Abstract
This research presents an adaptive, scalable RESTful API framework, aiming to optimize the deployment, management, and monitoring of IoT devices by integrating a dynamic RESTful API with Docker containerization. As demand increases for deploying IoT solutions that must respond against dynamic and heterogeneous environments, conventional methods of deployment are found inadequate in terms of scalability, flexibility, and real-time data processing. Such challenges are addressed in this research by adopting a RESTful API with FastAPI, optimized for asynchronous processing to allow high concurrency along with low latency. It promises to be robust in client-server interactions under all loads: high or low, of any kind. It simplifies deployment further with lightweight containerization using Docker and allows consistent application behaviors across multiple environments by encapsulating dependencies and configuration. Its practicality is demonstrated in an emulated IoT ecosystem with Raspberry Pi, ESP8266 NodeMCU and Arduino Uno devices equipped with DHT11 sensors that are able to monitor temperature and humidity. Performance evaluations of the research target also involve the throughput, response time, and scalability of the API by a set of performance evaluations that emulate different traffic scenarios, demonstrating how the framework can handle massive volumes of real-time data. Key findings show that the proposed framework can reduce deployment time by up to 99% and increased flexibility by 67% compared to traditional methods. The power of a dynamic API is its ability to refresh the endpoints without the inclusion of software changes, hence allowing real-time adaptability, which becomes quite necessary for applications dealing with a rapidly changing environment. The results ensure that this work encompasses developing resilient and flexible IoT infrastructures, sets up a framework that allows for scalability and dynamic deployment with efficiency in maintaining data integrity and low latency. Results obtained in this work confirm the suitability of the proposed framework for IoT applications and enable new developments in designing adaptive IoT systems, focusing on their application in unstable and resource-constrained environments.