## 2 Related Work
They discussed security and privacy concerns, scalability, interoperability, and data management challenges. The proposed solutions included lightweight encryption techniques, trust management frameworks, anomaly detection systems, edge analytics, and decentralized data storage. In [41], the authors explored process automation in an IoT-Fog-Cloud ecosystem, highlighting challenges related to the high latency in cloud computing, big data management, real-time processing, and heterogeneity of devices. Their proposed solutions included enhancing fog layer resiliency, efficient big data processing, addressing heterogeneity, ensuring scalability, and improving interoperability. The authors emphasized the importance of automating functions within the ecosystem to enhance efficiency, reduce latency, and manage complex environments. The authors of [42] surveyed cyberthreats and countermeasures in industrial IoT (IIoT) systems. They discussed vulnerabilities due to the complex integration of hardware and software and various types of cyberthreats such as phishing, ransomware, protocol attacks, supply chain attacks, and systems attacks. The paper suggested countermeasures, including phishing detection tools, next-generation firewalls, ML techniques, secure communication protocols, and blockchain-based solutions. The authors of [43] identified the high data generation, limited computational power, and substantial energy demands of IoT devices as significant issues in examining the challenges and strategies for enhancing energy efficiency in IoT environments. The energy-intensive operations of edge, fog, and cloud computing exacerbate these challenges. To address these, they proposed energy-aware architectures--cluster-based, centralized, and distributed--and techniques like data compression, the use of low-power hardware, energy-aware scheduling, task offloading, and energy harvesting. The study further discussed future challenges such as scalability, adaptability, integration of renewable energy, and maintaining security and privacy without compromising cost and practical implementation. Exploring the evolving security demands of systems of internet of things devices (SIoTD), the authors of [44] proposed adaptive, edge-based solutions to tackle these challenges. They identified complex security requirements, diverse and sophisticated cyberthreats, intermittent connectivity, significant data privacy concerns, latency issues, and the impracticality of centralized security measures as core challenges. To effectively address these issues, the authors advocated for shifting security processes closer to the data sources through edge-based processing, which reduces latency and enhances data privacy. Discussing the critical need for robust security mechanisms and forensic capabilities within the expansive networks of the IoT, the authors of [45] identified significant challenges in protecting the decentralized and distributed entities such as devices, the data these devices generate, and the digital evidence arising from data interactions, which are often inadequately secured by traditional centralized security frameworks. The paper also explored the role of mobile edge computing (MEC) in bringing computing resources closer to IoT devices to minimize latency and enhance communication while highlighting the security vulnerabilities inherent in the decentralized MEC-enabled IoT systems, including physical access to devices and data tampering. The surveys analyzed in our paper emphasize various AI approaches for detecting or countering cyberattacks in IoT environments. However, these studies often underscore persistent challenges related to scalability, resource constraints, and the dynamic nature of IoT systems. Our paper focuses on the specific paradigm of edge computing-assisted IoT (EC-IoT) incorporated with AI, highlighting its potential to enhance security measures in these environments.