Skip to main content
SpringerLink
Log in

A location-based fog computing optimization of energy management in smart buildings: DEVS modeling and design of connected objects

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

Nowadays, smart buildings rely on Internet of things (IoT) technology derived from the cloud and fog computing paradigms to coordinate and collaborate between connected objects. Fog is characterized by low latency with a wider spread and geographically distributed nodes to support mobility, real-time interaction, and location-based services. To provide optimum quality of user life in modern buildings, we rely on a holistic Framework, designed in a way that decreases latency and improves energy saving and services efficiency with different capabilities. Discrete EVent system Specification (DEVS) is a formalism used to describe simulation models in a modular way. In this work, the sub-models of connected objects in the building are accurately and independently designed, and after installing them together, we easily get an integrated model which is subject to the fog computing Framework. Simulation results show that this new approach significantly, improves energy efficiency of buildings and reduces latency. Additionally, with DEVS, we can easily add or remove sub-models to or from the overall model, allowing us to continually improve our designs.

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

Similar content being viewed by others

References

  1. Bellavista P, Berrocal J, Corradi A, Das S K, Foschini L, Zanni A. A survey on fog computing for the Internet of Things. Journal of Pervasive and Mobile Computing, 2019, 52: 71–99

    Article  Google Scholar 

  2. Jalali F, Khodadustan S, Gray C, Hinton K, Suits F. Greening IoT with fog: a survey. In: Proceedings of the 2017 IEEE International Conference on Edge Computing (EDGE). 2017, 25–31

  3. Atlam H F, Walters R J, Wills G B. Fog computing and the internet of things: a review. Big Data and Cognitive Computing, 2018, 2(2): 10

    Article  Google Scholar 

  4. Maatoug A, Belalem G. Conception and validation of smart building energy management system BEMS using the discrete event system specification DEVS. Journal of Communications Software and Systems, 2014, 10(2): 107–113

    Article  Google Scholar 

  5. Lau B P L, Marakkalage S H, Zhou Y, Hassan N U, Yuen C, Zhang M, Tan U X. A survey of data fusion in smart city applications. Information Fusion, 2019, 52: 357–374

    Article  Google Scholar 

  6. Zao J K, Gan T T, You C K, Méndez S J R, Chung C E, Wang Y T, Mullen T, Jung T P. Augmented brain computer interaction based on fog computing and linked data. In: Proceedings of the International Conference on Intelligent Environments. 2014, 374–377

  7. Maatoug A, Belalem G, Mahmoudi S. Fog computing framework for location-based energy management in smart buildings. Multiagent and Grid Systems, 2019, 15(1): 39–56

    Article  Google Scholar 

  8. Etemad M, Aazam M, St-Hilaire M. Using DEVS for modeling and simulating a Fog Computing environment. In: Proceedings of the 2017 International Conference on Computing, Networking and Communications (ICNC). 2017, 849–854

  9. Himeur Y, Alsalemi A, Al-Kababji A, Bensaali F, Amira A. Data fusion strategies for energy efficiency in buildings: overview, challenges and novel orientations. Information Fusion, 2020, 64: 99–120

    Article  Google Scholar 

  10. Stojkoska B L R, Trivodaliev K V. A review of internet of things for smart home: challenges and solutions. Journal of Cleaner Production, 2017, 140: 1454–1464

    Article  Google Scholar 

  11. Carrillo E, Benitez V, Mendoza C, Pacheco J. IoT framework for smart buildings with cloud computing. In: Proceedings of the 1st International Smart Cities Conference (ISC2). 2015, 1–6

  12. Tushar W, Yuen C, Li K, Wood K L, Wei Z, Xiang L. Design of cloud-connected IoT system for smart buildings on energy management (invited paper). EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 2016, 3(6): e3

    Article  Google Scholar 

  13. Al Faruque M A, Vatanparvar K. Energy management-as-a-service over fog computing platform. IEEE Internet of Things Journal, 2016, 3(2): 161–169

    Article  Google Scholar 

  14. Javed A, Rana O, Marmaras C, Cipcigan L. Fog paradigm for local energy management systems. In: Proceedings of the 2nd EAI International Conference on Cloud Infrastructures, Services, and IoT Systems for Smart Cities. 2018, 162–176

  15. Naranjo P G V, Pooranian Z, Shojafar M, Conti M, Buyya R. FOCAN: a Fog-supported smart city network architecture for management of applications in the Internet of Everything environments. Journal of Parallel and Distributed Computing, 2019, 132: 274–283

    Article  Google Scholar 

  16. Lin H, Liu G, Li F, Zuo Y. Where to go? Predicting next location in IoT environment. Frontiers of Computer Science, 2021, 15(1): 151306

    Article  Google Scholar 

  17. Pan J, Jain R, Paul S, Vu T, Saifullah A, Sha M. An internet of things framework for smart energy in buildings: designs, prototype, and experiments. IEEE Internet of Things Journal, 2015, 2(6): 527–537

    Article  Google Scholar 

  18. Moreno M V, Zamora M A, Skarmeta A F. User-centric smart buildings for energy sustainable smart cities. Transactions on Emerging Telecommunications Technologies, 2014, 25(1): 41–55

    Article  Google Scholar 

  19. Luan T H, Gao L, Li Z, Xiang Y, Wei G, Sun L. Fog computing: focusing on mobile users at the edge. 2015, arXiv preprint arXiv: 1502.01815

  20. Albataineh M, Jarrah M. DEVS-IoT: performance evaluation of smart home devices network. Multimedia Tools and Applications, 2021, 80(11): 16857–16885

    Article  Google Scholar 

  21. Bonino D, Corno F. DogSim: a state chart simulator for domotic environments. In: Proceedings of the 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops). 2010, 208–213

