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An Efficient Data Aggregation Method for Event-Driven WSNs: A Modeling and Evaluation Approach

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Abstract

This paper proposes and models an efficient data aggregation method for wireless sensor networks (WSNs). In the proposed data aggregation method, every cluster head (CH) node incorporates a local forwarding history to decide whether to forward or to drop a recently received packet. When a new packet arrives at a CH node, a threshold value is calculated based on the information of the forwarding history; then, a random number is generated and compared with the threshold value to determine whether the packet should be dropped or not. In fact, the CH node forwards the new packet with the probability of 1 − p and drops it with probability of p where the parameter p is determined based on the forwarding history. In order to evaluate the proposed data aggregation method, two approaches consisting of simulation and analytical modeling are used. Various scenarios are considered in simulations conducted with NS2 software to compare the proposed data aggregation method with four previously proposed methods. Results reveal that the proposed method (1) aggregates more than 70 % of redundant packets, (2) reduces the network end-to-end delay by at least 22 %, and (3) reduces the missed event rate compared with the other methods. The proposed method is also evaluated by means of an analytical model based on queuing networks. The model accurately estimates the network performance utilizing the proposed data aggregation method. Comparisons of the results obtained by the proposed model and simulations confirm that the proposed model has at most 7 % prediction error. The proposed model allows WSN designers to easily achieve useful information about their networks before the establishment and manufacturing of the networks.

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References

  1. 1.

    Kamarei, M., Patooghy, A., & Fazeli, M. (2013). LLDA: A low-latency data aggregation method to minimize data redundancy in wireless sensor networks. In The 5th conference on information and knowledge technology, Shiraz, Fars.

  2. 2.

    Bista, R., & Chang, J. W. (2010). Privacy-preserving data aggregation protocols for wireless sensor networks: A survey. Sensors, 10, 4577–4601.

  3. 3.

    Bhattacharjee, S., Roy, P., Ghosh, S., Misra, S., & Obaidat, M. S. (2012). Wireless sensor network-based fire detection, alarming, monitoring and prevention system for Bord-and-Pillar coal mines. The Journal of Systems and Software, 85(3), 571–581.

  4. 4.

    Feng, J., Wang, Z., & Henkel, J. (2012). An adaptive data gathering strategy for target tracking in cluster-based wireless sensor networks. In IEEE symposium on computers and communications (ISCC), Cappadocia, pp. 468–474.

  5. 5.

    Naderi, M. Y., Rabiee, H. R., Khansari, M., & Salehi, M. (2012). Error control for multimedia communications in wireless sensor networks: A comparative performance analysis. Ad Hoc Networks, 10(6), 1028–1042.

  6. 6.

    Chae, M. J., Yoo, H. S., Kim, J. Y., & Cho, M. Y. (2012). Development of a wireless sensor network system for suspension bridge health monitoring. Automation in Construction, 21, 237–252.

  7. 7.

    Egbogah, E. E., & Fapojuwo, A. O. (2011). A survey of system architecture requirements for health care-based wireless sensor networks. Sensors, 11(5), 4875–4898.

  8. 8.

    Cheng, B. C., Liao, G. T., Tseng, R. Y., & Hsu, P. H. (2012). Network lifetime bounds for hierarchical wireless sensor networks in the presence of energy constraints. Computer Networks, 56(2), 820–831.

  9. 9.

    Lin, K., Chen, M., & Zeadally, S. (2012). Balancing energy consumption with mobile agents in wireless sensor networks. Future Generation Computer Systems, 28(2), 446–456.

  10. 10.

    Barnawi, A. Y. (2012). Adaptive TDMA slot assignment using request aggregation in wireless sensor networks. Procedia Computer Science, 10, 78–85.

  11. 11.

    Chen, H., Mineno, H., & Mizuno, T. (2008). Adaptive data aggregation scheme in clustered wireless sensor networks. Computer Communications, 31(15, 25), 3579–3585.

  12. 12.

    Yeganeh, M. H., Yousefi, H., Alinaghipour, N., & Movaghar, A. (2011). RDAG: A structure-free real-time data aggregation protocol for wireless sensor networks. In 17th IEEE international conference on embedded and real-time computing systems and applications, Toyama, pp. 51–60.

  13. 13.

