Wireless Networks

, Volume 25, Issue 6, pp 3587–3603 | Cite as

Novel approach of distributed & adaptive trust metrics for MANET

  • De-gan Zhang
  • Jin-xin Gao
  • Xiao-huan LiuEmail author
  • Ting Zhang
  • De-xin Zhao


It is known to all that mobile ad hoc network (MANET) is more vulnerable to all sorts of malicious attacks which affects the reliability of data transmission because the network has the characteristics of wireless, multi-hop, etc. We put forward novel approach of distributed & adaptive trust metrics for MANET in this paper. Firstly, the method calculates the communication trust by using the number of data packets between nodes, and predicts the trust based on the trend of this value, and calculates the comprehensive trust by considering the history trust with the predict value; then calculates the energy trust based on the residual energy of nodes and the direct trust based on the communication trust and energy trust. Secondly, the method calculates the recommendation trust based on the recommendation reliability and the recommendation familiarity; adopts the adaptive weighting, and calculates the integrate direct trust by considering the direct trust with recommendation trust. Thirdly, according to the integrate direct trust, considering the factor of trust propagation distance, the indirect trust between nodes is calculated. The feature of the proposed method is its ability to discover malicious nodes which can partition the network by falsely reporting other nodes as misbehaving and then proceeds to protect the network. Simulation experiments and tests of the practical applications of MANET show that the proposed approach can effectively avoid the attacks of malicious nodes, besides, the calculated direct trust and indirect trust about normal nodes are more conformable to the actual situation.


Mobile Ad hoc Network Distributed Adaptive Trust metrics Malicious attacks 



This research work is supported by National Natural Science Foundation of China (Grant No. 61571328), Tianjin Key Natural Science Foundation (No.18JCZDJC96800), CSC Foundation (No. 201308120010), Major projects of science and technology in Tianjin (No.15ZXDSGX 00050), Training plan of Tianjin University Innovation Team (No.TD12-5016, TD13-5025), Major projects of science and technology for their services in Tianjin (No.16ZXFWGX00010, No.17YFZC GX00360), the Key Subject Foundation of Tianjin (No.15JC YBJC46500), Training plan of Tianjin 131 Innovation Talent Team (No.TD2015-23).


