Skip to main content
SpringerLink
Log in

A Distributed Anomaly Detection Scheme Based on Correlation Awareness in WSN

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Wireless sensor devices are affected by internal constraints and the external environment, generating abnormal data. Currently, many anomaly detection schemes ignore the correlation between screening nodes, wasting resources due to excessive communication. Therefore, this paper proposes a distributed anomaly detection scheme based on adaptive grouping using the correlation between nodes in wireless sensor networks. Limiting the scope of collaboration between nodes can reduce the waste of resources due to excessive communication. Since the computing resources of sensor nodes are limited, an edge-cloud framework is established. The scheme uses Spatio-temporal correlation and graph theory for wireless sensor networks to determine node groups with solid correlations on the cloud server. Based on the grouping results, anomaly detection is implemented locally. A Bayesian network model is constructed at the node within the group, and outlier detection is realized by inference on nodes. A correlation consistency evaluation method is proposed to improve anomaly detection accuracy to check the data consistency on the cluster head. The proposed scheme is verified by a generated data set and the real data of Intel Berkeley Research Lab. The effectiveness of the proposed method is verified by comparing it with three existing algorithms. Experimental results show that the method improves detection accuracy and reduces false detection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Algorithm 1
Algorithm 2
Algorithm 3
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Availability of Data

A portion of the data utilized in this study is obtainable through the reference [41], while another segment of the data can be derived through the computations outlined in Section Data sets.

References

  1. Kandris, D., Nakas, C., Vomvas, D., & Koulouras, G. (2020). Applications of wireless sensor networks: An up-to-date survey. Applied System Innovation. https://doi.org/10.3390/asi3010014

    Article  Google Scholar 

  2. Khan, M. A., Kumar, A., & Bandhu, K. C. (2022). Worldwide interoperability for microwave access network optimization with and without relay station for next generation internet access. International Journal of Communication Systems, 35(17), 5318. https://doi.org/10.1002/dac.5318

    Article  Google Scholar 

  3. Praveen Kumar, D., Amgoth, T., & Annavarapu, C. S. R. (2019). Machine learning algorithms for wireless sensor networks. A survey. Information Fusion, 49, 1–25. https://doi.org/10.1016/j.inffus.2018.09.013

    Article  Google Scholar 

  4. Gao, C., Wang, G., Shi, W., Wang, Z., & Chen, Y. (2022). Autonomous driving security: State of the art and challenges. IEEE Internet of Things Journal, 9(10), 7572–7595. https://doi.org/10.1109/JIOT.2021.3130054

    Article  Google Scholar 

  5. De Paola, A., Gaglio, S., Re, G. L., Milazzo, F., & Ortolani, M. (2015). Adaptive distributed outlier detection for WSNs. IEEE Transactions on Cybernetics, 45(5), 902–913. https://doi.org/10.1109/TCYB.2014.2338611

    Article  Google Scholar 

  6. Bosman, H. H., Iacca, G., Tejada, A., Wörtche, H. J., & Liotta, A. (2017). Spatial anomaly detection in sensor networks using neighborhood information. Information Fusion, 33, 41–56. https://doi.org/10.1016/j.inffus.2016.04.007

    Article  Google Scholar 

  7. Yu, X., Yang, X., Tan, Q., Shan, C., & Lv, Z. (2022). An edge computing based anomaly detection method in iot industrial sustainability. Applied Soft Computing, 128, 109486. https://doi.org/10.1016/j.asoc.2022.109486

    Article  Google Scholar 

  8. Dwivedi, R.K., Rai, A.K., & Kumar, R. (2020). A study on machine learning based anomaly detection approaches in wireless sensor network. In 2020 10th international conference on cloud computing, data science & engineering. https://doi.org/10.1109/Confluence47617.2020.9058311

  9. Gil, P., Martins, H., & Januário, F. (2019). Outliers detection methods in wireless sensor networks. Artificial Intelligence Review, 52(4), 2411–2436. https://doi.org/10.1007/s10462-018-9618-2

