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Memory Efficient Routing Using Bloom Filters in Large Scale Sensor Networks

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Abstract

Performance and lifetime of wireless sensor networks are tightly linked to the used routing protocol. Energy and memory efficiency are some of the main challenges of routing protocols. These challenges are more strict in large scale and dense networks. Numerous amount of routing approaches are published so far, emphasized on energy consumption. However, a few of them addresses the limitations of node memory. This paper introduces a new routing protocol called Bloom filter based routing protocol (BFRP). It reduces memory consumption by replacing a routing table with a Bloom filter. Since the approach is devised for clustered networks, a new clustering algorithm is introduced that takes remaining energy into the account for cluster head election. It also supports networks with churn. Several scenarios are simulated with NS2 and the results are compared to Coverage Preservation Clustering Protocol and Hybrid Energy-efficient Distributed Clustering algorithms. The results approve that BFRP improves energy consumption and show a significant decrease in memory usage.

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References

  1. Alotaibi, E., & Mukherjee, B. (2012). A survey on routing algorithms for wireless ad-hoc and mesh networks. Computer Networks, 56(2), 940–965.

    Article  Google Scholar 

  2. Al-Karaki, J. N., & Kamal, A. E. (2004). Routing techniques in wireless sensor networks: A survey. IEEE Wireless communications, 11(6), 6–28.

    Article  Google Scholar 

  3. Challal, Y., Ouadjaout, A., Lasla, N., Bagaa, M., & Hadjidj, A. (2011). Secure and efficient disjoint multipath construction for fault tolerant routing in wireless sensor networks. Journal of Network and Computer Applications, 34(4), 1380–1397. (Advanced Topics in Cloud Computing).

    Article  Google Scholar 

  4. Li, X., Wu, J., & Xu, J. (2006). Hint-based routing in wsns using scope decay bloom filters. In Networking, Architecture, and Storages. IWNAS’06. International workshop on (p. 8). IEEE.

  5. Tabatabaee Malazi, H., Zamanifar, K., & Dulman, S. O. (2011). Fed: Fuzzy event detection model for wireless sensor networks. International Journal of Wireless and Mobile Networks (IJWMN), 3(6), 29–45.

    Article  Google Scholar 

  6. Mouradian, A., Aug-Blum, I., & Valois, F. (2014). Rtxp: A localized real-time mac-routing protocol for wireless sensor networks. Computer Networks, 67, 43–59.

    Article  Google Scholar 

  7. Tabatabaee Malazi, H., Zamanifar, K., Pruteanu, A., & Dulman, S. (2014). Gossip-based density estimation in dynamic heterogeneous wireless sensor networks. International Journal of Autonomous and Adaptive Communications Systems, 7(1), 151–168.

    Article  Google Scholar 

  8. Soro, S., & Heinzelman, W. B. (2009). Cluster head election techniques for coverage preservation in wireless sensor networks. Ad Hoc Networks, 7(5), 955–972.

    Article  Google Scholar 

  9. Tabatabaee Malazi, H., Zamanifar, K., Khalili, A., & Dulman, S. O. (2013). Dec: Diversity-based energy-aware clustering for heterogeneous sensor networks. Ad Hoc and Sensor Wireless Networks, 17(1–2), 53–72.

    Google Scholar 

  10. Lotf, J. J., Hosseinzadeh, M., & Alguliev, R. M. (2010). Hierarchical routing in wireless sensor networks: A survey. In Computer Engineering and Technology (ICCET), 2nd international conference on (Vol. 3, pp 650–654).

  11. Kumar, D., Aseri, T. C., & Patel, R. B. (2009). Eehc: Energy efficient heterogeneous clustered scheme for wireless sensor networks. Computer Communications, 32(4), 662–667.

    Article  Google Scholar 

  12. Lindsey, S., & Raghavendra, C. S. (2002). Pegasis: Power-efficient gathering in sensor information systems. In Aerospace conference proceedings. IEEE (Vol. 3, pp. 3–1125). IEEE.

  13. Manjeshwar, A., & Agrawal, D. P. (2001) Teen: A routing protocol for enhanced efficiency in wireless sensor networks. In Parallel and distributed processing symposium, international (Vol. 3, pp. 30189a–30189a). IEEE Computer Society.

  14. Manjeshwar, A., & Agrawal, D. P. (2002). Apteen: A hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks. In Parallel and distributed processing symposium, international (Vol. 2, pp 0195b–0195b). IEEE Computer Society.

