The rapid growth in the smart era of Internet of Things (IoT) relies on the various applications that lead to the design wide range of routing protocols utilizing Machine learning techniques. Third party interference in the open network to perform malicious activities by using location information of the node is high. Many researchers have designed a wide range of protocols to improve security and energy efficiency but the dynamic nature of the Internet of Things suppressed the performance of those algorithms. This may lead to data drop, node death, delay, less network lifetime, and increased third party malicious activities. In this paper, a novel routing mechanism is developed to preserve source location privacy and prevent adversaries from doing backtracking attacks and traffic analysis for energy preservation. The proposed model consists of two key functions Node/Network Condition based Dynamic Phantom Node selection (NCDPNS) and Ant colony optimization Algorithm Aided Multi-Path based Routing (ACOMPR). Here, NCDPNS selects the phantom node based on the node/network conditions like node availability, link availability, node energy level, distance from other nodes in the network, and number of neighboring hops to preserve the location privacy. ACOMPR selects the path based on the ant colony optimization algorithm to choose more than one path for data transmission with very less common resources shared among multiple paths between the source and destination for energy efficient data transmission. The proposed mechanism is achieving the source location privacy at the first stage and energy efficient routing at the second stage. The proposed mechanism is implemented using a Network Simulator-2 (NS2) simulator with predefined network parameters. The results depict that it achieves high throughput, less delay, increased network lifetime, and low energy dissipation for data transmission by preserving the location of the node. The dynamic nature of the IoT is considered in the proposed work to make it more suitable for real-time applications. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
