, Volume 101, Issue 12, pp 1935–1970 | Cite as

On the performance, availability and energy consumption modelling of clustered IoT systems

  • Enver EverEmail author
  • Purav Shah
  • Leonardo Mostarda
  • Fredrick Omondi
  • Orhan Gemikonakli


Wireless sensor networks (WSNs) form a large part of the ecosystem of the Internet of Things (IoT), hence they have numerous application domains with varying performance and availability requirements. Limited resources that include processing capability, queue capacity, and available energy in addition to frequent node and link failures degrade the performance and availability of these networks. In an attempt to efficiently utilise the limited resources and to maintain the reliable network with efficient data transmission; it is common to select a clustering approach, where a cluster head is selected among the diverse IoT devices. This study presents the stochastic performance as well as the energy evaluation model for WSNs that have both node and link failures. The model developed considers an integrated performance and availability approach. Various duty cycling schemes within the medium-access control of the WSNs are also considered to incorporate the impact of sleeping/idle states that are presented using analytical modeling. The results presented using the proposed analytical models show the effects of factors such as failures, various queue capacities and system scalability. The analytical results presented are in very good agreement with simulation results and also present an important fact that the proposed models are very useful for identification of thresholds between WSN system characteristics.


WSNs IoT Energy consumption Stochastic models Performability Clustering 

Mathematics Subject Classification

68 90 



  1. 1.
    Abidoye AP, Obagbuwa IC (2017) Models for integrating wireless sensor networks into the internet of things. IET Wirel Sens Syst 7(3):65–72CrossRefGoogle Scholar
  2. 2.
    Ameen MA, Islam SMR, Kwak KS (2010) Energy saving mechanisms for MAC protocols in wireless sensor networks. IJDSN 2010Google Scholar
  3. 3.
    Anastasi G, Conti M, Francesco MD (2009) Extending the lifetime of wireless sensor networks through adaptive sleep. IEEE Trans Ind Inform 5(3):351–365CrossRefGoogle Scholar
  4. 4.
    Ashraf QM, Habaebi MH (2015) Autonomic schemes for threat mitigation in internet of things. J Netw Comput Appl 49:112–127CrossRefGoogle Scholar
  5. 5.
    Atzori L, Iera A, Morabito G (2017) Understanding the internet of things: definition, potentials, and societal role of a fast evolving paradigm. Ad Hoc Netw 56:122–140CrossRefGoogle Scholar
  6. 6.
    Banks J, Carson JS, Nelson BL, Nicol DM (2005) Discrete-event system simulation, 4th edn. Prentice Hall, Upper Saddle River, NJGoogle Scholar
  7. 7.
    Chakka R (1998) Spectral expansion solution for some finite capacity queus. Ann Oper Res 79:27–44MathSciNetCrossRefGoogle Scholar
  8. 8.
    Chakka R, Ever E, Gemikonakli O (2007) Joint-state modeling for open queuing networks with breakdowns, repairs and finite buffers. In: Modeling, analysis, and simulation of computer and telecommunication systems, 2007. MASCOTS ’07. 15th international symposium on. pp 260–266Google Scholar
  9. 9.
    Chan WHR, Zhang P, Zhang W, Nevat I, Valera AC, Tan H, Gautam N (2015) Adaptive duty cycling in sensor networks via continuous time Markov chain modelling. In: 2015 IEEE international conference on communications, ICC 2015, London, United Kingdom, June 8–12, 2015. pp 6669–6674Google Scholar
  10. 10.
