Skip to main content

A double-layer isolation mechanism for malicious nodes in wireless sensor networks

Abstract

Isolation plays an important role in the security of wireless sensor networks. Existing isolation models for wireless sensor networks possess excessive energy consumption. We propose a double-layer isolation mechanism based on an improved Dijkstra algorithm. The number of “optimal” nodes is obtained based on the calculation formula. Two groups of “optimal” nodes are determined by the arrival time difference ranging algorithm and the improved Dijkstra algorithm. The first layer isolation and the second layer isolation are constructed by the obtained two groups of “optimal” nodes, respectively. According to the status of receiving and sending data, the “optimal” routing protocol and working mode are selected. Based on the cooperation between two layers of isolation, intruded nodes are isolated from a wireless sensor network. The intrusion source is detected by monitoring the intruded nodes. Simulation results show that the proposed isolation mechanism can effectively reduce the energy consumption of wireless sensor nodes. High transmission efficiency, rapid response, and enhanced security are provided for wireless sensor networks.

This is a preview of subscription content, access via your institution.

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

References

  1. 1.

    Butun, I., Morgera, S. D., & Sankar, R. (2013). A survey of intrusion detection systems in wireless sensor networks. IEEE Communications Surveys & Tutorials, 16(1), 266–282.

    Article  Google Scholar 

  2. 2.

    Gungor, V. C., Lu, B., & Hancke, G. P. (2010). Opportunities and challenges of wireless sensor networks in smart grid. IEEE Transactions on Industrial Electronics, 57(10), 3557–3564.

    Article  Google Scholar 

  3. 3.

    Venkatraman, K., Daniel, J. V., & Murugaboopathi, G. (2013). Various attacks in wireless sensor network: Survey. International Journal of Soft Computing and Engineering (IJSCE), 3(1), 208–212.

    Google Scholar 

  4. 4.

    Dai, H.-N., Wang, Q., Li, D., & Wong, R.C.-W. (2013). On eavesdropping attacks in wireless sensor networks with directional antennas. International Journal of Distributed Sensor Networks, 9(8), 760834.

    Article  Google Scholar 

  5. 5.

    Kavitha, T., & Sridharan, D. (2010). Security vulnerabilities in wireless sensor networks: A survey. Journal of Information Assurance and Security, 5(1), 31–44.

    Google Scholar 

  6. 6.

    Illiano, V. P., & Lupu, E. C. (2015). Detecting malicious data injections in wireless sensor networks: A survey. ACM Computing Surveys (CSUR), 48(2), 1–33.

    Article  Google Scholar 

  7. 7.

    Can, O., & Sahingoz, O. K. (2015). A survey of intrusion detection systems in wireless sensor networks. In 2015 6th international conference on modeling, simulation, and applied optimization (ICMSAO) (pp. 1–6) IEEE.

  8. 8.

    Meghdadi, M., Ozdemir, S., & Güler, I. (2011). A survey of wormhole-based attacks and their countermeasures in wireless sensor networks. IETE Technical Review, 28(2), 89–102.

    Article  Google Scholar 

  9. 9.

    Yu, B., Xu, C.-Z., & Xiao, B. (2013). Detecting sybil attacks in VANETs. Journal of Parallel and Distributed Computing, 73(6), 746–756.

    Article  Google Scholar 

  10. 10.

    Singh, S. K., Singh, M., & Singh, D. K. (2011). A survey on network security and attack defense mechanism for wireless sensor networks. International Journal of Computer Trends and Technology, 1(2), 9–17.

    Google Scholar 

  11. 11.

    Ahmed, M., Mahmood, A. N., & Hu, J. (2015). A survey of network anomaly detection techniques. Journal of Network & Computer Applications, 60, 19–31.

    Article  Google Scholar 

  12. 12.

    Zhang, Y., Hamm, N. A., Meratnia, N., Stein, A., Van De Voort, M., & Havinga, P. J. (2012). Statistics-based outlier detection for wireless sensor networks. International Journal of Geographical Information Science, 26(8), 1373–1392.

