Advertisement

Background and Literature Review

  • Hayder Al-KashoashEmail author
Chapter
Part of the Springer Theses book series (Springer Theses)

Abstract

This chapter presents a comprehensive literature review on congestion control for WSNs and 6LoWPAN networks. \(\bullet \) It gives a review of performance metrics, operating systems and simulators used to evaluate and test proposed congestion control mechanisms as well as explaining which operating systems and simulators support the 6LoWPAN protocol stack.

References

  1. 1.
    Ghaffari A (2015) Congestion control mechanisms in wireless sensor networks: a survey. J Netw Comput Appl 52:101–115CrossRefGoogle Scholar
  2. 2.
    Kafi MA, Djenouri D, Ben-Othman J, Badache N (2014) Congestion control protocols in wireless sensor networks: a survey. IEEE Commun Surv Tutor 16(3):1369–1390CrossRefGoogle Scholar
  3. 3.
    Flora DJ, Kavitha V, Muthuselvi M (2011) A survey on congestion control techniques in wireless sensor networks. In: Proceedings of international conference on emerging trends in electrical and computer technology (ICETECT). IEEE, pp 1146–1149Google Scholar
  4. 4.
    Yuan H, Yugang N, Fenghao G (2014) Congestion control for wireless sensor networks: a survey. In: Proceedings of the 26th Chinese control and decision conference (2014 CCDC). IEEE, pp 4853–4858Google Scholar
  5. 5.
    Pant N, Singh M, Kumar P (2014) Traffic and resource based methods for congestion control in wireless sensor networks: a comparative analysis. In: Proceedings of 6th international conference on adaptive science and technology (ICAST). IEEE, pp 1–6Google Scholar
  6. 6.
    Zhao J, Wang L, Li S, Liu X, Yuan Z, Gao Z (2010) A survey of congestion control mechanisms in wireless sensor networks. In: Proceedings of 6th international conference on intelligent information hiding and multimedia signal processing (IIH-MSP). IEEE, pp 719–722Google Scholar
  7. 7.
    Gowthaman P, Chakravarthi R (2013) Survey on various congestion detection and control protocols in wireless sensor networks. Int J Adv Comput Eng Commun Technol (IJACECT) 2(4):15–19Google Scholar
  8. 8.
    Chakravarthi R, Gomathy C, Sebastian SK, Pushparaj K, Mon VB (2010) A survey on congestion control in wireless sensor networks. Int J Comput Sci Commun 1(1):161–164Google Scholar
  9. 9.
    Budhwar P (2015) A survey of transport layer protocols for wireless sensor networks. J Emerg Technol Innov Res (JETIR) 2:985–991Google Scholar
  10. 10.
    Sergiou C, Antoniou P, Vassiliou V (2014) A comprehensive survey of congestion control protocols in wireless sensor networks. IEEE Commun Surv Tutor 16(4):1839–1859CrossRefGoogle Scholar
  11. 11.
    Han Z, Niyato D, Saad W, Başar T, Hjørungnes A (2012) Game theory in wireless and communication networks: theory, models, and applications. Cambridge University Press, CambridgeGoogle Scholar
  12. 12.
    Matsumoto A, Szidarovszky F (2016) Game theory and its applications. Springer, BerlinCrossRefGoogle Scholar
  13. 13.
    Tzeng G-H, Huang J-J (2011) Multiple attribute decision making: methods and applications. CRC Press, Boca RatonGoogle Scholar
  14. 14.
    Kuo Y, Yang T, Huang G-W (2008) The use of grey relational analysis in solving multiple attribute decision-making problems. Comput Ind Eng 55(1):80–93CrossRefGoogle Scholar
  15. 15.
    Kelly FP, Maulloo AK, Tan DK (1998) Rate control for communication networks: shadow prices, proportional fairness and stability. J Oper Res Soc 49(3):237–252CrossRefGoogle Scholar
  16. 16.
    Tychogiorgos G, Leung KK (2014) Optimization-based resource allocation in communication networks. Comput Netw 66:32–45CrossRefGoogle Scholar
  17. 17.
    Wang L, Kuo G-S (2013) Mathematical modeling for network selection in heterogeneous wireless networks—a tutorial. IEEE Commun Surv Tutor 15(1):271–292CrossRefGoogle Scholar
  18. 18.
    Srikant R, Ying L (2013) Communication networks: an optimization, control, and stochastic networks perspective. Cambridge University Press, CambridgeGoogle Scholar
  19. 19.
