Skip to main content

Advertisement

Springer Nature Link
Log in

A comparative study of QoS performance for location based and corona based real-time routing protocol in mobile wireless sensor networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Mobile wireless sensor network (MWSN) is a special type of ad-hoc network which has a density of tiny sensor nodes that can interact with each other in which sensor nodes are equipped with either an engine for dynamic mobility or connected to mobile things for uninvolved mobility. A real-time routing method for sensor network means that information is provided according to their end-to-end deadlines. In location based real-time routing, each mobile sensor has a location determination mechanism that can contribute to design a real-time routing algorithm. However, in corona framework, the whole system is divided into virtual circle centered on the sink and each mobile sensor has a circle Identifier. The corona based real-time routing is made based on the corona Id and real-time criteria. This paper studies the quality of service (QoS) for location based and corona based real-time routing protocol in MWSN. A comparison study is implemented in real test bed and simulation experiments. The founding in this research concludes that the overall QoS performance for corona mechanism is better than location based real-time routing for MWSN in term of delivery ratio; power consumption; packet overhead and end-to-end delay.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Akyildiz, I., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

    Article  Google Scholar 

  2. Song, G., Zhou, Y., Ding, F., & Song, A. (2008). A mobile sensor network system for monitoring of unfriendly environments. Sensors Journal, 8(11), 7259–7274.

    Article  Google Scholar 

  3. Indu, S., et al. (2010). Self deployment of mobile sensor network for optimal coverage. International Journal of Engineering Science and Technology, 2(7), 2968–2975.

    Google Scholar 

  4. Li, M., et al. (2013). A survey on topology control in wireless sensor networks: Taxonomy. Comparative Study, and Open Issues, Proceedings of the IEEE, 101(12), 2538–2557.

    Google Scholar 

  5. Youssef, M., et al. (2014). Routing metrics of cognitive radio networks: A survey. IEEE Communications Surveys and Tutorials, 16(1), 92–109.

    Article  Google Scholar 

  6. Zhan, A., Xu, T., Chen, G., Ye, B., & Lu, S. (2008). A survey on real-time routing protocols for wireless sensor networks. Chinese Journal of Computer Science, 3(11), 234–238.

    Google Scholar 

  7. Li, Y., Chen, C., & Song, Y., et al. (2007). Real-time QoS support in wireless sensor networks: A survey, in the Proceedings of 7th IFAC Int Conf on Fieldbuses and networks in industrial and embedded systems (FeT’07), Toulouse, France, pp. 373–380.

  8. Ferng, H. W., Hadiputro, M., & Kurniawan, A. (2011). Design of novel node distribution strategies in corona-based wireless sensor networks. IEEE Transactions on Mobile Computing, 10(9), 1297–1311.

    Article  Google Scholar 

  9. Han, K., et al. (2013). Algorithm design for data communications in duty-cycled wireless sensor networks: A survey. IEEE Communications Magazine, 51(7), 107–113.

    Article  Google Scholar 

  10. Vasilakos, A., et al. (2012). Delay tolerant networks: Protocols and applications. Boca Raton: CRC Press.

    Google Scholar 

  11. Xiao, Y., et al. (2012). Tight performance bounds of multihop fair access for MAC protocols in wireless sensor networks and underwater sensor networks. IEEE Transactions on Mobile Computing, 11(10), 1538–1554.

    Article  Google Scholar 

  12. Chen, M., et al. (2011). Body area networks: A survey. MONET, 16(2), 171–193.

    Google Scholar 

  13. Wang, X., et al. (2012). A survey of green mobile networks: Opportunities and challenges. MONET, 17(1), 4–20.

    Google Scholar 

  14. Li, P., et al. (2012). CodePipe: An opportunistic feeding and routing protocol for reliable multicast with pipelined network coding. INFOCOM, pp. 100–108.

  15. Chilamkurti, N., Zeadally, S., Vasilakos, A., Sharma, V. (2009). Cross-layer support for energy efficient routing in wireless sensor networks, Journal of Sensors, 2009, 1–9.

  16. Cheng, H., et al. (2012). Nodes organization for channel assignment with topology preservation in multi-radio wireless mesh networks. Ad Hoc Networks, 10(5), 760–773.

    Article  Google Scholar 

  17. Sengupta, S., et al. (2012). An evolutionary multiobjective sleep-scheduling scheme for differentiated coverage in wireless sensor networks. IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews, 42(6), 1093–1102.

