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Congestion control in wireless sensor and 6LoWPAN networks: toward the Internet of Things

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Abstract

The Internet of Things (IoT) is the next big challenge for the research community where the IPv6 over low power wireless personal area network (6LoWPAN) protocol stack is a key part of the IoT. Recently, the IETF ROLL and 6LoWPAN working groups have developed new IP based protocols for 6LoWPAN networks to alleviate the challenges of connecting low memory, limited processing capability, and constrained power supply sensor nodes to the Internet. In 6LoWPAN networks, heavy network traffic causes congestion which significantly degrades network performance and impacts on quality of service aspects such as throughput, latency, energy consumption, reliability, and packet delivery. In this paper, we overview the protocol stack of 6LoWPAN networks and summarize a set of its protocols and standards. Also, we review and compare a number of popular congestion control mechanisms in wireless sensor networks (WSNs) and classify them into traffic control, resource control, and hybrid algorithms based on the congestion control strategy used. We present a comparative review of all existing congestion control approaches in 6LoWPAN networks. This paper highlights and discusses the differences between congestion control mechanisms for WSNs and 6LoWPAN networks as well as explaining the suitability and validity of WSN congestion control schemes for 6LoWPAN networks. Finally, this paper gives some potential directions for designing a novel congestion control protocol, which supports the IoT application requirements, in future work.

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References

  1. Shelby, Z., & Bormann, C. (2009). 6LoWPAN: The wireless embedded internet. Hoboken: Wiley.

    Book  Google Scholar 

  2. Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787–2805.

    Article  MATH  Google Scholar 

  3. Alcaraz, C., Najera, P., Lopez, J. & Roman, R. (2010). Wireless sensor networks and the Internet of Things: Do we need a complete integration? In: 1st International workshop on the security of the Internet of Things (SecIoT’10), Tokyo (Japan), 29 November.

  4. Khalil, N., Abid, M. R., Benhaddou, D. & Gerndt, M. (2014). Wireless sensors networks for Internet of Things. In: IEEE 9th international conference on intelligent sensors, sensor networks and information processing (ISSNIP), 2014 (pp. 1–6). IEEE.

  5. Al-Kashoash, H., & Kemp, A. H. (2017). Comparison of 6LoWPAN and LPWAN for the Internet of Things. Australian Journal of Electrical and Electronics Engineering, 13(4), 268–274.

    Article  Google Scholar 

  6. Ghaffari, A. (2015). Congestion control mechanisms in wireless sensor networks: A survey. Journal of Network and Computer Applications, 52, 101–115.

    Article  Google Scholar 

  7. Kafi, M. A., Djenouri, D., Ben-Othman, J., & Badache, N. (2014). Congestion control protocols in wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 16(3), 1369–1390.

    Article  Google Scholar 

  8. Flora, D. J., Kavitha, V. & Muthuselvi, M. (2011). A survey on congestion control techniques in wireless sensor networks. In International conference on emerging trends in electrical and computer technology (ICETECT), 2011 (pp. 1146–1149). IEEE.

  9. Yuan, H., Yugang, N., & Fenghao, G. (2014). Congestion control for wireless sensor networks: A survey. In: The 26th Chinese Control and Decision Conference (2014 CCDC) (pp. 4853–4858) IEEE.

  10. Pant, N., Singh, M. & Kumar, P. (2014). Traffic and resource based methods for congestion control in wireless sensor networks: A comparative analysis. In IEEE 6th international conference on adaptive science & technology (ICAST), 2014 (pp. 1–6). IEEE.

  11. Zhao, J., Wang, L., Li, S., Liu, X., Yuan, Z. & Gao, Z. (2010). A survey of congestion control mechanisms in wireless sensor networks. In 6th International conference on intelligent information hiding and multimedia signal processing (IIH-MSP), 2010 (pp. 719–722). IEEE.

  12. Gowthaman, P., & Chakravarthi, R. (2013). Survey on various congestion detection and control protocols in wireless sensor networks. International Journal of Advanced Computer Technology, 2(4), 15–19.

