## 2 Architecture and Framework of Computing Methods
### Fog Computing  
FC is an architecture designed to extend cloud capabilities by integrating computation, storage, and networking services along a continuum between end devices and centralized cloud data centers [52]. Introduced by Cisco Systems in 2012, FC is described as \"an extension of the CC paradigm that provides computation, storage, and networking services between end devices and traditional cloud servers\" [53]. Cisco applied this technology by extending the CC paradigm to the edge of networks, specifically wireless networks for the Internet of Things (IoT) [46]. The OpenFog Consortium defines FC as \"a horizontal system-level architecture that distributes compute, storage, control, and networking functions closer to users along the continuum from the cloud environment to objects\" [54]. For instance, compared to large-scale cloud data centers (e.g., Microsoft Azure regions), an IoT device typically communicates with a fog node that resides in closer proximity [55]. Figure 2b illustrates a three-tier fog system: the cloud layer, the fog layer, and the IoT/end-user layer. The fog layer can consist of one or more fog domains, each managed by different providers. Communication between the IoT/End-user layer and the fog layer occurs over the LAN, while communication with the cloud layer happens over the WAN, sometimes via the fog [56].  
FC facilitates a seamless service continuum between cloud infrastructure and end devices, enhancing computing flexibility in heterogeneous environments. These two technologies provide ubiquitous computing, storage, control, and communication capabilities, offering mutually dependent and beneficial services to create a continuous continuum of services. FC is a decentralized computing infrastructure situated between edge devices and the cloud, working with heterogeneous devices. Fog Computing Networks (FCN) support non-IP-based access technologies for communicating with end devices, as well as functions such as resource allocation, security, and device management [57]. By enabling data processing closer to the user, FC minimizes latency and enhances the efficiency of data handling.  
To address performance and efficiency requirements in FC environments, ref. [58] introduced a dynamic Integer Linear Programming (ILP) method to optimize resource allocation from the FC layer to IoT devices. This method aims to ensure that tasks are completed within time constraints while minimizing service latency and energy consumption, particularly with devices that have limited processing power and energy capacity. A fog orchestrator is proposed to centrally organize the resource pool, match applications to specific requests, and provide an automated workflow to physical resources [59]. This orchestrator manages deployment and scheduling, performs workload management with runtime quality of service (QoS) control, and aims to manage specific objects efficiently. Resource management remains a challenging problem due to constraints and dynamic workloads. While AI and ML algorithms have been employed to address these challenges, issues such as high variance, explainability, and the difficulty of online training persist [60].  
Security and privacy remain critical challenges in FC systems due to their distributed and dynamic architecture. Traditional intrusion detection mechanisms often prove inadequate in such environments. To address this, ref. [61] proposes a distributed deep learning framework designed to detect cyberattacks in edge and fog-based IoT networks. Furthermore, ref. [62] presents FogBus, a framework supporting seamless IoT-Fog-Cloud integration. FogBus supports platform-independent execution for multiple applications and leverages blockchain, encryption, and authentication technologies to ensure security across the continuum.