Defective node detection by employing DS theory and RP model for secure internet of things networks

Abstract

Internet of Things Networks (IOTN) are applied in all sectors as a result of their broad applicability. Even though the performance of IOTN is satisfactory, it still faces various obstacles, including energy constraints, dependability, and security. This work aims to improve the reliability of the network by identifying defective nodes. Defective nodes affect the normal functionality of the entire network; therefore, it is vital to detect the defective nodes, such that the Quality of Service (QoS) is improved. This work detects defective nodes by employing the Reward-and-Punishment Model (RPM) and the hypothetical analysis of Dempster-Shafer (DS) theory. The effectiveness of the proposed work is found to be satisfactory in terms of defective node detection, precision, energy consumption, and network lifetime.