Enhancing Interoperability and system performance through Automation and testing of IoT sensors

Seamless interoperability and enhanced system performance for real-time monitoring and efficient operation in smart environments are achieved through improved IoT sensor integration and automated testing and validation of backend APIs. By automating the processes of sensor data collection and API validation, the proposed method improves system efficiency, accuracy, and scalability within IoT environments. The central goal is to design a unified platform simplifying sensor deployment and ensuring consistent, reliable communication with backend systems. The proposed solution introduces a middleware layer that facilitates IoT sensor integration by unifying data formats and communication standards. It includes a backend API testing framework, leveraging tools like Cypress to verify API responses for accuracy, performance, and error resilience. Key components of this system include real-time simulation of sensor data, automated generation of test cases, and seamless integration with CI/CD pipelines for ongoing validation. Technologies such as Node.js, Python, and RESTful APIs are used to maintain scalability and system compatibility.The developed system offers a streamlined and automated approach to managing IoT sensors and verifying API interactions. Users can set up sensors, simulate their output, and validate backend responses without manual input. This level of automation reduces the time required for deployment, decreases the likelihood of errors, and ensures consistent performance of backend services. The proposed methodology demonstrates significant improvements in the efficiency and dependability of IoT-based systems.