• Title of article

    Multi Trust-based Secure Trust Model for WSNs

  • Author/Authors

    Khan,Tayyab School of Computer and Systems Sciences, JNU, New Delhi, India , Singh, Karan School of Computer and Systems Sciences, JNU, New Delhi, India , Gupta, Sakshi School of Computer and Systems Sciences, JNU, New Delhi, India , Manjul, Manisha School of Computer and Systems Sciences, JNU, New Delhi, India

  • Pages
    12
  • From page
    147
  • To page
    158
  • Abstract
    Trust “establishment (TE) among sensor nodes has become a vital requirement to improve security, reliability, and successful cooperation. Existing trust management approaches for large scale WSN are failed due to their low cooperation (i.e., dependability), higher communication and memory overheads (i.e., resource inefficient). This paper provides a new and comprehensive hybrid trust estimation approach for large scale WSN employing clustering to improve cooperation, trustworthiness, and security by detecting selfish sensor nodes with reduced resource (memory, power) consumption. The proposed scheme consists of unique features like authentication based data trust, scheduler based node trust, and attack resistant by giving the high penalty and minimum reward during node misbehavior. A task scheduling mechanism is employed for scheduling the significant task to reduce computation overhead. The proposed trust model would be capable to provide security against blackhole attack, grey hole attack, and badmouthing attack. Moreover, the proposed trust model feasibility has been tested with MATLAB. Simulation results exhibit the great performance of our proposed approach in terms of trust evaluation cost, prevention, and detection of malicious nodes with the help of analyzing consistency in trust values and communication” overhead.
  • Farsi abstract
    فاقد چكيده فارسي
  • Keywords
    Trust management , Resource scheduling , Attacks , WSN
  • Journal title
    Journal of Information Technology Management (JITM)
  • Serial Year
    2022
  • Record number

    2708033