Herminio Paucar
ABSTRACT
This paper proposes a software architecture model applied for context-aware monitoring of Smart Buildings(SB), and it works in two layers: Fog and Edge computing and uses the Multi-Agent System(MAS) and Federated Learning(FL). The objective of this architecture will allow context-aware monitoring and the capture of anomalies and offer specific functionalities that depend on the user roles. As a result, we modeled the complete software architecture considering Reactive, edge-fog computing, and MAS components, following the best practices from the literature and the 2413-IEEE standard.
- Matthias Baldauf, Schahram Dustdar, and Florian Rosenberg. 2007. A survey on context-aware systems. International Journal of Ad Hoc and Ubiquitous Computing 2 (2007), 263–277. Issue 4. https://doi.org/10.1504/IJAHUC.2007.014070Google Scholar
Digital Library
- Hemant Ghayvat, Subhas Mukhopadhyay, Xiang Gui, and Nagender Suryadevara. 2015. WSN- and IOT-Based Smart Homes and Their Extension to Smart Buildings. Sensors 15, 5 (2015), 10350–10379. https://doi.org/10.3390/s150510350Google ScholarCross Ref
- IEEE. 2020. IEEE Standard for an Architectural Framework for the Internet of Things (IoT). IEEE Std 2413-2019 (2020), 1–269. https://doi.org/10.1109/IEEESTD.2020.9032420Google ScholarCross Ref
- Cemal Keles, Abdulkerim Karabiber, Murat Akcin, Asim Kaygusuz, Baris Baykant Alagoz, and Ozan Gul. 2015. A smart building power management concept Smart socket applications with DC distribution. International Journal of Electrical Power and Energy Systems 64 (2015), 679–688. https://doi.org/10.1016/j.ijepes.2014.07.075Google ScholarCross Ref
- Koksal and B. Tekinerdogan. 2019. Architecture design approach for IoT-based farm management information systems. Precision Agriculture 20, 5 (2019), 926–958. https://doi.org/10.1007/s11119-018-09624-8Google ScholarCross Ref
- Song Liu, Shiyuan Yang, Hanze Zhang, and Weiguo Wu. 2023. A Federated Learning and Deep Reinforcement Learning-Based Method with Two Types of Agents for Computation Offload. Sensors 23, 4 (2023). https://doi.org/10.3390/s23042243Google ScholarCross Ref
- B. Schilit, N. Adams, and R. Want. 1994. Context-Aware Computing Applications. In 1994 First Workshop on Mobile Computing Systems and Applications. 85–90. https://doi.org/10.1109/WMCSA.1994.16Google ScholarDigital Library
- I. Sommerville. 2015. Software Engineering. Pearson Education. https://books.google.com.br/books?id=x_qZCgAAQBAJGoogle Scholar
Index Terms
- Context-aware monitoring for IoT: an approach based on Agents, and Federated Learning
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