    Google Scholar 

  13. Chakravarthi, R., Gomathy, C., Sebastian, S. K., Pushparaj, K., & Mon, V. B. (2010). A Survey on Congestion Control in Wireless Sensor Networks. International Journal of Computer Science & Communication, 1(1), 161–164.

    Google Scholar 

  14. Budhwar, P. (2015). A survey of transport layer protocols for wireless sensor networks. Journal of Emerging Technologies and Innovative Research (JETIR), 2, 985–991.

    Google Scholar 

  15. Sergiou, C., Antoniou, P., & Vassiliou, V. (2014). A comprehensive survey of congestion control protocols in wireless sensor networks. IEEE Communications Surveys & Tutorials, 16(4), 1839–1859.

    Article  Google Scholar 

  16. Shelby, Z., Hartke, K. & Bormann, C. (2014). The constrained application protocol (CoAP). IETF RFC 7252.

  17. Tanenbaum, A. S., & Wetherall, D. J. (2013). Computer Networks: Pearson New International Edition. Hertfordshire: University of Hertfordshire, Pearson Higher Ed.

    Google Scholar 

  18. Gomez, C., Kim, E., Kaspar, D. & Bormann, C. Problem statement and requirements for IPv6 over low-power wireless personal area network (6LoWPAN) routing. IETF RFC 6606.

  19. Chowdhury, A. H., Ikram, M., Cha, H.-S., Redwan, H., Shams, S., Kim, K.-H. & Yoo, S.-W. (2009). Route-over vs Mesh-under Routing in 6LoWPAN. In Proceedings of the 2009 international conference on wireless communications and mobile computing: Connecting the world wirelessly (pp. 1208–1212). ACM.

  20. Yoo, S., Lee, J., & Mulligan, G. (2007). Hierarchical routing over 6LoWPAN (HiLow), draft-daniel-6lowpan-hilow-hierarchical-routing-01, IETF Network Working Group. Internet-Draft: Technical Report.

  21. Kim, K., Park, S. D., Montenegro, G., Yoo, S. & Kushalnagar, N. (2007). 6LoWPAN ad hoc on-demand distance vector routing (LOAD). In Internet Draft, draft-daniel-6lowpan-load-adhoc-routing-03, IETF.

  22. Kim, K., Montenegro, G., Park, S., Chakeres, I., & Perkins, C. (2007). Dynamic MANET On-demand for 6LoWPAN (DYMO-low) Routing. Internet-Draft: IETF.

  23. Winter, T., Thubert, P., Brandt, A., Hui, J. & Kelsey, R. (2012). RPL: IPv6 routing protocol for low-power and Lossy networks. IETF, RFC 6550.

  24. Hui, J. & Thubert, P. (2011). Compression format for IPv6 datagrams over IEEE 802.15. 4-based networks. IETF RFC 6282.

  25. Montenegro, G., Kushalnagar, N., Hui, J. & Culler, D. (2007). Transmission of IPv6 packets over IEEE 802.15.4 networks. IETF RFC 4944.

  26. Committee, L. S. et al. (2003). Part 15.4: Wireless medium access control (MAC) and physical layer (PHY) specifications for low-rate wireless personal area aetworks (LR-WPANs). IEEE Computer Society.

  27. Yinbiao, S. et al. (2014). Internet of Things: Wireless sensor networks. White Paper, International Electrotechnical Commission (IEC).

  28. Reena, P., & Jacob, L. (2007) Hop-by-hop versus end-to-end congestion control in wireless multi-hop UWB networks. In: International conference on IEEE advanced computing and communications, ADCOM 2007 (pp. 255–261).

  29. Heimlicher, S., Nuggehalli, P., & May, M. (2007). End-to-end vs. Hop-by-hop transport. SIGMETRICS Performance Evaluation Review, 35(3), 59–60.

