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A CPN-based model for assessing energy consumption of IoT networks

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Abstract

Wireless sensor networks (WSN) commonly have energy consumption as a remarkable issue. Due to the popularity of Internet of Things (IoT) systems, attention has been devoted to low-power wide-area networks (LPWAN), concerning the influence of transceivers on energy utilization. For instance, the energy for a communication transceiver to send one single bit can be 1500 to 2000 times higher than the energy required to execute a single instruction. Therefore, evaluating the energy consumption of network physical layer for IoT systems is prominent for system design. This work presents a Colored Petri nets (CPN)-based framework for evaluating the energy consumption of LPWAN-based IoT systems, focusing on CPU and communication transceivers. The proposed model has been validated using a hardware platform with ARM7DMI and LoRa, and results indicate an error less than 1.4% and 0.14% for the CPU and network, respectively, and show the feasibility of the proposed model to estimate the impact of packet losses and timeout on the system performance.

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References

  1. Mckinsey. Disruptive technologies report. https://www.mckinsey.com/

  2. Parvez I, Rahmati A, Guvenc I, Sarwat AI, Dai H (2018) A survey on low latency towards 5G: ran, core network and caching solutions. IEEE Commun Surv Tutor 20(4):3098–3130. https://doi.org/10.1109/COMST.2018.2841349

    Article  Google Scholar 

  3. Al-Sakran A, Qutqut MH, Almasalha F, Hassanein HS, Hijjawi M (2018) In: 2018 14th International Wireless Communications Mobile Computing Conference (IWCMC), pp 291–297

  4. Adegbija T, Rogacs A, Patel C, Gordon-Ross A (2018) Microprocessor optimizations for the internet of things: a survey. IEEE Trans Comput Aided Des Integr Circuits Syst 37(1):7–20. https://doi.org/10.1109/TCAD.2017.2717782

    Article  Google Scholar 

  5. Kaur J, Reddy S (2017) Operating systems for low-end smart devices: a survey and a proposed solution framework. Int J Inform Technol 10:49–58. https://doi.org/10.1007/s41870-017-0044-5

    Article  Google Scholar 

  6. Ray P (2018) A survey on internet of things architectures. J King Saud Univ Comput Inform Sci 30(3):291–319. https://doi.org/10.1016/j.jksuci.2016.10.003

    Article  Google Scholar 

  7. Anwar F, D’Souza S, Symington A, Dongare A, Rajkumar R, Rowe A, Srivastava M (2016) In: 2016 IEEE Real-Time Systems Symposium (RTSS), pp 191–202

  8. Adegbija T, Rogacs A, Patel C, Gordon-Ross A (2018) Microprocessor optimizations for the internet of things: a survey. IEEE Trans Comput Aided Des Integr Circuits Syst 37(1):7–20

    Article  Google Scholar 

  9. Valdes Pena MD, Rodriguez-Andina JJ, Manic M (2017) The internet of things: the role of reconfigurable platforms. IEEE Ind Electron Mag 11(3):6–19. https://doi.org/10.1109/MIE.2017.2724579

    Article  Google Scholar 

  10. Javed F, Afzal MK, Sharif M, Kim B (2018) Internet of things (IoT) operating systems support, networking technologies, applications, and challenges: a comparative review. IEEE Commun Surv Tutor 20(3):2062–2100. https://doi.org/10.1109/COMST.2018.2817685

    Article  Google Scholar 

  11. Jensen K, Kristensen LM (2009) Coloured petri nets: modelling and validation of concurrent systems, 1st edn. Springer-Verlag, Berlin, Heidelberg

    Book  MATH  Google Scholar 

  12. Marsan MA, Balbo G, Conte G, Donatelli S, Franceschinis G (1994) Modelling with generalized stochastic petri nets, 1st edn. Wiley, USA

    MATH  Google Scholar 

  13. Stewart WJ (2009) Probability, Markov chains, queues, and simulation: the mathematical basis of performance modeling. Princeton University Press, USA