    Article  Google Scholar 

  10. Chander, B., & Kumaravelan, G. (2021). Outlier detection strategies for WSNs: A survey. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2021.02.012

    Article  Google Scholar 

  11. Ghorbel, O., Obeid, A.M., Abid, M., & Snoussi, H. (2016). One class outlier detection method in wireless sensor networks: Comparative study. In 2016 24th international conference on software, telecommunications and computer networks (SoftCOM) (pp. 1–8). https://doi.org/10.1109/SOFTCOM.2016.7772168

  12. Osanaiye, O., Alfa, A. S., & Hancke, G. P. (2018). A statistical approach to detect jamming attacks in wireless sensor networks. Sensors. https://doi.org/10.3390/s18061691

    Article  Google Scholar 

  13. Dai, T., & Ding, Z. (2020). Online distributed distance-based outlier clearance approaches for wireless sensor networks. Pervasive and Mobile Computing, 63, 101130. https://doi.org/10.1016/j.pmcj.2020.101130

    Article  Google Scholar 

  14. Arfaoui, A., Kribeche, A., Senouci, S. M., & Hamdi, M. (2019). Game-based adaptive anomaly detection in wireless body area networks. Computer Networks, 163, 106870. https://doi.org/10.1016/j.comnet.2019.106870

    Article  Google Scholar 

  15. Zheng, W., Yang, L., & Wu, M. (2018). An improved distributed pca-based outlier detection in wireless sensor network. In Cloud Computing and Security, Cham (pp. 37–49). https://doi.org/10.1007/978-3-030-00018-9_4

  16. Gan, G., & Ng, M.K.-P. (2017). K-means clustering with outlier removal. Pattern Recognition Letters, 90, 8–14. https://doi.org/10.1016/j.patrec.2017.03.008

    Article  Google Scholar 

  17. Rajasegarar, S., Leckie, C., & Palaniswami, M. (2014). Hyperspherical cluster based distributed anomaly detection in wireless sensor networks. Journal of Parallel and Distributed Computing, 74(1), 1833–1847. https://doi.org/10.1016/j.jpdc.2013.09.005

    Article  Google Scholar 

  18. Andrade, A. T. C., Montez, C., Moraes, R., Pinto, A. R., Vasques, F., & da Silva, G. L. (2016). Outlier detection using k-means clustering and lightweight methods for wireless sensor networks. In IECON 2016 - 42nd annual conference of the ieee industrial electronics society, pp. 4683–4688. https://doi.org/10.1109/IECON.2016.7794093

  19. Wazid, M., & Das, A. K. (2016). An efficient hybrid anomaly detection scheme using k-means clustering for wireless sensor networks. Wireless Personal Communications, 90(4), 1971–2000. https://doi.org/10.1007/s11277-016-3433-3

    Article  Google Scholar 

  20. Yuan, J., Guo, X., Xiang, H., Hu, Z., & Chen, B. (2020). An anomaly detection algorithm based on K-means and BP neural network in wireless sensor networks. 11430, 114300. https://doi.org/10.1117/12.2538333

  21. Titouna, C., Naït-Abdesselam, F., & Khokhar, A. (2019). DODS: A distributed outlier detection scheme for wireless sensor networks. Computer Networks, 161, 93–101. https://doi.org/10.1016/j.comnet.2019.06.014

    Article  Google Scholar 

  22. Miao, X., Liu, Y., Zhao, H., & Li, C. (2019). Distributed online one-class support vector machine for anomaly detection over networks. IEEE Transactions on Cybernetics, 49(4), 1475–1488. https://doi.org/10.1109/TCYB.2018.2804940

    Article  Google Scholar 

  23. Zhang, K., Yang, K., Li, S., Jing, D., & Chen, H.-B. (2019). ANN-based outlier detection for wireless sensor networks in smart buildings. IEEE Access, 7, 95987–95997. https://doi.org/10.1109/ACCESS.2019.2929550

