Authors - Nelson Herrera Herrera, Estevan Gomez-Torres, Paul Baldeon-Egas, Renato Toasa Abstract - The Context-Aware Mobile Systems (CAMS) framework simplifies the development of context-aware mobile applications using Model-Driven Development (MDD). CAMS incorporates a Domain-Specific Language (DSL) built with Xtext, enabling developers to model contextual information, define business rules, and establish event-driven behaviors. The generation of Android applications is automated via Acceleo, supporting the seamless integration of cloud services such as Azure Maps for geolocation, IoT Hub for sensor data management, and Twilio for contextual notifications. To enhance scalability and deployment efficiency, CAMS leverages Infrastructure-as-Code (IaC) solutions through Pulumi and Terraform, automating cloud resource provisioning. This approach reduces development complexity, minimizes manual configurations, and accelerates deployment cycles. A case study on package tracking showcases CAMS' ability to dynamically adjust application behavior in real time based on contextual data from IoT sensors and geolocation services. Evaluation results demonstrate a 40% reduction in development time compared to traditional methods, alongside improved scalability, supporting up to 10,000 IoT devices simultaneously. CAMS offers an innovative framework for developing intelligent mobile applications across various sectors, including logistics, smart cities, and real-time monitoring. Its modular architecture and automated deployment processes enable rapid prototyping and efficient scaling, making it adaptable to diverse application scenarios. Future enhancements include expanding support for iOS applications, integrating additional IoT capabilities, and incorporating AI-driven decision-making tools to enhance real-time responsiveness. CAMS positions itself as a robust and flexible solution for next-generation mobile applications, bridging the gap between contextual awareness and scalable cloud-based infrastructures.
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