    Yinbiao S et al (2014) Internet of things: wireless sensor networks. White paper. International Electrotechnical Commission (IEC)Google Scholar
  20. 20.
    Reena P, Jacob L (2007) Hop-by-hop versus end-to-end congestion control in wireless multi-hop UWB networks. In: Proceedings of international conference on advanced computing and communications (ADCOM 2007). IEEE, pp 255–261Google Scholar
  21. 21.
    Heimlicher S, Nuggehalli P, May M (2007) End-to-end vs. hop-by-hop transport. SIGMETRICS Perform Eval Rev 35(3):59–60CrossRefGoogle Scholar
  22. 22.
    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. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), p 20Google Scholar
  23. 23.
    Kafi MA, Djenouri D, Othman JB, Ouadjaout A, Badache N (2014) Congestion detection strategies in wireless sensor networks: a comparative study with testbed experiments. Procedia Comput Sci 37:168–175CrossRefGoogle Scholar
  24. 24.
    Yin X, Zhou X, Huang R, Fang Y, Li S (2009) A fairness-aware congestion control scheme in wireless sensor networks. IEEE Trans Veh Technol 58(9):5225–5234CrossRefGoogle Scholar
  25. 25.
    Wan C-Y, Eisenman SB, Campbell AT (2003) CODA: congestion detection and avoidance in sensor networks. In: Proceedings of the 1st international conference on embedded networked sensor systems. ACM, pp 266–279Google Scholar
  26. 26.
    Sankarasubramaniam Y, Akan ÖB, Akyildiz IF (2003) ESRT: event-to-sink reliable transport in wireless sensor networks. In: Proceedings of the 4th ACM international symposium on mobile ad hoc networking and computing. ACM, pp 177–188Google Scholar
  27. 27.
    Michopoulos V, Guan L, Oikonomou G, Phillips I (2012) DCCC6: duty cycle-aware congestion control for 6LoWPAN networks. In: Proceedings of international conference on pervasive computing and communications workshops (PERCOM workshops). IEEE, pp 278–283Google Scholar
  28. 28.
    Deshpande VS, Chavan PP, Wadhai VM, Helonde JB (2012) Congestion control in wireless sensor networks by using differed reporting rate. In: Proceedings of world congress on information and communication technologies (WICT). IEEE, pp 209–213Google Scholar
  29. 29.
    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. ACM, pp 134–147Google Scholar
  30. 30.
    Wang C, Li B, Sohraby K, Daneshmand M, Hu Y (2007) Upstream congestion control in wireless sensor networks through cross-layer optimization. IEEE J Sel Areas Commun 25(4):786–795CrossRefGoogle Scholar
  31. 31.
    Zawodniok M, Jagannathan S (2007) Predictive congestion control protocol for wireless sensor networks. IEEE Trans Wirel Commun 6(11):3955–3963CrossRefGoogle Scholar
  32. 32.
    Jaiswal S, Yadav A (2013) Fuzzy based adaptive congestion control in wireless sensor networks. In: Proceedings of 6th international conference on contemporary computing (IC3). IEEE, pp 433–438Google Scholar
  33. 33.
    Sergiou C, Vassiliou V, Paphitis A (2013) Hierarchical tree alternative path (HTAP) algorithm for congestion control in wireless sensor networks. Ad Hoc Netw 11(1):257–272CrossRefGoogle Scholar
  34. 34.
    Kim H-S, Paek J, Bahk S (2015) QU-RPL: queue utilization based RPL for load balancing in large scale industrial applications. In: Proceedings of 12th annual IEEE international conference on sensing, communication, and networking (SECON). IEEE, pp 265–273Google Scholar
  35. 35.
    Castellani AP, Rossi M, Zorzi M (2014) Back pressure congestion control for CoAP/6LoWPAN networks. Ad Hoc Netw 18:71–84CrossRefGoogle Scholar
  36. 36.
    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: Proceedings of wireless telecommunications symposium (WTS). IEEE, pp 1–6Google Scholar
  37. 37.
    Al-Kashoash HAA, Al-Nidawi Y, Kemp AH (2016) Congestion-aware RPL for 6LoWPAN networks. In: Proceedings of wireless telecommunications symposium (WTS 2016). IEEE, pp 1–6Google Scholar
  38. 38.
    Wan J, Xu X, Feng R, Wu Y (2009) Cross-layer active predictive congestion control protocol for wireless sensor networks. Sensors 9(10):8278–8310CrossRefGoogle Scholar
  39. 39.
