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Intelligent Query-Based Data Aggregation Model and Optimized Query Ordering for Efficient Wireless Sensor Network

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Abstract

Data aggregation algorithms play a primary role in WSN, as it collects and aggregates the data in an energy efficient manner so that the life expectancy of the network is extended. This paper intends to develop a query-based aggregation model for WSN using the advanced optimization algorithm called group search optimization (GSO). The proposed model is constructed in such a way that the querying order (QO) can be ranked based on latency and throughput. Accordingly, the main objective of the proposed GSO-based QO is to minimize the latency and maximize the throughput of WSN. The proposed data aggregation model facilitates the network administrator to understand the best queries so that the performance of the base station can be improved. After framing the model, it compares the performance of GSO-based QO with the traditional PSO-based QO, FF-based QO, GA-based QO, ABC-based QO and GSO-based QO in terms of idle time and throughput. Thus the data aggregation performance of proposed GSO-based QO is superior to the traditional algorithms by attaining high throughput and low latency.

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References

  1. Zhou, F., Chen, Z., Guo, S., & Li, J. (2016). Maximizing lifetime of data-gathering trees with different aggregation modes in WSNs. IEEE Sensors Journal, 16(22), 8167–8177.

    Article  Google Scholar 

  2. Wang, C., Jiang, C., Tang, S., & Li, X. Y. (2012). SelectCast: Scalable data aggregation scheme in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 23(10), 1958–1969.

    Article  Google Scholar 

  3. Chen, X., Hu, X., & Zhu, J. (2005). Minimum data aggregation time problem in wireless sensor networks. In International conference on mobile ad-hoc and sensor networks (pp. 133–142).

  4. Li, Y., Guo, L., & Prasad, S. K. (2010). An energy-efficient distributed algorithm for minimum-latency aggregation scheduling in wireless sensor networks. 2010 IEEE 30th international conference on distributed computing systems (pp. 827–836), Genova.

  5. Wu, C., Liu, Y., Wu, F., Fan, W., & Tang, B. (2017). Graph-based data gathering scheme in WSNs with a mobility-constrained mobile sink. IEEE Access, 5, 19463–19477.

    Article  Google Scholar 

  6. Cheng, C. F., Li, L. H., & Wang, C. C. (2017). Data gathering with minimum number of relay packets in wireless sensor networks. IEEE Sensors Journal, 17(21), 7196–7208.

    Article  Google Scholar 

  7. Nandhini, R., & Devarajan, N. (2013). An enhanced QoS provisioning for VoIP traffic in Wimax using channel aware cross layer scheduler. Jokull International journal, 63(8), 172–185.

    Google Scholar 

  8. Nandhini, R., & Devarajan, N. (2013). An improved channel aware cross layer designed scheduling for QoS enhancement in IEEE 802.16 wireless networks. Archives des science Journal, 66(3), 1–12.

    Google Scholar 

  9. Nandhini, R., & Devarajan, N. (2014). A cross-layer scheduler with channel state information for guaranteed quality of service. Journal of Computer Science, 10(2), 255–263.

    Article  Google Scholar 

  10. Nandhini, R., & Devarajan, N. (2014). Comparison for WiMAX scheduling algorithms and proposal for quality of service improvement in WiMAX networks. American Journal of Applied Sciences, 11(1), 8–16.

    Article  Google Scholar 

  11. Nandhini, R. (2015). Cross-layer designed scheduling algorithm for QoS enhancement in Wireless Broadband networks. International Journal of Applied Engineering Research, 10(13), 33191–33196. ISSN 0973-4562.

  12. Nandhini, R. (2015). Bandwidth reservation policy performance analysis in a wireless cellular network under non-exponential distributions. ARPN Journal of Engineering and Applied Sciences, 10(18). ISSN:1819-6608.

  13. Camillò, A., Nati, M., Petrioli, C., Rossi, M., & Zorzi, M. (2013). IRIS: Integrated data gathering and interest dissemination system for wireless sensor networks. Ad Hoc Networks, 11(2), 654–671.

    Article  Google Scholar 

  14. Karasabun, E., Korpeoglu, I., & Aykanat, C. (2013). Active node determination for correlated data gathering in wireless sensor networks. Computer Networks, 57(5), 1124–1138.

