DEVS-IoT: performance evaluation of smart home devices network


Advances in electronics and connectivity have enabled a wide range of applications that can harness data collection for better decision making and an improved lifestyle. The Internet of Things (IoT) provides the communication infrastructure that allows devices with sensing and control capabilities to be connected within a home network. Smart home systems are considered one of the prominent applications in IoT, where it is possible to control home devices to achieve a better usage in terms of cost and comfort. However, smart home networks contain a wide range of devices and finding an optimal schedule for their working hours is an NP-hard problem. Hence, rather than using mathematical optimization to find optimal solutions, this paper proposes a modeling and simulation methods in order to provide good decisions and recommendations for devices’ scheduling. Discrete Event System Specification (DEVS) formalism is used to develop a model of a smart home network. The devices are categorized into two groups: monitoring devices and control devices. Monitoring devices include sensors that capture climate, energy, power, performance, and occupant’s behavioral data. Control devices send signals remotely for setting and controlling different devices in the smart home network. The behavior in terms of power usage and cost is simulated under different scenarios and settings. The simulation results show that less energy consumption can be achieved if users adopt a behavior where the schedule of three devices is changed every week. As a result, the proposed method can be utilized to make better decisions in setting devices parameters and evaluating the performance of the smart devices network under different conditions, scenarios, and settings.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17


  1. 1.

    Aid L, Zaoui L, Mostefaoui SAM (2016) Using DEVS for modeling and simulation of ambient objects in intelligent buildings. J Ambient Intell Humaniz Comput 7(4):579–592

    Article  Google Scholar 

  2. 2.

    Akhtar N, Niazi MA, Mustafa F, Hussain A (2011) A discrete event system specification (DEVS)-based model of consanguinity. J Theor Biol 285(1):103–112

    MathSciNet  Article  Google Scholar 

  3. 3.

    Alansari Z, Anuar N, Kamsin A, Soomro S, Riyaz Belgaum M, Miraz D, et al (2018) Challenges of internet of things and big data integration. Emerging Technologies in Computing 47–55

  4. 4.

    Al-Fuqaha A, Guizani M, Mohammadi M, Aledhari M, Ayyash M (2015) Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun Surv Tutor 17(4):2347–2376

    Article  Google Scholar 

  5. 5.

    Balta-Ozkan N, Davidson R, Bicket M, Whitmarsh L (2013) The development of smart homes market in the UK. Energy 60:361–372

    Article  Google Scholar 

  6. 6.

    Bhati A, Hansen M, Chan CM (2017) Energy conservation through smart homes in a smart city: a lesson for Singapore households. Energy Policy 104:230–239

    Article  Google Scholar 

  7. 7.

    Bilal M (2017) A review of internet of things architecture, technologies and analysis smartphone-based attacks against 3D printers. arXiv:1708.04560 [cs.NI]

  8. 8.

    Chan M, Campo E, Estève D, Fourniols J-Y (2009) Smart homes – current features and future perspectives. Maturitas 64(2):90–97

    Article  Google Scholar 

  9. 9.

    Xuejun Chen (2002) Dependence management for dynamic reconfiguration of component-based distributed systems. In Proceedings 17th IEEE International Conference on Automated Software Engineering. 279–284

  10. 10.

    Chow ACH, Zeigler B (1994) Parallel DEVS: a parallel, hierarchical, modular modeling formalism. Winter Simulation Conference DOI:

  11. 11.

    Elzas MS, Zeigler BP, Oren TI (1989) Concepts for distributed knowledge maintenance in variable structure models. In: Modelling and simulation methodology: knowledge system paradigms. 45-54

  12. 12.

    Ferdinando F, William Y, Enrico P (2017) A Multiagent system approach to scheduling devices in smart homes. In proceedings of the. In: 16th conference on autonomous agents and MultiAgent systems (AAMAS ‘17), pp 981–989

    Google Scholar 

  13. 13.

    Firth S, Kane T, Dimitriou V, Hassan T, Fouchal F, Coleman M, Webb L (2019) REFIT smart home dataset. Accessed 20 December 2019

  14. 14.

    Hargreaves T, Wilson C, Hauxwell-Baldwin R (2018) Learning to live in a smart home. Build Res Inf 46(1):127–139

    Article  Google Scholar 

  15. 15.

    Hu X, Zeigler BP, Mittal S (2003) Dynamic reconfiguration in DEVS component-based modeling and simulation. SIMULATION

  16. 16.

    Hu X, Hu X, Zeigler BP, Mittal S (2005) Variable structure in DEVS component-based modeling and simulation. SIMULATION 81(2):91–102

    Article  Google Scholar 

  17. 17.

    Iqbal A, Ullah F, Anwar H, Kwak KS, Imran M, Jamal W, Rahman A (2018) Interoperable internet-of-things platform for smart home system using web-of-objects and cloud. Sustain Cities Soc 38:636–646

    Article  Google Scholar 

  18. 18.

    Jaradat M, Jarrah M, Jararweh Y, Al-Ayyoub M, Bousselham A (2014) Integration of renewable energy in demand-side management for home appliances. International renewable and sustainable energy conference (IRSEC). 571-576

  19. 19.

    Jaradat M, Jarrah M, Bousselham A, Jararweh Y, Al-Ayyoub M (2015) The internet of energy: smart sensor networks and big data management for smart grid. Procedia Comput Sci 56:592–597

    Article  Google Scholar 

  20. 20.

    Jarrah M (2016) Modeling and simulation of renewable energy sources in smart grid using DEVS formalism. Procedia Comput Sci 83:642–647

    Article  Google Scholar 

  21. 21.

