IAS: an IoT Architectural Self-adaptation Framework

International audienceThis paper develops a generic approach to model control loops and their interac- tion within the Internet of Things (IoT) environments. We take advantage of MAPE-K loops to enable architectural self-adaptation. The system’s architectural setting is aligned with the adaptation goals and the components run-time situation and constraints. We introduce an integrated framework for IoT Architectural Self-adaptation (IAS) where functional control elements are in charge of environmental adaptation and autonomic control elements handle the functional system’s architectural adaptation. A Queuing Networks (QN) approach was used for modeling the IAS. The IAS-QN can model control levels and their interaction to perform both architectural and environmental adaptations. The IAS-QN was modeled on a smart grid system for the Melle-Longchamp area (France). Our architectural adaptation approach successfully set the propositions to enhance the performance of the electricity trans- mission system. This industrial use-case is a part of CPS4EU European industrial innovation pro ject

Self-adaptive IoT Architectures

Along with the rapid growth of IoT technologies and devices, their solutions are currently being applied on various domains such as health-care, transportation and agriculture, but mainly on crowd monitoring and emergency handling. The latter is a safety critical IoT system based on collecting and analyzing the real-time data to perform proper actuation. In order to engineer such a high quality IoT application, a proper software architecture should be designed. In order for the software architecture to be able to optimize critical requirements such as fault-tolerance, performance and energy consumption, it ought to: 0 adapt itself to real-time environment transformation, ii) be designed in a proper level of elements distribution. In this paper, we critically analyze a set of IoT distribution and self-adaptation patterns to identify their suitable architectural combinations. Further, we use our IoT modeling framework (CAPS) to model an emergency handling system. Based on these, we design two quality driven architectures to be used for a forest monitoring and evacuation example and qualitatively evaluate and compare them

Real-time Emergency Response through Performant IoT Architectures

International audienc

ASSERT : A Blockchain-Based Architectural Approach for Engineering Secure Self-Adaptive IoT Systems

Internet of Things (IoT) systems are complex systems that can manage mission-critical, costly operations or the collection, storage, and processing of sensitive data. Therefore, security represents a primary concern that should be considered when engineering IoT systems. Additionally, several challenges need to be addressed, including the following ones. IoT systems’ environments are dynamic and uncertain. For instance, IoT devices can be mobile or might run out of batteries, so they can become suddenly unavailable. To cope with such environments, IoT systems can be engineered as goal-driven and self-adaptive systems. A goal-driven IoT system is composed of a dynamic set of IoT devices and services that temporarily connect and cooperate to achieve a specific goal. Several approaches have been proposed to engineer goal-driven and self-adaptive IoT systems. However, none of the existing approaches enable goal-driven IoT systems to automatically detect security threats and autonomously adapt to mitigate them. Toward bridging these gaps, this paper proposes a distributed architectural Approach for engineering goal-driven IoT Systems that can autonomously SElf-adapt to secuRity Threats in their environments (ASSERT). ASSERT exploits techniques and adopts notions, such as agents, federated learning, feedback loops, and blockchain, for maintaining the systems’ security and enhancing the trustworthiness of the adaptations they perform. The results of the experiments that we conducted to validate the approach’s feasibility show that it performs and scales well when detecting security threats, performing autonomous security adaptations to mitigate the threats and enabling systems’ constituents to learn about security threats in their environments collaboratively. © 2022 by the authors.open access</p