The Internet of Engineering Task (IETF) developed a lighter application protocol (Constrained Application Protocol (CoAP)) for the constrained IoT devices operating in lossy environments. Based on UDP, CoAP is a lightweight and efficient protocol compared to other IoT protocols such as HTTP, MQTT, etc. CoAP also provides reliable communication among nodes in wireless sensor networks in addition to features such as resource observation, resource discovery, congestion control, etc. These capabilities of CoAP have enabled the implementation of CoAP in various domains ranging from home automation to health management systems. The use of CoAP has highlighted its shortcomings over the time. To overcome shortcomings of CoAP, numerous enhancements have been made in basic CoAP architecture. This survey highlights the shortcomings of basic CoAP architecture and enhancements made in it throughout the time. Furthermore, existing challenges and issue in the current CoAP architecture are also discussed. Finally, some applications with CoAP implementation are mentioned in order to realize the viability of CoAP in real world use cases.

Cell-free massive multiple-input multiple-output (CF-mMIMO) networks are emerging as a promising technology for next-generation wireless communication. However, as the number of users increases in a CF-mMIMO network, scalability and optimal network performance become challenging. To tackle this issue, we propose a novel approach of dynamically clustering access points (APs) based on central processing unit (CPU) resources. The proposed method optimizes AP clustering by considering CPU resources, including bandwidth and power, distance, channel conditions, and APs' data demands. The joint optimization framework aims to resolve scalability issues and maximize network performance by balancing channel conditions, CPUs' computational strengths, and the user's varying data demands. The results from the simulations confirm that the proposed method effectively enhances both the network's scalability and performance.

The evolution of 5G technology towards 5G-Advanced has introduced advanced vehicular applications with stringent Quality-of-Service (QoS) requirements. Addressing these demands necessitates intelligent resource management within the standard 3GPP network slicing framework. This paper proposes a novel resource management scheme leveraging a Deep Deterministic Policy Gradient (DDPG) algorithm implemented in the Network Slice Subnet Management Function (NSSMF). The scheme dynamically allocates resources to network slices based on real-time traffic demands while maintaining compatibility with existing infrastructure, ensuring cost-effectiveness. The proposed framework features a two-level architecture: the gNodeB optimizes slice-level resource allocation at the upper level, and vehicles reserve resources dynamically at the lower level using the 3GPP Semi-Persistent Scheduling (SPS) mechanism. Evaluation in a realistic, trace-based vehicular environment demonstrates the scheme’s superiority over traditional approaches, achieving higher Packet Delivery Ratio (PDR), improved Spectral Efficiency (SE), and adaptability under varying vehicular densities. These results underscore the potential of the proposed solution in meeting the QoS demands of critical 5G-Advanced vehicular applications.