    Jarrah M and Al-Shrida F (2017) A multi-objective evolutionary solution to improve the quality of life in smart cities. The 14th international conference on smart cities: improving quality of life using ICT & IoT (HONET-ICT). 36-39

  22. 22.

    Jarrah M, Jaradat M, Jararweh Y, Al-Ayyoub M, Bousselham A (2015) A hierarchical optimization model for energy data flow in smart grid power systems. Inf Syst 53:190–200

    Article  Google Scholar 

  23. 23.

    Kane T, Firth SK, Hassan TM, Dimitriou V (2017) Heating behaviour in English homes: an assessment of indirect calculation methods. Energy Build 148:89–105

    Article  Google Scholar 

  24. 24.

    Karaboghossian T, Zito M (2018) Easy knapsacks and the complexity of energy allocation problems in the smart grid. Optim Lett 12(7):1553–1568

    MathSciNet  Article  Google Scholar 

  25. 25.

    Khan M, Tanveer H, Mohammad T, Giovanna S, Victor HCD. (2019) Cost-effective video summarization using deep CNN with hierarchical weighted fusion for IoT surveillance networks. J IEEE Internet of Things.

  26. 26.

    Knuth D (1969) The art of computer programming. Vol. 2. Addison Wesley Longman Publishing Co

  27. 27.

    Kortuem G, Kawsar F, Sundramoorthy V, Fitton D (2010) Smart objects as building blocks for the internet of things. IEEE Internet Comput 14(1):44–51

    Article  Google Scholar 

  28. 28.

    Lee E, Bahn H (2013) Electricity usage scheduling in smart building environments using smart devices. ScientificWorldJournal 2013:1–11

    Google Scholar 

  29. 29.

    Li M, Gu W, Chen W, He Y, Wu Y, Zhang Y (2018) Smart home: architecture, technologies and systems. Procedia Comput Sci 131:393–400

    Article  Google Scholar 

  30. 30.

    Maatoug A, Belalem G, Mostefaoui K (2014) Modeling and simulation of energy management system for smart city with the formalism DEVS: towards reducing the energy consumption. Int J Comput Appl 90(18):38–43

    Google Scholar 

  31. 31.

    Mehdi G, Roshchin M (2015) Electricity consumption constraints for smart-home automation: an overview of models and applications. Energy Procedia 83:60–68

    Article  Google Scholar 

  32. 32.

    Mehdi L, Ouallou Y, Mohamed O, Hayar A (2018) New smart home’s energy management system design and implementation for frugal smart cities. In the 2018 international conference on selected topics in Mobile and wireless networking (MoWNeT). 149–153

  33. 33.

    Mubdir B, Al-Hindawi A, Hadi N (2016) Design of smart home energy management system for saving energy. Eur Sci J ESJ 12(33):521

    Google Scholar 

  34. 34.

    Nasir NH, Shabir AP, Javaid AS, Fadi A, Khan M (2019) Secure data transmission framework for confidentiality in IoTs. Ad Hoc Netw 95:101989

    Article  Google Scholar 

  35. 35.

    Piyare R, Tazil M (2011) Bluetooth based home automation system using cell phone. In 2011 IEEE 15th international symposium on consumer electronics (ISCE). 192–195

    Google Scholar 

  36. 36.

    Ray AK, Roshan R (2017) Issues, challenges and application of big data in smart home. IJCA Proc Int Conf Comput Syst Math Sci 14–17

  37. 37.

    Tendeloo YV, Vangheluwe H (2018) Discrete event system specification modeling and simulation. Winter Simulation Conference (WSC) 162–176.

  38. 38.

    Theodoridis E, Mylonas G, Chatzigiannakis I (2013) Developing an IoT smart city framework. IISA. doi:

  39. 39.

    Ullman JD (1975) NP-complete scheduling problems. J Comput Syst Sci 10(3):384–393

    MathSciNet  Article  Google Scholar 

  40. 40.

    Van Woensel L, Archer G, Panades-Estruch L, Vrscaj D (2019) Ten technologies which could change our lives: potential impacts and policy implications. Accessed 20 December 2019

  41. 41.

    Wainer GA, Goldstein R, Khan A (2018) Introduction to the discrete event system specification formalism and its application for modeling and simulating cyper-physical systems. Winter Simulation Conference (WSC) 177–191

  42. 42.

    Wilson C, Hargreaves T, Hauxwell-Baldwin R (2017) Benefits and risks of smart home technologies. Energy Policy 103:72–83

    Article  Google Scholar 

  43. 43.

    Zeigler BP (2003) DEVS today: recent advances in discrete event-based information technology. In the11th IEEE/ACM international symposium on modeling, analysis and simulation of computer telecommunications systems, MASCOTS. 148–161

  44. 44.

    Zeigler BP, Louri A (1993) A simulation environment for intelligent machine architectures. J parallel Distrib. Comput 18(1):77–88

    MATH  Google Scholar 

  45. 45.

    Zeigler BP, Reynolds RG (1985) Towards a theory of adaptive computer architectures. In the 5th international conference on distributed computing systems. Computer Society Press 19:468–475

    Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Moath Jarrah.

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

Verify currency and authenticity via CrossMark

Cite this article

Albataineh, M., Jarrah, M. DEVS-IoT: performance evaluation of smart home devices network. Multimed Tools Appl (2020).

Download citation


  • DEVS formalism
  • Smart home network
  • Scheduling
  • Modeling and simulation
  • Poisson process