    Rangwala S, Gummadi R, Govindan R, Psounis K (2006) Interference-aware fair rate control in wireless sensor networks. ACM SIGCOMM Comput Commun Rev 36(4):63–74CrossRefGoogle Scholar
  40. 40.
    Fang W-W, Chen J-M, Shu L, Chu T-S, Qian D-P (2010) Congestion avoidance, detection and alleviation in wireless sensor networks. J Zhejiang Univ Sci C 11(1):63–73CrossRefGoogle Scholar
  41. 41.
    Sheu J-P, Hu W-K (2008) Hybrid congestion control protocol in wireless sensor networks. In: Proceedings of vehicular technology conference (VTC). IEEE, pp 213–217Google Scholar
  42. 42.
    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):919–931CrossRefGoogle Scholar
  43. 43.
    Lee J-H, Jung I-B (2010) Adaptive-compression based congestion control technique for wireless sensor networks. Sensors 10(4):2919–2945CrossRefGoogle Scholar
  44. 44.
    Sheu JP, Hsu CX, Ma C (2015) A game theory based congestion control protocol for wireless personal area networks. In: Proceedings of 39th annual computer software and applications conference (COMPSAC), vol 2Google Scholar
  45. 45.
    Ma C, Sheu J-P, Hsu C-X (2015) A game theory based congestion control protocol for wireless personal area networks. J SensGoogle Scholar
  46. 46.
    Fahmy HMA (2016) Simulators and emulators for WSNs. Wireless sensor networks. Springer, Berlin, pp 381–491Google Scholar
  47. 47.
    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. Ambient intelligence. Springer, Berlin, pp 115–148Google Scholar
  48. 48.
    Dunkels A, Grönvall B, Voigt T (2004) Contiki - a lightweight and flexible operating system for tiny networked sensors. In: Proceedings of 29th annual IEEE international conference on local computer networks. IEEE, pp 455–462Google Scholar
  49. 49.
    Dunkels A (2009) Contiki: bringing IP to sensor networks. ERCIM News 76:2009Google Scholar
  50. 50.
    Dunkels A, Eriksson J, Finne N, Tsiftes N (2011) Powertrace: network-level power profiling for low-power wireless networks. Swedish Institute of Computer Science (SICS), Technical reportGoogle Scholar
  51. 51.
    Thingsquare (2016) Why choose Contiki. http://www.contiki-os.org/
  52. 52.
    Dunkels A, Schmidt O, Voigt T, Ali M (2006) Protothreads: simplifying event-driven programming of memory-constrained embedded systems. In: Proceedings of the 4th international conference on embedded networked sensor systems. ACM, pp 29–42Google Scholar
  53. 53.
    Baccelli E, Hahm O, Gunes M, Wahlisch M, Schmidt TC (2013) RIOT OS: towards an OS for the internet of things. In: Proceedings of the 32nd international conference on computer communications (INFOCOM). IEEE, pp 79–80Google Scholar
  54. 54.
    Will H, Schleiser K, Schiller J (2009) A real-time kernel for wireless sensor networks employed in rescue scenarios. In: Proceedings of the 34th conference on local computer networks (LCN). IEEE, pp 834–841Google Scholar
  55. 55.
    Levis P, Lee N (2003) TOSSIM: a simulator for TinyOS networks. UC Berkeley, vol 24Google Scholar
  56. 56.
    Osterlind F, Dunkels A, Eriksson J, Finne N, Voigt T (2006) Cross-level sensor network simulation with COOJA. In: Proceedings of 31st IEEE conference on local computer networks. IEEE, pp 641–648Google Scholar
  57. 57.
    Stehlık M (2011) Comparison of simulators for wireless sensor networks. Master’s thesis, Faculty of Informatics, Masaryk University, Brno, Czech RepublicGoogle Scholar
  58. 58.
    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. ACM, pp 385–386Google Scholar
  59. 59.
    Downard IT (2004) Simulating sensor networks in NS-2. DTIC document.Technical reportGoogle Scholar
  60. 60.
    Henderson TR, Lacage M, Riley GF, Dowell C, Kopena J (2008) Network simulations with the NS-3 simulator. SIGCOMM Demonstr 15:17Google Scholar
  61. 61.
    Simon G, Volgyesi P, Maróti M, Lédeczi Á (2003) Simulation-based optimization of communication protocols for large-scale wireless sensor networks. In: Proceedings of IEEE aerospace conference, vol 3, pp 1339–1346Google Scholar
  62. 62.
