## 2 Related Work
The key contributions of our review include the following:  
* Exploring the emerging paradigm of EC-IoT: We explore how EC-IoT integrates AI to improve the efficiency and responsiveness of IoT systems. * Discussing current threats and their effects on EC-IoT: We analyze the specific security challenges posed by integrating edge computing with IoT and the implications of these threats. * Analyzing countermeasures in the current literature: We review existing solutions, particularly AI-based techniques, that address the security issues within EC-IoT frameworks. * Proposing future directions to improve countermeasures for these threats: We suggest research avenues focused on developing scalable, efficient, and robust security solutions that can adapt to the evolving landscape of IoT threats and vulnerabilities. \begin{table}
\begin{tabular}{l l l l l l} \hline
**Article** & **Main Fores** & **Isures Discussed** & **Contementaneous/Solutions** & **Future Challenges** \\ \hline
[48] & Energy efficiency & Network congestion, power limits & Energy-aware techniques & Scalability, renewable integration, ML, enhancement \\ \hline
[44] & IoT security & Connectivity, privacy, latency & Edge processing, ML, models & Hyperparameter tuning, complex topologies \\ \hline
[46] & Blockchain formulas & IoT security, MEC, blockchain & Blockchain integration & Scalability, delay optimization, mobility \\ \hline
[44] & Resource-limited IoT & Security vulnerabilities & IoT proxy, TVN, IPS & Anomaly detection, live traffic analysis \\ \hline
[42] & IoT security & Cybernetics & Pushing, rennowware, protocol attack countermeasures & Unconventional attack methods \\ \hline
[43] & IoT-log-closed & High latency, big data & Fog computing & Fog efficiency, big data management \\ \hline
[44] & Security analysis & IoT security, privacy & Deep learning & Efficiency, adaptability, heterogeneity \\ \hline
[45] & Vulnerability analysis & Attack surfaces & Finnowrate updates, secure boot & Device heterogeneity, lightweight security \\ \hline
[46] & AI for security & Edge node vulnerability & AI integration, blockchain & Computation costs, evolving threats \\ \hline
[47] & RL for IoT & Security challenges, RL & RL-based solutions & Scalability, resource constraints \\ \hline
[48] & Edge security & Resource constraints, edge vulnerability & Edge security architectures & Secure edge layer, data quality \\ \hline
[49] & LoR edge integration & Cloud limits, low-power, long-range & Edge integration, regulatory compliance & Scalability, data privacy, standardization \\ \hline
[48] & Secure analytics & Security trade-offs, trust, privacy & Lightweight security, trust management & Advanced trust models, scalable frameworks \\ \hline
[49] & IoT security & IoT threats, attack types & Blockchain, fog computing, ML, edge computing & Scalability, resource constraints \\ \hline
[48] & Edge architectures & IoT edge architecture, challenges & Security, data management, scalability & Resource constraints, evolving threats \\ \hline
[49] & ML/DL methods & Complexity, vulnerability, attack surfaces & ML/DL techniques, anomaly detection & Scalability, data privacy, standardization \\ \hline
[49] & Cybersecurity & Data, network, service security & Data encryption, IDS, secure software & Scalability, privacy concerns \\ \hline
[49] & IoT security & IoT threats, attack types & Fog computing, ML, edge, blockchain & Scalability, real-time adaptation \\ \hline
[49] & Secure aggregation & Security, privacy in healthcare IoT & Data encryption, secure aggregation & Device heterogeneity, dynamic trust \\ \hline
[100] & Edge security & Security, privacy risks, attacks & Secure updates, IDS, lightweight cryptography & Scalability, dynamic trust management \\ \hline
[48] & Security architectures & Resource management, privacy & Packet filters, firewalls, IDS & Balancing security, trust management \\ \hline \end{tabular}
\end{table} 
Table 1: Related work on edge computing-assisted IoT security.