    Article  Google Scholar 

  15. Arjmandi, H., Taki, M., & Lahouti, F. (2011). Lifetime maximized data gathering in wireless sensor networks using limited-order distributed source coding. Signal Processing, 91(11), 2661–2666.

    Article  MATH  Google Scholar 

  16. Kalpakis, K., & Tang, S. (2009). A combinatorial algorithm for the maximum lifetime data gathering with aggregation problem in sensor networks. Computer Communications, 32(15), 1655–1665.

    Article  Google Scholar 

  17. Huang, R., Zhang, Z., & Xu, G. (2011). Predictive model-aided filtering scheme of data-collection in WSN. The Journal of China Universities of Posts and Telecommunications, 18(2), 17–24.

    Article  Google Scholar 

  18. Nanda, A., & Rath, A. K. (2016). Energy efficient and secure data aggregation with malicious aggregator node detection (ESMD) in WSNs. In IEEE 7th power India international conference (PIICON) (pp. 1–6), Bikaner, Rajasthan, India.

  19. Nandhini, R., & Wategaonkar, D. N. (2016). A survey on reliability in wireless sensor network. Indian Journal of Science and Technology, 9(38). ISSN (Print): 0974-6846, ISSN (Online): 0974-5645.

  20. Sarode, P., & Nandhini, R, APDA: Adaptive pruning & data aggregation algorithms for query based wireless sensor networks. In 2016 International conference IEEE global trends in signal processing, information computing and communication (ICGTSPICC) (pp. 219–224), Electronic ISBN: 978-1-5090-0467-6.

  21. Nandhini, R., & Wategaonkar, D. N. (2016). Modelling of clustering and sectoring approach in WSN. In International conference on global trends in signal processing, information computing and communication (ICSPICC).

  22. Huangfu, W., Liu, Y., Duan, B., Sun, L., Ma, J., & Chen, C. (2008). EATA: Effectiveness based aggregation time allocation algorithm for wireless sensor networks. 2008 IEEE symposium on computers and communications (pp. 981–987) Marrakech.

  23. Madden, S., Franklin, M. J., Hellerstein, J. M., & Hong, W. (2002). TAG: A tiny aggregation service for ad-hoc sensor networks. ACM SIGOPS operating systems review—OSDI ‘02: Proceedings of the 5th symposium on operating systems design and implementation (vol. 36 no. SI, pp. 131–146).

  24. Bagaa, M., Challal, Y., Ksentini, A., Derhab, A., & Badache, N. (2014). Data aggregation scheduling algorithms in wireless sensor networks: Solutions and challenges. IEEE Communications Surveys & Tutorials, 16(3), 1339–1368.

    Article  Google Scholar 

  25. Bachir, A., Dohler, M., Watteyne, T., & Leung, K. K. (2010). MAC essentials for wireless sensor networks. IEEE Communications Surveys & Tutorials, 12(2), 222–248.

    Article  Google Scholar 

  26. Ha, N. P. K., Zalyubovskiy, V., & Choo, H. (2012). Delay-efficient data aggregation scheduling in duty-cycled wireless sensor networks. In Proceeding RACS ‘12 proceedings of the 2012 ACM research in applied computation symposium (pp. 203–208).

  27. Brayner, A., Lopes, A., Meira, D., Vasconcelos, R., & Menezes, R. (2008). An adaptive in-network aggregation operator for query processing in wireless sensor networks. Journal of Systems and Software, 81(3), 328–342.

    Article  MATH  Google Scholar 

  28. Chatterjea, S., Nieberg, T., Meratnia, N., & Havinga, P. (2008). A distributed and self-organizing scheduling algorithm for energy-efficient data aggregation in wireless sensor networks. ACM Transactions on Sensor Networks (TOSN), 4(4), 20.

    Article  Google Scholar 

  29. Bagaa, M., Younis, M., & Balasingham, I. (2015). Optimal strategies for data aggregation scheduling in wireless sensor networks. In 2015 IEEE global communications conference (GLOBECOM) (pp. 1–6), San Diego, CA.