    Chang X (1999) Network simulations with OPNET. In: Proceedings of the 31st conference on winter simulation: simulation—a bridge to the future-volume 1. ACM, pp 307–314Google Scholar
  63. 63.
    Varga A (2001) The OMNeT++ discrete event simulation system. In: Proceedings of the European simulation multiconference (ESM’2001), pp 185–192Google Scholar
  64. 64.
    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. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp 330–333Google Scholar
  65. 65.
    Ee CT, 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. ACM, pp 148–161Google Scholar
  66. 66.
    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. ACM, pp 274–285Google Scholar
  67. 67.
    Chen S, Yang N (2006) Congestion avoidance based on lightweight buffer management in sensor networks. IEEE Trans Parallel Distrib Syst 17(9):934–946CrossRefGoogle Scholar
  68. 68.
    Monowar MM, Rahman MO, Hong CS (2008) Multipath congestion control for heterogeneous traffic in wireless sensor network. In: Proceedings of 10th international conference on advanced communication technology (ICACT), vol 3. IEEE, pp 1711–1715Google Scholar
  69. 69.
    Wang G, Liu K (2009) Upstream hop-by-hop congestion control in wireless sensor networks. In: Proceedings of 20th international symposium on personal, indoor and mobile radio communications. IEEE, pp 1406–1410Google Scholar
  70. 70.
    Alam MM, Hong CS (2009) CRRT: congestion-aware and rate-controlled reliable transport in wireless sensor networks. IEICE Trans Commun 92(1):184–199CrossRefGoogle Scholar
  71. 71.
    Brahma S, Chatterjee M, Kwiat K (2010) Congestion control and fairness in wireless sensor networks. In: Proceedings of 8th IEEE international conference on pervasive computing and communications workshops (PERCOM workshops). IEEE, pp 413–418Google Scholar
  72. 72.
    Heikalabad SR, Ghaffari A, Hadian MA, Rasouli H (2011) DPCC: dynamic predictive congestion control in wireless sensor networks. IJCSI Int J Comput Sci Issues 8(1)Google Scholar
  73. 73.
    Munir SA, Bin YW, Biao R, Jian M (2007) Fuzzy logic based congestion estimation for QoS in wireless sensor network. In: Proceedings of wireless communications and networking conference (WCNC 2007). IEEE, pp 4336–4341Google Scholar
  74. 74.
    Wei J, Fan B, Sun Y (2012) A congestion control scheme based on fuzzy logic for wireless sensor networks. In: Proceedings of 9th international conference on fuzzy systems and knowledge discovery (FSKD). IEEE, pp 501–504Google Scholar
  75. 75.
    He T, Ren F, Lin C, Das S (2008) Alleviating congestion using traffic-aware dynamic routing in wireless sensor networks. In: Proceedings of 5th annual IEEE communications society conference on sensor, mesh and ad hoc communications and networks (SECON’08). IEEE, pp 233–241Google Scholar
  76. 76.
    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. ACM, pp 14–27Google Scholar
  77. 77.
    Rahman MO, Monowar MM, Hong CS (2008) A QoS adaptive congestion control in wireless sensor network. In: Proceedings of 10th international conference on advanced communication technology (ICACT), vol 2. IEEE, pp 941–946Google Scholar
  78. 78.
    Wang C, Sohraby K, Li B (2005) SenTCP: a hop-by-hop congestion control protocol for wireless sensor networks. In: Proceedings of IEEE INFOCOM, 2005, pp 107–114Google Scholar
  79. 79.
    Sergiou C, Vassiliou V, Paphitis A (2014) Congestion control in wireless sensor networks through dynamic alternative path selection. Comput Netw 75:226–238CrossRefGoogle Scholar
  80. 80.
    Dasgupta R, Mukherjee R, Gupta A (2015) Congestion avoidance topology in wireless sensor network using Karnaugh map. In: Proceedings of applications and innovations in mobile computing (AIMoC). IEEE, pp 89–96Google Scholar
  81. 81.
    Razzaque MA, Hong CS (2009) Congestion detection and control algorithms for multipath data forwarding in sensor networks. In: Proceedings of 11th international conference on advanced communication technology (ICACT), vol 1. IEEE, pp 651–653Google Scholar
  82. 82.
    Sergiou C, Vassiliou V (2014) HRTC: a hybrid algorithm for efficient congestion control in wireless sensor networks. In: Proceedings of 6th international conference on new technologies, mobility and security (NTMS). IEEE, pp 1–5Google Scholar
  83. 83.