  1. 1.
    Zhang, D. G., Li, G., & Zheng, K. (2014). An energy-balanced routing method based on forward-aware factor for wireless sensor network. IEEE Transactions on Industrial Informatics, 10, 766–773.CrossRefGoogle Scholar
  2. 2.
    Tan, S. S., Li, X. P., & Dong, Q. K. (2015). Trust based routing mechanism for securing OLSR-Based MANET. Ad Hoc Networks, 30, 84–98.CrossRefGoogle Scholar
  3. 3.
    Gungor, V. C., Bin, L., & Hancke, G. P. (2010). Opportunities and challenges of wireless sensor networks in smart grid. IEEE Transactions on Industrial Electronics, 57(10), 3557–3564.CrossRefGoogle Scholar
  4. 4.
    Chen, J. Q., & Mao, G. Q. (2018). Capacity of cooperative vehicular networks with infrastructure support: Multi-user case. IEEE Transactions on Vehicular Technology, 67(2), 1546–1560.CrossRefGoogle Scholar
  5. 5.
    Wang, X., Liu, L., & Su, J. (2012). RLM: A general model for trust representation and aggregation. IEEE Transactions on Services Computing, 5, 131–143.CrossRefGoogle Scholar
  6. 6.
    Song, X. D., & Wang, X. (2015). Extended AODV routing method based on distributed minimum transmission (DMT) for WSN. International Journal of Electronics and Communications, 69(1), 371–381.CrossRefGoogle Scholar
  7. 7.
    Wang, X. (2017). A kind of novel VPF-based energy-balanced routing strategy for wireless mesh network. International Journal of Communication Systems, 30(6), 1–15.CrossRefGoogle Scholar
  8. 8.
    Zhang, D. G. (2012). A new approach and system for attentive mobile learning based on seamless migration. Applied Intelligence, 36, 75–89.CrossRefGoogle Scholar
  9. 9.
    Niu, H. L. (2017). Novel positioning service computing method for WSN. Wireless Personal Communications, 92(4), 1747–1769.CrossRefGoogle Scholar
  10. 10.
    Zhang, D. G., & Zhu, Y. N. (2012). A new constructing approach for a weighted topology of wireless sensor networks based on local-world theory for the internet of things (IOT). Computers & Mathematics with Applications, 64, 1044–1055.zbMATHCrossRefGoogle Scholar
  11. 11.
    Artz, D., & Gil, Y. (2007). A survey of trust in computer science and the semantic web. Web Semantics, 5, 58–71.CrossRefGoogle Scholar
  12. 12.
    Shao, K., & Luo, F. (2012). Normal distribution based dynamical recommendation trust model. Journal of Software, 23(12), 3130–3148.CrossRefGoogle Scholar
  13. 13.
    Govindan, K., & Mohapatra, P. (2012). Trust computations and trust dynamics in mobile ad hoc networks: A survey. IEEE Communications Surveys & Tutorials, 14(2), 279–298.CrossRefGoogle Scholar
  14. 14.
    Nordheimer, K., & Schulze, T. (2010). Trustworthiness in networks: A simulation approach for approximating local trust and distrust values. IEEE Communications Surveys and Tutorials, 321, 157–171.Google Scholar
  15. 15.
    Hsieh, M. Y., Huang, Y. M., & Chao, H. C. (2007). Adaptive security design with malicious node detection in cluster-based sensor networks. Computer Communications, 30, 2385–2400.CrossRefGoogle Scholar
  16. 16.
    Li, W. B. (2016). Novel ID-based anti-collision approach for RFID. Enterprise Information Systems, 10(7), 771–789.CrossRefGoogle Scholar
  17. 17.
    Safa, H., Artail, H., & Tabet, D. (2010). A cluster-based trust-aware routing protocol for mobile ad hoc networks. Wireless Networks, 16, 969–984.CrossRefGoogle Scholar
  18. 18.
    Li, L., Fan, L., & Hui, H. (2009). Behavior-driven role-based trust management. Journal of Software, 20, 2298–2306.CrossRefGoogle Scholar
  19. 19.
    Cho, J. H., Swami, A., & Chen, I. R. (2012). Modeling and analysis of trust management with trust chain optimization in mobile ad hoc networks. Journal of Network and Computer Applications, 35, 1001–1012.CrossRefGoogle Scholar
  20. 20.
    Li, N. H., & Mitchell, J. C. (2005). Beyond proof-of- compliance: Security analysis in trust management. Journal of the ACM, 52, 474–514.MathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    Wang, X., & Song, X. D. (2015). New medical image fusion approach with coding based on SCD in wireless sensor network. Journal of Electrical Engineering & Technology, 10(6), 2384–2392.CrossRefGoogle Scholar
  22. 22.
    Niu, H. L. (2017). Novel PEECR-based clustering routing approach. Soft Computing, 21(24), 7313–7323.CrossRefGoogle Scholar
  23. 23.
    Feng, R. J., Xu, X. F., & Zhou, X. (2011). A trust evaluation algorithm for wireless sensor networks based on node behaviors and D-S evidence theory. Sensors, 11, 1345–1360.CrossRefGoogle Scholar
  24. 24.
    Jiang, J. F., Han, G. J., & Wang, F. (2015). An efficient distrubuted trust model for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 26, 1228–1237.CrossRefGoogle Scholar
  25. 25.
    Zhou, S., & Chen, J. (2017). New Dv-distance method based on path for wireless sensor network. Intelligent Automation and Soft Computing, 23(2), 219–225.CrossRefGoogle Scholar
  26. 26.
    Zhang, T. (2018). Novel optimized link state routing protocol based on quantum genetic strategy for mobile learning. Journal of Network and Computer Applications, 2018(122), 37–49.CrossRefGoogle Scholar
  27. 27.
    Li, G., & Pan, Z. H. (2014). A new anti-collision algorithm for RFID tag. International Journal of Communication Systems, 27(11), 3312–3322.Google Scholar
  28. 28.