    Book  MATH  Google Scholar 

  14. NXP. Lpc2103 user manual. https://www.nxp.com/docs/en/user-guide/UM10161.pdf

  15. Semtech. Sx1276-7-8-9 datasheet. https://www.semtech.com/products/wireless-rf/lora-transceivers

  16. Martinez B, Montón M, Vilajosana I, Prades JD (2015) The power of models: modeling power consumption for IoT devices. IEEE Sens J 15(10):5777–5789

    Article  Google Scholar 

  17. Sun M, Shi Z, Chen S, Zhou Z, Duan Y (2017) Energy-efficient composition of configurable internet of things services. IEEE Access 5:25609–25622

    Article  Google Scholar 

  18. Berrachedi A, Boukala-Ioualalen M (2016) In: 2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp 772–777

  19. Shareef A, Zhu Y (2012) Effective stochastic modeling of energy-constrained wireless sensor networks. J Comput Netw Commun. https://doi.org/10.1155/2012/870281

    Article  Google Scholar 

  20. Bor M, Roedig U, (2017) In: 2017 13th International Conference on Distributed Computing in Sensor Systems (DCOSS), pp 27–34. https://doi.org/10.1109/DCOSS.2017.10

  21. Rajab H, Cinkler T, Bouguera T (2021) IoT scheduling for higher throughput and lower transmission power. Wirel Netw. https://doi.org/10.1007/s11276-020-02307-1

    Article  Google Scholar 

  22. Rajab H, Cinkler T, Bouguera T (2021) Evaluation of energy consumption of lpwan technologies. https://doi.org/10.21203/rs.3.rs-343897/v1

  23. Gupta S, Snigdh I (2022) Clustering in lora networks, an energy-conserving perspective. Wirel Pers Commun 122:1–14. https://doi.org/10.1007/s11277-021-08894-2

    Article  Google Scholar 

  24. Farooq MO (2020) Clustering-based layering approach for uplink multi-hop communication in lora networks. IEEE Netw Lett 2(3):132–135. https://doi.org/10.1109/LNET.2020.3003161

    Article  Google Scholar 

  25. Delgado C, Sanz JM, Blondia C, Famaey J (2021) Batteryless lorawan communications using energy harvesting: modeling and characterization. IEEE Internet Things J 8(4):2694–2711. https://doi.org/10.1109/JIOT.2020.3019140

    Article  Google Scholar 

  26. Berrachedi A, Boukala-Ioualalen M (2016) In: 2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp 772–777. https://doi.org/10.1109/WAINA.2016.86

  27. Lages D, Borba E, Araujo J, Tavares E, Sousa E (2021) In: 2021 IEEE International Systems Conference (SysCon), pp 1–8. https://doi.org/10.1109/SysCon48628.2021.9447103

  28. Krug S, O’Nils M (2019) Modeling and comparison of delay and energy cost of IoT data transfers. IEEE Access 7:58654–58675. https://doi.org/10.1109/ACCESS.2019.2913703

    Article  Google Scholar 

  29. Martinez B, Montón M, Vilajosana I, Prades JD (2015) The power of models: modeling power consumption for IoT devices. IEEE Sens J 15(10):5777–5789. https://doi.org/10.1109/JSEN.2015.2445094

    Article  Google Scholar 

  30. Ju Q, Zhang Y (2018) Predictive power management for internet of battery-less things. IEEE Trans Power Electron 33(1):299–312. https://doi.org/10.1109/TPEL.2017.2664098

    Article  Google Scholar 

  31. Guizani M, Rayes A, Khan B, Al-Fuqaha A (2010) Network modeling and simulation: a practical perspective. Wiley, USA

  32. Gosavi A (2014) Simulation-based optimization: parametric optimization techniques and reinforcement learning. In: Operations Research/Computer Science Interfaces Series, Springer, US, USA

