IoT operational intelligence

IoT, sensors, networks, data communications, microcontrollers, and microprocessors are frequently used terms in successfully functioning processes. However, terms like “process optimisation”, “intelligent devices”, and “Inference Models” using Machine Learning (ML) are new and related to the functional integrity of systems and may need context, so they add value to our understanding of the domains they perform in. This chapter explains sensors or sensor clusters, ML & the relevance of inference. We explore the intelligent edge using microcontroller devices enhanced by “inference models”, using machine learning placed next to where data is generated/collected. We explore enhanced microcontroller technology with inference models that analyse data at the edge, generating “edge intelligence” and improving outcomes by sending analysed, verified results data over trusted (IoT) wired or wireless networks to meet the needs of real-time domains. We establish that systems work efficiently and effectively with credible feedback from sensors. By customising the training of intelligent devices, we show how “edge” devices generate “actionable intelligence” at the edge to optimise workloads and reduce the repetitive work humans need to do manually. With the “invention” of Large Language Models (LLMs), knowledge is placed at our fingertips. Both humans and machines now have access to knowledge using published APIs. We explore the benefits of emerging technologies in solving operational problems in real-time domains such as health and emergencies (fire).