## 2 Related Work  
In recent years, numerous techniques have been proposed to mitigate malicious attacks on IoT networks. Some of the frequently used methodologies for mitigating black hole attacks in RPL-based IoT networks include acknowledgment-based methods, statistical/mathematical-based methods, specification-based techniques, and trust-based mechanisms [10]. Acknowledgment-based methods are utilized to detect and respond to black hole attacks through techniques like node marking or ICMPv6 echo messages. The authors in [12] present an acknowledgment-based security mechanism for combating black hole attacks in RPL networks within Cyber-Physical Systems (CPSs). It employs features like IPv6 over Low-Power Wireless Personal Area Networks (6 LoWPANs) network discovery and avoids reliance on complex Intrusion Detection Systems (IDSs). Instead, a distributed timer-based approach is utilized for detecting malicious nodes. Statistical/mathematical-based methods contribute to the detection and mitigation of black hole attacks by analyzing network traffic patterns, identifying anomalies indicative of suspicious behavior, and implementing algorithms to dynamically compute routing paths or isolate malicious nodes. In [13], the authors introduced an IDS focused on early detection and isolation of malicious nodes. This is achieved by leveraging local information shared among neighbors and employing a universal sink detection method in graph theory. The proposed system significantly enhanced the Packet Delivery Ratio and throughput, thereby preserving overall network performance. Specification-based methods, as demonstrated by the security mechanism in Secure RPL, offer comprehensive approaches to identify and respond to black hole threats. In [14], the authors introduce GBG-RPL, a technique leveraging Gini-index and blockchain to detect and mitigate black hole attacks in smart health monitoring CPSs. GBG-RPL not only identifies anomalous activities but also proposes a resilient framework, achieving notable improvements in terms of reduced packet loss, enhanced residual energy utilization, decreased energy consumption, improved attack-detection rate, faster attack-detection time, optimized network management, and reduced end-to-end delay. A trust-based mechanism involves the computation of trust values for each node by its neighboring nodes in the network. These trust values can be assessed and evaluated at various hierarchical levels. In the first case, all trust values can be propagated to the root, enabling it to take the necessary steps to maintain the integrity of the network [15]. In another scenario, the entire network can be divided into equal clusters [16]. The trust value of each node in a cluster is then transmitted to the cluster's head (CH). Subsequently, the CH assumes the responsibility of directing these trust values to the Base Station (BS) of the network. In the last case, only the parents in the entire network topology are monitored, and their trust values are then propagated to the root. Several trust-based models have been proposed in the literature to identify and isolate compromised nodes within the network, ensuring the preservation of the network's integrity. To isolate malicious nodes, a trust score is computed for each node, which is subsequently compared either within a fixed time span or without any time constraints against a pre-defined threshold. In addition to trust-based models, several alternative schemes have also been proposed. However, these schemes lack an optimized method for efficiently propagating the credibility of a specific node to the network's root. The authors in [17] propose a blockchain-based trust model for the Wireless Sensor Internet of Things. Using the Dijkstra algorithm, an efficient routing protocol is introduced to prevent void holes between ordinary sensor nodes and a sink node. All transactions are recorded in an immutable blockchain for transparency, employing the Proof of Authority consensus algorithm, which outperforms the proof-of-work algorithm.