IoT based traffic congestion control for environmental applications
Abstract
Recently, the demands of the Internet of Things (IoT) have been steadily growing .The main idea of the Internet of Things (IoT) is to revolutionize the current Internet by enriching it with a large number of intelligent objects that communicate with each other. Heterogeneous communication technologies are integrated into the Internet to achieve the IoT application. The function of these objects is to collect data from sensors, process it, and communicate it. Therefore, sensors are main features of IoT. The Heterogeneous Wireless sensor network (HWSN) is a key technology element of the Internet of Things (IoT), which is considered as the future evolution of the Internet. The integration of HWSNs into IoT makes communication with any type of object possible and opens the gates to a multitude of application areas. We have proposed: (Load-Balancing based Clustering Multipath routing protocol for Heterogeneous sensor networks). After having detailed the functioning of our protocol, we present the realization of the simulations by the NS3 simulator, whose objective is to allow us to evaluate the performances of the proposed (Load-Balancing based Clustering Multipath routing protocol for HWSN) by comparing the results with the two protocols: (Weight based clustering in wireless sensor networks) and (Distributed Energy efficient Adaptive Clustering Protocol). We thus take into account several metrics for the performance of evaluation. We also note that the proposed protocol is 47% better than DEACP with respect to fist node die, and 35% better than WBCHN with respect to last node die while maintaining the average data transmission delay. We were able to show through the simulation results obtained that the objectives have been reached.
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Abbasi, A. A., and Younis, M. 2007. A survey on clustering algorithms for wireless sensor networks, Computer Communications, Elsevier, 30(14–15), 2826–2841.
Chirihane Gherbi, Zibouda Aliouat, Mohamed Benmohammed, 2016, An adaptive clustering approach to dynamic load balancing and energy efficiency in wireless sensor networks. Elsevier, journal of energy 114 / 647-662.
N. Meghanathan, P. MumfordLin , A Benchmarking Algorithm to Determine the Sequence of Stable Data Gathering Trees for Wireless Mobile Sensor Networks journal of INFORMATICA . Vol 37, No 3 (2013)
C. R., and Gerla, M. (1997), Adaptive clustering for mobile wireless networks, IEEE Journal on Selected Areas in Communications, 15(7), 1265–1275.
Youssef, M. A., Youssef, A., and Younis, M. F. (2009), Overlapping multi hop clustering for wireless sensor networks, IEEE Transactions on Parallel and Distributed Systems, 20(12), 1844–1856.
Youssef, M. A., Youssef, A., and Younis, M. F. 2009, Overlapping multi hop clustering for wireless sensor networks, IEEE Transactions on Parallel and Distributed Systems, 1844–1856.
Cheng, C. T., Tse, C. K., and Lau, F. C. M. 2011, A clustering algorithm for wireless sensor networks based on social insect colonies, IEEE Sensors Journal, 1(3), 711–721.
Younis, O., Krunz, M., and Ramasubramanian, S. 2006, Node clustering in wireless sensor networks: Recent developments and deployment challenges, IEEE Network, 20(3), 20–25.
Chirihane Gherbi, Zibouda Aliouat, and Mohamed Benmohammed, 2019, A novel load balancing scheduling algorithm for wireless sensor networks, Journal of Network and Systems Management, 27(2) :430–462.
Nadjet Khoulalene, Louiza Bouallouche-Medjkoune, Djamil Aissani, Adel Mani, and Halim Ariouat, 2018, Clusterin with load balancing-based routing protocol for wireless sensor networks, Wireless Personal Communications, 103(3) :2155–2175.
Ravi Tandon, Biswanath Dey, and Sukumar Nandi, 2013, Weight based clustering in wireless sensor networks, the National Conference on Communications (NCC) IEEE, pages 1–5.
Chirihane Gherbi, Zibouda Aliouat, and Mohamed Benmohammed, 2019, Comparative analysis of hierarchical cluster protocols for wireless sensor networks, International Journal of High Performance Computing and Networking, 13(4) :366–377.
Peng Jun-jie, Chen Yuan-yuan, 2015, A low energy consumption WSN node, Int J Embed Syst, 7(3/4).
Shao Xing, Wang Cui-Xiang, Rao Yuan 2015, Network coding aware QoS routing for wireless sensor network, J Commun ,10(1).
Kamal ARM, Hamid MA. 2016, Supervisory routing control for dynamic load balancing in low data rate wireless sensor networks, Wirel Netw February 15(01).
Kumar, B., Singh, S., & Chand, S. (2016), Energy efficient clustering protocol using fuzzy logic for heterogeneous wsns, Wireless Personnel Communication, 86(2), 451–475.
Ding, X. X., Ling, M., Wang, Z. J., & Song, F. L. (2017), Dk-leach: A optimized cluster structure routing method based on leach in wireless sensor networks, Wireless Personnel Communication, 96(4),1–11.
Wendi Rabiner Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan. Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences, pages 10–pp. IEEE, 2000.
Yousef Jaradat; Mohammad Masoud; Saleh Al-Jazzar
A comparative study of the effect of node distributions on 2D and 3D heterogeneous WSN. International Journal of Sensor Networks 2020 Vol.33 No.4
DOI: https://doi.org/10.31449/inf.v45i7.3671
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