Abstract
A cognitive environment (CE) is a smart environment having self-learning and self-adaptation capabilities. It is obtained by augmenting a physical environment by using IT equipment and artificial intelligence technologies. The goal is to furnish the environment’s dwellers and the environment itself with advanced services devoted to (i) improving the quality of life of the people, (ii) optimizing the use of shared resources and spaces, (iii) increasing security and safety, (iv) assisting people in daily life activities, (v) extending the lifetime of devices and infrastructures, and (vi) enforcing and actuating policies promoting sustainability, green-aware behaviors, and energy-saving management of the whole system. Realizing a CE is a complex and multidisciplinary task. It requires transversal skills among which those related to distributed systems, IoT and artificial intelligence technologies, and networking. In addition, suitable methodological approaches and platforms could be leveraged in order to deal with the complex process of designing and implementing a CE. In this chapter, as enabling technology, the COGITO platform is introduced. COGITO is an agent-based IoT platform tailored to the development of CEs in a heterogeneous continuum computing environment comprising cloud, fog, and edge resources. The practical use of the platform is demonstrated through some use cases developed at the ICAR-CNR headquarter at Rende (Italy).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
COGITO project—A COGnItive dynamic sysTem to allOw buildings to learn and adapt—https://www.icar.cnr.it/en/progetti/cogito-sistema-dinamico-e-cognitivo-per-consentire-agli-edifici-di-apprendere-ed-adattarsi/.
- 2.
Raspberry Pi 4 website. https://www.raspberrypi.com/products/raspberry-pi-4-model-b/.
- 3.
Libelium Waspmote. https://www.libelium.com/iot-products/waspmote/.
- 4.
Intel Neural Compute Stick 2 (Intel NCS2). https://www.intel.com/content/www/us/en/developer/tools/neural-compute-stick/overview.html.
References
Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)
Cicirelli, F., Guerrieri, A., Spezzano, G., Vinci, A.: An edge-based platform for dynamic smart city applications. Future Gener. Comput. Syst. 76, 106–118 (2017). https://doi.org/10.1016/j.future.2017.05.034. https://www.sciencedirect.com/science/article/pii/S0167739X16308342
Cicirelli, F., Guerrieri, A., Mercuri, A., Spezzano, G., Vinci, A.: Itema: A methodological approach for cognitive edge computing iot ecosystems. Future Gener. Comput. Syst. 92, 189–197 (2019). https://doi.org/10.1016/j.future.2018.10.003. http://www.sciencedirect.com/science/article/pii/S0167739X17330224
Cicirelli, F., Guerrieri, A., Spezzano, G., Vinci, A.: A cognitive enabled, edge-computing architecture for future generation iot environments. In: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), pp. 35–40. IEEE, Piscataway (2019)
Cicirelli, F., Guerrieri, A., Mastroianni, C., Spezzano, G., Vinci, A.: Thermal comfort management leveraging deep reinforcement learning and human-in-the-loop. In: 2020 IEEE International Conference on Human-Machine Systems (ICHMS), pp. 1–6 (2020). https://doi.org/10.1109/ICHMS49158.2020.9209555
Cicirelli, F., Guerrieri, A., Mastroianni, C., Scarcello, L., Spezzano, G., Vinci, A.: Balancing energy consumption and thermal comfort with deep reinforcement learning. In: 2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS), pp. 1–6 (2021). https://doi.org/10.1109/ICHMS53169.2021.9582638
Ergen, S.C.: Zigbee/IEEE 802.15. 4 summary. UC Berkeley 10(17), 11 (2004)
Forsyth, D., Ponce, J.: Computer Vision: A Modern Approach. Prentice Hall, Hoboken (2011)
Garcia Lopez, P., Montresor, A., Epema, D., Datta, A., Higashino, T., Iamnitchi, A., Barcellos, M., Felber, P., Riviere, E.: Edge-centric computing: vision and challenges. SIGCOMM Comput. Commun. Rev. 45(5), 37–42 (2015). https://doi.org/10.1145/2831347.2831354
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Li, Z., Dong, B.: A new modeling approach for short-term prediction of occupancy in residential buildings. Build. Environ. 121, 277–290 (2017)
Liang, T.J., Huang, J.F., Yadav, P.K.: Design and implementation of dimmable led control circuit with dali protocol. In: 2016 IEEE International Conference on Power and Energy (PECon), pp. 121–126. IEEE, Piscataway (2016)
Luck, M., McBurney, P., Shehory, O., Willmott, S.: Agent technology, Computing as Interaction: A Roadmap for Agent Based Computing. University of Southampton on Behalf of AgentLink III (2005)
Mahesh, B.: Machine learning algorithms-a review. Int. J. Sci. Res. 9, 381–386 (2020)
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529 (2015)
Ploennigs, J., Ba, A., Barry, M.: Materializing the promises of cognitive IoT: how cognitive buildings are shaping the way. IEEE Int. Things J. 5(4), 2367–2374 (2017)
Qian, L., Luo, Z., Du, Y., Guo, L.: Cloud computing: An overview. In: IEEE International Conference on Cloud Computing, pp. 626–631. Springer, Berlin (2009)
Soni, D., Makwana, A.: A survey on MQTT: A protocol of internet of things (IoT). In: International Conference on Telecommunication, Power Analysis and Computing Techniques (ICTPACT-2017), vol. 20, pp. 173–177 (2017)
Tran, V.V., Park, D., Lee, Y.C.: Indoor air pollution, related human diseases, and recent trends in the control and improvement of indoor air quality. Int. J. Environ. Res. Public Health 17(8), 2927 (2020)
Voulodimos, A., Doulamis, N., Doulamis, A., Protopapadakis, E.: Deep learning for computer vision: a brief review. Comput. Intell. Neurosci. 2018 (2018). https://doi.org/10.1155/2018/7068349
Wooldridge, M.: An Introduction to MultiAgent Systems, 2nd edn. Wiley Publishing, Hoboken (2009)
Xia, F., Yang, L.T., Wang, L., Vinel, A.: Internet of things. Int. J. Commun. Syst. 25(9), 1101–1102 (2012)
Yannuzzi, M., van Lingen, F., Jain, A., Parellada, O.L., Flores, M.M., Carrera, D., Pérez, J.L., Montero, D., Chacin, P., Corsaro, A., Olive, A.: A new era for cities with fog computing. IEEE Int. Comput. 21(2), 54–67 (2017)
Yu, Z., Nakamura, Y.: Smart meeting systems: a survey of state-of-the-art and open issues. ACM Comput. Surv. 42(2), 1–20 (2010)
Acknowledgements
This work has been partially supported by the COGITO (A COGnItive dynamic sysTem to allOw buildings to learn and adapt) project, funded by the Italian government (PON ARS01 00836) and by the CNR project “Industrial transition and resilience of post-Covid19 Societies - Sub-project: Energy Efficient Cognitive Buildings.”
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Amadeo, M., Cicirelli, F., Guerrieri, A., Ruggeri, G., Spezzano, G., Vinci, A. (2023). COGITO: A Platform for Developing Cognitive Environments. In: Cicirelli, F., Guerrieri, A., Vinci, A., Spezzano, G. (eds) IoT Edge Solutions for Cognitive Buildings. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-031-15160-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-15160-6_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15159-0
Online ISBN: 978-3-031-15160-6
eBook Packages: Computer ScienceComputer Science (R0)