© 2019 Elsevier B.V. With the emergence of WSNs in the recent times, providing trustworthy and reliable data delivery is challenging task due to unique characteristics and constraints of nodes. Malicious node can easily disrupt the integrity of network through the inclusion of false and malicious data and initiate internal attacks. Detection of malicious nodes using trust-based security is an effective and lightweight countermeasure as compared to key based security schemes which incurs higher overhead costs. The WSNs will play greater role in the next-generation IoT systems and a compromised node can jeopardize the availability and authenticity of sensory layer. In this paper, an efficient Belief based trust evaluation mechanism (BTEM) is proposed which isolates the malicious node from trust-worthy nodes and defend against Bad-mouth, On–Off and Denial of Service (DoS) attacks. Bayesian estimation approach is used in gathering direct and In-direct trust values of the sensor nodes which further considers the correlation of the data collected over the time and then estimate imprecise knowledge in decision making for secure delivery of data thus avoiding the malicious nodes. Compared with existing approaches, the proposed BTEM performs better in the detection of malicious node (MN), with lesser delay and improved network throughput.