    Chatterjee, P., & Sengupta, I. (2014). A trust enhanced secure clustering framework for wireless ad hoc networks. Wireless Networks, 20, 1669–1684.CrossRefGoogle Scholar
  29. 29.
    Wang, G., & Gui, X. L. (2013). The selection of trading node and the calculation method of trust relationship in social network. Chinese Journal of Computers, 36, 368–383. (in Chinese with English abstract).CrossRefGoogle Scholar
  30. 30.
    Zhu, Y. N., Liu, S., & Zhang, X. D. (2016). Multi-radio multi-channel (MRMC) resource optimization method for wireless mesh network. Journal of Information Science and Engineering, 32(2), 501–519.MathSciNetGoogle Scholar
  31. 31.
    Li, X. Y., & Gui, X. L. (2010). The cognitive model of dynamic trust. Journal of Software, 21, 163–176. (in Chinese with English abstract).CrossRefGoogle Scholar
  32. 32.
    Yuan, W. W., Guan, D. H., & Lee, Y. K. (2010). The small-world trust network. Appliede Intelligence, 35, 399–410.CrossRefGoogle Scholar
  33. 33.
    Liu, S. (2017). Novel unequal clustering routing protocol considering energy balancing based on network partition & distance for mobile education. Journal of Network and Computer Applications, 88(15), 1–9. Scholar
  34. 34.
    Ma, Z. (2016). A novel compressive sensing method based on SVD sparse random measurement matrix in wireless sensor network. Engineering Computations, 33(8), 2448–2462.CrossRefGoogle Scholar
  35. 35.
    Zhang, X. D. (2012). Design and implementation of embedded un-interruptible power supply system (EUPSS) for web-based mobile application. Enterprise Information Systems, 6(4), 473–489.CrossRefGoogle Scholar
  36. 36.
    Tang, Y. M. (2018). Novel reliable routing method for engineering of internet of vehicles based on graph theory. Engineering Computations. Scholar
  37. 37.
    Zhao, C. P. (2012). A new medium access control protocol based on perceived data reliability and spatial correlation in wireless sensor network. Computers & Electrical Engineering, 38(3), 694–702.CrossRefGoogle Scholar
  38. 38.
    Liang, Y. P. (2013). A kind of novel method of service-aware computing for uncertain mobile applications. Mathematical and Computer Modelling, 57(3–4), 344–356.Google Scholar
  39. 39.
    Ma, Z. (2017). Shadow detection of moving objects based on multisource information in internet of things. Journal of Experimental & Theoretical Artificial Intelligence, 29(3), 649–661.MathSciNetCrossRefGoogle Scholar
  40. 40.
    Kang, X. J. (2012). A novel image de-noising method based on spherical coordinates system [J]. EURASIP Journal on Advances in Signal Processing, 2012(110), 1–10. Scholar
  41. 41.
    Zhang, D. G. (2014). A novel approach to mapped correlation of ID for RFID anti-collision. IEEE Transactions on Services Computing, 7(4), 741–748.CrossRefGoogle Scholar
  42. 42.
    Liu, S., & Liu, X. H. (2018). Novel dynamic source routing protocol (DSR) based on genetic algorithm-bacterial foraging optimization (GA-BFO). International Journal of Communication Systems, 31(18), 1–20. Scholar
  43. 43.
    Song, X. D. (2015). New agent-based proactive migration method and system for big data environment (BDE). Engineering Computations, 32(8), 2443–2466.CrossRefGoogle Scholar
  44. 44.
    Chen, C., & Cui, Y. Y. (2018). New method of energy efficient subcarrier allocation based on evolutionary game theory. Mobile Networks and Applications. Scholar
  45. 45.
    Wang, X. (2015). New clustering routing method based on PECE for WSN. EURASIP Journal on Wireless Communications and Networking, 2015(162), 1–13. Scholar
  46. 46.
    Zheng, K. (2016). Novel quick start (QS) method for optimization of TCP. Wireless Networks, 22(1), 211–222.CrossRefGoogle Scholar
  47. 47.
    Zheng, K. (2015). A novel multicast routing method with minimum transmission for WSN of cloud computing service. Soft Computing, 19(7), 1817–1827.CrossRefGoogle Scholar
  48. 48.
    Zhang, D. G., & Ge, H. (2018). New multi-hop clustering algorithm for vehicular Ad Hoc Networks. IEEE Transactions on Intelligent Transportation Systems. Scholar
  49. 49.
    Zhou, S., & Tang, Y. M. (2018). A low duty cycle efficient MAC protocol based on self-adaption and predictive strategy. Mobile Networks and Applications, 23(4), 828–8392. Scholar
  50. 50.
    Zhang, T., & Zhang, J. (2018). A kind of effective data aggregating method based on compressive sensing for wireless sensor network. EURASIP Journal on Wireless Communications and Networking, 2018(159), 1–15. Scholar
  51. 51.
    Zhao, D. X., Ma, Z., & Zhang, D. G. (2016). A distributed and adaptive trust evaluation algorithm for MANET. ACM MSWiM/Q2SWinet’16, 11, 47–54.Google Scholar
  52. 52.
    Zhang, T. (2018). Novel self-adaptive routing service algorithm for application of VANET. Applied Intelligence, 11, 1–5. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • De-gan Zhang
    • 1
    • 2
    • 3
  • Jin-xin Gao
    • 1
    • 2
  • Xiao-huan Liu
    • 1
    • 2
    Email author
  • Ting Zhang
    • 1
    • 2
  • De-xin Zhao
    • 1
    • 2
  1. 1.Key Laboratory of Computer Vision and System, Ministry of EducationTianjin University of TechnologyTianjinChina
  2. 2.Tianjin Key Lab of Intelligent Computing & Novel Software TechnologyTianjin University of TechnologyTianjinChina
  3. 3.School of Electronic and Information EngineeringUniversity of SydneySydneyAustralia

Personalised recommendations