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Enhancing IoT Performance via Using Mobility Aware for Dynamic RPL Routing Protocol Technique (MA-RPL)

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Języki publikacji
EN
Abstrakty
EN
Nodes' aware-mobility in the Internet of Things (IoTs) stills open defy for researchers, due to the dynamic changing of routing path and networks’ resource limitations. Therefore, in this study a new method is proposed called Mobility Aware - “Routing Protocol for Low power and Lossy Networks” (MARPL), that consists of two phases: in the first phase splitting the entire network into sub areas based on reference nodes with “Time Difference of Arrival” (TDoA) technique. While, the second phase, is about managing mobile nodes (MNs) in RPL according to the sub areas' ID. The Cooja simulator software has been used to implement and assess MA-RPL method performance, according to the data packet metrics (lost packet, packet delivery ratio PDR), latency and nodes' power usage in comparison with two methods: Corona (Co-RPL) and Mobility Enhanced (ME-RPL). The simulation results have been shown that the MA-RPL method consumes less nodes' energy usage, gives less latency with minimum data packet loss in comparison with Co-RPL and MERPL.
Twórcy
  • Directorate of Inspection, Ministry of Health, Baghdad, Iraq
Bibliografia
  • [1] R. Prasad and V. Rohokale, “Internet of Things (IoT) and Machine to Machine (M2M) Communication,” Cyber Security: The Lifeline of Information and Communication Technology, Springer Series in Wireless Technology, pp. 125-141, 2020. https://doi.org/10.1007/978-3-030-31703-4_9
  • [2] H. Tschofenig. and E. Baccelli, “Cyberphysical Security for the Masses: A Survey of the Internet Protocol Suite for Internet of Things Security,” In: IEEE Security and Privacy Magazine, Institute of Electrical and Electronics Engineers, vol. 17, no. 5, pp.47-57, 2019. https://doi.org/10.1109/MSEC.2019.2923973
  • [3] [H. Kharrufa, H. Al-Kashoash and A Kemp, “RPL-Based Routing Protocols in IoT Applications: A Review, ” In: IEEE Sensors Journal, vol.19, no.15, pp 5952-5967, 2019. https://doi.org/10.1109/JSEN.2019.2910881
  • [4] A. Zarzoor, “Optimizing RPL performance based on the selection of best route between child and root node using E-MHOF method,” International Journal of Electrical and Computer Engineering (IJECE), vol. 11, no. 1, pp. 2088-8708, 2021. http://doi.org/10.11591/ijece.v11i1.pp224-231
  • [5] M. Farooq and D. Pesch, “Reduced Overhead Routing in Short-Range Low-Power and Lossy Wireless Networks,” u Sensors, vol. 19, no. 5, pp. 1-10, 2019. https://dx.doi.org/10.3390%2Fs19051240
  • [6] K. Haque, A. Abdelgawad, V. Yanambaka and K. Yelamarthi, “An Energy-Efficient and Reliable RPL for IoT,” in 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), pp 1-2, 2020. https://doi.org/10.1109/WF-IoT48130.2020.9221450
  • [7] P. Satanasaowapak and C. Khunboa, “The improvement of node Mobility in RPL to increase transmission efficiency,” International Journal of Electrical and Computer Engineering, vol. 5, no. 9, pp. 4238-4249, 2019. http://doi.org/10.11591/ijece.v9i5.pp4238-4249
  • [8] D. Bendouda, L. Mokdad and H. Haffaf, “Exploiting node mobility for fault management in RPL-based wireless sensor networks,” International Journal of High Performance Computing and Networking (IJHPCN), vol. 12, no. 1, pp. 26-38, 2018. http://doi.org/10.1504/IJHPCN.2018.093839
  • [9] A. Musaddiq, Y. Zikria, Zulqarnain and S. Won Kim, “Routing protocol for Low-Power and Lossy Networks for heterogeneous traffic network,” EURASIP Journal on Wireless Communications and Networking, pp. 1-23, vol. 2020, no. 21,pp. 1-23, 2020. https://doi.org/10.1186/s13638-020-1645-4
