## 2 Related Works
This work firstly introduces the benefits of bringing the data processing closer to the network edge in other words to the edge nodes or the edge devices. Then the authors provide context for measuring the performance of deep learning models and outline some frameworks for training and inference. The overall purpose of this article was to introduce edge computing and deep learning and present methods for accelerating and evaluating a deep learning inference on edge devices. Earlier studies also focused on object recognition applications at the network edge. [PERSON] et al. [22] suggested a cooperative framework for image object recognition, which integrates the process of AIoT devices. They also formulated a task-offloading network optimization problem for this environment and solved it using a heuristic algorithm based on PSO. The testbed that was used in this paper was a practical surveillance system for monitoring human flow, and a technique for detecting error data were also introduced. The encouraging results of the proposed approach could be applied in a real-world surveillance system. Scholars also discuss how edge computing technology would improve the performance of IoT. [PERSON] et al. in [23] conducted an analytical survey to investigate the relationship between edge computing and the Internet of Things, and how the integration of edge computing can enhance the overall performance of IoT networks. More specifically, this study lists the benefits and divides various edge computing architectures into several categories. In terms of response time computation, capacity, and storage space, it also compares these categories' performance analytically. The performance of IoT devices in the cloud versus edge computing was also compared, and an analytical analysis of the edge computing architecture's performance, task scheduling, security, and privacy was presented. The study outlined the overbalance of edge computing concerning transmission, storage, and computation and provides a discussion about new challenges such as system integration resource management security, and privacy. Additional studies to understand more completely the key differences between AI for Edge and AI on Edge were introduced by [PERSON] et al. [24]. The authors defined AI for Edge as a potential research direction for giving solutions in terms of optimization problems in edge computing. On the other hand, AI on Edge is a research direction on studying how to run an AI model on Edge. In other words, it is a framework that has been designed to carry out both training and inference operations, with the aim of achieving a particular objective such as optimal algorithm performance, cost savings, privacy, etc. Moreover, in this paper, the authors categorize research efforts in edge computing to certain topologies, content, and services and introduce how to initiate an edge computing application with intelligence. Researchers also proposed an intelligent and cooperative approach to edge computing in IoT networks, which seeks to achieve a mutually beneficial integration of AI and edge computing [25]. The authors' focus was on redesigning the AI-related modules of edge computing to enable the distribution of AI's core functions from the cloud to the edge. In more detail, the authors introduced a new approach to intelligent and cooperative edge computing in an IoT system, which enables edge computing and AI collaboration. The authors presented different scenarios for their proposed approach. In the first scenario, the edge-AI enables collaboratively efforts between the cloud and the edge. This is accomplished through the implementation of a novel data schema for storage where data are kept locally to ensure privacy. The additional scenario builds a smart edge by leveraging locally adaptive AI. Reservation-based approach for computation offloading that is able to adapt to AI predictions of time sequences.