  30. Yousefi, H., Malekimajd, M., Ashouri, M., & Movaghar, A. (2015). Fast aggregation scheduling in wireless sensor networks. IEEE Transactions on Wireless Communications, 14(6), 3402–3414.

    Article  Google Scholar 

  31. Chang, C. L., & Ho, K. Y. (2016). Slot assignment for TDMA MAC in industrial wireless sensor network. In 2016 IEEE/ACIS 15th international conference on computer and information science (ICIS) (pp. 1–5), Okayama.

  32. Yan, M., Han, M., Ai, C., Cai, Z., & Li, Y. (2016). Data aggregation scheduling in probabilistic wireless networks with cognitive radio capability. In 2016 IEEE global communications conference (GLOBECOM) (pp. 1–6), Washington, DC.

  33. Ebrahimi, D., & Assi, C. (2016). On the interaction between scheduling and compressive data gathering in wireless sensor networks. IEEE Transactions on Wireless Communications, 15(4), 2845–2858.

    Article  Google Scholar 

  34. Nandhini, R., & Devarajan, N. (2013). Channel quality based cross layer scheduling algorithm in Wimax networks. Life Sciences Journal, 10(2), 2498–2506.

    Google Scholar 

  35. Xu, X., Ansari, R., Khokhar, A., & Vasilakos, A. V. (2015). Hierarchical data aggregation using compressive sensing (HDACS) in WSNs. ACM Transactions on Sensor Networks (TOSN), 11(3), 45.

    Article  Google Scholar 

  36. Xu, X., Li, X. Y., & Song, M. (2013). Efficient aggregation scheduling in multihop wireless sensor networks with SINR constraints. IEEE Transactions on Mobile Computing, 12(12), 2518–2528.

    Article  Google Scholar 

  37. Xu, X., Li, X. Y., Wan, P. J., & Tang, S. (2012). Efficient scheduling for periodic aggregation queries in multihop sensor networks. IEEE/ACM Transactions on Networking, 20(3), 690–698.

    Article  Google Scholar 

  38. Ma, J., Lou, W., & Li, X. Y. (2014). Contiguous link scheduling for data aggregation in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 25(7), 1691–1701.

    Article  Google Scholar 

  39. Bagaa, M., Younis, M., Djenouri, D., Derhab, A., & Badache, N. (2015). Distributed low-latency data aggregation scheduling in wireless sensor networks. ACM Transactions on Sensor Networks (TOSN), 11(3), 49.

    Article  Google Scholar 

  40. Kasirajan, P., Larsen, C., & Jagannathan, S. (2012). A new data aggregation scheme via adaptive compression for wireless sensor networks. ACM Transactions on Sensor Networks (TOSN), 9(1), 5.

    Article  Google Scholar 

  41. Jhumka, A., Bradbury, M., & Saginbekov, S. (2014). Efficient fault-tolerant collision-free data aggregation scheduling for wireless sensor networks. Journal of Parallel and Distributed Computing, 74(1), 1789–1801.

    Article  MATH  Google Scholar 

  42. Wang, P., He, Y., & Huang, L. (2013). Near optimal scheduling of data aggregation in wireless sensor networks. Ad Hoc Networks, 11(4), 1287–1296.

    Article  Google Scholar 

  43. Davoodi, E., Zare, K., & Babaei, E. (2017). A GSO-based algorithm for combined heat and power dispatch problem with modified scrounger and ranger operators. Applied Thermal Engineering, 120, 36–48.

    Article  Google Scholar 

  44. He, S., Wu, Q. H., & Saunders, J. R. (2009). Group search optimizer: An optimization algorithm inspired by animal searching behavior. IEEE Transactions on Evolutionary Computation, 13(5), 973–990.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computing Science and Engineering, VIT Chennai, Vellore, Tamilnadu, India

    Prachi Sarode & R. Nandhini

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  1. Prachi Sarode
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  2. R. Nandhini
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Correspondence to Prachi Sarode.

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Sarode, P., Nandhini, R. Intelligent Query-Based Data Aggregation Model and Optimized Query Ordering for Efficient Wireless Sensor Network. Wireless Pers Commun 100, 1405–1425 (2018). https://doi.org/10.1007/s11277-018-5646-0

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  • DOI: https://doi.org/10.1007/s11277-018-5646-0

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