## 7 Open Challenges and Future Research Opportunities  
The implementation of EC-IoT security faces several critical challenges, including power constraints, memory limitations, data privacy, and ethical concerns, as well as local processing risks and training constraints. Addressing these challenges requires a proper understanding of the new and emerging technologies and the development of new, robust solutions designed to meet the specific needs of EC-IoT systems. Therefore, this section discusses some future research directions and emerging trends aimed at tackling these challenges through innovative solutions. The issues discussed in this section are summarized in Table 3. The table highlights the importance of ongoing research and development to ensure that EC-IoT systems can operate efficiently and securely. By exploring advanced methodologies and cutting-edge technologies, we aim to provide a roadmap for addressing these critical challenges, ultimately fostering a more resilient and secure EC-IoT ecosystem. \begin{table}
\begin{tabular}{p{85.4 pt} p{142.3 pt} p{142.3 pt}} \hline
**Challenge** & **Description** & **Future Research Directions** \\ \hline Power constraints & Limited processing power in edge devices requires solutions like model pruning, on-device learning, and federated learning (FL), which can reduce data transmission but require complex implementation. & Develop advanced pruning algorithms to maintain accuracy \\ \hline Training constraints & Task offloading helps with intensive tasks but depends on network reliability and may introduce latency. Edge-centric training can enhance autonomy and efficiency. & Create efficient task offloading strategies \\ \hline Memory limitations & Quantization reduces memory usage but can affect model precision. Mixed precision training can optimize performance and memory efficiency. & Enhance post-training quantization methods \\ \hline Local processing risks & Hybrid processing ensures real-time processing and reduces latency but requires sophisticated architecture. & Develop optimized hybrid architectures \\ \hline Data privacy & Edge-cloud collaboration enhances data safety but can introduce latency and requires robust communication channels. & Enhance secure multi-party computation methods \\ \hline Ethical concerns & AI integration in EC-IoT raises ethical concerns such as potential bias, transparency, accountability, and informed consent. & Address AI bias and ensure fairness \\ \hline \end{tabular}
\end{table} 
Table 3: Summary of challenges and future research directions for EC-IoT security. Power constraints: The challenge of limited processing power in edge devices necessitates innovative solutions like model pruning, which reduces model size and complexity. However, it may lead to accuracy loss and requires careful tuning. Future research should focus on developing sophisticated pruning algorithms, such as structured and adaptive pruning, that maintain accuracy while reducing size [31; 120]. Also, quantization and knowledge distillation can enhance model efficiency. On-device learning optimizes energy usage and improves device longevity but is limited by capabilities and may need frequent updates. Efficient on-device learning algorithms should be designed for low power consumption without sacrificing performance [11; 20]. Federated learning (FL) reduces data transmission needs, enhancing privacy and efficiency, but requires complex implementation. Research should develop energy-efficient FL algorithms to minimize power consumption by optimizing communication protocols and reducing update frequencies [35; 120]. Hierarchical FL structures can balance power consumption and computational load. Integrating edge caching and opportunistic computing can reduce energy demands while maintaining a robust performance. Training constraints: Task offloading helps offload intensive tasks to the cloud while retaining critical processing at the edge, but it depends on network reliability and may introduce latency.