## 3 Security and Privacy-Preserving Solutions for IoE
### Integration of Different Security Layers
#### 3.5.2 Multi-Layered Security Architectures  
In the evolving IoE ecosystem, singular security solutions are no longer sufficient, and there is a growing need for holistic and integrated architectures to address complex and multi-layered security threats [1; 2; 3; 10; 14]. In this context, when blockchain-based solutions, Edge AI-based anomaly detection systems, and quantum-resilient cryptographic approaches, which were discussed separately in the previous sections, are considered together, they present more robust and sustainable security strategies [25; 33; 54; 108]. Table 16 compares the three main approaches proposed to address the security and privacy challenges of IoE: Blockchain-Based Security, Edge AI-Enabled Security, and Quantum-Resilient Security Solutions.  
* Blockchain-Based Security provides security for IoE in critical areas such as identity authentication, access control, and data storage through its decentralized  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Approach** & **Features/Definition** & **Advantages** & **Disadvantages** & **Application Areas** \\ \hline Blockchain-Based Security [11; 13; 83; 83; 85; 88; 92; 93] & Access control via smart contracts, Decentralized identity authentication, Immutable data storage & No single point of failure, Transparency and auditability, Increased trust & High computational cost, Scalability issues, Regulatory barriers & IoE identity management, Data integrity, Supply chain security \\ \hline Edge AI for Secure Computing [15; 16; 25; 26; 27; 28; 59; 106; 112; 113] & Real-time anomaly detection, Privacy-preserving AI model training, Low-latency security decisions & Fast decision-making, Reduces network congestion, Less dependency on central servers & Limited hardware capacity, AI model security risks, Adversarial attacks & Smart cities, IoT security monitoring, Cyber-attack detection \\ \hline Quantum-Resilient Security [33; 34; 35; 52; 56; 70; 120; 132; 132] & PQC, hash-based signatures, ZKP, and Quantum AI for long-term data security & Future-proof security, Resistance to quantum attacks, Stronger cryptographic structure & Lack of widespread adoption, Complex implementation processes, High computational cost & Healthcare data security, Financial transactions, IoE data transmission computational cost & Healthcare data security, Financial transactions, IoE data transmission \\ \hline \hline \end{tabular}
\end{table}
Table 15: Security layer combinations according to IoE use cases.  
structure [9; 11; 38; 83]. However, it faces challenges such as scalability issues and high transaction costs [88; 92; 93].
* Edge AI for Secure Computing aims to perform real-time attack detection and security decisions using local AI models while preserving data privacy [16; 25; 59]. Nevertheless, it has limitations including adversarial attacks and restricted hardware capacity [28; 137; 146].
* Quantum-Resilient Security offers more robust solutions against future threats through PQC and quantum-enhanced AI methods [33; 34; 35; 54; 127]. However, it also presents challenges such as complex implementation processes and high computational costs [52; 53; 130].  
In conclusion, Blockchain, Edge AI, and Quantum Security solutions provide different advantages and limitations across IoE security layers. Blockchain creates a robust security framework with its decentralized structure, while Edge AI enhances security through real-time data processing capabilities. Quantum Security plays a critical complementary role in ensuring long-term resilience. It is clear that IoE security cannot be achieved through a single solution alone. Therefore, adopting hybrid approaches based on layered architectures is essential for establishing sustainable security strategies.