A novel and comprehensive trust estimation clustering based approach for large scale wireless sensor networks

• Main Authors: Khan, Tayyab, Singh, Karan, Singh, Satya P., Manjul, Manisha, Mohamed Abdel-Basset, Le, Hoang Son, Hoang, Viet Long
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2019
• Subjects: DRNTU::Engineering::Computer science and engineering; Trust Management; Data Trust
• Online Access: https://hdl.handle.net/10356/106451; http://hdl.handle.net/10220/48931; http://dx.doi.org/10.1109/ACCESS.2019.2914769
• Institution: Nanyang Technological University

With the wide applications of wireless sensor networks (WSNs) in various fields, such as environment monitoring, battlefield surveillance, healthcare, and intrusion detection, trust establishment among sensor nodes becomes a vital requirement to improve security, reliability, and successful cooperation. The existing trust management approaches for large-scale WSN are failed due to their low dependability (i.e., cooperation), higher communication, and memory overheads (i.e., resource inefficient). In this paper, we propose a novel and comprehensive trust estimation approach (LTS) for large-scale WSN that employs clustering to improve cooperation, trustworthiness, and security by detecting malicious (faulty or selfish) sensor nodes with reduced resource (memory and power) consumption. The proposed scheme (LTS) operates on two levels, namely, intra-cluster and inter-cluster along with distributed approach and centralized approach, respectively, to make accurate trust decision of sensor nodes with minimum overheads. LTS consists of unique features, such as robust trust estimation function, attack resistant, and efficient trust aggregation at the cluster, head to obtain the global feedback trust value. Data trust along with communication trust plays a significant role to cope with malicious nodes. In LTS, punishment and trust severity can be tuned according to the application requirement, which makes it an innovative LTS. Moreover, dishonest recommendations (outliers) are eliminated before aggregation at the base station by observing the statistical dispersion. The theoretical and mathematical validations along with simulation results exhibit the great performance of our proposed approach in terms of trust evaluation cost, prevention, and detection of malicious nodes as well as communication overhead.