Skip to main content

Advertisement

SpringerLink
Log in

A fog-based hybrid intelligent system for energy saving in smart buildings

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

In recent years, the widespread diffusion of pervasive sensing devices and the increasing need for reducing energy consumption have encouraged research in the energy-aware management of smart environments. Following this direction, this paper proposes a hybrid intelligent system which exploits a fog-based architecture to achieve energy efficiency in smart buildings. Our proposal combines reactive intelligence, for quick adaptation to the ever-changing environment, and deliberative intelligence, for performing complex learning and optimization. Such hybrid nature allows our system to be adaptive, by reacting in real time to relevant events occurring in the environment and, at the same time, to constantly improve its performance by learning users’ needs. The effectiveness of our approach is validated in the application scenario of a smart home by extensive experiments on real sensor traces. Experimental results show that our system achieves substantial energy savings in the management of a smart environment, whilst satisfying users’ needs and preferences.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Amadeo M, Molinaro A, Paratore SY, Altomare A, Giordano A, Mastroianni C (2017) A cloud of things framework for smart home services based on information centric networking. In: Proc. of the 2017 IEEE 14th international conference on networking, sensing and control (ICNSC), IEEE, pp 245–250

  • Amigoni F, Gatti N, Pinciroli C, Roveri M (2005) What planner for ambient intelligence applications? IEEE Trans Syst Man Cybern Part A Syst Hum 35(1):7–21

    Article  Google Scholar 

  • Atallah L, Yang G (2009) The use of pervasive sensing for behaviour profiling—a survey. Pervas Mob Comput 5:447–464

    Article  Google Scholar 

  • Baghban A, Bahadori M, Rozyn J, Lee M, Abbas A, Bahadori A, Rahimali A (2016) Estimation of air dew point temperature using computational intelligence schemes. Appl Therm Eng 93:1043–1052

    Article  Google Scholar 

  • Bagis A (2008) Fuzzy rule base design using tabu search algorithm for nonlinear system modeling. ISA Trans 47(1):32–44

    Article  Google Scholar 

  • Bhargava K, McManus G, Ivanov S (2017) Fog-centric localization for ambient assisted living. In: Proc. of the 2017 international conference on engineering, technology and innovation (ICE/ITMC), IEEE, pp 1424–1430

  • Bilgin A, Dooley J, Whittington L, Hagras H, Henson M, Wagner C, Malibari A, Al-Ghamdi A, Alhaddad MJ, Alghazzawi D (2012) Dynamic profile-selection for zslices based type-2 fuzzy agents controlling multi-user ambient intelligent environments. In: Proc. of the 2012 IEEE international conference on fuzzy systems, pp 1–8

  • Calvaresi D, Cesarini D, Sernani P, Marinoni M, Dragoni AF, Sturm A (2017) Exploring the ambient assisted living domain: a systematic review. J Ambient Intell Hum Comput 8(2):239–257

    Article  Google Scholar 

  • Castellano G, Fanelli AM, Mencar C (2002) Generation of interpretable fuzzy granules by a double-clustering technique. Arch Control Sci 12(4):397–410

    MathSciNet  MATH  Google Scholar 

  • Cook DJ (2010) Learning setting-generalized activity models for smart spaces. IEEE Intell Syst 2010(99):1

    Google Scholar 

  • Cook DJ, Das SK (2007) How smart are our environments? An updated look at the state of the art. Pervas Mob Comput 3(2):53–73

    Article  Google Scholar 

  • Cook DJ, Augusto JC, Jakkula VR (2009) Ambient intelligence: technologies, applications, and opportunities. Pervas Mob Comput 5(4):277–298

    Article  Google Scholar 

  • Corucci F, Anastasi G, Marcelloni F (2011) A WSN-based testbed for energy efficiency in buildings. In: Proc. of the 2011 IEEE symposium on computers and communications (ISCC), pp 990–993

  • De Paola A, Gaglio S, Lo Re G, Ortolani M (2012) Sensor9k: a testbed for designing and experimenting with WSN-based ambient intelligence applications. Pervas Mob Comput 8(3):448–466

