Abstract-In today's "smart era" there is a growing ecosystem of Internet of Things (IoT)-enabled devices, which exploit (wireless) Internet connectivity and use standard communication protocols to interact with each other and the environment. As various IoT components are becoming widely available in the marketplace, a key challenge from a feedback control viewpoint is the ability to seamlessly integrate new IoT components or modify existing configurations in feedback control settings without having to halt the operation of the system and redesign the overall feedback control scheme. This article exploits technologies from the semantic web domain, for the design of a novel Semanticallyenhanced IoT-enabled Intelligent Control Systems (SEMIoTICS) architecture. The proposed SEMIoTICS scheme incorporates a supervisor module able to facilitate the semantic modelling of IoT components and the subsequent online composition/reconfiguration of feedback control loops. We demonstrate the applicability of the SEMIoTICS architecture through illustrative scenarios from the Smart Buildings domain.

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This paper presents a novel architecture for the design of fault-detection schemes, aiming to automate the cognitive process performed by human experts when designing fault detection schemes for certain systems. The work starts with the identification of types of cyber-physical components participating in a fault-detection scheme. These are semantically characterized, adopting a model driven by previous efforts of the World Wide Web Consortium on the semantic composition of Web services. The semantic characterizations of the components are then exploited by a Cognitive Agent with semantic reasoning capabilities, to achieve the configuration of a fault-detection scheme, given a set of specifications and available components. The Cognitive Agent has access to a knowledge representation model and is able to interact with human operators and with the components to enrich its knowledge for making and enforcing decisions about the configuration. The applicability of the architecture and the reasoning steps are demonstrated through the configuration of a water contamination event-detection scheme with learning capabilities within a smart water distribution network.

The design of feedback control systems typically assumes a fixed number of sensors and actuators. However, in today's large-scale complex systems there is often a need to replace faulty sensors/actuators with new ones or to deploy additional sensors of possibly different characteristics compared to the old sensors. In such cases, it may be necessary to deactivate, redesign and reconfigure the feedback control system, which implies system interruptions and significant economic costs. This paper provides a first step towards a semantically enhanced feedback control architecture, where it is possible to automatically reconfigure the sensor/actuator components without having to redesign the feedback control law. The proposed architecture uses ontology-based semantic mediation. This paper focuses on the sensor case and utilizes a simple example to illustrate the approach. It is emphasized that the proposed architecture will be even more beneficial for achieving interoperability in the new paradigm of system of systems where systems may be added or removed and the expectation is that the overall system should continue to operate optimally.