Abstract
Realizing efficient network control requires intelligent methods of network management, especially for sensitive network data. Different network types implement diverse methods to control and administer network traffic as well as effectively manage network resources. As with wireless sensor networks (WSNs), communication traffic and network resource control are performed depending on independently employed mechanisms to deal with events occurring on different levels of the network. It is therefore challenging to realize efficient network performance with guaranteed quality of service (QoS) in WSNs, given their computing limitations. Software defined networking (SDN) carries the potential to improve and evolve WSNs in terms of capacity and application. A means to apply SDN strategies to these compute-limited WSNs, formulates software defined wireless sensor networks (SDWSN). This work proposes a QoS Path Selection and Resource-associating (Q-PSR) scheme for adaptive load balancing and intelligent resource control for optimal network performance. Our experimental results indicate better performance in terms of computation with load balancing and efficient resource alignment for different networking tasks when compared with other competing schemes.
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References
Abdala MA, Younis SB, Jaseem SA (2017) Software-Defined Networking for Wireless Sensor Networks, In: Proceedings of Al-Salam University College First Scientific Conference SFSC’17, pp 513–524
D’Aniello G, Gaeta M, Hong T (2018) Effective quality-aware sensor data management. IEEE Trans Emerg Topics Comput Intell 2(1):65–77. https://doi.org/10.1109/TETCI.2017.2782800
Di Dio P, Faraci S, Galluccio L, Milardo S, Morabito G, Palazzo S, Livreri P (2016) Exploiting state information to support QoS in Software-Defined WSNs. IN: 2016 Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net), pp 1–7. https://doi.org/10.1109/MedHocNet.2016.7528421
Esguerra RAD, Truelen ADP, Festin CAM, Tan WM (2017) SDN-enabled loss mitigation of wireless sensor network data traffic. In: TENCON 2017–2017 IEEE Region 10 Conference, pp 387–392. https://doi.org/10.1109/TENCON.2017.8227895
Fotouhi H, Vahabi M, Ray A, Björkman M (2016) SDN-TAP: An SDN-based traffic aware protocol for wireless sensor networks. In: 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), pp 1–6. https://doi.org/10.1109/HealthCom.2016.7749527
Gaeta M, Loia V, Tomasiello S (2015) Multisignal 1-d compression by f-transform for wireless sensor networks applications. Appl Soft Comput 30:329–340. https://doi.org/10.1016/j.asoc.2014.11.061
Galluccio L, Milardo S, Morabito G, Palazzo S (2015) SDN-WISE: Design, prototyping and experimentation of a stateful SDN solution for WIreless SEnsor networks. In: 2015 IEEE Conference on Computer Communications (INFOCOM), pp 513–521. https://doi.org/10.1109/INFOCOM.2015.7218418
IETF (2010) YANG—a data modeling language for the network configuration protocol (NETCONF). https://tools.ietf.org/html/rfc6020
Kang J, Zhang Y, Nath B (2007) TARA: topology-aware resource adaptation to alleviate congestion in sensor networks. IEEE Trans Parallel Distrib Syst 18(7):919–931. https://doi.org/10.1109/TPDS.2007.1030
Lan Z, Ma W, Xia W, Shen L, Yan F, Ren L (2017) Design and implementation of flow-based programmable nodes in software-defined sensor networks. In: 2017 3rd IEEE International Conference on Computer and Communications (ICCC), pp 734–738. https://doi.org/10.1109/CompComm.2017.8322640
Levis P, Gay DE (2009) TinyOS. Cambridge University Press, pp 55
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. https://doi.org/10.1109/JIOT.2018.2797906
MEMSIC (2010) TelosB Mote, TPR2420 Platform
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 66:274–287. https://doi.org/10.1016/j.compeleceng.2017.02.026
Modieginyane KM, Malekian R, Letswamotse BB (2018) Flexible network management and application service adaptability in software defined wireless sensor networks. J Ambient Intell Humaniz Comput 9:44. https://doi.org/10.1007/s12652-018-0766-7
Mukherjee M, Shu L, Zhao T, Li K, Wang H (2016) Low Control Overhead-Based Sleep Scheduling in Software-Defined Wireless Sensor Networks. In: 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp 1236–1237. https://doi.org/10.1109/HPCC-SmartCity-DSS.2016.0174
OpenDayLight (2017) YANG Tools, https://wiki.opendaylight.org/view/YANG_Tools
Razzaque MA, Bleakley C, Dobson S (2013) Compression in wireless sensor networks: a survey and comparative evaluation. ACM Trans Sensor Netw 10(1):1–44
Santos LFDS, Mendonça FFD, Dias KL (2017) µSDN: An SDN-Based Routing Architecture for Wireless Sensor Networks. In: 2017 VII Brazilian Symposium on Computing Systems Engineering (SBESC), Curitiba, pp 63–70. https://doi.org/10.1109/SBESC.2017.15
Semenciuc E, Pastrav A, Palade T, Puschita E (2018) SDWN for End-to-End QoS Path Selection in a Wireless Network Ecosystem. In: Fratu O, Militaru N, Halunga S (eds) Future Access Enablers for Ubiquitous and Intelligent Infrastructures. FABULOUS 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 241. https://doi.org/10.1007/978-3-319-92213-3_20
Sergiou C, Vassiliou V (2011) DAlPaS: A performance aware congestion control algorithm in Wireless Sensor Networks. In: 2011 18th International Conference on Telecommunications, pp 167–173. https://doi.org/10.1109/CTS.2011.5898912
Shu L, Hauswirth M, Chao H, Chen M, Zhang Y (2011) NetTopo: a framework of simulation and visualization for wireless sensor networks. Ad Hoc Netw 9(5):799–820. https://doi.org/10.1016/j.adhoc.2010.09.003
Wei Y, Muqing W, Wenxing L, Min Z (2017) The design of load-balance based routing algorithm in software defined wireless sensor networks. In: 2017 IEEE/CIC International Conference on Communications in China (ICCC), pp 1–6. https://doi.org/10.1109/ICCChina.2017.8330522
Zhang Y, Zhu Y, Yan F, Li Z, Shen L (2016) Semidefinite programming based resource allocation for energy consumption minimization in software defined wireless sensor networks. In: 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pp 1–6. https://doi.org/10.1109/PIMRC.2016.7794902
Acknowledgements
This work has been achieved through the continued support from the National Research Foundation (NRF) of South Africa (grant numbers: IFR160118156967 and RDYR160404161474). We also acknowledge the University of Pretoria for providing hardware facilities for the purpose of this work.
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Letswamotse, B.B., Malekian, R. & Modieginyane, K.M. Adaptable QoS provisioning for efficient traffic-to-resource control in software defined wireless sensor networks. J Ambient Intell Human Comput 11, 2397–2405 (2020). https://doi.org/10.1007/s12652-019-01263-9
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DOI: https://doi.org/10.1007/s12652-019-01263-9