    Article  Google Scholar 

  • De Paola A, Ortolani M, Lo Re G, Anastasi G, Das SK (2014) Intelligent management systems for energy efficiency in buildings: a survey. ACM Comput Surv 47(1):13:1–13:38

    Article  Google Scholar 

  • De Paola A, Lo Re G, Morana M, Ortolani M (2015) Smartbuildings: an ami system for energy efficiency. SustainIT 2015:1–7

    Google Scholar 

  • De Paola A, Ferraro P, Gaglio S, Lo Re G, Das SK (2017) An adaptive bayesian system for context-aware data fusion in smart environments. IEEE Trans Mobile Comput 16(6):1502–1515

    Article  Google Scholar 

  • Denna M, Mauri G, Zanaboni AM (1999) Learning fuzzy rules with tabu search-an application to control. IEEE Trans Fuzzy Syst 7(3):295–318

    Article  Google Scholar 

  • Doctor F, Hagras H, Callaghan V (2005) A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments. IEEE Trans Syst Man Cybern Part A Syst Hum 35(1):55–65

    Article  Google Scholar 

  • European Commission (2017) Energy efficiency in buildings. http://ec.europa.eu/energy/en/topics/energy-efficiency/buildings. Accessed 1 Feb 2019

  • Fazzolari M, Alcala R, Nojima Y, Ishibuchi H, Herrera F (2013) Improving a fuzzy association rule-based classification model by granularity learning based on heuristic measures over multiple granularities. In: Proc. of the 2013 IEEE Int. workshop on genetic and evolutionary fuzzy systems (GEFS), pp 44–51

  • Georgievski I, Nguyen TA, Aiello M (2013) Combining activity recognition and ai planning for energy-saving offices. In: Proc. of the 2013 IEEE 10th Int. Conf. on ubiquitous intelligence and computing and 10th int. conf. on autonomic and trusted computing (UIC/ATC), IEEE, pp 238–245

  • Gruber P, Gwerder M, J T (2001) Predictive control for heating applications. In: Proc. of the 7th REHVA world congress, clima 2000. Naples, Italy, pp 1–15

  • Hagras H, Alghazzawi D, Aldabbagh G (2015) Employing type-2 fuzzy logic systems in the efforts to realize ambient intelligent environments [application notes]. IEEE Comput Intell Mag 10(1):44–51

    Article  Google Scholar 

  • Herrera F (2008) Genetic fuzzy systems: taxonomy, current research trends and prospects. Evol Intell 1(1):27–46

    Article  Google Scholar 

  • Hinojosa CE, Camargo HA (2013) Multi-objective iterative genetic approach for learning fuzzy classification rules with semantic-based selection of the best rule. In: IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS), 2013 Joint, pp 292–297

  • Hosoya Y, Umano M (2012) Dynamic fuzzy q-learning with facility of tuning and removing fuzzy rules. In: Proc. of the 2012 IEEE int. conf. on fuzzy systems (FUZZ-IEEE), IEEE, pp 1–8

  • Hosseini R, Qanadli SD, Barman S, Mazinani M, Ellis T, Dehmeshki J (2012) An automatic approach for learning and tuning gaussian interval type-2 fuzzy membership functions applied to lung cad classification system. IEEE Trans Fuzzy Syst 20(2):224–234

    Article  Google Scholar 

  • Kulkarni AA (2002) A reactive behavioral system for the intelligent room. PhD thesis, Massachusetts Institute of Technology

  • Lhotska L, Macek J, Peri D (2004) Evaluation of ecg: comparison of decision tree and fuzzy rules induction. In: European meetings on cybernetics and systems research (EMCSR), pp 713–718

  • Nauck D, Kruse R (1993) A fuzzy neural network learning fuzzy control rules and membership functions by fuzzy error backpropagation. In: Proc. of the IEEE int. conf. on neural networks, IEEE, pp 1022–1027

