Unleashing the Future of Smart Homes: A Revelation of Cutting-Edge Distributed Architecture

  • Joselito Jr UFBA
  • Luana Martins UFBA
  • Dhyego Tavares UFBA
  • Denivan Campos UFBA
  • Frederico Durão UFBA
  • Cássio Prazeres UFBA
  • Maycon Peixoto UFBA
  • Gustavo B. Figueiredo UFBA
  • Ivan Machado UFBA
  • Eduardo Almeida UFBA

Resumo


Recent discussions have delved extensively into Smart Homes, focusing on constructing and integrating services into an architecture capable of supporting the daily routine of the homes of several users and providing a stable operation. When artificial intelligence is added to a development architecture with devices that interface between man and machine, creating a system that operates in conjunction with users becomes a real challenge. How the system may or may not behave in the day-to-day lives of users becomes the genuine concern of developers. This brings challenges to be overcome and decisions to be made. Implementation decisions, division and creation of modules, communications between applications, user actions, communication interfaces, server response times, and other issues arise amidst all the complexity of developing for the real world. Observing the current scenario, we present our proposal for a distributed Smart Home architecture integrated with a third-party SAAS-based cloud service with an extensive catalog of smart devices used in a smart home environment. Our architecture obtains data about devices from users’ homes registered in a third-party cloud, and uses Artificial Intelligence to train a model using the user’s routine based on the behavior and use of devices in the house. The system provides recommendations sent directly to the smartphone or smartwatch to help with user comfort or to reduce energy consumption. This work presents the system architecture, technologies, and communications between services. Ultimately, we list the lessons learned in architectural design, solution coding, module integration, communication with smartphones and smartwatches, and working with intelligent physical devices in the user’s environment.

Palavras-chave: smart homes, software architecture, software design, machine learning applied, lessons learned

Referências

Kevin Bouchard, Bruno Bouchard, and Abdenour Bouzouane. 2012. Guidelines to efficient smart home design for rapid AI prototyping: a case study. In proceedings of the 5th international conference on pervasive technologies related to assistive environments. 1–8.

Padma Nyoman Crisnapati, I Nyoman KusumaWardana, and I Komang Agus Ady Aryanto. 2016. Rudas: Energy and sensor devices management system in home automation. In 2016 IEEE region 10 symposium (TENSYMP). IEEE, 184–187.

Xiao Guo, Zhenjiang Shen, Yajing Zhang, and Teng Wu. 2019. Review on the application of artificial intelligence in smart homes. Smart Cities 2, 3 (2019), 402–420.

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-termmemory. Neural computation 9, 8 (1997), 1735–1780.

J Jithish and Sriram Sankaran. 2017. A Hybrid Adaptive Rule based System for Smart Home Energy Prediction.. In DIAS/EDUDM@ ISEC.

Farzeem D Jivani, Manohar Malvankar, and Radha Shankarmani. 2018. A voice controlled smart home solution with a centralized management framework implemented using ai and nlp. In 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT). IEEE, 1–5.

Stephen Makonin, Lyn Bartram, and Fred Popowich. 2012. A smarter smart home: Case studies of ambient intelligence. IEEE pervasive computing 12, 1 (2012), 58–66.

Sunil Manandhar, Kevin Moran, Kaushal Kafle, Ruhao Tang, Denys Poshyvanyk, and Adwait Nadkarni. 2020. Towards a natural perspective of smart homes for practical security and safety analyses. In 2020 ieee symposium on security and privacy (sp). IEEE, 482–499.

Prianka Mandal, Sunil Manandhar, Kaushal Kafle, Kevin Moran, Denys Poshy- vanyk, and Adwait Nadkarni. 2023. Helion: Enabling Natural Testing of Smart Homes. In Proceedings of the 31st ACM Joint European Software Engineering Con- ference and Symposium on the Foundations of Software Engineering. 2147–2151.

Bauyrzhan Ospan, Nawaz Khan, Juan Augusto, Mario Quinde, and Kenzhegali Nurgaliyev. 2018. Context aware virtual assistant with case-based conflict resolution in multi-user smart home environment. In 2018 international conference on computing and network communications (coconet). IEEE, 36–44.

Debayan Paul, Tanmay Chakraborty, Soumya Kanti Datta, and Debolina Paul. 2018. IoT and machine learning based prediction of smart building indoor temperature. In 2018 4th International Conference on Computer and Information Sciences (ICCOINS). IEEE, 1–6.

Samad Sepasgozar, Reyhaneh Karimi, Leila Farahzadi, Farimah Moezzi, Sara Shirowzhan, Sanee M. Ebrahimzadeh, Felix Hui, and Lu Aye. 2020. A systematic content review of artificial intelligence and the internet of things applications in smart home. Applied Sciences 10, 9 (2020), 3074.

Amit Kumar Sikder, Leonardo Babun, and A Selcuk Uluagac. 2021. Aegis+ a context-aware platform-independent security framework for smart home systems. Digital Threats: Research and Practice 2, 1 (2021), 1–33.

Huaizhou Su, Yande Li, and Li Liu. 2018. Gesture recognition based on accelerometer and gyroscope and its application in medical and smart homes. InWeb and Big Data: APWeb-WAIM 2018 International Workshops: MWDA, BAH, KGMA, DM-MOOC, DS, Macau, China, July 23–25, 2018, Revised Selected Papers 2. Springer, 90–100.
Publicado
30/09/2024
JR, Joselito et al. Unleashing the Future of Smart Homes: A Revelation of Cutting-Edge Distributed Architecture. In: SIMPÓSIO BRASILEIRO DE COMPONENTES, ARQUITETURAS E REUTILIZAÇÃO DE SOFTWARE (SBCARS), 18. , 2024, Curitiba/PR. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2024 . p. 41-50. DOI: https://doi.org/10.5753/sbcars.2024.3854.