    Chiasserini C, Garetto M (2006) An analytical model for wireless sensor networks with sleeping nodes. IEEE Trans Mob Comput 5(12):1706–1718CrossRefGoogle Scholar
  11. 11.
    El Kafhali S, Salah K (2017) Efficient and dynamic scaling of fog nodes for iot devices. J Supercomput 73(12):5261–5284CrossRefGoogle Scholar
  12. 12.
    Ever E (2014) Fault-tolerant two-stage open queuing systems with server failures at both stages. IEEE Commun Lett 18(9):1523–1526CrossRefGoogle Scholar
  13. 13.
    Ever E, Gemikonakli O, Koçyigit A, Gemikonakli E (2013) A hybrid approach to minimize state space explosion problem for the solution of two stage tandem queues. J Netw Comput Appl 36(2):908–926CrossRefGoogle Scholar
  14. 14.
    Ezdiani S, Acharyya IS, Sivakumar S, Al-Anbuky A (2017) Wireless sensor network softwarization: towards wsn adaptive qos. IEEE Internet Things J 4(5):1517–1527CrossRefGoogle Scholar
  15. 15.
    Instruments T (2003) Msp430\(\times \)13\(\times \), msp430\(\times \)14 mixed signal microcontroller user guide. Tech. rep. Accessed Jan 2017
  16. 16.
    Instruments T (2011) Msp430f47\(\times \)3, msp430f47\(\times \)4 mixed signal microcontroller user guide. Tech. rep. Accessed Jan 2017
  17. 17.
    Javed A, Larijani H, Ahmadinia A, Emmanuel R, Mannion M, Gibson D (2017) Design and implementation of a cloud enabled random neural network-based decentralized smart controller with intelligent sensor nodes for hvac. IEEE Internet Things J 4(2):393–403CrossRefGoogle Scholar
  18. 18.
    Jurdak R, Ruzzelli AG, O’Hare GM (2010) Radio sleep mode optimization in wireless sensor networks. IEEE Trans Mob Comput 9(7):955–968CrossRefGoogle Scholar
  19. 19.
    Kougianos E, Mohanty SP, Coelho G, Albalawi U, Sundaravadivel P (2016) Design of a high-performance system for secure image communication in the internet of things. IEEE Access 4:1222–1242CrossRefGoogle Scholar
  20. 20.
    Krishna D, Ali T, Enver E, Fredrick AO, Purav S, Leonardo M, Orhan G (2016) Does the assumption of exponential arrival distributions in wireless sensor networks hold? Int J Sens Netw (IJSNet) 26(2):81–100Google Scholar
  21. 21.
    Laranjeira LA, Rodrigues GN (2014) Border effect analysis for reliability assurance and continuous connectivity of wireless sensor networks in the presence of sensor failures. IEEE Trans Wirel Commun 13(8):4232–4246CrossRefGoogle Scholar
  22. 22.
    Li J, Zhao YQ, Yu FR, Huang X (2016) Queuing analysis of two-hop relay technology in lte/lte-a networks with unsaturated and asymmetric traffic. IEEE Internet Things J 3(3):378–385CrossRefGoogle Scholar
  23. 23.
    Mitrani I (1997) Probabilistic modelling. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  24. 24.
    Mukter Z, Yand HW, Shabiul MI, Amin N (2014) An integrated hybrid energy harvester for autonomous wireless sensornetwork nodes. Int J Photoenergy 2014:760534Google Scholar
  25. 25.
    Munir A, Gordon-Ross A (2011) Markov modeling of fault-tolerant wireless sensor networks. In: Proceedings of 20th international conference on computer communications and networks, ICCCN 2011, Maui, Hawaii, July 31–August 4, 2011. pp 1–6Google Scholar
  26. 26.
    Munir A, Antoon J, Gordon-Ross A (2015) Modeling and analysis of fault detection and fault tolerance in wireless sensor networks. ACM Trans Embed Comput Syst 14(1):3:1–3:43CrossRefGoogle Scholar
  27. 27.
    Muralidharan S, Sahu BJR, Saxena N, Roy A (2017) Ppt: A push pull traffic algorithm to improve qos provisioning in iot-ndn environment. IEEE Commun Lett 21(6):1417–1420CrossRefGoogle Scholar
  28. 28.
    Nguyen TD, Khan JY, Ngo DT (2017) Energy harvested roadside ieee 802.15. 4 wireless sensor networks for iot applications. Ad Hoc Netw 56:109–121CrossRefGoogle Scholar
