Empowering Efficient Drone Monitoring with Low-Latency Edge-Cloud Continuum Platforms

Drones used for activities such as environmental monitoring and infrastructure inspection generate vast amounts of data, requiring dedicated infrastructure for efficient management. While the cloud is a widely adopted solution, it often faces limitations such as latency, bandwidth constraints, and scalability challenges. To this scope, this paper presents a novel framework that leverages edge-cloud continuum platforms to overcome these issues. By combining the immediacy of edge computing with the computational power of the cloud, the framework processes data close to its source for real-time responsiveness and efficiently distributes tasks across multiple infrastructure layers, from edge devices to regional data centers and centralized clouds. This hybrid approach enhances scalability, efficiency, and responsiveness, addressing the demands of modern monitoring systems. The paper also addresses the lack of standardized protocols in edge-cloud configurations, a key obstacle to seamless interoperability. The proposed framework supports developers in designing and deploying applications across the edge-cloud continuum in a platform-independent manner, optimizing deployment configurations and services to meet strict quality of service (QoS) requirements. A case study on fire monitoring validates the framework, demonstrating substantial improvements in latency and scalability for critical applications such as disaster management and environmental conservation. By enabling scalable, adaptable, and cross-platform applications, the framework provides a robust solution for the complex needs of real-time, mission-critical scenarios.