# Key Considerations for Real-Time Object Recognition on Edge Computing Devices
## 5 Future Directions  
The previous sections discussed the current state of research and development in edge AI for object recognition. In this section, future issues and potential research directions are discussed. Some potential future research directions for future research and development in edge AI for object recognition are as follows:  
1. Optimizing deep neural networks for edge devices: Edge AI systems for object detection often rely on deep neural networks (DNNs), which can be computationally intensive and require significant memory resources. Research should focus on developing more efficient DNN models specifically designed for edge devices with limited resources. This includes exploring lightweight models, which is to design DNN architectures with fewer parameters and operations, such as the RTSD-Net model proposed in [23], which simplifies the YOLOv4-tiny architecture for real-time strawberry detection on a Jetson Nano. In another work, [PERSON] et al. proposed DeepCompress-VIT, which can achieve a high compression ratio (more than 14 times) to enable the entire model of vision transformer (VIT) to be stored in the limited storage space of edge devices [118]. 2. Addressing real-time processing requirements: Object detection in many edge AI applications, such as autonomous driving, requires real-time or near real-time performance. Future research should focus on optimizing AI algorithms for speed, such as designing algorithms that can process data quickly and efficiently, such as the Edge YOLO system presented in [15], which modifies the YOLOv4 architecture for real-time object detection on edge devices in autonomous vehicles. On the other hand, it is also necessary to leverage hardware accelerators. Hardware accelerators can be designed to speed up specific parts of the object detection pipeline, such as convolutional operations or object tracking. There are many studies developed in this theme. For example, [PERSON] et al. proposed a fast and scalable multicore YOLOv3-Tinyaccelerator using input stationery systolic architecture, which improved the FPS up to 2.23 times, compared to the existing method [45]. * Improving energy efficiency: Edge devices often rely on battery power, making energy efficiency a crucial consideration for object detection systems. Research should focus on designing energy-efficient algorithms that minimize energy consumption without sacrificing accuracy. This may involve using lightweight models [43], optimizing the inference process [119], and employing techniques like model compression [77]. Another direction is to optimize hardware for low-power use, which is to develop energy-efficient hardware platforms and components specifically designed for edge AI applications. This includes utilizing low-power processors, optimizing memory access patterns, employing energy harvesting techniques, and employing cross-layer optimization techniques [92]. The edge AI architecture consists of a cloud server, edge server, and edge devices. Therefore, research related to task allocation, scheduling, and offloading among the architecture components can be developed further to improve the energy efficiency [37; 107]. * Addressing security and privacy concerns: Edge AI systems for object detection can be vulnerable to security threats and privacy breaches, for example, adversarial and overload attacks [120]. Future research should focus on developing secure and privacy-preserving algorithms that protect sensitive data and prevent malicious attacks. This includes techniques like federated learning [121], differential privacy, homomorphic encryption, or a combination of them. For example, [PERSON] et al. proposed leveraging homomorphic encryption to design an efficient edge AI architecture with federated learning [122]. Another direction is to implement secure hardware and software platforms, as well as secure communication protocols for data transmission. For example, [PERSON] et al.