    Article  Google Scholar 

  13. 13.

    Wang, S.-S., Yan, K.-Q., Wang, S.-C., & Liu, C.-W. (2011). An integrated intrusion detection system for cluster-based wireless sensor networks. Expert Systems with Applications, 38(12), 15234–15243.

    Article  Google Scholar 

  14. 14.

    Shafiei, H., Khonsari, A., Derakhshi, H., & Mousavi, P. (2014). Detection and mitigation of sinkhole attacks in wireless sensor networks. Journal of Computer and System Sciences, 80(3), 644–653.

    Article  Google Scholar 

  15. 15.

    Crosby, G. V., Hester, L., & Pissinou, N. (2011). Location-aware, trust-based detection and isolation of compromised nodes in wireless sensor networks. IJ Network Security, 12(2), 107–117.

    Google Scholar 

  16. 16.

    Ahmed, A., Bakar, K. A., Channa, M. I., Haseeb, K., & Khan, A. W. (2015). A survey on trust based detection and isolation of malicious nodes in ad-hoc and sensor networks. Frontiers of Computer Science, 9(2), 280–296.

    MathSciNet  Article  Google Scholar 

  17. 17.

    Khalil, I., Bagchi, S., Rotaru, C. N., & Shroff, N. B. (2010). UnMask: Utilizing neighbor monitoring for attack mitigation in multihop wireless sensor networks. Ad Hoc Networks, 8(2), 148–164.

    Article  Google Scholar 

  18. 18.

    Sen, J. (2009). A survey on wireless sensor network security. International Journal of Communication Networks & Information Se, 1(2), 59–82.

    Google Scholar 

  19. 19.

    Madkour, A., Aref, W. G., Rehman, F. U., Rahman, M. A., & Basalamah, S. (2017). A survey of shortest-path algorithms, pp. 1–26. Preprint retrieved from arXiv:1705.02044

  20. 20.

    Broumi, S., Bakal, A., Talea, M., Smarandache, F., & Vladareanu, L. (2016). Applying Dijkstra algorithm for solving neutrosophic shortest path problem. In 2016 international conference on advanced mechatronic systems (ICAMechS) (pp. 412–416) IEEE.

  21. 21.

    Chen, H., Liu, B., Huang, P., Liang, J., & Gu, Y. (2012). Mobility-assisted node localization based on TOA measurements without time synchronization in wireless sensor networks. Mobile Networks and Applications, 17(1), 90–99.

    Article  Google Scholar 

  22. 22.

    Ke, M., Xu, Y., Anpalagan, A., Liu, D., & Zhang, Y. (2017). Distributed TOA-based positioning in wireless sensor networks: A potential game approach. IEEE Communications Letters, 22, 316–319.

    Article  Google Scholar 

  23. 23.

    Singh, P., & Agrawal, S. (2013). TDOA based node localization in WSN using neural networks. In 2013 international conference on communication systems and network technologies (pp. 400–404) IEEE.

  24. 24.

    Xu, B., Sun, G., Yu, R., & Yang, Z. (2013). High-accuracy TDOA-based localization without time synchronization. IEEE Transactions on Parallel & Distributed Systems, 24(8), 1567–1576.

    Article  Google Scholar 

  25. 25.

    Lee, Y. S., Park, J. W., & Barolli, L. (2012). A localization algorithm based on AOA for ad-hoc sensor networks. Mobile Information Systems, 8(1), 61–72.

    Article  Google Scholar 

  26. 26.

    Liu, C., Yang, J., & Wang, F. (2013). Joint TDOA and AOA location algorithm. Journal of Systems Engineering and Electronics, 24(2), 183–188.

    Article  Google Scholar 

  27. 27.

    Yaghoubi, F., Abbasfar, A.-A., & Maham, B. (2014). Energy-efficient RSSI-based localization for wireless sensor networks. IEEE Communications Letters, 18(6), 973–976.

    Article  Google Scholar 

  28. 28.

