## 2 Architecture and Framework of Computing Methods
### Mobile Cloud Computing and Mobile Edge Computing  
The IoT enables a wide range of innovative applications aimed at enhancing user experience and quality of life [63]. However, the centralized architecture of CC introducesseveral limitations, including constrained bandwidth, increased latency, and higher energy consumption, which impede the fulfillment of real-time performance demands. To overcome these challenges, MEC has emerged as a distributed computing paradigm that brings computational and storage resources closer to end-users by integrating edge capabilities into mobile networks [64]. By embedding EC functionalities into mobile infrastructures, MEC supports real-time responsiveness for latency-sensitive applications and enhances network efficiency [65]. In 2014, the European Telecommunications Standards Institute (ETSI), through its Industry Specification Group (ISG), formalized the MEC architecture to enable seamless convergence of cloud functionalities within mobile network environments. MCC combines mobile computing and cloud computing paradigms by employing computation offloading techniques to mitigate the inherent resource constraints of mobile devices [66]. Despite offering benefits such as extended battery life, enhanced storage capabilities, scalability, and reliability, MCC still faces significant challenges in terms of security, privacy, trust, bandwidth limitations, data management, synchronization, energy efficiency, and system heterogeneity [67]. To alleviate these concerns, MEC shifts control, storage, and processing operations to the network edge, allowing resource-constrained mobile devices to execute computationally intensive, low-latency applications more effectively [68]. In contrast to MCC, where computational tasks are offloaded to remote cloud servers via the Internet, MEC executes processing tasks closer to the end-user, thereby enhancing both Quality of Experience (QoE) and Quality of Service (QoS). A hierarchical deployment architecture for MCC and MEC is illustrated in Figures 2c and 2d, respectively. The Introductory Technical White Paper defines MEC as a computing paradigm that allows computationally intensive and latency-sensitive tasks to be executed on mobile devices by leveraging unused processing power and storage capacity at the network edge [69]. In parallel, Cisco introduced FC as a broader concept that extends MEC beyond smartphones to include other edge devices, such as set-top boxes [53]. One of the key applications of MEC is face recognition, which involves five main computational steps: image acquisition, face detection, preprocessing, feature extraction, and classification [70]. Moreover, MEC enhances the efficiency and responsiveness of Augmented Reality (AR) applications on mobile devices [71]. These systems typically comprise five essential components: a video source, tracker, mapper, object recognizer, and renderer. While the video source and renderer are executed locally, the tracker, mapper, and object recognizer can be offloaded to the edge server due to their high computational demand [71]. This offloading strategy minimizes redundant data transmission by aggregating user input in shared geographic regions, thereby improving overall system performance and energy efficiency. This approach allows mobile users to fully leverage MEC's benefits, particularly in reducing latency and improving energy efficiency. A comparison of MEC and MCC systems is provided in Table 1.