An edge-to-cloud framework for privacy-aware management of geospatial data

In today’s fast-paced technological landscape, characterized by rapid innovation and transformation across sectors, a compelling demand emerges for sophisticated and adaptable systems within Smart Environments. These dynamic settings, marked by an influx of data from diverse sources and intricate distributed systems, offer remarkable opportunities alongside distinct challenges. This Thesis responds to the need for a flexible, scalable framework that adeptly navigates the complexities of modern distributed data aggregation and processing systems, while upholding the paramount principles of data privacy and security. At its core, the framework introduces a distributed architecture, housing a service placement algorithm that seamlessly spans from the Edge to the Cloud, meticulously crafted in accordance with the tenets of Fog Computing. This architectural approach indispensably relies on data privacy, significantly influencing applications and prioritizing reliable data management. A second pivotal contribution is the Seamless Data Acquisition Protocol (SEAMDAP), a standard-based and modern approach meticulously designed to facilitate data collection within distributed systems. Engineered to be both user-friendly and highly customizable, SEAMDAP streamlines the intricate process of gathering data from a multitude of sources, reducing friction, and enhancing flexibility. Lastly, the Thesis ventures deeply into the critical realms of data integrity and security, acutely acknowledging the inherent importance of georeferenced data and location verification. A robust architecture is proposed within the framework’s toolkit, ensuring that data remains secure during transmission, storage, and processing, and culminating in the exploration of advanced processing techniques such as Homomorphic Encryption and Multi-Party Computation. Crucially, the direction taken with this framework is firmly anchored in the pursuit of standardizing the realm of smart environments while proactively addressing identified issues. Several tools presented herein have undergone rigorous testing in Smart Farming environments, each accompanied by compelling use cases. The framework holds particular promise in settings characterized by high heterogeneity, an abundance of georeferenced data, a critical need for interoperability among systems operated by diverse stakeholders, and an strong commitment to data privacy.