A Quality and Privacy-Aware Edge-Cloud Continuum Framework for Internet of Things Applications

Abstract

The exponential growth of the Internet of Things (IoT) has created new opportunities to provide diverse data to applications in critical domains. By leveraging IoT data, applications can achieve real-time situation awareness. Cloud computing supports the acquisition, processing, and dissemination of IoT data. But cloud-based solutions can lead to high latency and privacy-related issues. Introducing an edge computing layer between IoT devices and the cloud helps address these issues by bringing processing and storage closer to the data sources, while offloading computationally intensive tasks to the cloud. This approach to designing storage and processing components is known as the computing continuum. Existing computing continuum frameworks are often domain-specific and difficult to adapt for seamless data acquisition from heterogeneous sources. This paper addresses this limitation by proposing Q-PRIME (Quality- and Privacy-aware Management for Edge-Cloud IoT). It is a platform that dynamically acquires and delivers IoT data with quality and privacy awareness, while also providing storage and visualisation services. Q-PRIME’s effectiveness in enabling quality- and privacy-aware situational awareness is evaluated based on Quality of Service (QoS) and privacy preservation metrics. Q-PRIME serves latency-critical queries locally with a 95th-percentile query latency below 250 ms, while forwarding latency-tolerant context to the cloud. It demonstrates consistent system performance and storage decisions that conform to the configured quality and privacy policy; no Personally Identifiable Information (PII) was observed in cloud responses under the evaluated configuration and the trusted-edge assumption.