## 4 Results: Proof of Concept
### Identity Management Implementation
#### 4.1.3 Verifiable Presentations (VPs)
### Security and Privacy Implications

Security and privacy are paramount in IoT ecosystems, where devices often handle sensitive and personal data. The decentralized nature of the IOTA framework inherently improves security by eliminating single points of failure. Each transaction on the Tangle is independently validated by multiple nodes, making it highly resistant to tampering and attacks. The implementation of DIDs and VCs within IOTA ensures that identity-related information is securely managed and verifiable. The cryptographic techniques underlying DIDs and VCs provide strong guarantees of data integrity and authenticity. In addition, the use of revocation mechanisms allows for the dynamic management of credentials, ensuring that compromised or outdated credentials can be efficiently invalidated. Our study also highlights the importance of privacy in identity management. By using cryptographic methods and decentralized storage, users retain control over their identity data, deciding what information to share and with whom. This approach minimizes the exposure of personal data and reduces the risk of data breaches. ### Challenges and Limitations

Despite the promising results, our study encountered several challenges that warrant further investigation. One of the primary issues is the limited integration of the DID-Comm protocol within the IOTA framework, which limits the simulation of real-world interactions between roles in a decentralized identity ecosystem. Future work should focus on integrating DIDComm to facilitate more robust and interoperable communication between entities. Moreover, the resource constraints of IoT devices, such as the Raspberry Pi, posed significant challenges during cryptographic operations. The high computational demands of these operations, reflected in increased CPU utilization, underscore the need to optimize cryptographic algorithms to ensure efficient performance on low-power devices. This issue is exacerbated in large-scale deployments, where the cumulative load of cryptographic tasks across many devices can significantly impact overall system performance. While the Raspberry Pi is widely used due to its affordability and flexibility, it is not optimized for high-throughput cryptographic functions, which can result in slower processing speeds and higher latency for identity verification tasks. Furthermore, managing non-permanent nodes in the IOTA network has raised concerns about data persistence and reliability. While permanent nodes provide a solution by ensuring that identity records and other essential data are always available, they come at the cost of higher operational costs. Non-permanent nodes, while cost-effective, can suffer from unreliable availability and reduced redundancy, which could compromise the integrity and availability of identity data, especially when dealing with time-sensitive applications such as authentication and authorization in IoT environments. Balancing the cost of maintaining persistent nodes with the need for reliable data access will be a critical aspect of optimizing the system for widespread deployment. As the number of devices in the IoT network grows, additional bottlenecks are likely to emerge, particularly in terms of scalability. While IOTA's Tangle offers promising scalability advantages through its unique Directed Acyclic Graph (DAG) structure, the increase in the number of devices introduces new challenges related to network congestion and transaction throughput. Each IoT device will generate a large number of identity-related transactions, creating potential bottlenecks in the IOTA network. If not carefully managed, these bottlenecks can lead to delays in identity validation and data synchronization, negatively impacting the overall performance and responsiveness of the system. This is particularly concerning when considering devices such as the Raspberry Pi, which can struggle with high data volumes and real-time processing requirements in large-scale deployments.