## 8 Methods for Designing, Testing, and Deploying the GBG-RPL Framework in Smart Healthcare CPS  
The methods for designing, testing, and deploying the GBG-RPL framework can be categorized into several types, as described below:  
1. Designing the GBG-RPL Framework  
The design of the GBG-RPL framework incorporates various methods to ensure a robust and adaptable system. Mathematical design plays a key role, involving the formulation of the Gini index-based algorithm along with trust metrics [97]. By lever  
Figure 6: The proposed methodology. aging mathematical principles, this method defines the trust assessment and adaptation mechanisms within the framework. Additionally, interdisciplinary collaboration is a cornerstone of the design process, fostering cooperation between cybersecurity and healthcare experts [98]. This collaborative approach ensures a holistic design that aligns with both mathematical rigor and the real-world requirements of healthcare systems. Furthermore, allowing tailoring of the GBG-RPL to specific characteristics of smart healthcare systems enables the customization of the parameters and algorithm based on the unique deployment requirements. To mitigate the security risks associated with the implementation of the proposed framework in a smart healthcare system, a comprehensive set of measures is undertaken. First and foremost, a decentralized architecture is adopted to distribute trust-management functions across multiple nodes, reducing the vulnerability to centralized attacks [85]. In the proposed framework, the trust calculations, the ledger in the form of BRGTL, device registration, and smart contracts have been implemented in the fog servers, which are deployed in the fog layer. The implementation of the fog layer mitigates the direct exposure of the trust calculation and database from the device layer where the resource-constrained devices are exposed to the attackers [17]. Regular updates and security audits would be conducted for the blockchain network and smart contracts to address vulnerabilities and ensure the robustness of the system. Scalability challenges are managed using a layered architecture, where multiple fog servers are implemented to accommodate large numbers of nodes, large data volumes, and geographic expansion. To navigate regulatory complexities, close collaboration with legal experts would ensure that the proposed framework aligns with relevant healthcare data protection regulations [99]. Thorough testing and validation during integration, the involvement of cybersecurity experts, and seamless communication between the proposed framework and existing components would address concerns related to integration complexity. These multifaceted measures collectively contribute to a resilient security infrastructure for the smart healthcare system. 2. Testing the GBG-RPL Framework  
The evaluation of the GBG-RPL framework involves two distinct testing methodologies to ensure its effectiveness and reliability. Simulation testing is conducted using the Cooja simulator, wherein trust parameters and attack configurations are systematically varied. This process helps identify potential issues and assesses the framework's scalability and adaptability under different conditions [100]. The second part complements the simulation testing with real-world testing, which takes place in actual smart healthcare environments. The evaluation process offers an in-depth understanding of the framework's effectiveness across diverse scenarios by utilizing the simulations and real-world testing, which add to the framework's robustness and applicability in real-world healthcare settings. 1. Deployment Methods for the GBG-RPL Framework  
The implementation of the GBG-RPL framework necessitates the smooth integration of GBG-RPL into the existing infrastructure of intelligent healthcare systems [9]. This integration guarantees compatibility and cooperation with other components.