Adaptation is crucial for the success of IoT systems since these are part of continuously changing environments. Changes may come from the different layers of an IOT system, either service, connectivity or physical layer. Most existing literature pays special attention to changes in the service layer leaving out the rest of the layers. We have presented eight challenges that may impact the three layers of IoT systems. These challenges have served us as a research agenda to foster IoT systems.
New code in projects can introduce violations that deviate the code implementation from the intended architecture. This process is known as architecture erosion. In this project, we study how to recover the implemented architecture, detect/fix violations when comparing it with the intended architecture. We aim at integrating our solution in the CI process.
IoT systems are subjected to changes in the dynamic environments they operate in. These changes (e.g. variations in the bandith consumption or new devices joining/leaving) may impact the Quality of Service (QoS) of the IoT system. A number of self-adaptation strategies for IoT architectures to better deal with these changes have been proposed in the literature. Nevertheless, they focus on isolated types of changes. We lack a comprehensive view of the trade-offs of each proposal and how they could be combined to cope with dynamic situations involving simultaneous types of events. The gist of this project is to develop an approach to identify different dynamic events and adapt the architecture to them. The adaptation mechanism may include software deployment patterns. This project is funded by the colombian government and its main case study is an IoT system for coal mines.
In the Industry 4.0 vision, Prognostics and Health Management (PHM) is expected to assist domain experts in the generation of maintenance decisions. PHM relies on the processing of data sensed from the manufacturing plant for inferring the future performance of production systems. The acquisition and management of data brings different challenges such as data integration, heterogeneity, search usability and volume. To be best of authors' knowledge, an information system for sharing maintenance data able to fulfill the aforementioned challenges is not available yet. XRepo is proposed within this paper and faces three of the identified challenges through selected functionality: i) Heterogeneity: stored data is complaint with a standard format that includes the information necessary for performing PHM analysis; ii) Integration: data is uploaded to the repository through files or web services; iii) Search usability: stored data can be filtered by criteria and downloaded. This work is meant to be an effort towards the generation of a common information system for PHM analysis.