue for the IIoT framework. The development of a smart and integrated IIoT infrastructure can be a possible solution that can efficiently handle the aforementioned issues. This paper proposes an AI-integrated, secured IIoT infrastructure incorporating heterogeneous data collection and storing capability, global inter-communication, and a real-time anomaly detection model. To this end, smart data acquisition devices are designed and developed through which energy data are transferred to the edge IIoT servers. Hash encoding credentials and transport layer security protocol are applied to the servers. Furthermore, these servers can exchange data through a secured message queuing telemetry transport protocol. Edge and cloud databases are exploited to handle big data. For detecting the anomalies of individual electrical appliances in real-time, an algorithm based on a group of isolation forest models is developed and implemented on edge and cloud servers as well. In addition, remote-accessible online dashboards are implemented, enabling users to monitor the system. Overall, this study covers hardware design; the development of open-source IIoT servers and databases; the implementation of an interconnected global networking system; the deployment of edge and cloud artificial intelligence; and the development of real-time monitoring dashboards. Necessary performance results are measured, and they demonstrate elaborately investigating the feasibility of the proposed IIoT framework at the end.

A Compact IIoT System for Remote Monitoring and Control of a Micro Hydropower Plant

“…The development of an IIoT data concentrator to transfer the measurements from simple devices to free web technologies for analysis is another approach explored in [ 31 ]. Complex studies of extended solutions for real-time anomaly detection using IIoT technology, cloud computing, and edge AI, such as those presented in [ 32 ], have been proposed using the same model of application, in which IIoT servers collect data from smart devices, which are exchanged through secured protocols through edge and cloud databases. The compact solution for micro hydropower plant management allows a different design in which the data concentrator provides various means of serially or directly gathering the data, assuring overall information coding according to the establish data protocol.…”

Cybersecurity in Cyber–Physical Power Systems

“…Hardware design, server, and database creation in open source and computer simulation [123] Adaptive method and multicriteria optimization Cyberattacks and network traffic anomaly detection. Creating an adaptive system to manage and monitor information security.…”

“…In the industrial sector, the use of the Industrial Internet of Things technology has seen continuous growth encompassing artificial intelligence (AI), computing, and cybersecurity. In this scenario, Reference [123] proposes an approach for data acquisition, fault identification, management, and real-time monitoring of energy data based on AI algorithms.…”

Anomaly Detection in a Smart Industrial Machinery Plant Using IoT and Machine Learning

“…This research gap highlights the critical need for this case study. The smart manufacturing plant addressed here represents a highly specialized and challenging scenario where safety and efficiency are paramount [19]. Previous works on IoT security and anomaly detection laid the theoretical and methodological foundations.…”