Advertisement

Soft Computing

, Volume 23, Issue 2, pp 483–495 | Cite as

Event-based k-nearest neighbors query processing over distributed sensory data using fuzzy sets

  • Yinglong LiEmail author
  • Hong Chen
  • Mingqi Lv
  • Yanjun Li
Methodologies and Application
  • 92 Downloads

Abstract

K-nearest neighbor (kNN) query is an effective way to extract information of interest from distributed sensing devices. Most of the existing kNN query processing approaches rely on using raw sensor readings, which is costly in terms of communication and time overhead. This paper investigates the event-based kNN query problem in distributed sensor systems and proposes a novel e-kNN query scheme using fuzzy sets. Our key technique is that linguistic e-kNN event information instead of raw sensory data is used for e-kNN information storage and in-networks kNN query processing, which is very beneficial to energy efficiency. In addition, event confidence-based grid storage method and e-kNN query processing algorithm are devised for e-kNN information storage and retrieval, respectively. Experimental results based on real-life data set further show that our e-kNN scheme outperforms the conventional methods in terms of communication cost and response time with accuracy guarantee.

Keywords

K-nearest neighbors (kNN) Event detection Fuzzy sets Energy efficiency Sensor network 

Notes

Acknowledgements

We are grateful to the anonymous reviewers and the editor for their constructive suggestions for improving the quality of this paper. This work was funded by the National Natural Science Foundation of China (Nos. 61502421 and 61532021), as well as the Zhejiang Provincial Natural Science Foundation of China (Nos. LY15F020026 and LY15F020025).