    Article  Google Scholar 

  24. Luo, T., & Nagarajan, S. G. (2018). Distributed anomaly detection using autoencoder neural networks in WSN for IoT. In 2018 IEEE international conference on communications (ICC), pp. 1–6. https://doi.org/10.1109/ICC.2018.8422402

  25. Livani, M. A., & Abadi, M. (2010). Distributed PCA-based anomaly detection in wireless sensor networks. In 2010 international conference for internet technology and secured transactions (pp. 1–8). IEEE. https://ieeexplore.ieee.org/document/5678106

  26. Fawzy, A., Mokhtar, H. M., & Hegazy, O. (2013). Outliers detection and classification in wireless sensor networks. Egyptian Informatics Journal, 14(2), 157–164. https://doi.org/10.1007/s10462-018-9618-2

    Article  Google Scholar 

  27. Abid, A., Khediri, S. E., & Kachouri, A. (2021). Improved approaches for density-based outlier detection in wireless sensor networks. Computing, 103(10), 2275–2292. https://doi.org/10.1007/s00607-021-00939-5

    Article  MathSciNet  Google Scholar 

  28. De Paola, A., Gaglio, S., Re, G. L., Milazzo, F., & Ortolani, M. (2015). Adaptive distributed outlier detection for WSNs. IEEE Transactions on Cybernetics, 45(5), 902–913. https://doi.org/10.1109/TCYB.2014.2338611

    Article  Google Scholar 

  29. Chen, P.-Y., Yang, S., & McCann, J. A. (2015). Distributed real-time anomaly detection in networked industrial sensing systems. IEEE Transactions on Industrial Electronics, 62(6), 3832–3842. https://doi.org/10.1109/TIE.2014.2350451

    Article  Google Scholar 

  30. Yuan, H., Zhao, X., & Yu, L. (2015). A distributed Bayesian algorithm for data fault detection in wireless sensor networks. In 2015 international conference on information networking (ICOIN) (pp. 63–68). https://doi.org/10.1109/ICOIN.2015.7057858

  31. Safaei, M., Ismail, A. S., Chizari, H., Driss, M., Boulila, W., Asadi, S., & Safaei, M. (2020). Standalone noise and anomaly detection in wireless sensor networks: A novel time-series and adaptive Bayesian-network-based approach. Software: Practice and Experience, 50(4), 428–446. https://doi.org/10.1002/spe.2785

    Article  Google Scholar 

  32. Ramos, R. G. D. S., Ribeiro, P., & Cardoso, J. V. D. M. (2016). Anomalies detection in wireless sensor networks using bayesian changepoints. In 2016 IEEE 13th international conference on mobile ad hoc and sensor systems (MASS) (pp. 384–385). https://doi.org/10.1109/MASS.2016.064

  33. Feng, R., Han, X., Liu, Q., & Yu, N. (2015). A credible Bayesian-based trust management scheme for wireless sensor networks. International Journal of Distributed Sensor Networks, 11(11), 678926. https://doi.org/10.1155/2015/678926

    Article  Google Scholar 

  34. Kumar Dwivedi, R., Pandey, S., & Kumar, R. (2018). A study on machine learning approaches for outlier detection in wireless sensor network. In 2018 8th international conference on cloud computing, data science & engineering (confluence) (pp. 189–192). https://doi.org/10.1109/CONFLUENCE.2018.8442992

  35. Wu, C., Peng, Q., Lee, J., Leibnitz, K., & Xia, Y. (2021). Effective hierarchical clustering based on structural similarities in nearest neighbor graphs. Knowledge-Based Systems, 228, 107295. https://doi.org/10.1016/j.knosys.2021.107295

    Article  Google Scholar 

  36. Scanagatta, M., Salmerón, A., & Stella, F. (2019). A survey on Bayesian network structure learning from data. Progress in Artificial Intelligence, 8(4), 425–439. https://doi.org/10.1007/s13748-019-00194-y

