Cognitive and Autonomic IoT System Design

Abstract

Currently, the Internet of Things (IoT) usage observes a drastic growth in several areas and participates in the rapid universe digitalization. Henceforward, the IoT systems next generation will be more difficult to develop and monitor. Gathering real-time data created by IoT triggers some novel opportunities for businesses to take at the right time more accurate and precise decisions. However, several challenges (such as IoT systems complexity and heterogeneous data management, and IoT system scalability) restrain the elaboration of IoT systems that are smart and impel business decision-making. This paper proposes to automatize IoT systems management using an autonomic computing approach. Nevertheless, autonomic computing is insufficient for smart IoT systems development. Actually, a smart IoT system implements cognitive abilities that allow its learning and decision-making in real-time. Therefore, this study proposes a model for designing smart IoT systems. It defines a set of cognitive design patterns that delineate the dynamiccooperation between management processes (MPs) (to handle the requirements evolvement and the system's environment unpredictability) and add cognitive capabilities to IoT systems (to generate new insights, perceive big data, and communicate with users). The study's primary goal is to support the developer in designing smart IoT systems that are flexible by choosing an appropriate pattern (or a set of patterns) to meet complex system requirements.