# Effect of Compressed Sensing Rates and Video Resolutions on a PoseNet Model in an AIoT System
## 1 Introduction  
The Internet of Things (IoT) denotes a global infrastructure, enabling advanced services by interconnecting physical and virtual things based on existing and evolving interoperable information and communication technologies [1; 2; 3; 4]. Artificial intelligence (AI) is defined as an artificial creation of human-like intelligence and designed to imitate human abilities to understand and solve problems [5; 6]. Recently, the Artificial Intelligence of Things (AIoT), combining AI and IoT, has attracted widespread attention from researchers, because it enables more effective IoT operations and creates AI services based on IoT data [7]. The AIoT systems generally collect and process a large amount of data (audio, image, video, etc. ), and then they analyze and train the data to provide AI services (smart home control, object detection, pose estimation, etc.). In [8], recent developments in AIoT systems and their applications supported by AI (i.e., machine learning/deep learning algorithms) were introduced. In addition, [PERSON] et al. in [9] proposed an AIoT-based smart agricultural system that identifies pests by using YOLOv3 object detection built from the image from drones and mobile devices and predicts the occurrence of pests by using the LSTM built from environmental data from weather stations. As mentioned before, the AIoT systems have to send and receive a large amount of data between things such as IoT devices and an AIoT edge server through the Internet. Inevitably, this causes serious data traffic problems, which may lead to time delays and unstable channel conditions (congestion, collisions, etc.) due to limited bandwidth [10; 11]. Many research studies have been conducted to reduce the problems in the AIoT system [12; 13; 14; 15; 16; 17]. A general distributed federated learning framework was presented to avoid the communication bottleneck of most centrally managed AIoT systems in [12]. [PERSON] et al. in [13] proposed an algorithm using transform techniques for cardiac data compression to reduce the data traffic caused by a large amount of electrocardiogram data in theArtificial Intelligence of Medical things (AIoMT) system and [PERSON] et al. in [14] presented a discrete atomic transformation-based lossless image compression algorithm to reduce the resources required for image processing, storage, and transmission over the network in the IoT system. [PERSON] et al. in [15] reviewed compressed sensing (CS)-based IoT applications and identified avenues for future CS-based IoT research. In [16], the authors presented a gradient CS method for image data reduction in unmanned aerial vehicles. Finally, we proposed a oneM2M-compliant AIoT system with the CS to reduce data traffic and analyzed the effects of CS rates on the performance of YOLOv5 object detection in [17]. Pose estimation is a research field in computer vision, which plays a prominent role in various applications, such as healthcare, sports sciences, animations, robotics, intelligent video analytics, etc. [18; 19; 20; 21]. This aims to determine the position or spatial location of body keypoints (e.g., parts, joints) of a person from a given image or video in [22]. [PERSON] et al. in [23], proposed an IoT technology integration device for a smart home system with a human posture recognition function using the kNN method, and human pose is detected through a PoseNet model. In [24], the authors showed an intelligent collaborative inference approach for a PoseNet model-based service in mobile edge computing networks. [PERSON] et al. in [25] presented a oneM2M-compliant AIoT system with an AIoT edge device and a floating population-monitoring server applicable to commercial area analysis in a smart city, where the AIoT edge device estimates human poses using two PoseNet models built on MobileNet v1 and ResNet-50 backbone architectures. However, to the best of our knowledge, we could not find any literature considering the use of CS for data traffic mitigation in AIoT systems for pose estimation services.