## 1 Introduction  
The increasing demand for smart energy systems has led to a rapid rise in the number and variety of Internet of Things (IoT)-based smart devices in power systems [1]. IoT has emerged as a key enabler of smart grids, facilitating real-time monitoring, predictive maintenance, and decentralized control of energy systems [2]. This technological transformation not only enhances system efficiency but also lays the foundation for more resilient and adaptable grids, which are critical for the future of energy management. However, the exponential growth in the number of IoT devices has resulted in massive data generation, necessitating high bandwidth and powerful data processing infrastructures. Projections made over a decade ago foreshadowed this trend: Cisco Internet Business Solutions Group (IBSG) predicted that the number of internet-connected smart devices would reach approximately 25 billion by 2015 and 50 billion by 2020 [3]. According to the International Data Corporation (IDC), the amount of digital data generated in 2010 exceeded 1 zettabyte, and as of 2012, approximately 2.5 exabytes of data are generated every day [4]. Dell Technologies estimated that by 2025, there would be 41.6 billion IoT devices worldwide, generating a total of 79.4 zettabytes of data [5]. This exponential data growth calls for innovative solutions to handle the surge in data traffic and to ensure the effective management of network resources. The increasing volume of data generated by IoT devices necessitates advanced big data analytics and processing capabilities. Cisco's 2018-2023 report highlights the urgent need for innovative solutions in network architecture and data management to address the challenges posed by this data explosion [6]. This underscores the importance of developing efficient network infrastructures and management frameworks capable of accommodating the exponential growth in connected devices and data traffic. In response to the increasing demands of modern data management, cloud computing (CC) emerged as a significant technological advancement. Introduced by IBM and Google in 2007, CC provided new methodologies for software development, particularly in addressing the challenges of internet-scale applications. These technologies offered scalability, flexibility, and distributed data management, enabling businesses to manage their data without the need for on-premises infrastructure [7]. CC provides flexibility and scalability in storing, accessing, and processing data. This technology is seen as a unique solution for delivering applications to businesses [8]. However, while CC offered scalable solutions, it also introduced new challenges, particularly in the area of security. As cloud providers increasingly host vast amounts of sensitive data and core business applications, ensuring the confidentiality, integrity, and security of these data have become a pressing issue [9]. Today, security and privacy issues have emerged as major challenges when cloud providers hosting large datasets and core applications share these data with their customers [10]. Thus, although CC has provided a flexible and scalable solution for businesses, its rapid adoption has also highlighted the need for robust security frameworks to address the growing risks associated with cloud environments. The rapid expansion of the IoT has led to an unprecedented surge in data generation at the network edge. Traditional CC architectures struggle to efficiently process this influx of information while maintaining low latency and high security. Relying solely on centralized cloud processing increases response times, exacerbates bandwidth congestion, and elevates privacy risks. To address these limitations, fog computing (FC) emerged as an intermediate solution that reduces network traffic and provides storage and computation near the edge of the network [11]. Existing solutions such as FC attempt to bridge this gap, yet they remain insufficient for real-time, latency-sensitive applications [12].