Semantics-driven Programming of Self-Adaptive Reactive-Systems

In recent years, new classes of highly dynamic, complex systems are gaining momentum. These classes include, but are not limited to IoT, smart cities, cyber-physical systems andsensor networks. These systems are characterized by the need to express behaviors drivenby external and/or internal changes, i.e. they are reactive and context-aware. A desirabledesign feature of these systems is the ability of adapting their behavior to environmentchanges. In this article, we propose an approach to support adaptive, reactive systemsbased on semantic runtime representations of their context, enabling the selection ofequivalent behaviors, i.e. behaviors that have the same effect on the environment. The context representation and the related knowledge are managed by an engine designed according to a reference architecture and programmable through the declarative definitionof sensors and actuators. The knowledge base of sensors and actuators (hosted by anRDF triplestore) is bound to the real world by grounding semantic elements to physicaldevices via REST APIs. The proposed architecture along with the defined ontology tryto address the main problems of dynamically re-configurable systems by exploiting adeclarative, queryable approach to enable runtime reconfiguration with the help of (a)semantics to support discovery in heterogeneous environment, (b) composition logic todefine alternative behaviors for variation points, (c) bi-causal connection life-cycle toavoid dangling links with the external environment. The proposal is validated in a casestudy aimed at designing an edge node for smart buildings dedicated to cultural heritagepreservation.