  15. Chan, H., & Perrig, A. (2004). Ace: An emergent algorithm for highly uniform cluster formation. In H. Karl., A. Wolisz & A. Willig (Eds.), Wireless sensor networks (pp. 154–171). Heidelberg: Springer.

  16. Demirbas, M., Arora, A., & Mittal, V. (2004) Floc: A fast local clustering service for wireless sensor networks. In Workshop on dependability issues in wireless ad hoc networks and sensor networks (pp. 1–6).

  17. Heinzelman, W. R., Kulik, J., & Balakrishnan, H. (1999). Adaptive protocols for information dissemination in wireless sensor networks. In Proceedings of the 5th annual ACM/IEEE international conference on mobile computing and networking (pp. 174–185). ACM.

  18. Younis, O., & Fahmy, S. (2004). Heed: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379.

    Article  Google Scholar 

  19. Osano, T., Uchida, Y., & Ishikawa, N. (2008). Routing protocol using bloom filters for mobile ad hoc networks. In Mobile ad-hoc and sensor networks. The 4th international conference on (pp. 89–94). IEEE.

  20. Guo, D., He, Y., & Liu, Y. (2014). On the feasibility of gradient-based data-centric routing using bloom filters. IEEE Transactions on Parallel and Distributed Systems, 25(1), 180–190.

  21. Pasquini, R., Magalhaes, M. F., Verdi, F. L., & Welin, A. (2010). Bloom filters in a landmark-based flat routing. In Communications (ICC), IEEE international conference on (pp. 1–5). IEEE.

  22. Jerzak, Z., & Fetzer, C. (2008). Bloom filter based routing for content-based publish/subscribe. In Proceedings of the second international conference on distributed event-based systems (pp. 71–81). ACM.

  23. Koloniari, G., & Pitoura, E. (2004). Content-based routing of path queries in peer-to-peer systems. In E. Bertino., S. Christodoulakis., D. Plexousakis., V. Christophides., M. Koubarakis., K. Böhm & E. Ferrari (Eds.), Advances in database technology-EDBT (pp. 29–47). Heidelberg: Springer.

  24. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In System sciences. Proceedings of the 33rd annual hawaii international conference on (pp 10–pp.) IEEE.

  25. Gupta, I., Riordan, D., & Sampalli, S. (2005). Cluster-head election using fuzzy logic for wireless sensor networks. In Communication networks and services research conference. Proceedings of the 3rd annual (pp 255–260). IEEE.

  26. Ahmadi, M., & Wong, S. (2009). K-stage pipelined bloom filter for packet classification. In Computational science and engineering. CSE’09. international conference on (Vol. 2, pp. 64–70). IEEE.

  27. Geravand, S., & Ahmadi, M. (2013). Bloom filter applications in network security: A state-of-the-art survey. Computer Networks, 57(18), 4047–4064.

    Article  Google Scholar 

  28. Ghanbari, P., Ahmadi, M., & Ahmadi, A. (2012). Error management and detection in computer networks using bloom filters. In Proceedings of the international conference on advances in computing, communications and informatics (pp. 551–556). ACM.

  29. Geravand, S., & Ahmadi, M. (2014). An efficient and scalable plagiarism checking system using bloom filters. Computers and Electrical Engineering, 40(6), 1789–1800.

    Article  Google Scholar 

  30. Bloom, B. H. (1970). Space/time trade-offs in hash coding with allowable errors. Communications of the ACM, 13(7), 422–426.

    Article  MATH  Google Scholar 

  31. Guo, D., Jie, W., Chen, H., Yuan, Y., & Luo, X. (2010). The dynamic bloom filters. IEEE Transactions on Knowledge and Data Engineering, 22(1), 120–133.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Technology, Islamic Azad University, Kermanshah Branch, Kermanshah, Iran

    Seyedeh Mahboubeh Sajjadian Amiri

  2. Faculty of Computer Science and Engineering, GC, Shahid Beheshti University, Tehran, Iran

    Hadi Tabatabaee Malazi

  3. Department of Computer Engineering, University of Razi, Kermanshah, Iran

    Mahmood Ahmadi

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  1. Seyedeh Mahboubeh Sajjadian Amiri
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  2. Hadi Tabatabaee Malazi
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  3. Mahmood Ahmadi
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Correspondence to Hadi Tabatabaee Malazi.

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Sajjadian Amiri, S.M., Tabatabaee Malazi, H. & Ahmadi, M. Memory Efficient Routing Using Bloom Filters in Large Scale Sensor Networks. Wireless Pers Commun 86, 1221–1240 (2016). https://doi.org/10.1007/s11277-015-2985-y

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