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Anastasi G, Conti M, Francesco MD, Passarella A (2009) Energy conservation in wireless sensor networks: a survey. Ad Hoc Netw 7(3):537–568CrossRefGoogle Scholar
  2. Bilski P, Mazurek P, Wagner J (2015) Application of k nearest neighbors approach to the fall detection of elderly people using depth-based sensors. In: Proceedings of 8th IEEE intelligent data acquisition and advanced computing systems: technology and applications. IEEE, pp 733–739Google Scholar
  3. Chirici G, Mura M, McInerney D et al (2016) A meta-analysis and review of the literature on the k-nearest neighbors technique for forestry applications that use remotely sensed data. Remote Sens Environ 176(1):282–294CrossRefGoogle Scholar
  4. Cho HJ (2013) Continuous range k-nearest neighbor queries in vehicular ad hoc networks. J Syst Softw 86(1):1323–1332CrossRefGoogle Scholar
  5. Fu TY, Peng WC, Lee WC (2010) Parallelizing itinerary-based kNN query processing in wireless sensor networks. IEEE T Knowl Data Eng 22(5):711–729CrossRefGoogle Scholar
  6. Galindo J (2008) Handbook of research on fuzzy information processing in databases. IGI Global, HersheyCrossRefGoogle Scholar
  7. Guttman A (1984) R-Tree: a dynamic index structure for spatial searching. In: Proceedings of ACM special interest group on management of data. ACM, pp 47–57Google Scholar
  8. Han Y, Park K, Hong J, Ulamin N, Lee YK (2015) Distance-constraint \(k\)-nearest neighbor searching in mobile sensor networks. Sensors 15(8):18209–18228CrossRefGoogle Scholar
  9. Islam RUI, Hossain MS, Andersson K (2016) A novel anomaly detection algorithm for sensor data under uncertainty. Soft Comput. doi: 10.1007/s00500-016-2425-2 (published online)
  10. Komai Y, Sasaki Y, Hara T, Nishio S (2015) K nearest neighbor search for location-dependent sensor data in MANETs. Ind Sens Netw Adv Data Manag Des Secur 3(1):942–954Google Scholar
  11. Lai Y, Chen H, Li C (2007) Processing the v-KNN queries in wireless sensor networks. In: Proceedings of parallel processing. IEEE, pp 1–6Google Scholar
  12. Li YY, Parker LE (2014) Nearest neighbor imputation using spatial—temporal correlations in wireless sensor networks. Inf Fusion 15(1):64–79CrossRefGoogle Scholar
  13. Li X, Tang Y (2014) Two-dimensional nearest neighbor classification for agricultural remote sensing. Neurocomputing 142(22):182–189Google Scholar
  14. Lin WC, Ke SW, Tsai CF (2014) An intrusion detection system based on combining cluster centers and nearest neighbors. Knowl Based Syst 78(1):13–21Google Scholar
  15. Liu Y, Fu JS, Zhang Z (2016) \(k\)-Nearest neighbors tracking in wireless sensor networks with coverage holes. Pers Ubiquitous Comput 20(3):431–446CrossRefGoogle Scholar
  16. OMNET++ (2016). http://www.omnetpp.org
  17. Rodger JA (2014) A fuzzy nearest neighbor neural network statistical model for predicting demand for natural gas and energy cost savings in public buildings. Expert Syst Appl 41(1):1813–1829CrossRefGoogle Scholar
  18. Rodger JA, George JA (2017) Triple bottom line accounting for optimizing natural gas sustainability: a statistical linear programming fuzzy ILOWA optimized sustainment model approach to reducing supply chain global cybersecurity vulnerability through information and communications technology. J Clean Prod 142(4):1931–1949CrossRefGoogle Scholar
  19. Sharma G, Busch C (2015) Optimal nearest neighbor queries in sensor networks. Theor Comput Sci 608(p2):146–165MathSciNetCrossRefzbMATHGoogle Scholar
  20. Silva RI, Macedo DF et al (2014) Spatial query processing in wireless sensor networks–a survey. Inf Fusion 15(1):32–43CrossRefGoogle Scholar
  21. Su J, Long Y, Qiu X, Li S, Liu D (2015) Anomaly detection of single sensors using OCSVM_KNN. In: Proceedings of big data computing and communications (BigCom). Springer, pp 217–230Google Scholar
  22. Wang MT (2016) Nearest neighbor query processing using the network voronoi diagram. Data Knowl Eng 103(1):19–43CrossRefGoogle Scholar
  23. Winter J, Lee W (2016) KPT: a dynamic KNN query processing algorithm for location-aware sensor networks. In: Proceedings of international workshop on data management for sensor networks. ACM, pp 119–124Google Scholar
  24. Wu S, Chuang K, Chen C, Chen M (2007) DIKNN: an itinerary-based KNN query processing algorithm for mobile sensor networks. In: Proceedings of the 23rd international conference on data engineering (ICDE). IEEE, pp 456–465Google Scholar
  25. Xie M, Hu J, Han S, Chen HH (2013) Scalable hypergrid k-NN-based online anomaly detection in wireless sensor networks. IEEE Trans parallel Distrib 24(8):1661–1670CrossRefGoogle Scholar
  26. Xie W, Li X, Venkat N, Amiya N (2014) K nearest neighbour query processing in wireless sensor and robot networks. In: Proceedings of ad-hoc networks and wireless. Springer, pp 251–264Google Scholar
  27. Xu MWJ, Tong K, Kong H et al (2007) Monitoring top-k query in wireless sensor networks. IEEE T Knowl Data Eng 19(1):962–976Google Scholar
  28. Yanga KT, Chiu GM (2017) Monitoring continuous all k-nearest neighbor query in mobile network environments. Pervasive Mob Comput 39(2017):231–248CrossRefGoogle Scholar
  29. Zhao Z, Yu G, Li B, Yao L, Yang X (2007) An algorithm for optimizing multidimensional k-NN queries in wireless sensor networks. J Softw 18(5):1186–1197 (in Chinese) CrossRefGoogle Scholar
  30. Zheng Y, Ling HF (2013) Emergency transportation planning in disaster relief supply chain management: a cooperative fuzzy optimization approach. Soft Comput 17(7):1301–1314CrossRefGoogle Scholar
  31. Zheng Y, Ling H, Chen S, Xue J (2015a) A hybrid neuro-fuzzy network based on differential biogeography-based optimization for online population classification in earthquakes. IEEE Trans Fuzzy Syst 23(4):1070–1083CrossRefGoogle Scholar
  32. Zheng Y, Jeon B, Xu D et al (2015b) Image segmentation by generalized hierarchical fuzzy C-means algorithm. J Intell Fuzzy Syst 28(2):961–973Google Scholar
  33. Zhu J, Kan B, Liu Y et al (2014) A probabilistic group reverse k-nearest-neighbor query in sensor networks. In: Proceedings of advanced technologies in ad hoc and sensor networks. Springer, pp 121–130Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.College of Computer and TechnologyZhejiang University of TechnologyHangzhouChina
  2. 2.School of InformationRenmin University of ChinaBeijingChina

Personalised recommendations