Abstract
This article presents iBuilding: distributed artificial intelligence embedded into Intelligent or Smart Buildings in an Industry 4.0 application that enables the adaptation to the external environment and the different building users. Buildings are becoming more intelligent in the way they monitor the usage of its assets, functionality and space. The more efficiently a building can be monitored or predicted, the more return of investment can deliver as unused space or energy can be redeveloped or commercialized, therefore reducing energy consumption while increasing functionality. This article proposes distributed artificial intelligence embedded into a Building based on neural networks with a deep learning structure. (1) Sensorial neurons at the device level are dispersed through the intelligent building to gather, filter environment information and predict its next values. (2) Management neurons based on reinforcement learning algorithm at the edge level make predictions about values and trends for building managers or developers to make commercial or operational informed decisions. (3) Finally, transmission neurons based on the genetic algorithms and the genome codify, transmit iBuilding information and also multiplex its data entirely to generate clusters of buildings interconnected with each other at the cloud level. The proposed iBuilding based on distributed learning is validated with a public research dataset; the results show that artificial intelligence embedded into the intelligent building enables real-time monitoring and successful predictions about its variables. The key concept proposed by this article is that the learned information obtained by iBuilding after its adaptation to the environment is never lost when the building changes over time or is decommissioned but transmitted to future generations.
Similar content being viewed by others
References
Ghaffarianhoseini A, Berardi U, AlWaer H, Chang S, Halawa E, Ghaffarianhoseini A, Clements D (2016) What is an intelligent building? Analysis of recent interpretations from an international perspective. Archit Sci Rev 59(5):338–357
Kirschner M, Gerhart J (2005) The plausibility of life resolving Darwin’s dilemma. Yale University Press, London, pp 1–336
Hinton G, Nowlan S (1996) How learning can guide evolution. Complex Syst 1:495–502
Smith D, Bullmore E (2007) Small-world brain networks. Neuroscientist 12:512–523
Sporns O, Chialvo D, Kaiser M, Hilgetag C (2004) Organization, development and function of complex brain networks. Trends Cogn Sci 8(9):418–425
Clements D (1997) What do we mean by intelligent buildings? Autom Constr 6(5–6):395–400
Omar O (2018) Intelligent building, definitions, factors and evaluation criteria of selection. Alex Eng J 57:2903–2910
Leclercq A, Isaac H (2016) The new office: how coworking changes the work concept. J Bus Strateg 37(6):3–9
Hui JK, Zhang Y (2018) Sharing space: urban sharing, sharing a living space, and shared social spaces. Space Cult, pp 1–13
Yacoub G (2018) How do collaborative practices emerge in coworking spaces? Evidence from fintech start-ups. Acad Manag Proc 1:1–10
Honga T, Taylor-Langea S, D’Ocab S, Yanc D, Corgnati S (2016) Advances in research and applications of energy-related occupant behaviour in buildings. Energy Build 116:694–702
Masoso OT, Grobler LJ (2010) The dark side of occupants’ behaviour on building energy use. Energy Build 42:173–177
Hong T, Yan D, D’Oca S, Chen C (2017) Ten questions concerning occupant behaviour in buildings: the big Picture. Build Environ 114:518–530
Nguyen T, Aiello M (2013) Energy intelligent buildings based on user activity: a survey. Energy Build 56:244–257
Shaikh P, Nor N, Nallagownden P, Elamvazuthi I, Ibrahim T (2014) A review on optimized control systems for building energy and comfort management of smart sustainable buildings. Renew Sustain Energy Rev 34:409–429
Oldewurtel F, Sturzenegger D, Morari M (2013) Importance of occupancy information for building climate control. Appl Energy 101:521–532
Labeodan T, Zeiler W, Boxem G, Zhao Y (2015) Occupancy measurement in commercial office buildings for demand-driven control applications—a survey and detection system evaluation. Energy Build 93:303–314
Ko JH, Kong DS, Huh JH (2017) Baseline building energy modelling of cluster inverse model by using daily energy consumption in office buildings. Energy Build 140:317–323
Cetina KS, Tabares-Velascob PC, Novoselac A (2014) Appliance daily energy use in new residential buildings: use profiles and variation in time-of-use. Energy Build 84:716–726
Tüfekci P (2014) Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods. Electr Power Energy Syst 60:126–140