  • Navara M, Peri D (2004) Automatic generation of fuzzy rules and its applications in medical diagnosis. In: Proc. of the 10th int. conf. on information processing and management of uncertainty, pp 657–663

  • Nguyen T, Khosravi A, Creighton D, Nahavandi S (2015) Fuzzy system with tabu search learning for classification of motor imagery data. Biomed Signal Process Control 20:61–70

    Article  Google Scholar 

  • Nguyen TA, Aiello M (2013) Energy intelligent buildings based on user activity: a survey. Energy Build 56:244–257

    Article  Google Scholar 

  • Perera C, Qin Y, Estrella JC, Reiff-Marganiec S, Vasilakos AV (2017) Fog computing for sustainable smart cities: a survey. ACM Comput Surv CSUR 50(3):32

    Google Scholar 

  • Rahmani AM, Gia TN, Negash B, Anzanpour A, Azimi I, Jiang M, Liljeberg P (2018) Exploiting smart e-health gateways at the edge of healthcare internet-of-things: a fog computing approach. Future Gen Comput Syst 78:641–658

    Article  Google Scholar 

  • Rasul F, Rahman M, Shelia V, Hill SE, Islam A, Iosiliani O, Loyd N, Hoogenboom G (2015) Agricultural Weather Network (AgWeatherNet) Science for Society. In: Conference on science at IAREC (Irrigated agricultural research and extension center), Washington State University

  • Sahni Y, Cao J, Zhang S, Yang L (2017) Edge mesh: a new paradigm to enable distributed intelligence in internet of things. IEEE Access 5:16441–16458

    Article  Google Scholar 

  • Santini S, Romer K (2006) An adaptive strategy for quality-based data reduction in wireless sensor networks. In: Proc. of the 3rd international conference on networked sensing systems (INSS 2006), pp 29–36

  • Stantchev V, Barnawi A, Ghulam S, Schubert J, Tamm G (2015) Smart items, fog and cloud computing as enablers of servitization in healthcare. Sens Transducers 185(2):121

    Google Scholar 

  • Stojkoska BLR, Trivodaliev KV (2017a) A review of internet of things for smart home: challenges and solutions. J Clean Prod 140:1454–1464

    Article  Google Scholar 

  • Stojkoska BR, Trivodaliev K (2017b) Enabling internet of things for smart homes through fog computing. In: Proc. of the 2017 25th telecommunication forum (TELFOR), IEEE, pp 1–4

  • Stojmenovic I, Wen S (2014) The fog computing paradigm: scenarios and security issues. In: Proc. of the 2014 federated conference on computer science and information systems (FedCSIS), IEEE, pp 1–8

  • Talbi N, Belarbi K (2013) Designing fuzzy rule base using hybrid elite genetic algorithm and tabu search: application for control and modeling. Int J Hybrid Intell Syst 10(4):205–214

    Article  Google Scholar 

  • Verma P, Sood SK (2018) Fog assisted-iot enabled patient health monitoring in smart homes. IEEE Internet Things J 5:991–1

    Article  Google Scholar 

  • Yan Y, Su W (2016) A fog computing solution for advanced metering infrastructure. In: Proc. of the 2016 IEEE/PES transmission and distribution conference and exposition (T&D), IEEE, pp 1–4

  • Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning. Inf Sci 8(3):199–249

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Engineering, University of Palermo, Viale delle Scienze, ed. 6, 90128, Palermo, Italy

    Alessandra De Paola, Pierluca Ferraro, Giuseppe Lo Re & Marco Morana

  2. Keele University, Keele, Staffordshire, UK

    Marco Ortolani

Authors
  1. Alessandra De Paola
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pierluca Ferraro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giuseppe Lo Re
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marco Morana
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Marco Ortolani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alessandra De Paola.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

De Paola, A., Ferraro, P., Lo Re, G. et al. A fog-based hybrid intelligent system for energy saving in smart buildings. J Ambient Intell Human Comput 11, 2793–2807 (2020). https://doi.org/10.1007/s12652-019-01375-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-019-01375-2

Keywords

Search

Navigation