Internet of Things Systems: Architectures and Qualities

Internet of Things (IoT) systems have several characteristics which make their design a challenging task, e.g. they are often large, data driven, distributed, dynamic, evolving, and heterogeneous. Moreover, different applications have different desired quality characteristics, e.g. performance, reliability, power consumption, interoperability, security, privacy, trust, and standards compliance which influence the design. Assessing IoT systems with respect to such qualities is challenging and requires clearly-defined and appropriate metrics. Since many IoT systems are very large, scalability is a key factor. Artificial Intelligence (AI) has been proposed as a key technology to enable the realization of IoT systems meeting the requirements related to such quality characteristics. The integration of AI into IoT systems is sometimes referred to as AIoT. To make scalable AIoT systems, key stakeholders have proposed utilizing distributed resources at the “edge” of the network, i.e. edge computing, and providing the required capabilities closer to the source of data. Edge computing, and in particular edge intelligence (AI at the edge), may help to address the challenges of current cloud solutions for hosting IoT applications by lowering latency, handling privacy issues, and reducing data communication. Moreover, distributed edge computing can be integrated with centralized processing in the cloud, resulting in hybrid edge-cloud architectures that may combine the strengths of both approaches. However, this adds more complexity when designing IoT systems. Moreover, AIoT entails that IoT systems will be able to analyze data and make proactive decisions with very limited or no involvement of humans. On the one hand, such systems can process data and make decisions faster and more accurate than those involving human analysts and/or decision-makers. On the other hand, human users need to be able to trust the analyses and the decisions made by the AI components of the system. Thus, how and when human users interact with the system becomes a key concern to support trust, e.g. through transparency, explainability, and so on.

This Topical Collection aims to address some of the above raised challenges about architectures for IoT systems, as well as appropriate qualities and metrics. We encourage both researchers and practitioners to contribute by submitting their most recent theoretical and practical investigations and findings.

Keywords:

1. Architectures for (Intelligent) IoT systems

2. Quality Characteristics of (Intelligent) IoT systems

3. Metrics for assessing (Intelligent) IoT systems

4. Edge-computing, Cloud-computing, Hybrid Edge-Cloud Architectures

5. Energy / Power Consumption and Sustainability in (Intelligent) IoT systems

6. Security and Privacy for (Intelligent) IoT systems

7. Trust and Scalability in (Intelligent) IoT systems

8. Interoperability and Standards Compliance within (Intelligent) IoT systems

9. AIoT and Explainability for IoT systems

10.Human Factors in (Intelligent) IoT systems