## 1 Introduction  
Edge computing has emerged as a critical paradigm for real-time processing in distributed environments, enabling computation closer to data sources such as Internet-of-Things (IoT) devices, sensors, or local servers [1]. The edge computing approach presents key benefits like reduced latency, bandwidth optimization, enhanced security and privacy, real-time decision-making, offline functionality, cost efficiency, and scalable and distributed architecture. These advantages make edge computing a crucial component in supporting IoT systems, and different organizations have proposed various IoT architectures, taking various perspectives into account. Edge architecture involves various components like edge devices, edge nodes, edge data centers, edge cloud, and edge analytics [2]. The key techniques that enable decentralized edge computing are virtual machines (VMs), containers, software-defined networking (SDN), content delivery networks (CDN), cloudlets, and micro data centers (MDC)/edge data centers (EDC) [3]. Security is crucial in edge computing for protecting data and maintaining system integrity. A comprehensive security architecture for edge computing involves robust technologies like encryption, secure authentication protocols, and other privacy-preserving techniques. Advanced cryptographic techniques ensure confidentiality and integrity, while technologies like Artificial Intelligence (AI) and ML enable intelligent decision-making and analytics [4]. However, ensuring security at the edge presents significant challenges due to the heterogeneous nature of computing environments and the presence of resource-constrained devices. The availability of high-performance computing and secure network infrastructure varies widely across applications, from well-established smart cities to resource-limited smart villages. The increase in devices generating data has led to massive volumes of data that require efficient processing. Consequently, the computing environment has evolved, incorporating various architectures such as cloud, fog, edge, cloudlet, and Mobile Edge Computing (MEC). These architectures facilitate data processing at different levels of the IoT structure. Edge computing, as shown in Figure 1, is one such paradigm that brings computation closer to where data are generated, primarily to reduce latency and alleviate network bandwidth constraints. This shift in computing has paved the way for real-time applications that demand faster response times. Building upon edge computing, collaborative edge computing enables resource sharing across the network to complete computational tasks efficiently. This approach is inherently distributed, with participating computing servers, often referred to as _edge nodes_, located in different geographical regions [5]. Edge computing is characterized by processing data closer to its source, enabling faster response times and reducing the amount of data sent to the cloud, which helps conserve network resources. It enhances security and privacy by keeping sensitive data on local devices while also improving system reliability. Additionally, distributed computing minimizes data movement between the cloud and edge devices. While applications  
Figure 1: Edge computing architecture. requiring extensive computation may still rely on the cloud, real-time applications such as surveillance, real-time monitoring systems, healthcare, and autonomous vehicles benefit from processing at the edge to ensure low latency and quick decision-making. Edge nodes within this architecture provide the necessary infrastructure, including data processing, storage, networking capabilities, software, and power backup, enabling efficient and resilient operations. However, task computation can be delayed due to node overload. To address this, edge computing incorporates computation offloading through a controlled process known as load balancing, which transfers queued tasks to the next available edge node (server or data center), ensuring seamless processing without delays.