    Article  Google Scholar 

  18. Liu, et al. CDC: Compressive data collection for wireless sensor networks, IEEE Transactions on Parallel and Distributed Systems, doi:10.1109/TPDS.2014.2345257.

  19. Wei, G., et al. (2011). Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter. Computer Communications, 34(6), 793–802.

    Article  Google Scholar 

  20. Sheng, Z., et al. (2014). A survey on the ietf protocol suite for the internet of things: Standards, challenges, and opportunities. Wireless Communications. IEEE, 20(6), 91–98.

    Google Scholar 

  21. Peng, M., et al. (2011). Impacts of sensor node distributions on coverage in sensor networks. Journal of Parallel and Distributed Computing, 71(12), 1578–1591.

    Article  MATH  Google Scholar 

  22. Xiang, L., et al. (2011). Compressed data aggregation for energy efficient wireless sensor networks. SECON 2011, pp. 46–54.

  23. Felemban, E., Lee, C. G., Ekici, E., Boder R., & Vural, S. (2005). Probabilistic QoS guarantee in reliability and timeliness domains in wireless sensor networks, 24th Annual Joint Conference of the IEEE Computer and Communications Societies, IEEE Proceedings, pp. 2646–2657.

  24. Li, H., Shenoy, P., & Ramamritham, K. (2005). Scheduling messages with deadlines in multi-hop real-time sensor networks, IEEE real-time and embedded technology and applications symposium (RTAS 2005), San Francisco, California, pp. 415–425.

  25. Chipara, O., He, Z., Xing, G., Chen, Q., Wang, X., Lu, C., Stankovic, J., & Abdelzaher, T. (2006). Real-time power-aware routing in sensor networks, quality of service, IWQoS 2006, 14th IEEE International Workshop, pp. 83–92.

  26. Ahmed, A., & Fisal, N. (2008). A real-time routing protocol with load distribution in wireless sensor networks. Elsevier Computer Communication Journal, 31(14), 3190–3203.

    Article  Google Scholar 

  27. Ali, A., Latiff, L. A., & Fisal, N. (2010). Simulation-based real-time routing protocol with load distribution in wireless sensor networks. Wirel. Commun. Mob. Comput, 10(7), 1002–1016.

    Google Scholar 

  28. Lee, E., Park, S., Oh, S., Kim, S.H., & Nam, K.D. (2011) Real-time routing protocol based on expect grids for mobile sinks in wireless sensor networks, vehicular technology conference (VTC Fall), pp. 1–5.

  29. Park, S., Lee, E., Park, H., Jung, J., Kim, S.H. (2010) Strategy for real-time data dissemination to mobile sinks in wireless sensor networks, IEEE 21st International symposium on personal indoor and mobile radio communications (PIMRC), pp. 1905–1910.

  30. Araú jo, G. M., & Becker, L. B. (2011). A network conditions aware geographical forwarding protocol for real-time applications in mobile wireless sensor networks, in AINA 2011 IEEE international conference, pp. 38–45.

  31. Singh, M. et al. A tree based routing protocol for mobile sensor networks (IJCSE). International Journal on Computer Science and Engineering, 02(01), 55–60.

  32. Shee, S.H., Wang, K., Hsieh, I. L. (2005). Color-theory-based dynamic localization in mobile wireless sensor networks, in Proceedings of workshop on wireless, Ad Hoc, Sensor Networks, pp. 73–78.

  33. Hu, L., & Evans, D. (2004). Localization for mobile sensor networks, in tenth international conference on mobile computing and networking (MobiCom’04), Philadelphia, Pennsylvania, USA, pp. 45–57.

  34. Baggio, A., & Langendoen, K. (2008). Monte Carlo localization for mobile wireless sensor networks. Ad Hoc Networks Journal, 6(5), 718–733.

    Article  Google Scholar 

  35. Pavithra, G., & Devaki, P. (2014). Link and location based routing mechanism for energy efficiency in wireless sensor networks. IJETT Journal, 8(4), 212–217.

    Article  Google Scholar 

  36. Zeng, Y., Li, D., & Vasilakos, A. V. (2013). Real-time data report and task execution in wireless sensor and actuator networks using self-aware mobile actuators”. Computer Communications, 36(9), 988–997.