    Kwon, S., Ko, J. H., Kim, J., & Kim, C. (2011). Dynamic timeout for data aggregation in wireless sensor networks. Computer Networks, 55(3), 650–664.

  14. 14.

    Wang, P., He, Y., & Huang, L. (2013). Near optimal scheduling of data aggregation in wireless sensor networks. Ad Hoc Networks, 11, 1287–1296.

  15. 15.

    Feng, J., Eager, D. L., & Makaroff, D. (2007). Asynchronous data aggregation for real-time monitoring in sensor networks. In Proceedings of the 6th international IFIP-TC6 conference on ad hoc and sensor networks, wireless networks, next generation internet, Heidelberg, pp. 73–84.

  16. 16.

    Chakravarthi, R., Gomathy, C., Sebastian, S., Pushparaj, K., & Mon, K. (2010). A survey on congestion control in wireless sensor networks. International Journal of Computer Science & Communication, 1(1), 161–164.

  17. 17.

    Lin, C., et al. (2011). Energy efficient ant colony algorithms for data aggregation in wireless sensor networks. Journal of Computer and System Sciences, 78(6), 1686–1702.

  18. 18.

    Solis, L., & Obraczka, K. (2004). The impact of timing in data aggregation for sensor networks. In IEEE international conference on communications (ICC), pp. 3640–3645.

  19. 19.

    Xu, X., Li, X., Mao, X., Tang, S., & Wang, S. (2011). A delay-efficient algorithm for data aggregation in multihop wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 22(1), 163–175.

  20. 20.

    Yousefi, H., Yeganeh, M. H., Alinaghipour, N., & Movaghar, A. (2012). Structure-free real-time data aggregation in wireless sensor networks. Computer Communications, 35(9), 1132–1140.

  21. 21.

    Yue, J., Zhang, W., Xiao, W., Tang, D., & Tang, J. (2012). Energy efficient and balanced cluster-based data aggregation algorithm for wireless sensor networks. Procedia Engineering, 29, 2009–2015.

  22. 22.

    Jung, W., Lim, K., & Ko, Y. (2011). Efficient clustering-based data aggregation techniques for wireless sensor networks. Wireless Networks, 17(5), 1387–1400.

  23. 23.

    Shrestha, A., Xing, L., & Liu, H. (2006). Infrastructure communication reliability of wireless sensor networks. In Proceedings of the 2nd IEEE international symposium on dependable, autonomic and secure computing (DASC’06), North Dartmouth, pp. 250–257.

  24. 24.

    Yu, Z., Fu, X., Cai, Y., & Vuran, M. C. (2011). A reliable energy-efficient multi-level routing algorithm for wireless sensor networks using Fuzzy Petri Nets. Sensors, 11(3), 3381–3400.

  25. 25.

    Katiyar, V., Chand, N., & Soni, S. (2011). A survey on clustering algorithms for heterogeneous wireless sensor networks. International Journal of Advanced Networking and Applications, 2(4), 745–754.

  26. 26.

    Sadat, A., & Karmakar, G. (2011). Optimum clusters for reliable and energy efficient wireless sensor networks. In 10th IEEE international symposium on network computing and applications, Cambridge, pp. 342–347.

  27. 27.

    Yu, J., Qi, Y., Wang, G., & Gu, X. (2012). A cluster-based routing protocol for wireless sensor networks with nonuniform node distribution. AEU - International Journal of Electronics and Communications, 66(12), 54–61.

  28. 28.

    Saleem, M., Ullah, I., & Farooq, M. (2012). BeeSensor: A bee-inspired, energy-efficient and scalable routing protocol for wireless sensor networks. Information Sciences, 200, 38–56.

  29. 29.

    Kamarei, M., Hajimohammadi, M., Patooghy, A., & Fazeli, M. (2013). OLDA: An efficient on-line data aggregation method for wireless sensor networks. In International conference on broadband and wireless computing, communication and applications (BWCCA), Compiegne, France, pp. 49–53.

  30. 30.

    Feng, X., Shan, C., Xin, L., & Yu, L. (2009). Reliability evaluation of wireless sensor networks using an enhanced OBDD algorithm. The Journal of China Universities of Posts and Telecommunications, 16(5), 62–70.

  31. 31.

