## 4 Overview of Included Studies
Authors of [78] introduce AloTtalk, a SIP-based platform designed to support a variety of heterogeneous AloT applications. Unlike existing IoT platforms that rely on lightweight protocols such as MQTT, AloTtalk makes use of the Session Initiation Protocol (SIP) for its robust capabilities in managing real-time multimedia and messaging sessions. This platform addresses critical requirements of modern AloT applications, such as high-quality data streaming, low latency. The platform's modular design simplifies IoT application development. Sensors collect data, which are processed and later get sent to actuators, while SIP proxies ensure smooth communication. A hybrid blockchain architecture secures data transmission, and AI models such as long short-term memory (LSTM) and a CNN model, are integrated as IoTtalk devices to perform tasks such as prediction and recognition. The platform was implemented in two real-world AloT applications and tested in both cloud and edge computing environments. The first case regarded a real-time road traffic prediction application where roadside units (RSUs) collected vehicle speed data and transmitted it to the AloTtalk platform via SIP messaging. The LSTM model processed the data to predict traffic speeds over the next five minutes and achieved an average error of just 4%. The second case was a neighborhood violence detection application, where smart home systems streamed audio data via SIP sessions to a CNN model to identify suspicious sounds such as gunshots or screams and sent notifications to emergency services when needed. It effectively identified urban sounds with high accuracy, but had difficulty with certain sounds such as children playing. The experiments demonstrated AloTtalk's efficiency under various scenarios. In the cloud setup, the AloTtalk server and SIP proxy were located remotely from the user agents, simulating a geographically distributed deployment. In the edge setup, all components were co-located. Results showed that edge computing reduced latency by approximately 10 ms compared to cloud. However, it should be mentioned that the study lacked details on user experience and did not specify the locations or scale of the experiments. In [79] a metaverse avatar teleport system is presented, with its applications primarily focusing on entertainment sectors such as video games. The system employs an AloT pose estimation device to control avatar movements, aiming to address traditional VR interface limitations by implementing a oneM2M IoT-compliant network-based solution. Its architecture consists of an AloT device (Raspberry Pi 4 with a Google Coral USB Accelerator), a webcam for image capture, PoseNet for pose estimation, a Teleport Information Extractor, middleware, and a metaverse interworking module. The system resizes and analyzeswebcam input to estimate human pose skeletons, triggering avatar teleportation via HTTP and MQTT protocols. Testing in the Minecraft video game environment using the system demonstrated its ability to recognize three distinct poses differentiated by the relative positions of wrists and shoulders and successfully adjusted coordinates and direction vectors, displaying guidance messages to users. However, the system appears to suffer from significant limitations that hinder its practical applicability, while its functionality is also limited. Firstly, it is constrained by specific lighting conditions, while metrics regarding latency and recognition accuracy are not reported. The implementation is particularly narrow in scope, showcasing only basic teleportation between two points within a Minecraft environment, which doesn't represent the variety of metaverse applications. Furthermore, while the system adheres to the oneM2M standard, it overlooks other important industry standards that would be crucial for real-world deployment.