  22. Yi S, Hao Z, Qin Z, Li Q. Fog computing: platform and applications. In: Proceedings of the 3rd IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb). 2015, 73–78

  23. Zeigler B P, Praehofer H, Kim T G. Theory of Modelling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems. 2nd ed. San Diego: Academic Press, 2000

    Google Scholar 

  24. Capocchi L, Bernardi F, Federici D, Bisgambiglia P A. BFS-DEVS: a general DEVS-based formalism for behavioral fault simulation. Simulation Modelling Practice and Theory, 2006, 14(7): 945–970

    Article  Google Scholar 

  25. Stojmenovic I, Wen S. The fog computing paradigm: scenarios and security issues. In: Proceedings of the 2014 Federated Conference on Computer Science and Information Systems (FedCSIS). 2014, 1–8

  26. Varghese B, Wang N, Nikolopoulos D S, Buyya R. Feasibility of fog computing. 2017, arXiv preprint arXiv: 1701.05451

  27. Guo X, Ansari N, Hu F, Shao Y, Elikplim N R, Li L. A survey on fusion-based indoor positioning. IEEE Communications Surveys & Tutorials, 2020, 22(1): 566–594

    Article  Google Scholar 

  28. Cesconetto J, Augusto Silva L, Bortoluzzi F, Navarro-Cáceres M, Zeferino C A, Leithardt V R Q. PRIPRO—privacy profiles: user profiling management for smart environments. Electronics, 2020, 9(9): 1519

    Article  Google Scholar 

  29. Abyaneh A Y, Pourahmadi V, Foumani A H G. CSI-based authentication: extracting stable features using deep neural networks. Transactions on Emerging Telecommunications Technologies, 2020, 31(2): e3795

    Google Scholar 

  30. Belhadi A, Djenouri Y, Srivastava G, Djenouri D, Lin J C W, Fortino G. Deep learning for pedestrian collective behavior analysis in smart cities: a model of group trajectory outlier detection. Information Fusion, 2021, 65: 13–20

    Article  Google Scholar 

  31. Bittencourt L F, Diaz-Montes J, Buyya R, Rana O F, Parashar M. Mobility-aware application scheduling in fog computing. IEEE Cloud Computing, 2017, 4(2): 26–35

    Article  Google Scholar 

  32. Filippi J B, Bisgambiglia P. JDEVS: an implementation of a DEVS based formal framework for environmental modelling. Environmental Modelling & Software, 2004, 19(3): 261–274

    Article  Google Scholar 

  33. GeoTools. Geotools Users Group, 2002. Available from Geotools.org.website

  34. Shah A S, Nasir H, Fayaz M, Lajis A, Shah A. A review on energy consumption optimization techniques in IoT based smart building environments. Information, 2019, 10(3): 108

    Article  Google Scholar 

Download references

Acknowledgements

We sincerely thank the General Directorate of the Scientific Research and Technological Development (DGRSTD) of the Ministry of Higher Education and Scientific Research of Algeria, for all the resources provided for the accomplishment of this project.

Author information

Authors and Affiliations

  1. Computer Science Department, Faculty of Exact and Applied Sciences, University of Oran 1 Ahmed Ben Bella, Oran, 31000, Algeria

    Abdelfettah Maatoug & Ghalem Belalem

  2. Science and Technology Department, Faculty of Science and Technology, University of TIARET, Tiaret, 14000, Algeria

    Abdelfettah Maatoug

  3. Computer Science Department, Faculty of Engineering, University of Mons, Mons, 7000, Belgium

    Saïd Mahmoudi

Authors
  1. Abdelfettah Maatoug
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ghalem Belalem
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Saïd Mahmoudi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdelfettah Maatoug.

Additional information

Abdelfettah Maatoug received his Master degree in Computer Science from the National Superior School of Computer Science of Algiers, Algeria in 2015. He is an assistant professor in the University of TIARET, Algeria and is a PhD candidate in Department of computer science, Faculty of exact and applied sciences, University of Oran1 Ahmed Ben Bella, Algeria. He is a member of Computer Science Laboratory of Oran. His research interests include Distributed Systems, IoT, Grid computing, Cloud and Fog computing, Energy Management, Economic models, Smart Cities, Smart Homes and Smart Buildings.

Ghalem Belalem graduated from Department of Computer Science, Faculty of Exact and Applied Sciences, University of Oran1 Ahmed Ben Bella, Algeria, where he received PhD degree in computer science in 2007. His current research interests are distributed system, grid computing, cloud computing, replication, consistency, fault tolerance, resource management, economic models, energy consumption, Big data, IoT, mobile environment, images processing, supply chain optimization, decision support systems, high performance computing.

Saïd Mahmoudi received his MS in Computer Science from University of Lille 1, France in 1999. In 2003, he obtained his PhD in Computer Science at the University of Lille 1, France. Between 2003 and 2005, he was an Associate Lecturer at the University of Lille 3, France. Since September 2005, he is an Associate Professor at the Faculty of Engineering of the University of Mons, Belgium. His research interests include artificial intelligence, internet of things, medical images analysis, 2D and 3D retrieval and indexing, Big Data and large scale multimedia management. He published over then 150 journal and conference papers to date. He participates in numerous scientific and academic activities (member of the organizing committee for some conferences, reviewer and guest editor for several internationals journals).

Electronic supplementary material

11704_2021_375_MOESM1_ESM.pdf

A location-based fog computing optimization of energy management in smart buildings: DEVS modeling and design of connected objects

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maatoug, A., Belalem, G. & Mahmoudi, S. A location-based fog computing optimization of energy management in smart buildings: DEVS modeling and design of connected objects. Front. Comput. Sci. 17, 172501 (2023). https://doi.org/10.1007/s11704-021-0375-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11704-021-0375-z

Keywords

Search

Navigation