    Huang, F., Jiang, Z., Zhang, S., & Gao, S. (2010). Reliability evaluation of wireless sensor networks using logistic regression. In International conference on communications and mobile computing, Shenzhen, pp. 334–338.

  32. 32.

    Silva, I., Guedes, L., Portugal, P., & Vasques, F. (2012). Reliability and availability evaluation of wireless sensor networks for industrial applications. Sensors, 12(1), 806–838.

  33. 33.

    Xiao, Y., Chen, S., Li, X., & Li, Y. (2008). An enhanced factoring algorithm for reliability evaluation of wireless sensor networks. In The 9th international conference for young computer scientists, Hunan, pp. 2175–2179.

  34. 34.

    Shaikh, F., Khelil, A., & Suri, N. (2007). On modeling the reliability of data transport in wireless sensor networks. In 15th EUROMICRO international conference on parallel, distributed and network-based processing (PDP’07), Napoli, pp. 395–402.

  35. 35.

    Mizanian, K., Yousefi, H., & Jahangir, A. H. (2009). Modeling and evaluating reliable real-time degree in multi-hop wireless sensor networks. In IEEE Sarnoff symposium, SARNOFF ‘09, Princeton, NJ, pp. 1–6.

  36. 36.

    Yousefi, H., Mizanian, K., & Jahangir, A. H. (2010). Modeling and evaluating the reliability of cluster-based wireless sensor networks. In 24th IEEE international conference on advanced information networking and applications, Perth, Australia, pp. 827–834.

  37. 37.

    Zhu, C., Zheng, C., Shu, L., & Han, G. (2012). A survey on coverage and connectivity issues in wireless sensor networks. Journal of Network and Computer Applications, 35(2), 619–632.

  38. 38.

    Bagayoko, A. B., Paillassa, B., & Almeida, C. B. (2009). Transport and routing redundancy for MANETs robustness. In IEEE international symposium on parallel and distributed processing with applications, Sichuan, China, pp. 348–353.

  39. 39.

    Boudewijn, H. (1998). Performance of computer communication systems: A model-based (1st ed.). New York: Wiley.

  40. 40.

    Suhonen, J., Hamalainen, T. D., & Hannikainen, M. (2009). Availability and end-to-end reliability in low duty cycle multihop wireless sensor networks. Sensors, 9(3), 2088–2116.

  41. 41.

    Speer, A. P., & Chen, I. R. (2006). Effect of redundancy on mean time to failure of wireless sensor networks. In Proceedings of the 20th international conference on advanced information networking and applications (AINA’06), Vienna, Austria, pp. 373–382.

  42. 42.

    Zhang, J., Wu, Q., Ren, F., He, T., & Lin, C. (2010). Effective data aggregation supported by dynamic routing in wireless sensor networks. In IEEE international conference on communications (ICC’10), Cape Town, pp. 1–6.

  43. 43.

    He, T., Stankovic, J., Lu, C., & Abdelzaher, T. (2003). SPEED: A stateless protocol for real-time communication in sensor networks. In International conference on distributed computing systems, pp. 46–55.

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Acknowledgments

This research was is part supported by a grant from IPM (No. CS1392-4-23).

Author information

Correspondence to Ahmad Patooghy.

Additional information

The submitted manuscript is an extended version of the work previously proposed in [1]. The extensions of the current version include: (1) a wide range of simulation experiments added to the current version. To do this the proposed data aggregation method is compared with previously proposed methods i.e., RAG, SPEED, DASDR, and RDAG is terms of network performance, data aggregation rate, event miss rate. The previous version of the paper does not offer any comparison experiments, (2) an analytical performance model is proposed and validated in the paper. The proposed model estimates network performance, data aggregation rate, event miss rate of WSN using the proposed data aggregation method with an acceptable prediction error. The model provides a remarkable speed up in the evaluation process of data aggregation methods in the field of wireless sensor networks.

This research was in part supported by a grant from IPM (No. CS1392-4-23).

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Kamarei, M., Hajimohammadi, M., Patooghy, A. et al. An Efficient Data Aggregation Method for Event-Driven WSNs: A Modeling and Evaluation Approach. Wireless Pers Commun 84, 745–764 (2015). https://doi.org/10.1007/s11277-015-2659-9

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Keywords

  • Data aggregation
  • Modeling
  • Wireless sensor networks