    Article  Google Scholar 

  37. Keally, M., Zhou, G., Xing, G. (2009) Sidewinder: A predictive data forwarding protocol for mobile wireless sensor networks, secon09, 6th Annual IEEE Communications Society Conference, pp. 1–9.

  38. Chuang, P.J., Hu, T.T. (2010). A new and efficient hierarchy-based any cast routing protocol for wireless sensor networks, international symposium on parallel and distributed processing with applications (ISPA), pp. 334–341.

  39. Thepvilojanapong, N., Tobe, Y., & Sezaki, K. (2005). HAR: Hierarchy-based any cast routing protocol for wireless sensor networks, Proc. IEEE Symp. On Applications and the Internet, pp. 204–212.

  40. Ahmed, A. A. (2013). An enhanced real-time routing protocol with load distribution for mobile wireless sensor networks. Computer Networks, 57(6), 1459–1473.

    Article  Google Scholar 

  41. Zeng, Y., et al. (2013). Directional routing and scheduling for green vehicular delay tolerant networks. Wireless Networks, 19(2), 161–173.

    Article  Google Scholar 

  42. Latiff, L., Ali, A., Ooi, C., & Fisal, N. (2005). Development of an indoor gps-free self positioning system for mobile ad hoc network (MANET), In: Proceedings of the 13th IEEE International Conference on networks, MICC-ICON 2005. Malaysia, November, 16–18, 2–6.

    Google Scholar 

  43. Ali, A., Latiff, L., Fisal, N. (2004). GPS-free indoor location tracking in mobile ad hoc network (MANET) using RSSI, in: RF and microwave conference, IEEE proceedings, pp. 251–255.

  44. Zuniga, M., & Krishnamachari, B. (2004). Analyzing the transitional region in low power wireless links, sensor and ad-hoc communications and networks, IEEE SECON 2004, 1st Annual IEEE communications society conference, pp. 517–526.

  45. MEMSIC Technology, http://www.memsic.com/products/wireless-sensor-networks.html. Accessed on March 2012.

  46. Croce, S., Marcelloni, F., & Vecchio, M. (2008). Reducing power consumption in wireless sensor networks using a novel approach to data aggregation. (Oxford Journals). The Computer Journal, 51(2), 227–239.

    Article  Google Scholar 

  47. IEEE 802.15.4 Standard, 2003, Part 15.4: Wireless medium access control (MAC) and physical layer (PHY) specifications for low-rate wireless personal area networks (LR-WPANs), IEEE standard for information technology, IEEE-SA standards board.

  48. T. Dimitriou, A. Kalis, Efficient delivery of information in sensor networks using smart antennas, 1st international workshop on algorithmic aspects of wireless sensor networks (ALGOSENSORS 2004), lecture notes in computer science, LNCS, 3121, Springer, pp. 109–122.

  49. Ali, A., Latiff, L.A., Ooi, C-C., & Fisal, N., (2005). Location based geocasting and forwarding (LGF) Strategy in mobile ad hoc network (MANET), ICT 2005, Cape Town, South Africa, pp. 536–541.

  50. Latiff, L., Ali, A., Fisal, N. (2007) Power reduction quadrant based directional routing protocol in mobile ad hoc network, proceedings of the 2007 IEEE international conference on telecommunication and malaysia international conference on communications, pp. 208–213.

  51. Lymberopoulos, D., Lindsey, Q & Savvides, A. (2005). An empirical analysis of radio signal strength variability’ in IEEE 802.15.4 networks using monopole antennas, Yale ENALAB Technical Report 050501.

  52. Ahmed, A. A., & Fisal, N. (2014). A real–time routing protocol with mobility support and load distribution for mobile wireless sensor networks. International Journal of Sensor Networks, 15(2), 95–111.

    Article  Google Scholar 

Download references

Acknowledgments

This work was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under Grant No. (830-004-D1434). The authors, therefore, acknowledge with thanks DSR technical and financial support.

Author information

Authors and Affiliations

  1. Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh, KSA

    Adel Ali Ahmed

Authors
  1. Adel Ali Ahmed
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Adel Ali Ahmed.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ahmed, A.A. A comparative study of QoS performance for location based and corona based real-time routing protocol in mobile wireless sensor networks. Wireless Netw 21, 1015–1031 (2015). https://doi.org/10.1007/s11276-014-0834-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-014-0834-7

Keywords