  33. Damaso A, Freitas D, Rosa N, Silva B, Maciel P (2013) Evaluating the power consumption of wireless sensor network applications using models. Sensors (Basel, Switzerland) 13:3473–3500. https://doi.org/10.3390/s130303473

    Article  Google Scholar 

  34. Zairi S, Mezni A, Zouari B (2015) In: Aguayo-Torres MC, Gómez G, Poncela J (eds) 13th International Conference on Wired/Wireless Internet Communication (WWIC), Wired/Wireless Internet Communications, Springer International Publishing, Malaga, Spain, pp 381–395. https://doi.org/10.1007/978-3-319-22572-2_28. https://hal.inria.fr/hal-01728799.

  35. Coronado E, Valero V, Orozco-Barbosa L, Cambronero M, Pelayo FL (2021) Modeling and simulation of the IEEE 802.11e wireless protocol with hidden nodes using colored petri nets. Softw. Syst. Model. 20(2):505–538

    Article  Google Scholar 

  36. Petajajarvi J, Pettissalo M, Mikhaylov K, Roivainen A, Hänninen T (2015) In: 2015 14th International Conference on ITS Telecommunications (ITST). https://doi.org/10.1109/ITST.2015.7377400

  37. Semtech. Sx1272/3/6/7/8: Lora modem designer’s guide. https://www.semtech.com/

  38. Murata T (1989) Petri nets: properties, analysis and applications. Proc IEEE 77(4):541–580. https://doi.org/10.1109/5.24143

    Article  Google Scholar 

  39. Hahm O, Baccelli E, Petersen H, Tsiftes N (2016) Operating systems for low-end devices in the internet of things: a survey. IEEE Internet Things J 3(5):720–734. https://doi.org/10.1109/JIOT.2015.2505901

    Article  Google Scholar 

  40. Anwar F, D’Souza S, Symington A, Dongare A, Rajkumar R, Rowe A, Srivastava M (2016) In: 2016 IEEE Real-Time Systems Symposium (RTSS), pp 191–202. https://doi.org/10.1109/RTSS.2016.027

  41. Javed F, Afzal MK, Sharif M, Kim B (2018) Internet of things (iot) operating systems support, networking technologies, applications, and challenges: a comparative review. IEEE Commun Surv Tutor 20(3):2062–2100

    Article  Google Scholar 

  42. Ratzer AV, Wells L, Lassen HM, Laursen M, Qvortrup JF, Stissing MS, Westergaard M, Christensen S, Jensen K (2003) In: Proceedings of the 24th International Conference on Applications and Theory of Petri Nets, Springer-Verlag, Berlin, Heidelberg, ICATPN’03, pp 450–462

  43. Heltec. Heltec lora 32 (v2). https://heltec.org/proudct_center/lora/lora-node/

  44. Keysight. 34465a digital multimeter, 6 \(1/2\) digit, truevolt dmm. https://www.keysight.com/

  45. Davison A, Hinkley D (1997) Bootstrap methods and their application. J Am Statist Assoc. https://doi.org/10.2307/1271471

    Article  MATH  Google Scholar 

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Author notes

  1. Eric Borba, Eduardo Tavares, Andson Balieiro and Erica Souza have contributed equally to this work.

Authors and Affiliations

  1. CIn, UFPE, Av. Jorn. Aníbal Fernandes, s/n, Recife, PE, 50740-560, Brazil

    Diogo Lages, Eric Borba, Eduardo Tavares & Andson Balieiro

  2. UFRPE, Rua Dom Manuel de Medeiros, s/n, Recife, PE, 52171-900, Brazil

    Erica Souza

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  1. Diogo Lages
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Lages, D., Borba, E., Tavares, E. et al. A CPN-based model for assessing energy consumption of IoT networks. J Supercomput 79, 12978–13000 (2023). https://doi.org/10.1007/s11227-023-05185-4

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