## 1 Introduction
Several techniques have been proposed in the literature to detect and prevent black hole attacks, such as exponential smoothing-based [1], trust-based [8], encryption-based [10], and root-based [11] defense methods. Exponential smoothing-based methods are limited in providing comprehensive protection against evolving black hole attacks due to their inherent difficulty in adapting to rapidly changing attack patterns. Trust-based techniques struggle to create an efficient reporting module for isolating malicious nodes in Green IoT systems, primarily due to challenges in establishing and maintaining trust among resource-constrained devices. Encryption-based mechanisms are often deemed impractical for low resource environments, such as Green IoT, due to the resource-intensive and costly nature of encryption models. Finally, the root-based schemes, while effective in identifying malicious nodes, often rely on centralized decision making, which can be a bottleneck in large-scale LLNs and create single points of failure. Hence, there is a need to devise methods that improve network stability and efficiency. This paper addresses the black hole attack by proposing a mechanism to protect LLN integrity and enhance its performance. The proposed scheme outlines a comprehensive methodology for trust evaluation and propagation in an IoT network. It incorporates both direct and indirect trust mechanisms to assess node integrity, considering various parameters like energy, unselfishness, honesty, dishonesty, and similarity. The trust computation employs forgetting curves to prioritize recent behavior and mitigate the influence of old feedback on trust assessment. The trust propagation involves a delta (\(\Delta\))-based strategy to determine the trustworthiness of nodes over time, allowing for the isolation of potentially malicious nodes. Comparedto existing methods, this approach optimizes trust evaluation by avoiding immediate isolation, reducing root node feedback, and not limiting evaluations to specific timeframes. The proposed mechanism aims to counter the severity of black hole attacks, preventing packet loss, reducing end-to-end delay, and improving overall network performance. The key contributions of this work include the following:  
1. The development of a novel trust-based Optimized Reporting Module (ORM) that alleviates the burden from the root node, preventing it from being a single point of failure. In the proposed scheme, the root node only receives the trust value of a node when action is required, rather than receiving trust values whenever they are calculated. 2. Reduction of the overhead cost associated with sending trust values of a specific node to the root within a fixed time span. 3. Implementation of RPL's optimized routing algorithm to enhance network efficiency by mitigating latency, reducing overall transmission load, and lowering the burden on the root node. The remainder of the paper is organized as follows. Section 2 discusses the related work. In Section 3, we discuss the classification of attacks on RPL-based IoT. The proposed scheme is presented in Section 4. Section 5 presents simulation results, comparisons, and performance evaluations. Finally, Section 6 concludes the paper.