    Article  Google Scholar 

  30. Heimlicher, S., Karaliopoulos, M., Levy, H. & May, M. (2007). End-to-end vs. Hop-by-hop transport under intermittent connectivity. In: Proceedings of the 1st international conference on Autonomic computing and communication systems (p. 20). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).

  31. Kafi, M. A., Djenouri, D., Othman, J. B., Ouadjaout, A., & Badache, N. (2014). Congestion detection strategies in wireless sensor networks: A comparative study with testbed experiments. Procedia Computer Science, 37, 168–175.

    Article  Google Scholar 

  32. Yin, X., Zhou, X., Huang, R., Fang, Y., & Li, S. (2009). A fairness-aware congestion control scheme in wireless sensor networks. IEEE Transactions on Vehicular Technology, 58(9), 5225–5234.

    Article  Google Scholar 

  33. Wan, C.-Y., Eisenman, S. B. & Campbell, A. T. (2003). CODA: Congestion detection and avoidance in sensor networks. In Proceedings of the 1st international conference on Embedded networked sensor systems (pp. 266–279). ACM.

  34. Sankarasubramaniam, Y., Akan, Ö. B., & Akyildiz, I. F. (2003). ESRT: Event-to-sink reliable transport in wireless sensor networks. In Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing (pp. 177–188). ACM.

  35. Michopoulos, V., Guan, L., Oikonomou, G. & Phillips, I. (2012). DCCC6: Duty cycle-aware congestion control for 6LoWPAN networks. In IEEE International conference on pervasive computing and communications workshops (PERCOM Workshops), 2012 (pp. 278–283). IEEE.

  36. Deshpande, V. S., Chavan, P. P., Wadhai, V. M. & Helonde, J. B. (2012). Congestion control in wireless sensor networks by using differed reporting rate. In World congress on information and communication technologies (WICT) (209–213). IEEE.

  37. Wang, C., Li, B., Sohraby, K., Daneshmand, M., & Hu, Y. (2007). Upstream congestion control in wireless sensor networks through cross-layer optimization. IEEE Journal on Selected Areas in Communications, 25(4), 786–795.

    Article  Google Scholar 

  38. Hull, B., Jamieson, K. & Balakrishnan, H. (2004). Mitigating congestion in wireless sensor networks. In Proceedings of the 2nd international conference on Embedded networked sensor systems (pp. 134–147). ACM.

  39. Jaiswal, S. & Yadav, A. (2013). Fuzzy based adaptive congestion control in wireless sensor networks. In 6th International conference on contemporary computing (IC3), 2013 (pp. 433–438). IEEE.

  40. Sergiou, C., Vassiliou, V., & Paphitis, A. (2013). Hierarchical tree alternative path (HTAP) algorithm for congestion control in wireless sensor networks. Ad Hoc Networks, 11(1), 257–272.

    Article  Google Scholar 

  41. Kim, H.-S., Paek, J., & Bahk, S. (2015). QU-RPL: Queue utilization based RPL for load balancing in large scale industrial applications. In: 12th Annual IEEE international conference on sensing, communication, and networking (SECON) (pp. 265–273). IEEE.

  42. Castellani, A. P., Rossi, M., & Zorzi, M. (2014). Back pressure congestion control for CoAP/6LoWPAN networks. Ad Hoc Networks, 18, 71–84.

    Article  Google Scholar 

  43. Huang, J.-M., Li, C.-Y., & Chen, K.-H. (2009). TALONet: A power-efficient grid-based congestion avoidance scheme using multi-detouring technique in wireless sensor networks. In Wireless Telecommunications Symposium, WTS (pp. 1–6). IEEE.

  44. Al-Kashoash, H. A. A., Al-Nidawi, Y., & Kemp, A. H. (2016). Congestion-aware RPL for 6LoWPAN networks. In Accepted at Wireless Telecommunications Symposium (WTS) (1–6). IEEE.

  45. Wan, J., Xu, X., Feng, R., & Wu, Y. (2009). Cross-layer active predictive congestion control protocol for wireless sensor networks. Sensors, 9(10), 8278–8310.