  • [10] M. Zhao, I. W. Wang-Hei Ho, and P. Chong. “An energy-efficient region based rpl routing protocol for low-power and lossy networks,” IEEE Internet of Things Journal, vol. 3, no. 6, pp. 1319–1333, 2016. https://doi.org/10.1109/JIOT.2016.2593438
  • [11] O. Gaddour, A. Koubâa, R. Rangarajan, O. Cheikhrouhou, E. Tovar, and M. Abid, “Co-RPL: RPL routing for mobile low power wireless sensor networks using Corona mechanism,” in IEEE Industrial Embedded Systems (SIES), 2014 9th IEEE International Symposium, pp. 200-209, 2019. https://doi.org/10.1109/SIES.2014.6871205
  • [12] Z. Latib, A. Jamil, N. Alduais, J. Abdullah, L. Audah, and R. Alias, “Strategies for a better performance of RPL under mobility in wireless sensor networks,” in Advances in Electrical and Electronic Engineering: From Theory to Applications AIP Conference Proceedings, pp. 1-8, 2017. https://doi.org/10.1063/1.5002020
  • [13] I. El Korbi, M. Ben Brahim, C. Adjih and L. Saidane, “Mobility Enhanced RPL for Wireless Sensor Networks,” in 2012 Third International Conference on The Network of the Future (NOF), 2012: 1-8. https://doi.org/10.1109/NOF.2012.6463993
  • [14] P. Sharma, V. Jain and A. Kumar Uprawal, “EMAEER: Enhanced Mobility Aware Energy Efficient Routing Protocol for Internet of Things,” in 2018 Conference on Information and Communication Technology (CICT), pp.1-6, 2018. https://doi.org/10.1109/INFOCOMTECH.2018.8722396
  • [15] H. Kharrufa, H. Al-Kashoash, Y. Al-Nidawi, M. Mosquera and A. Kemp, “Dynamic RPL for multi-hop routing in IoT applications,” in 2017 13th Annual Conference on Wireless On-demand Network Systems and Services (WONS), Jackson, WY, pp. 100-103, 2017. https://doi.org/10.1109/WONS.2017.7888753
  • [16] M. Radhesh Anand and M. Tahiliani, “mRPL++: Smarter-HOP for optimizing mobility in RPL,” in 2016 IEEE Region 10 Symposium (TENSYMP). IEEE, pp. 36–41, 2016. https://doi.org/10.1109/TENCONSpring.2016.7519374
  • [17] M. Barcelo, A. Correa, J. Vicario, A. Morell and X. Vilajosana, “Addressing Mobility in RPL With Position Assisted Metrics,” IEEE Sensors Journal, vol. 16, no. 7, pp.2151-2161, 2016. https://doi.org/10.1109/JSEN.2015.2500916
  • [18] J. Kniess and V. De Figueiredo Marques, “MARPL: A crosslayer approach for Internet of things based on neighbor variability for mobility support in RPL,” Trans Emerging Tel Tech, pp. 1-17, 2020. https://doi.org/10.1002/ett.3931
  • [19] V. de Figueiredo Marques, J. Kniess, “Mobility Aware RPL (MARPL): Mobility to RPL on Neighbor Variability,” in Green, Pervasive, and Cloud Computing. GPC 2019. Lecture Notes in Computer Science, vol. 11484, pp.59-73, 2019. https://doi.org/10.1007/978-3-030-19223-5_5
  • [20] P. Wu, S. Su. Z. Zuo, , X. Guo, B. Sun, X. Wen, “Time Difference of Arrival (TDoA) Localization Combining Weighted Least Squares and Firefly Algorithm,” Sensors, vol.19, no.2554, pp. 1-14, 2019. http://doi.org/10.3390/s19112554
  • [21] M. Bai, S. Lan and J. Huang, “Time Difference of Arrival (TDOA)-Based Acoustic Source Localization and Signal Extraction for Intelligent Audio Classification,” In: 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM), pp. 632-636, 2018. https://doi.org/10.1109/SAM.2018.8448583
  • [22] F. Khelifi, A. Bradai, A. Benslimane, R. Priyanka and A. Mohamed, “A Survey of Localization Systems in Internet of Things,” Mobile Networks and Applications, vol. 24, pp. 761–785, 2019. https://doi.org/10.1007/s11036-018-1090-3
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-80a687dd-bfd2-4c0d-b165-c4b67b191259
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