Semantically Enhanced Online Configuration of Feedback Control Schemes

Abstract

Abstract-Recent progress towards the realization of the "Internet of Things" has improved the ability of physical and soft/cyber entities to operate effectively within large-scale, heterogeneous systems. It is important that such capacity be accompanied by feedback control capabilities sufficient to ensure that the overall systems behave according to their specifications and meet their functional objectives. To achieve this, such systems require new architectures that facilitate the online deployment, composition, interoperability and scalability of control system components. Most current control systems lack scalability and interoperability because their design is based on a fixed configuration of specific components, with knowledge of their individual characteristics only implicitly passed through the design. This work addresses the need for flexibility when replacing components or installing new components, which might occur when an existing component is upgraded or when a new application requires a new component, without the need to readjust or redesign the overall system. A semantically-enhanced feedback control architecture is introduced for a class of systems, aimed at accommodating new components into a closed-loop control framework by exploiting the semantic inference capabilities of an ontology-based knowledge model. This architecture supports continuous operation of the control system, a crucial property for large-scale systems for which interruptions have negative impact on key performance metrics that may include human comfort and welfare or economy costs. A case-study example from the smart buildings domain is used to illustrate the proposed architecture and semantic inference mechanisms.Index Terms-cyber-physical systems, feedback control, internet of things, semantic composition, semantic knowledge models. W E currently live in the "smart era" where people and machines intelligently interact in work and social ecosystems [1]. This interaction is facilitated by a variety of smart machines, from small personal devices to hardware and software-equipped smart buildings [2], [3], to even larger and more complex systems such as electric power grids [4]. The high penetration of smart portable and embedded devices connected to networks, suggest a future where machines will interact with each other and the environment, in a contextaware framework [5]. Thus, new sensing, actuation and control capabilities can be created.In this context, consider the case of a smart building, equipped with various sensors, actuators and controllers, designed to maintain the comfort and safety of the inhabitants, while reducing operating costs. A smart building may consist of various sub-systems that control the lighting, the temperature and the humidity, the condition of the air, fire alarm systems, sprinkler systems and many more. In most cases, the design of these systems is based on a fixed configuration of specific components, with certain knowledge of their specific characteristics. For example, specific temperature se...

SEMIoTICS: Semantically Enhanced IoT-Enabled Intelligent Control Systems

“…All operations of Σ are built on top of the "Knowledge Model" G, which is a supergraph of many bipartite graphs formed by semantic relations between modelled knowledge objects of pre-defined types [6]. To model the knowledge we use declarative language, i.e.…”

“…For instance, the semantic annotation of a sensor's output {window 1, temperature, "state", Celsius}, can be transformed to {room 1, temperature, "State", Celsius} provided that the linguistic terms "window 1" and "room 1" are linked through the relation "within", defined using the concepts of the GeoSPARQL presented earlier. As defined in our previous work [6], these rules comprise composite relations as compositions of "relation graphs"; the edges of these graphs are not explicitly defined but they are implemented as paths of length > 2. The relations and the semantic rules are exploited by the supervisor Σ in the reasoning process, to evaluate whether certain semantic transformations can be applied for enabling the semantic matching.…”

“…In previous work [6], the authors addressed some aspects of the above challenges by developing an initial design of a novel architecture that incorporates a supervisor module with logical inference capabilities. The supervisor uses declarative language (ontologies) to model the expert knowledge about individual components in a consistent way, so as to be exploitable by machines.…”

“…As mentioned in Section I In a recent work [6] we advance the above presented state-of-the-art, by entering into the details of the self-adaptation and reconfiguration need for the feedback control system and designing the basics of the semantic modelling and reasoning mechanisms. In that work, we propose the modelling of expert knowledge in a supergraph of many bipartite graphs formed by meaningful relations between modelled knowledge entities of pre-defined types [34], [35].…”