Top-K監測查詢持續監測網路中感測器的讀數，且從回傳的讀數中得出最大的k個值所相對應的節點並排出其大小順序。Top-K 監測查詢被廣泛應用於許多不同的分散式網路。本論文中我們討論此問題應用於 WSN 的情況，而 WSN 的運作主要受限於感測器的電力並非無限。我們以遮罩方法為基礎提出一個新的方法並稱為非相依自動調節遮罩監測，目的是避免感測器傳送對使用者來說不需要的訊息來減少感測器的電力消耗。本篇我們提出的方法改善了過去沒有考慮到的監測環境的數值變化是否有特性的問題。我們希望能依監測環境的讀數變化的情形來調整遮罩的設定以達到減少感測器新的讀數超出遮罩的機率，所以我們提出的新遮罩設定步驟讓每個點皆能依過去的部分數據套用高斯分布來調節遮罩的設定。另外，我們也提出遮罩參數來縮小傳送遮罩的所需的訊息量。最後我們使用虛擬的數據與實際的數據來測試我們的方法。可以從模擬的結果證明出我們的方法在不同的情況下都比起先前的方法更可以減少網路整體的功率消耗且延長了網路的使用期。

Top-k monitoring facilitates the selection of the highest k numbers of sensor readings from the serial feedbacks of the nodes, and is widely utilized in distributed network applications. The major problem to the practice of top-k monitoring query is the limited energy supply of the sensors; therefore, aiming to retrench energy consumption of the wireless sensor network using top-k monitoring, we bring forth in this paper a filter-based algorithm named as Independent Adaptive Filter-based Monitoring to lessen the transmission of unnecessary messages in every sensor node. Thereby, considering the long noteless issue of probable regularity in the reading’s variation, a filter-setting step being introduced, we use probability techniques and Gaussian distribution to set an adaptive filter to each node, which automatically
adjust the future setting of filters according to the past monitoring data, to reduce the probability of the new readings going beyond the filter; moreover, we devise parameter transmission between base station and each sensor node to assure non-overlapped filter and the least feedbacks. Our proposed new algorithm is examined with both virtual and real datasets, and the simulation results prove that the new algorithm effectively reduces the energy consumption so as to considerably extend the networks lifetime compared with the previous top-k algorithms.

[1] S. Madden, M. Franklin, J. Hellerstein, and W. Hong, “Tag: a tiny aggregation service for ad-hoc sensor networks,” ACM SIGOPS Operating Systems Review,
vol. 36, no. SI, pp. 131–146, 2002.
[2] N. Xu, S. Rangwala, K. Chintalapudi, D. Ganesan, A. Broad, R. Govindan, and D. Estrin, “A wireless sensor network for structural monitoring,” in Proceedings
of the 2nd international conference on Embedded networked sensor systems. ACM, 2004, pp. 13–24.
[3] R. Szewczyk, E. Osterweil, J. Polastre, M. Hamilton, A. Mainwaring, and D. Estrin, “Habitat monitoring with sensor networks,” Communications of the ACM, vol. 47, no. 6, pp. 34–40, 2004.
[4] K. Romer and F. Mattern, “The design space of wireless sensor networks,” Wireless Communications, IEEE, vol. 11, no. 6, pp. 54–61, 2004.
[5] C. Olston, B. Loo, and J. Widom, “Adaptive precision setting for cached approximate values,” in ACM SIGMOD Record, vol. 30, no. 2. ACM, 2001, pp.355–366.
[6] M. Wu, J. Jianliang Xu, X. Xueyan Tang, and W. Wang-Chien Lee, “Top-k monitoring in wireless sensor networks,” IEEE Transactions on Knowledge and Data Engineering,, vol. 19, no. 7, pp. 962–976, 2007.
[7] H. Mai, Y. Lee, K. Lee, and M. Kim, “Distributed adaptive top-k monitoring in wireless sensor networks,” Journal of Systems and Software, vol. 84, no. 2, pp. 314–327, 2011.
[8] B. Malhotra, M. Nascimento, and I. Nikolaidis, “Exact top-k queries in wireless sensor networks,” IEEE Transactions on Knowledge and Data Engineering,no. 99, pp. 1–1, 2010.
[9] J. Niedermayer, M. Nascimento, M. Renz, P. Kroger, and H. Kriegel, “Exploiting local node cache in top-k queries within wireless sensor networks,” in 33 Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 2010, pp. 434–437.
[10] M. Ye, W. Lee, D. Lee, and X. Liu, “Distributed processing of probabilistic top-k queries in wireless sensor networks,” IEEE Transactions on Knowledge and Data Engineering,, no. 99, pp. 1–1, 2011.
[11] C. Zhu, L. Yang, L. Shu, T. Hara, and S. Nishio, “Implementing top-k query in duty-cycled wireless sensor networks,” in Wireless Communications and Mobile
Computing Conference (IWCMC), 2011 7th International. IEEE, 2011, pp.553–558.
[12] I. Ilyas, G. Beskales, and M. Soliman, “A survey of top-k query processing techniques in relational database systems,” ACM Computing Surveys (CSUR), vol. 40, no. 4, p. 11, 2008.
[13] R. Fagin, A. Lotem, and M. Naor, “Optimal aggregation algorithms for middleware,” in Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. ACM, 2001, pp. 102–113.
