The growth of the Internet of Things (IoT) causes an increase in data produced by diverse IoT devices and sensors. The capacity to monitor extensive data within an IoT network comprising multiple servers is gaining significance. Real-time data observation allows devices to monitor and gather data, facilitating decision-making and optimization across various applications, including smart cities, healthcare, agriculture, and industrial automation. Observing a substantial amount of data in an IoT network presents challenges due to the resource limitations of the constrained devices utilized within these networks. The Internet Engineering Task Force has introduced the Constrained Application Protocol (CoAP), which includes two significant features: observed and block-wise transfer. These features present a viable solution for managing communication between clients and servers in environments with limited network resources. The implementation of CoAP with block-wise transfer may result in congestion issues for the client. The default congestion control algorithm employed by CoAP, known as Binary Exponential Back-Off (BEB), is inadequate for resource observation in group communication involving block-wise transfer. This inadequacy can cause substantial congestion, leading to buffer overflow, data loss, and connection drops. We conducted a study to evaluate the effectiveness of the EnCoCo-RED buffer control algorithm in CoAP Client to address this problem. We evaluated these algorithms through a Cooja simulation and assessed their performance against the default BEB algorithm. The results indicated that EnCoCo-RED surpassed the default CoAP congestion control BEB in throughput by as much as 32%. The EnCoCo-RED algorithm demonstrates a markedly higher throughput in the CoAP observe with Block-Wise transfer scenario.