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 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, 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 like robust trust estimation function, attack resistant and efficient trust aggregation at cluster head to obtain 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 makes it an innovative trust estimation approach. Moreover, dishonest recommendations (outliers) are eliminated before aggregation at the base station by observing statistical dispersion. Theoretical and mathematical validation along with simulation results exhibit great performance of our proposed approach in terms of trust evaluation cost, prevention, and detection of malicious nodes as well as communication overhead.