[14] A. Natsev, Y. Chang, J. Smith, C. Li, and J. Vitter, “Supporting incremental join queries on ranked inputs,” in Proceedings of the International Conference on Very Large Data Bases, 2001, pp. 281–290.
[15] I. Ilyas, W. Aref, and A. Elmagarmid, “Joining ranked inputs in practice,” in Proceedings of the 28th international conference on Very Large Data Bases. VLDB Endowment, 2002, pp. 950–961.
[16] V. Hristidis, N. Koudas, and Y. Papakonstantinou, “Prefer: A system for the efficient execution of multi-parametric ranked queries,” ACM SIGMOD Record, vol. 30, no. 2, pp. 259–270, 2001.
[17] C. Li, K. Chen-Chuan Chang, and I. Ilyas, “Supporting ad-hoc ranking aggregates,” in Proceedings of the 2006 ACM SIGMOD international conference on Management of data. ACM, 2006, pp. 61–72.
[18] U. Guntzer, W. Balke, and W. Kiesling, “Optimizing multi-feature queries for image databases,” in Proceedings of the 26th International Conference on Very Large Data Bases. Morgan Kaufmann Publishers Inc., 2000, pp. 419–428.
[19] J. Guntzer, W. Balke, and W. Kiesling, “Towards efficient multi-feature queries in heterogeneous environments,” in Information Technology: Coding and Com-
puting, 2001. Proceedings. International Conference on. IEEE, 2001, pp. 622–628.
[20] Y. Chang, L. Bergman, V. Castelli, C. Li, M. Lo, and J. Smith, “The onion technique: indexing for linear optimization queries,” in ACM SIGMOD Record, vol. 29, no. 2. ACM, 2000, pp. 391–402.
[21] I. Ilyas, W. Aref, and A. Elmagarmid, “Supporting top-kjoin queries in relational databases,” The VLDB Journal, vol. 13, no. 3, pp. 207–221, 2004.
[22] Z. Zhang, S. Hwang, K. Chang, M. Wang, C. Lang, and Y. Chang, “Boolean+ ranking: querying a database by k-constrained optimization,” in Proceedings of the 2006 ACM SIGMOD international conference on Management of data. ACM, 2006, pp. 359–370.
[23] S. Borzsony, D. Kossmann, and K. Stocker, “The skyline operator,” in Data Engineering, 2001. Proceedings. 17th International Conference on IEEE, 2001, pp. 421–430.
[24] Y. Yuan, X. Lin, Q. Liu, W. Wang, J. Yu, and Q. Zhang, “Efficient computation of the skyline cube,” in Proceedings of the 31st international conference on Very large data bases. VLDB Endowment, 2005, pp. 241–252.
[25] A. Deligiannakis, Y. Kotidis, and N. Roussopoulos, “Processing approximate aggregate queries in wireless sensor networks,” Information Systems, vol. 31, no. 8, pp. 770–792, 2006.
[26] S. Madden, M. Franklin, J. Hellerstein, and W. Hong, “Tinydb: an acquisitional query processing system for sensor networks,” ACM Transactions on Database Systems (TODS), vol. 30, no. 1, pp. 122–173, 2005.
[27] A. Vlachou, C. Doulkeridis, K. Norvag, and M. Vazirgiannis, “On efficient top-k query processing in highly distributed environments,” in Proc. of SIGMOD,
2008, pp. 753–764.
[28] C. Jin, K. Yi, L. Chen, J. Yu, and X. Lin, “Sliding-window top-k queries on uncertain streams,” Proceedings of the VLDB Endowment, vol. 1, no. 1, pp.301–312, 2008.
[29] B. Babcock and C. Olston, “Distributed top-k monitoring,” in Proceedings of the 2003 ACM SIGMOD international conference on Management of data. ACM, 2003, pp. 28–39.
[30] D. Zeinalipour-Yazti, Z. Vagena, D. Gunopulos, V. Kalogeraki, V. Tsotras, M. Vlachos, N. Koudas, and D. Srivastava, “The threshold join algorithm for top-k queries in distributed sensor networks,” in Proceedings of the 2nd inter-national workshop on Data management for sensor networks. ACM, 2005, pp.61–66.
[31] A. Deshpande, C. Guestrin, and S. Madden, “Using probabilistic models for data management in acquisitional environments,” in Proc. CIDR. Citeseer, 2005, pp. 317–328.
[32] A. Deshpande, C. Guestrin, S. Madden, J. Hellerstein, and W. Hong, “Model-driven data acquisition in sensor networks,” in Proceedings of the Thirtieth international conference on Very large data bases-Volume 30. VLDB Endow-
ment, 2004, pp. 588–599.
[33] ——, “Model-based approximate querying in sensor networks,” The VLDB journal, vol. 14, no. 4, pp. 417–443, 2005.
[34] X. Tang and J. Xu, “Extending network lifetime for precision-constrained data aggregation in wireless sensor networks,” in INFOCOM 2006. 25th IEEE International Conference on Computer Communications. Proceedings. IEEE, 2006,pp. 1–12.
[35] L. from Earth, “Live from earth and mars (lem) project,” http://www-k12.atmos.washington.edu/k12/grayskies/, 2006.
[36] W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks,” IEEE Transactions on Wireless Communications,, vol. 1, no. 4, pp. 660–670, 2002.