    Article  Google Scholar 

  46. Rangwala, S., Gummadi, R., Govindan, R., & Psounis, K. (2006). Interference-aware fair rate control in wireless sensor networks. ACM SIGCOMM Computer Communication Review, 36(4), 63–74.

    Article  Google Scholar 

  47. Fang, W.-W., Chen, J.-M., Shu, L., Chu, T.-S., & Qian, D.-P. (2010). Congestion avoidance, detection and alleviation in wireless sensor networks. Journal of Zhejiang University SCIENCE C, 11(1), 63–73.

    Article  Google Scholar 

  48. Sheu, J.-P., & Hu, W.-K. (2008). Hybrid congestion control protocol in wireless sensor networks. In Vehicular Technology Conference. VTC Spring 2008 (pp. 213–217). IEEE.

  49. Kang, J., Zhang, Y., & Nath, B. (2007). TARA: Topology-aware resource adaptation to alleviate congestion in sensor networks. IEEE Transactions on Parallel and Distributed Systems, 18(7), 919–931.

    Article  Google Scholar 

  50. Lee, J.-H., & Jung, I.-B. (2010). Adaptive-compression based congestion control technique for wireless sensor networks. Sensors, 10(4), 2919–2945.

    Article  Google Scholar 

  51. Sheu, J. P., Hsu, C. X. & Ma, C. (2015). A game theory based congestion control protocol for wireless personal area networks. In IEEE 39th Annual Computer Software and Applications Conference (COMPSAC) (Vol. 2, July).

  52. Ma, C., Sheu, J.-P. & Hsu, C.-X. (2015). A game theory based congestion control protocol for wireless personal area networks. Journal of Sensors, 2016.

  53. Fahmy, H. M. A. (2016). Simulators and emulators for WSNs. In Wireless sensor networks (pp. 381–491). Springer.

  54. Levis, P., Madden, S., Polastre, J., Szewczyk, R., Whitehouse, K., Woo, A., Gay, D., Hill, J., Welsh, M. & Brewer, E., et al. (2005). TinyOS: An operating system for sensor networks. In: Ambient intelligence (pp. 115–148). Springer.

  55. Dunkels, A., Grönvall, B. & Voigt, T. (2004). Contiki—A lightweight and flexible operating system for tiny networked sensors. In 29th Annual IEEE international conference on local computer networks, 2004 (pp. 455–462). IEEE.

  56. Dunkels, A. (2009). Contiki: Bringing IP to sensor networks. ERCIM News, 76.

  57. Dunkels, A., Eriksson, J., Finne, N. & Tsiftes, N. (2011). Powertrace: Network-level power profiling for low-power wireless networks.

  58. Baccelli, E., Hahm, O., Gunes, M., Wahlisch, M. & Schmidt, T. C. (2013). RIOT OS: Towards an OS for the Internet of Things. In Proceedings of the 32nd IEEE International Conference on Computer Communications (INFOCOM) (pp. 79–80). IEEE.

  59. Will, H., Schleiser, K. & Schiller, J. (2009). A real-time kernel for wireless sensor networks employed in rescue scenarios. In IEEE 34th Conference on Local Computer Networks (LCN 2009) (pp. 834–841). IEEE.

  60. Levis, P. & Lee, N. (2003). TOSSIM: A simulator for TinyOS networks. UC Berkeley (September, Vol. 24).

  61. Osterlind, F., Dunkels, A., Eriksson, J., Finne, N. & Voigt, T. (2006). Cross-Level Sensor Network Simulation with COOJA. In Proceedings 31st IEEE conference on local computer networks (pp. 641–648). IEEE.

  62. Österlind, F., Eriksson, J. & Dunkels, A. (2010). Cooja TimeLine: A power visualizer for sensor network simulation. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems (pp. 385–386). ACM.

