AdaptThing: modelo computacional para gerenciamento dinâmico e adaptativo de objetos da IoT utilizando histórico de contextos

Abstract

It's currently expected that any environment will become an Intelligent Environment, capable of responding to events, especially in unexpected situations. Intelligent environments are favored by technological evolution, which has enabled the emergence of the Internet of Things (IoT) paradigm, which allows connectivity between different systems and devices, whether physical or virtual, through the Internet. Both systems and devices must adapt to the needs of environments, responding dynamically to existing changes. This requires sensory elements, called sensory objects, used to collect information from the environment. In addition to collecting and obtaining data from heterogeneous sources, it is necessary to store the data in such a way as to constitute a historical basis for consultation and inference, considering the characteristics of the event, location, and moment in which it occurred, thus generating a history of contexts. Based on contextual history, coupled with new events, it is possible to infer the need to reconfigure the operational behavior of sensitive objects, even relocating mobile resources to less densely monitored areas, thus enabling more reliable and detailed data. Thus, this thesis proposes the AdaptThing computational model, which supports heterogeneous sensitive object networks, with the ability to dynamically adapt the operational behavior of the elements involved, to improve data resolution and detail. The computational model was implemented and evaluated in two application scenarios. One scenario was educational, where the system provided questions according to the average knowledge of the class where applied, reducing the number of questions of the same subject by 33.3%. The other implementation scenario was applied to a set of 14 professional climate stations, where one of the stations had its operation adapted based on contextual information, reducing its computational consumption by 67%. Thus, it is considered that the AdaptThing computational model can manage IoT sensitive objects, dynamically adapting operational operating behavior, allowing for more detailed information.