  29. 29.
    Odey AJ, Li D (2012) Low power transceiver design parameters for wireless sensor networks. Wirel Sens Netw 4(10):243–249CrossRefGoogle Scholar
  30. 30.
    Omondi FA, Ever E, Shah P, Gemikonakli O, Mostarda L (2014) Performability modelling and analysis of clustered wireless sensor networks with limited storage capacities. In: International conference on internet and distributed computing systems. Springer, pp 369–382Google Scholar
  31. 31.
    Omondi FA, Shah P, Gemikonakli O, Ever E (2015a) An analytical model for bounded wsns with unreliable cluster heads and links. In: 2015 IEEE 40th conference on local computer networks (LCN). IEEE, pp 201–204Google Scholar
  32. 32.
    Omondi FA, Shah P, Gemikonakli O, Ever E (2015b) An analytical model for bounded wsns with unreliable cluster heads and links. In: 40th IEEE conference on local computer networks, LCN 2015, Clearwater Beach, FL, USA, October 26–29, 2015. pp 201–204Google Scholar
  33. 33.
    Qiu T, Feng L, Xia F, Wu G, Zhou Y (2011) A packet buffer evaluation method exploiting queueing theory for wireless sensor networks. Comput Sci Inf Syst 8(4):1028–1049CrossRefGoogle Scholar
  34. 34.
    Silva I, Guedes LA, Portugal P, Vasques F (2012) Reliability and availability evaluation of wireless sensor networks for industrial applications. Sensors 12(1):806–838CrossRefGoogle Scholar
  35. 35.
    Sivieri A, Mottola L, Cugola G (2016) Building internet of things software with eliot. Comput Commun 89:141–153CrossRefGoogle Scholar
  36. 36.
    Tilak S, Abu-Ghazaleh NB, Heinzelman W (2002) A taxonomy of wireless micro-sensor network models. ACM SIGMOBILE Mob Comput Commun Rev 6(2):28–36CrossRefGoogle Scholar
  37. 37.
    Trivedi KS, Ma X, Dharmaraja S (2003) Performability modelling of wireless communication systems. Int J Commun Syst 16(6):561–577CrossRefGoogle Scholar
  38. 38.
    Wang C, Xing L, Vokkarane V, Sun YL (2014) Reliability and lifetime modeling of wireless sensor nodes. Microelectron Reliab 54(1):160–166CrossRefGoogle Scholar
  39. 39.
    Wang Y, Vuran MC, Goddard S (2012) Cross-layer analysis of the end-to-end delay distribution in wireless sensor networks. IEEE/ACM Trans Netw (TON) 20(1):305–318CrossRefGoogle Scholar
  40. 40.
    Xu L, Collier R, GM OHare (2017) A survey of clustering techniques in wsns and consideration of the challenges of applying such to 5g iot scenarios. IEEE Internet Things J 4(5):1229–1249CrossRefGoogle Scholar
  41. 41.
    Yang O, Heinzelman W (2013) An adaptive sensor sleeping solution based on sleeping multipath routing and duty-cycled MAC protocols. TOSN 10(1):10CrossRefGoogle Scholar
  42. 42.
    Zhang Y, Li WW (2012) Modeling and energy consumption evaluation of a stochastic wireless sensor network. EURASIP J Wirel Commun Netw 2012:282CrossRefGoogle Scholar
  43. 43.
    Zhen CF, Liu W, Liu Y, Yan A (2014) Energy-efficient sleep/wake scheduling for acoustic localization wireless sensor network node. Int J Distrib Sens Netw 10(2):1–14CrossRefGoogle Scholar
  44. 44.
    Zhou H, Luo D, Gao Y, Zuo DC (2011) Modeling of node energy consumption for wireless sensor networks. Wirel Sens Netw 3(1):18–23CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Engineering, Middle East Technical UniversityKalkanlı, Güzelyurt, Mersin 10Turkey
  2. 2.Faculty of Science and TechnologyMiddlesex UniversityThe BurroughUK
  3. 3.Scuola di Scienze e TecnologieUniversita’ degli Studi diCamerinoItaly
  4. 4.CT-CentreUniversity of NairobiNairobi, GPOKenya

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