    Oguejiofor, O. S., Okorogu, V. N., Abe, A., & Osuesu, B. O. (2013). Outdoor localization system using RSSI measurement of wireless sensor network. International Journal of Innovative Technology & Exploring Engineering, 2(2), 1–6.

    Google Scholar 

  29. 29.

    Shah, A. F. M. S., & Islam, M. S. (2014). A survey on cooperative communication in wireless networks. International Journal of Intelligent Systems and Applications, 6(7), 66–78.

    Article  Google Scholar 

  30. 30.

    Ghosh, R. K. (2017). Routing protocols for mobile ad hoc network. Singapore: Springer.

    Book  Google Scholar 

  31. 31.

    Ramos, H. S., Guidoni, D., Boukerche, A., Nakamura, E. F., Frery, A. C., & Loureiro, A. A. F. (2011). Topology-related modeling and characterization of wireless sensor networks. In Proceedings of the 8th ACM symposium on performance evaluation of wireless ad hoc, sensor, and ubiquitous networks, association for computing machinery (pp. 33–40).

  32. 32.

    Younis, M., Lee, S., Senturk, I. F., & Akkaya, K. (2014). Topology management techniques for tolerating node failure. Amsterdam: Elsevier.

    Book  Google Scholar 

  33. 33.

    Verma, A., Singh, M. P., Singh, J. P., & Kumar, P. (2015) Survey of MAC protocol for wireless sensor networks. In 2015 second international conference on advances in computing and communication engineering (pp. 92–97).

  34. 34.

    Huang, P., Xiao, L., Soltani, S., Mutka, M. W., & Xi, N. (2013). The evolution of MAC protocols in wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 15(1), 101–120.

    Article  Google Scholar 

  35. 35.

    Kwon, Y., Chae, Y. (2006). Traffic adaptive IEEE 802.15.4 MAC for wireless sensor networks. In International conference on embedded and ubiquitous computing (pp. 864–873) Springer.

  36. 36.

    Chen, M., Kwon, T., Mao, S., Yuan, Y., & Leung, V. C. M. (2008). Reliable and energy-efficient routing protocol in dense wireless sensor networks. International Journal of Sensor Networks, 4(1), 104–117.

    Article  Google Scholar 

  37. 37.

    Kiani, F., Amiri, E., Zamani, M., Khodadadi, T., & Manaf, A. A. (2015). Efficient intelligent energy routing protocol in wireless sensor networks. International Journal of Distributed Sensor Networks, 2015, 15.

    Google Scholar 

  38. 38.

    Ahmed, M. B., Gonnade, S., & Xaxa, D. (2016). Energy-efficient protocols for wireless sensor networks. International Journal of Computer Applications, 44(3), 9–11.

    Article  Google Scholar 

  39. 39.

    Wehrle, K., Günes, M., & Gross, J. (2010). Modeling and tools for network simulation. ISBN 978-3-642-12330-6.

  40. 40.

    Haojun, T., Kuan, Z., Mianxiong, D., Kaoru, O., Anfeng, L., Ming, Z., & Tian, W. (2018). Adaptive transmission range based topology control scheme for fast and reliable data collection. Wireless Communications and Mobile Computing, 2018, 1–21.

    Google Scholar 

Download references

Acknowledgements

This work was supported by Science and Technology Project in Shaanxi Province of China (Program No. 2019ZDLGY07-08), the International Science and Technology Cooperation Program of the Science and Technology Department of Shaanxi Province, China (Grant No. 2018KW-049), the Special Scientific Research Program of Education Department of Shaanxi Province, China (Grant No. 17JK0711), the International Science and Technology Cooperation Program of the Science and Technology Department of Shaanxi Province, China (Grant No. 2019KW-008).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Qing Zhang.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wang, Z., Zhang, Q. & Gao, C. A double-layer isolation mechanism for malicious nodes in wireless sensor networks. Wireless Netw 27, 2391–2407 (2021). https://doi.org/10.1007/s11276-021-02595-1

Download citation

Keywords

  • Wireless sensor networks
  • Improved Dijkstra algorithm
  • Double-layer isolation mechanism
  • Security