  63. Downard, I. T. (2004). Simulating Sensor Networks in NS-2. DTIC Document: Technical Report.

  64. Henderson, T. R., Lacage, M., Riley, G. F., Dowell, C., & Kopena, J. (2008). Network Simulations with the ns-3 Simulator. SIGCOMM Demonstration, 15, 17.

    Google Scholar 

  65. Simon, G., Volgyesi, P., Maróti, M. & Lédeczi, Á. (2003). Simulation-based optimization of communication protocols for large-scale wireless sensor networks. In IEEE aerospace conference (Vol. 3, pp. 1339–1346).

  66. Chang, X. (1999). Network simulations with OPNET. In Proceedings of the 31st conference on Winter simulation: Simulation—A bridge to the future-Volume 1 (pp. 307–314). ACM.

  67. Varga, A. (2001). The OMNeT++ discrete event simulation system. In: Proceedings of the European simulation multiconference (ESM’2001) (pp. 185–192).

  68. Kirsche, M. & Hartwig, J. (2013). A 6LoWPAN Model for OMNeT++: Poster Abstract. In Proceedings of the 6th International ICST Conference on Simulation Tools and Techniques (pp. 330–333). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).

  69. Ee, C. T. & Bajcsy, R. (2004). Congestion control and fairness for many-to-one routing in sensor networks. In Proceedings of the 2nd international conference on Embedded networked sensor systems (pp. 148–161). ACM.

  70. Zawodniok, M., & Jagannathan, S. (2007). Predictive congestion control protocol for wireless sensor networks. IEEE Transactions on Wireless Communications, 6(11), 3955–3963.

    Article  Google Scholar 

  71. Chen, S. & Zhang, Z. (2006). Localized algorithm for aggregate fairness in wireless sensor networks. In Proceedings of the 12th annual international conference on Mobile computing and networking (pp. 274–285). ACM.

  72. Chen, S., & Yang, N. (2006). Congestion avoidance based on lightweight buffer management in sensor networks. IEEE Transactions on Parallel and Distributed Systems, 17(9), 934–946.

    Article  Google Scholar 

  73. Monowar, M. M., Rahman, M. O. & Hong, C. S. (2008). Multipath congestion control for heterogeneous traffic in wireless sensor network. In 10th International Conference on Advanced Communication Technology, ICACT 2008 (Vol. 3, pp. 1711–1715). IEEE.

  74. Wang, G. & Liu, K. (2009). Upstream Hop-by-Hop Congestion Control in Wireless Sensor Networks. In IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications (pp. 1406–1410). IEEE.

  75. Alam, M. M., & Hong, C. S. (2009). CRRT: Congestion-aware and rate-controlled reliable transport in wireless sensor networks. IEICE Transactions on Communications, 92(1), 184–199.

    Article  Google Scholar 

  76. Brahma, S., Chatterjee, M. & Kwiat, K. (2010). Congestion control and fairness in wireless sensor networks. In 8th IEEE international conference on pervasive computing and communications workshops (PERCOM Workshops) (pp. 413–418). IEEE.

  77. Heikalabad, S. R., Ghaffari, A., Hadian, M. A. & Rasouli, H. (2011). DPCC: Dynamic predictive congestion control in wireless sensor networks. IJCSI International Journal of Computer Science Issues, 8 (1).

  78. Munir, S. A., Bin, Y. W., Biao, R. & Jian, M. (2007). Fuzzy logic based congestion estimation for QoS in wireless sensor network. In Wireless communications and networking conference, WCNC 2007 (pp. 4336–4341). IEEE.

  79. Wei, J., Fan, B. & Sun, Y. (2012). A congestion control scheme based on fuzzy logic for wireless sensor networks. In 9th International conference on fuzzy systems and knowledge discovery (FSKD) (pp. 501–504). IEEE.

  80. He, T., Ren, F., Lin, C. & Das, S. (2008). Alleviating congestion using traffic-aware dynamic routing in wireless sensor networks. In 5th Annual IEEE communications society conference on sensor, mesh and ad hoc communications and networks, 2008. SECON’08 (pp. 233–241). IEEE.

