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Game Theory Based Congestion Control Framework

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

Abstract

WSNs connected to the Internet through 6LoWPAN have wide applications in industrial, automation, health care, military, environment, logistics, etc. An estimate by Bell Labs suggests that from 50 to 100 billion things are expected to be connected to the Internet by 2020 [1], and the number of the wireless sensor devices will account for a majority of these. Generally, the applications can be categorized into four types: event-based, continuous, query-based and hybrid applications based on the data delivery method [2, 3]. In the hybrid application type, the first three categories are combined into hybrid application, i.e. sensor nodes send packets in response to an event (event based) and at the same time send packets periodically (continuous) as well as send a reply to a sink query (query based). This type of application will be common in the future as WSNs are integrated with the Internet to form the IoT [4]. In the IoT applications, the sensor nodes host many different application types simultaneously (event based, continuous and query based) with varied requirements. Some of them are real-time applications, where the application data is time-critical and delay-constrained, while others are non-real-time applications. Some applications send very important data and losing this data is not permitted, e.g. medical applications and fire detection applications. This brings new challenges to the congestion control algorithms and mechanisms designed to be aware of application priorities as well as node priorities. However, according to our best knowledge, none of the existing congestion control literature in WSNs and 6LoWPAN networks supports awareness of both node priorities and application priorities. To address this, later we define a ‘priority cost function’ to support node priority awareness and distinguish between high-priority nodes and low-priority nodes.

References

  1. 1.
    Weldon M (2016) The future X network: a bell labs perspective. CRC PressGoogle Scholar
  2. 2.
    Ghaffari A (2015) Congestion control mechanisms in wireless sensor networks: a survey. J Netw Comput Appl 52:101–115CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805CrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    Dunkels A, B. 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
  7. 7.
    Osterlind E, 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
  8. 8.
    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
  9. 9.
    Nikaido H, Isoda K (1955) Note on noncooperative convex games. Pac J Math 5(5):807–815CrossRefGoogle Scholar
  10. 10.
    Rosen JB (1965) Existence and uniqueness of equilibrium points for concave N-person games. Econ: J Econ Soc 520–534Google Scholar
  11. 11.
    Brown RG (2004) Smoothing, Forecasting and prediction of discrete time series. Courier CorporationGoogle Scholar
  12. 12.
    Michopoulos V, Guan L, Oikonomou G, Phillips I (2002) 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
  13. 13.
    Castellani AP, Rossi M, Zorzi M (2014) Back pressure congestion control for CoAP/6LoWPAN networks. Ad Hoc Netw 18:71–84CrossRefGoogle Scholar
  14. 14.
    Stehlık M (2011) Comparison of simulators for wireless sensor networks, Master’s thesis, Faculty of informatics, Masaryk university, Brno, Czech RepublicGoogle Scholar
  15. 15.
    Dunkels A, Eriksson J, Finne N, Tsiftes N (2011) Powertrace: network-level power profiling for low-power wireless networks. Technical report, Swedish Institute of Computer Science (SICS)Google Scholar
  16. 16.
    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
  17. 17.
    Michopoulos V (2012) Congestion and medium access control in 6LoWPAN WSN, Ph.D. dissertation, Computer science, Loughborough UniversityGoogle Scholar
  18. 18.
    Zawodniok M, Jagannathan S (2007) Predictive congestion control protocol for wireless sensor networks. IEEE Trans Wirel Commun 6(11):3955–3963CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Technical Institute/QurnaSouthern Technical UniversityBasraIraq

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