Flexible network management and application service adaptability in software defined wireless sensor networks

  • Kgotlaetsile Mathews Modieginyane
  • Reza MalekianEmail author
  • Babedi Betty Letswamotse
Original Research


The need for highly responsive and adaptable computing systems is essential in today’s network computing age. This is principally due to the drastic evolution in broad computing platforms operating at highly descriptive and abstracted mediums such as; reconfigurable computing systems, smart automation systems, cognitive and parallel programming systems which communicate using very complex resources or modes. Hence, such systems must incorporate the best forms of technologies to cater for the rapidly growing and heterogeneously connected platforms such as with Internet of Things (IoT). However, to effectively manage these network platforms with such high-end computing resources, requires a well-structured and carefully implemented systems. This work implements a Software Defined Wireless Sensor Network (SDWSN) approach coupled with Discrete Event Simulation (DES) and a highly extensible and scalable Software Defined Networking (SDN) controller–OpenDayLight (ODL), to implement a software-oriented network environment to increase network service adaptability and simplify network management. The implemented approach uses the ODL’s Model-Driven Service Abstraction Layer (MD-SAL) to facilitate the forwarding layer by applying state procedures to manage flow rules and introduce software-oriented network services. Experimental results indicate that in this approach, the traffic flow routing is significantly improved, with reduced transmission delays and that the underlying sensor nodes uses less energy since energy demanding tasks are performed on the controller.


Internet of things Software defined wireless sensor networks Discrete event simulation Software defined networking Model-driven service abstraction layer 



We are grateful of the National Research Foundation (NRF) of South Africa as well as Telkom South Africa for their continuous financial support through this work. We also acknowledge the University of Pretoria (UP) for lab resources that are provided to us for the success of our work.

Compliance with ethical standards

Conflict of interest

There are no conflicts of interest in this work. Every aspect of this work was a collective effort and agreement of all the authors herewith.


  1. Anadiotis AG, Galluccio L, Milardo S, Morabito G, Palazzo S (2017) SD-WISE: a software-defined wireless sensor network. ArXiv:1710.09147Google Scholar
  2. Can Z, Demirbas M (2016) Querying on federated sensor networks. J Sensor Actuator Netw 5(3):14, 1–15. Google Scholar
  3. Dallaglio M, Sambo N, Cugini F, Castoldi P (2017) Control and management of transponders with NETCONF and YANG. IEEE/OSA J Opt Commun Netw 9(3):B43–B52. CrossRefGoogle Scholar
  4. Huang R, Chu X, Zhang J, Hu YH (2015) Energy-efficient monitoring in software defined wireless sensor networks using reinforcement learning: a prototype. Int J Distrib Sens Netw 2015:360428. CrossRefGoogle Scholar
  5. Junli F, Yawen W, Haibin S (2017) An improved energy-efficient routing algorithm in software define wireless sensor network. In: Signal Processing, Communications and Computing (ICSPCC), 2017 IEEE International Conference on, pp 1–5.
  6. Kadel R, Ahmed K, Nepal A (2017) Adaptive error control code implementation framework for software defined wireless sensor network (SDWSN). In: Telecommunication Networks and Applications Conference (ITNAC), 2017 27th International, pp 1–6.
  7. Li G, Guo S, Yang Y, Yang Y (2018) Traffic load minimization in software defined wireless sensor networks. Internet Things J IEEE (99):1–9.
  8. Luo T, Tan H, Quek TQS (2012) Sensor OpenFlow: enabling software-defined wireless sensor networks. IEEE Commun Lett 16(11):1896–1899. CrossRefGoogle Scholar
  9. McKeown N, Anderson T, Balakrishnan H, Parulkar G, Peterson L, Rexford J, Shenker S, Turner J (2008) OpenFlow: enabling innovation in campus networks. ACM SIGCOMM Comput Commun Rev.
  10. Miguel MLF, Penna MC, Jamhour E, Pellenz E (2017) A CoAP based control plane for software defined wireless sensor networks. J Commun Netw 19(6):555–562. CrossRefGoogle Scholar
  11. Misra S, Bera S, Achuthananda MP, Pal SK, Obaidat MS (2017) Situation-aware protocol switching in software-defined wireless sensor network systems. IEEE Syst J (99):1–8.
  12. Modieginyane KM, Letswamotse BB, Malekian R, Abu-Mahfouz AM (2017) Software defined wireless sensor networks application opportunities for efficient network management: a survey. Comput Electr Eng 1–14.
  13. Mukherjee M, Shu L, Zhao T, Wang D, Wang H (2017) Lightweight flow management for software-defined wireless sensor networks with link fault in data plane. In: 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp 998–999.
  14. Nguyen TMC, Hoang DB, Chaczko Z (2016) Can SDN technology be transported to software-defined WSN/IoT? In: 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp 234–239.
  15. Reza M, Bogatinoska DC, Karadimce A, Trengoska J, Nyako WA (2015) A novel smart ECO model for energy consumption optimization. Elektronika ir Elektrotechnika 21(6):75–80. Google Scholar
  16. Spachos P, Lin J, Bannazadeh H, Leon-Garcia A (2015) Smart room monitoring through wireless sensor networks in software defined infrastructures. In: 2015 IEEE 4th International Conference on Cloud Networking (CloudNet), pp 216–218.
  17. Van Adrichem NLM, Doerr C, Kuipers FA (2014) OpenNetMon: network monitoring in OpenFlow software-defined networks. In: Network operations and management symposium (NOMS), 2014 IEEE, pp 5–9.
  18. Wang J, Zhai P, Zhang Y, Shi L, Wu G, Shi X, Zhou P (2018) Software defined network routing in wireless sensor network. In: Wan J et al (eds) Cloud computing, security, privacy in new computing environments. CloudComp 2016, SPNCE 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 197.
  19. Wannenburg J, Malekian R (2017) Physical activity recognition from smartphone accelerometer data for user context awareness sensing. IEEE Trans Syst Man Cybern Syst 47(12):3142–3149. CrossRefGoogle Scholar
  20. Xiang W, Wang N, Zhou Y (2016) An energy-efficient routing algorithm for software-defined wireless sensor networks. IEEE Sens J 16(20):7393–7400. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical, Electronic and Computer EngineeringUniversity of PretoriaPretoriaSouth Africa

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