  81. Woo, A., Tong, T. & Culler, D. (2003). Taming The underlying challenges of reliable multihop routing in sensor networks. In Proceedings of the 1st international conference on Embedded networked sensor systems (pp. 14–27). ACM.

  82. Rahman, M. O., Monowar, M. M. & Hong, C. S. (2008). A QoS adaptive congestion control in wireless sensor network. In 10th International conference on advanced communication technology, 2008, ICACT 2008 (Vol. 2, pp. 941–946). IEEE.

  83. Wang, C., Sohraby, K. & Li, B. (2005). SenTCP: A hop-by-hop congestion control protocol for wireless sensor networks. In IEEE INFOCOM (pp. 107–114).

  84. Sergiou, C., Vassiliou, V., & Paphitis, A. (2014). Congestion control in wireless sensor networks through dynamic alternative path selection. Computer Networks, 75, 226–238.

    Article  Google Scholar 

  85. Dasgupta, R., Mukherjee, R., & Gupta, A. (2015). Congestion avoidance topology in wireless sensor network using Karnaugh Map. In Applications and innovations in mobile computing (AIMoC) (pp. 89–96). IEEE.

  86. Razzaque, M. A. & Hong, C. S. (2009). Congestion detection and control algorithms for multipath data forwarding in sensor networks. In 11th International conference on advanced communication technology, ICACT (Vol. 1, pp. 651–653).

  87. Sergiou, C. & Vassiliou, V. (2014). HRTC: A hybrid algorithm for efficient congestion control in wireless sensor networks. In 6th International conference on new technologies, mobility and security (NTMS) (pp. 1–5). IEEE.

  88. Tassiulas, L., & Ephremides, A. (1992). Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks. IEEE Transactions on Automatic Control, 37(12), 1936–1948.

    Article  MATH  MathSciNet  Google Scholar 

  89. Hellaoui, H. & Koudil, M. (2015). Bird flocking congestion control for CoAP/RPL/6LoWPAN networks. In Proceedings of the 2015 Workshop on IoT challenges in Mobile and Industrial Systems (pp. 25–30). ACM.

  90. Kim, H.-S., Kim, H., Paek, J. & Bahk, S. (2016). Load balancing under heavy traffic in rpl routing protocol for low power and lossy networks. IEEE Transactions on Mobile Computing.

  91. Thubert, P. (2012). Objective function zero for the routing protocol for low-power and Lossy networks (RPL). RFC 6552.

  92. Gnawali, O. & Levis, P. (2010). The ETX objective function for RPL. Internet Draft: Draft-gnawali-roll-etxof-00.

  93. Tang, W., Ma, X., Huang, J. & Wei, J. (2015). Toward improved rpl: A congestion avoidance multipath routing protocol with time factor for wireless sensor networks. Journal of Sensors, 2016.

  94. Kamgueu, P. O., Nataf, E., Ndié, T. D. & Festor, O. (2013). Energy-based routing metric for RPL. [Research Report] RR-8208, INRIA (p. 14).

  95. Lodhi, M. A., Rehman, A., Khan, M. M. & Hussain, F. B. (2015). Multiple path RPL for low power Lossy networks. In IEEE Asia Pacific conference on wireless and mobile (APWiMob), 2015 (pp. 279–284). IEEE.

  96. Ha, M., Kwon, K., Kim, D. & Kong, P.-Y. (2014). Dynamic and distributed load balancing scheme in multi-gateway based 6LoWPAN. In IEEE International Conference on, and Green Computing and Communications (GreenCom), IEEE and Cyber, Physical and Social Computing (CPSCom) Internet of Things (iThings) (pp. 87–94). IEEE.

  97. Liu, X., Guo, J., Bhatti, G., Orlik, P. & Parsons, K. (2013). Load balanced routing for low power and Lossy networks. In IEEE Wireless Communications and Networking Conference (WCNC) (pp. 2238–2243). IEEE.

