Multifaceted infrastructure for self-adaptive IoT systems

Abstract Background: Internet of Things (IoT) enables the interaction among objects to provide services to their users. Areas such as eHealth, smart energy, and smart buildings have been benefiting from the IoT potential. However, the development of IoT systems is still complex because it deals with a highly dynamic, volatile, and heterogeneous environment. These characteristics require discovering devices, managing these devices’ context, and self-adapt their behavior. Goal : In this work, we propose a self-adaptive IoT infrastructure to support multiple facets, i.e., the contextual discovery of smart objects, the context management, and the self-adaptation process of the development of these systems. Methods : We evaluated the proposed infrastructure by developing a smart building application with and without it. The evaluation focused on four issues: the feasibility of integrating the context management through middleware platforms with adaptation based on workflows in a request/response communication model, the impact of our infrastructure on the development of self-adaptive IoT systems considering cyclomatic complexity and coupling code metrics; the impact of using contextual filters on the orchestrator of self-adaptation; and the impact on the quality of the self-adaptation. Results : The results suggest that: (i) it is feasible to use the proposed infrastructure in the development of self-adaptive IoT systems; (ii) there is a reduction in the cyclomatic complexity and the coupling with our approach, (iii) there is a considerable decrease in the number of rules evaluated at runtime, (iv) our infrastructure reduces the execution time of the adaptations when using contextual filters, and (v) the self-adaptation process was effective when using the orchestrator of self-adaptations. Conclusion : With these results, we observed that the proposed multifaceted infrastructure could reduce the complexity related to the development of IoT systems, in addition to optimizing their self-adaptation process.