    Article  Google Scholar 

  37. Rienstra, T., Thimm, M., Kersting, K., & Shao, X. (2020). Independence and D-separation in abstract argumentation. In Proceedings of the 17th international conference on principles of knowledge representation and reasoning (pp. 713–722). https://doi.org/10.24963/kr.2020/73

  38. Schrago, C. G., Aguiar, B. O., & Mello, B. (2018). Comparative evaluation of maximum parsimony and Bayesian phylogenetic reconstruction using empirical morphological data. Journal of Evolutionary Biology, 31(10), 1477–1484. https://doi.org/10.1111/jeb.13344

    Article  Google Scholar 

  39. Fitriyah, H., & Budi, A. S. (2019). Outlier detection in object counting based on hue and distance transform using median absolute deviation (MAD). In 2019 international conference on sustainable information engineering and technology (SIET) (pp. 217–222). https://doi.org/10.1109/SIET48054.2019.8985993

  40. Gil, P., Martins, H., Cardoso, A., & Palma, L. (2016). Outliers detection in non-stationary time-series: Support vector machine versus principal component analysis. In 2016 12th IEEE international conference on control and automation (ICCA) (pp. 701–706). https://doi.org/10.1109/ICCA.2016.7505361

  41. Madden, S. (2004). Intel Lab Data. Website. http://db.csail.mit.edu/labdata/labdata.html

  42. Gao, C., Yang, P., Chen, Y., Wang, Z., & Wang, Y. (2021). An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks. Complex & Intelligent Systems, 7(5), 2453–2468. https://doi.org/10.1007/s40747-021-00442-6

    Article  Google Scholar 

  43. Li, G., He, J., & Fu, Y. (2008). Group-based intrusion detection system in wireless sensor networks. Computer Communications, 31(18), 4324–4332. https://doi.org/10.1016/j.comcom.2008.06.020

    Article  Google Scholar 

Download references

Funding

This work is partly supported by the Scientific Research Program of the Science and Technology Department of Shaanxi Province, China (Grant No. 2023-YBGY-211), the Scientific Research Program of the Shaanxi Provincial Education Department, China (Grant No. 21JP115), the Scientific Research Program of the Science and Technology Bureau of Xi’an, China (Grant No. 22GXFW0129), the Scientific Research Program of the Science and Technology Bureau of Yulin, China (Grant No. CXY-2022-162), and the Shaanxi Province Qinchuangyuan "Scientist + Engineer" Team Construction Project (Grant No. 2023KXJ-241).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, 710121, Shaanxi, China

    Zhongmin Wang, Rui Gao, Cong Gao & Yanping Chen

  2. Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an University of Posts and Telecommunications, Xi’an, 710121, Shaanxi, China

    Zhongmin Wang, Cong Gao & Yanping Chen

  3. Xi’an Key Laboratory of Big Data and Intelligent Computing, Xi’an University of Posts and Telecommunications, Xi’an Shaanxi, 710121, China

    Zhongmin Wang

  4. Xi’an Zhongxing New Software Co., Ltd., Xi’an, China

    Fengwei Wang

Authors
  1. Zhongmin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rui Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cong Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yanping Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fengwei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The authors W.Z. and G.R.: contributed equally to this work. Z.W.: Conceptualization, Methodology. R.G.: Methodology, Validation, Formal analysis, Writing. C.G.: Software, Data Curation. Y.C.: Resources, Supervision. F.W.: Software, Investigation.

Corresponding author

Correspondence to Rui Gao.

Ethics declarations

Conflict of interest

The authors declare that they have no Conflict of interest.

Code availability

The simulation tool employed in this study can be accessed by clicking [https://github.com/mesepulveda/wsnsim]. The code for the data processing and algorithm used in detecting data abnormal can be provided by the corresponding author Rui Gao on request.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Z., Gao, R., Gao, C. et al. A Distributed Anomaly Detection Scheme Based on Correlation Awareness in WSN. Wireless Pers Commun 134, 519–541 (2024). https://doi.org/10.1007/s11277-024-10930-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-024-10930-w

Keywords

Search

Navigation