Wang Z, Srinivasan RS (2017) A review of artificial intelligence-based building energy use prediction: contrasting the capabilities of single and ensemble prediction models. Renew Sustain Energy Rev 75:796–808
Tsanas A, Xifara A (2012) Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy Build 49:560–567
Arghira N, Hawarah L, Ploix S, Jacomino M (2012) Prediction of appliances energy use in smart homes. Energy 48:128–134
Candanedo LM, Feldheim V, Deramaix D (2017) Data-driven prediction models of energy use of appliances in a low-energy house. Energy Build 140:81–97
Bi H, Gelenbe E (2014) A cooperative emergency navigation framework using mobile cloud computing. In: International symposium computer and information sciences, pp 41–48
Gelenbe E, Bi H (2014) Emergency navigation without an infrastructure. Sensors 14(8):15142–15162
Ghosh A, Chakraborty D, Law A (2018) Artificial intelligence in internet of things. Chin Assoc Artif Intell Trans Intell Technol 3(4):208–218
Osifeko M, Hancke G, Mahfouz A (2020) Artificial intelligence techniques for cognitive sensing in future IoT: state-of-the-art, potentials, and challenges. J Sens Actuator Netw 9(21):1–31
Zhang J (2020) Real-time detection of energy consumption of IoT network nodes based on artificial intelligence. Comput Commun 153:188–195
Alama F, Mehmoodb R, Katiba I, Albeshri A (2016) Analysis of eight data mining algorithms for smarter internet of things (IoT). Int Workshop Data Min IoT Syst Proc Comput Sci 98:437–442
Skouby K, Lynggaard P (2014) Smart home and smart city solutions enabled by 5G, IoT, AAI and CoT services. In: International conference on contemporary computing and informatics, pp 874–878
Lloret J, Tomas J, Canovas A, Parra L (2016) An integrated IoT architecture for smart metering. Internet of things. IEEE Commun Mag 54(12):50–57
Osuwa AA, Ekhoragbon EB, Fat LT (2017) Application of artificial intelligence in internet of things. In: International conference on computational intelligence and communication networks, pp 169–173
Shafique M, Theocharides T, Bouganis C, Hanif M, Khalid F, Hafız R, Rehman S (2018) An overview of next-generation architectures for machine learning: roadmap, opportunities and challenges in the IoT era. In: Design, automation and test in Europe conference and exhibition, pp 827–832
Rego A, Canovas A, Jiménez JM, Lloret J (2018) An intelligent system for video surveillance in IoT environments. IEEE Access 6:31580–31598
Liu L, Zhou B, Zou Z, Yeh S, Zheng L (2018) A smart unstaffed retail shop based on artificial intelligence and IoT. In: International workshop on computer-aided modelling and design of communication links and networks, pp 1–4
Zhou J, Wang Y, Ota K, Dong M (2019) AAIoT: accelerating artificial intelligence in IoT systems. IEEE Wirel Commun Lett 8(3):825–828
Soomro S, Miraz M, Prasanth A, Abdullah M (2018) Artificial intelligence enabled IoT: traffic congestion reduction in smart cities. In: Smart cities symposium, pp 1–6
Garcia I, Muttukrishnan R, Lloret J (2019) Human-centric AI for trustworthy IoT systems with explainable multilayer perceptrons. IEEE Access 7:125562–125574
Prutyanov V, Melentev N, Lopatkin D, Menshchikov A, Somov A (2019) Developing IoT devices empowered by artificial intelligence: experimental study. Global IoT Summit, pp 1–6
Gonzalez P, Lloret J, Taha M (2018) Architecture to integrate IoT networks using artificial intelligence in the cloud. In: International conference on computational science and computational intelligence, pp 996–1001
Lv Z, Han Y, Singh A, Manogaran G, Lv H (2021) Trustworthiness in industrial IoT systems based on artificial intelligence. IEEE Trans Industr Inf 17(2):1496–1504
Trakadas P et al (2020) An artificial intelligence-based collaboration approach in industrial IoT manufacturing: key concepts. Archit Ext Potential Appl Sens 20(19):5480
Adi E, Anwar A, Baig Z, Zeadally S (2020) Machine learning and data analytics for the IoT. Neural Comput Appl 32:16205–16233
Pramanik P, Pal S, Choudhury P (2018) Beyond automation: the cognitive IoT. Artificial intelligence brings sense to the internet of things. Cognitive computing for big data systems over IoT, pp 1–37
Gladence L, Anu V, Rathna R, Brumancia E (2020) Recommender system for home automation using IoT and artificial intelligence. J Ambient Intell Humaniz Comput 2020:1–9
Chiang M, Zhang T (2016) Fog and IoT: an overview of research opportunities. IEEE Internet Things J 3(6):854–864
Sodhro A, Pirbhulal S, Albuquerque V (2019) Artificial intelligence-driven mechanism for edge computing-based industrial applications. IEEE Trans Industr Inf 15(7):4235–4243
