Internet of Things (IoT) technologies are already playing an important role in our daily activities as we use them and rely on them to increase our abilities, connectivity, productivity and quality of life. However, there are still obstacles to achieving a unique interface able to transfer full control to users given the diversity of protocols, properties and specifications in the varied IoT ecosystem. Particularly for the case of home automation systems, there is a high degree of fragmentation that limits interoperability, increasing the complexity and costs of developments and holding back their real potential of positively impacting users. In this article, we propose implementing W3C’s Web of Things Standard supported by home automation ontologies, such as SAREF and UniversAAL, to deploy the Living Lab Gateway that allows users to consume all IoT devices from a smart home, including those physically wired and using KNX^® technology. This work, developed under the framework of the EC funded Plan4Act project, includes relevant features such as security, authentication and authorization provision, dynamic configuration and injection of devices, and devices abstraction and mapping into ontologies. Its deployment is explained in two scenarios to show the achieved technology’s degree of integration, the code simplicity for developers and the system’s scalability: one consisted of external hardware interfacing with the smart home, and the other of the injection of a new sensing device. A test was executed providing metrics that indicate that the Living Lab Gateway is competitive in terms of response performance.

This paper describes a framework for web-based monitoring, orchestration and simulation for industrial settings such as highly automated factories and warehouses. HTML5 is used for a 2.5D visualization with local prediction for smooth animation and 3D models for robot arms. The web server aggregates data from the devices for streaming to the web page, for generating situation reports, and enabling users to intervene as needed. Simulation is possible using simple looping sequences of actions. A cognitive architecture is described for richer behaviour that dynamically adapts to the context, decoupling reasoning from real-time control using asynchronous intents. Stochastic rules allow agents to escape from futile patterns of behaviour. Agents can control multiple devices. For a larger numbers of devices, control can be distributed across multiple agents in a way that decouples applications from the underlying protocols and addressing schemes. Iterative refinement is applied as new requirements come to light, e.g. to handle faults, or where humans have intervened in unexpected ways. The paper closes with a short summary of previous work and suggestions for future work on integrating generative AI to further simplify development and provide supervisory control.