    Tassiulas L, Ephremides A (1992) Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks. IEEE Trans Autom Control 37(12):1936–1948MathSciNetCrossRefGoogle Scholar
  84. 84.
    Hellaoui H, Koudil M (2015) Bird flocking congestion control for CoAP/RPL/6LoWPAN networks. In: Proceedings of the workshop on IoT challenges in mobile and industrial systems. ACM, pp 25–30Google Scholar
  85. 85.
    Shelby Z, Hartke K, Bormann C (2014) The constrained application protocol (CoAP). IETF RFC 7252Google Scholar
  86. 86.
    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 Trans Mob ComputGoogle Scholar
  87. 87.
    Winter T, Thubert P, Brandt A, Hui J, Kelsey R (2012) RPL: IPv6 routing protocol for low-power and lossy networks. IETF, RFC 6550Google Scholar
  88. 88.
    Thubert P (2012) Objective function zero for the routing protocol for low-power and lossy networks (RPL). RFC 6552Google Scholar
  89. 89.
    Gnawali O, Levis P (2010) The ETX objective function for RPL. Internet draft: draft-gnawali-roll-etxof-00Google Scholar
  90. 90.
    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. J Sens 2016Google Scholar
  91. 91.
    Lodhi MA, Rehman A, Khan MM, Hussain FB (2015) Multiple path RPL for low power lossy networks. In: Proceedings of Asia Pacific conference on wireless and mobile (APWiMob). IEEE, pp 279–284Google Scholar
  92. 92.
    Ha M, Kwon K, Kim D, Kong P-Y (2014) Dynamic and distributed load balancing scheme in multi-gateway based 6LoWPAN. In: Proceedings of international conference on internet of things (iThings), green computing and communications (GreenCom) and cyber, physical and social computing (CPSCom). IEEE, pp 87–94Google Scholar
  93. 93.
    Liu X, Guo J, Bhatti G, Orlik P, Parsons K (2013) Load balanced routing for low power and lossy networks. In: Proceedings of wireless communications and networking conference (WCNC). IEEE, pp 2238–2243Google Scholar
  94. 94.
    Guo J, Liu X, Bhatti G, Orlik P, Parsons K (2013) Load balanced routing for low power and lossy networks, 21 January 2013, US Patent Application 13/746,173Google Scholar
  95. 95.
    Tang W, Wei Z, Zhang Z, Zhang B (2014) Analysis and optimization strategy of multipath RPL based on the COOJA simulator. Int J Comput Sci Issues (IJCSI) 11(5):27–30Google Scholar
  96. 96.
    Kamgueu PO, Nataf E, Ndié TD, Festor O (2013) Energy-based routing metric for RPL. [Research report] RR-8208, INRIA, p 14Google Scholar
  97. 97.
    Zheng T, Ayadi A, Jiang X (2011) TCP over 6LoWPAN for industrial applications: an experimental study. In: Proceedings of 4th IFIP international conference on new technologies, mobility and security (NTMS). IEEE, pp 1–4Google Scholar
  98. 98.
    Ayadi A, Maillé P, Ros D (2011) TCP over low-power and lossy networks: tuning the segment size to minimize energy consumption. In: Proceedings of 4th IFIP international conference on new technologies, mobility and security (NTMS). IEEE, pp 1–5Google Scholar
  99. 99.
    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. J Commun Netw 17(6):647–655CrossRefGoogle Scholar
  100. 100.
    Antoniou P, Pitsillides A, Blackwell T, Engelbrecht A, Michael L (2013) Congestion control in wireless sensor networks based on bird flocking behavior. Comput Netw 57(5):1167–1191CrossRefGoogle Scholar
  101. 101.
    Michopoulos V, Guan L, Oikonomou G, Phillips I (2011) A comparative study of congestion control algorithms in IPv6 wireless sensor networks. In: Proceedings of international conference on distributed computing in sensor systems and workshops (DCOSS). IEEE, pp 1–6Google Scholar
  102. 102.
    Weldon M (2016) The future X network: a Bell Labs perspective. CRC Press, Boca RatonGoogle Scholar
  103. 103.
    Dunlap J (2011) From billing and technology convergence to ecosystem convergence: Why M2M matters to your business. Pipeline: Technol Serv Provid 8(7):14Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Technical Institute/QurnaSouthern Technical UniversityBasraIraq

Personalised recommendations