  98. Guo, J., Liu, X., Bhatti, G., Orlik, P. & Parsons, K. (2013). Load balanced routing for low power and Lossy networks. Jan. 21, US Patent App. 13/746,173.

  99. Tang, W., Wei, Z., Zhang, Z., & Zhang, B. (2014). Analysis and optimization strategy of multipath RPL based on the COOJA simulator. International Journal of Computer Science Issues (IJCSI), 11(5), 27–30.

    Google Scholar 

  100. Al-Kashoash, H., Hafeez, M. & Kemp, A. (2017). Congestion control for 6LoWPAN networks: A game theoretic framework. IEEE Internet of Things Journal.

  101. Al-Kashoash, H. A., Amer, H. M., Mihaylova, L. & Kemp, A. H. (2017). Optimization based hybrid congestion alleviation for 6LoWPAN networks. IEEE Internet of Things Journal.

  102. Al-Kashoash, H. A., Hassen, F., Kharrufa, H. & Kemp, A. H. (2018). Analytical modelling of congestion for 6LoWPAN networks. ICT Express.

  103. Zheng, T., Ayadi, A. & Jiang, X. (2011). TCP over 6LoWPAN for industrial applications: An experimental study. In 4th IFIP International Conference on New Technologies, Mobility and Security (NTMS) (pp. 1–4). IEEE.

  104. Ayadi, A., Maillé, P. & Ros, D. (2011). TCP over low-power and lossy networks: Tuning the segment size to minimize energy consumption. In 4th IFIP international conference on new technologies, mobility and security (NTMS), 2011 (pp. 1–5). IEEE.

  105. Kim, H.-S., Im, H., Lee, M.-S., Paek, J., & Bahk, S. (2015). A measurement study of TCP over RPL in low-power and Lossy networks. Journal of Communications and Networks, 17(6), 647–655.

    Article  Google Scholar 

  106. Antoniou, P., Pitsillides, A., Blackwell, T., Engelbrecht, A., & Michael, L. (2013). Congestion control in wireless sensor networks based on bird flocking behavior. Computer Networks, 57(5), 1167–1191.

    Article  Google Scholar 

  107. Michopoulos, V., Guan, L., Oikonomou, G. & Phillips, I. (2011). A comparative study of congestion control algorithms in IPv6 wireless sensor networks. In International conference on distributed computing in sensor systems and workshops (DCOSS) (pp. 1–6). IEEE.

  108. Al-Kashoash, H. A. A., Al-Nidawi, Y., & Kemp, A. H. (2016). Congestion analysis for low power and Lossy networks. In Accepted at Wireless Telecommunications Symposium (WTS), 2016 (1–6). IEEE.

  109. Weldon, M. (2016). The future X network: A bell labs perspective. Boca Raton: CRC Press.

    Book  Google Scholar 

  110. Dunlap, J. (2011). From billing and technology convergence to ecosystem convergence: Why M2M matters to your business. Pipeline: Technology for Service Providers, 8(7), 14.

    Google Scholar 

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Authors and Affiliations

  1. Technical Institute/Qurna, Southern Technical University, Basra, Iraq

    Hayder A. A. Al-Kashoash

  2. The Faculty of Engineering, Al-Mustansiriya University, Baghdad, 10047, Iraq

    Yaarob Al-Nidawi

  3. The Electronic and Electrical Engineering School, University of Leeds, Leeds, LS2 9JT, UK

    Harith Kharrufa & Andrew H. Kemp

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  1. Hayder A. A. Al-Kashoash
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Correspondence to Hayder A. A. Al-Kashoash.

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Al-Kashoash, H.A.A., Kharrufa, H., Al-Nidawi, Y. et al. Congestion control in wireless sensor and 6LoWPAN networks: toward the Internet of Things. Wireless Netw 25, 4493–4522 (2019). https://doi.org/10.1007/s11276-018-1743-y

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