Jiang H (2019) Mobile fire evacuation system for large public buildings based on artificial intelligence and IoT. IEEE Access 7:64101–64109
Chen X, Hao X (2019) Feature reduction method for cognition and classification of IoT devices based on artificial intelligence. IEEE access 7:103291–103298
Debauche O, Mahmoudi S, Mahmoudi S, Manneback P, Manneback P, Lebeau F (2020) A new edge architecture for AI-IoT services deployment. Int Conf Mob Syst Pervasive Comput Proc Comput Sci 175:10–19
Ke R, Zhuang Y, Pu Z, Wang Y (2020) A smart, efficient, and reliable parking surveillance system with edge artificial intelligence on IoT devices. IEEE Trans Intell Transport Syst 2020:1–13
Yang S, Xu K, Cui L, Ming Z, Chen Z, Ming Z (2020) EBI-PAI: towards an efficient edge-based IoT platform for artificial intelligence. IEEE Internet Things J 2020:1–15
Hasan M, Islam M, Zarif MI, Hashem M (2019) Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet Things 7:100059
Cañedo J, Skjellum A (2016) Using machine learning to secure IoT systems. In: Annual conference on privacy, security and trust, pp 219–222
Xiao L, Wan X, Lu X, Zhang Y, Wu D (2018) IoT security techniques based on machine learning. IEEE Signal Process Mag 35(5):41–49
Farivar F, Sayad M, Jolfaei A, Alazab M (2020) Artificial intelligence for detection, estimation, and compensation of malicious attacks in nonlinear cyber-physical systems and industrial IoT. IEEE Trans Industr Inf 16(4):2716–2725
Hussain F, Hussain R, Hassan S, Hossain E (2020) Machine learning in IoT security: current solutions and future challenges. IEEE Commun Surv Tutor 22(3):1686–1721
Fang H, Qi A, Wang X (2020) Fast authentication and progressive authorization in large-scale IoT: how to leverage AI for security enhancement. IEEE Network 34(3):24–29
Ganesh R, Karthika P, Aravinda V (2019) Secure IoT systems using raspberry Pi machine learning artificial intelligence. In: International conference on computer networks and inventive communication technologies, pp 797–805
Shafique K, Khawaja BA, Sabir F, Qazi S, Mustaqim M (2020) Internet of things (IoT) for next-generation smart systems: a review of current challenges, future trends and prospects for emerging 5G-IoT scenarios. IEEE Access 8:23022–23040
Wang D, Chen D, Song B, Guizani N, Yu X, Du X (2018) From IoT to 5G I-IoT: the next generation IoT-based intelligent algorithms and 5G technologies. IEEE Commun Mag 56:114–120
Javaid N, Sher A, Nasir H, Guizani N (2018) Intelligence in IoT-based 5G networks: opportunities and challenges. IEEE Commun Mag 56(10):94–100
Kiss P, Reale A, Ferrari CJ, Istenes Z (2018) Deployment of IoT applications on 5G edge. In: IEEE international conference future IoT technologies, pp 1–9
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780
Gelenbe E (1989) Random neural networks with negative and positive signals and product form solution. Neural Comput 1:502–510
Gelenbe E (1993) Learning in the recurrent random neural network. Neural Comput 5:154–164
Gelenbe E (2004) Cognitive Packet Network. Patent US 6804201:B1
Gelenbe E, Xu Z, Seref E (1999) Cognitive packet networks. In: International conference on tools with artificial intelligence, pp 47–54
Huang G, Zhu Q, Siew C (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501
Huang G, Chen L, Siew C (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Networks 17(4):879–892
Gelenbe E, Yin Y (2016) Deep learning with random neural networks. In: International joint conference on neural networks, pp 1633–1638
Yin Y, Gelenbe E (2016) Deep learning in multi-layer architectures of dense nuclei. CoRR abs/1609.07160, pp 1–10
Serrano W (2018) The random neural network with a genetic algorithm and deep learning clusters in fintech: smart investment. In: IFIP international conference on artificial intelligence applications and innovations, pp 297–310
Serrano W (2019) Genetic and deep learning clusters based on neural networks for management decision structures. Neural Comput Appl 2019:1–25
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The author declares that he has no conflict of interest against any company or institution.
Ethical approval
This research has not involved human participants and/or animals, except for the author contribution.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Rights and permissions
About this article
Cite this article
Serrano, W. iBuilding: artificial intelligence in intelligent buildings. Neural Comput & Applic 34, 875–897 (2022). https://doi.org/10.1007/s00521-021-05967-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-021-05967-y