LAUSR

The vast development of the Internet of Things (IoT) and cloud-enabled data processing solutions provide the opportunity to build novel and fascinating smart, connected healthcare systems. Smart healthcare systems analyze the IoT-generated patient data to both enhance the quality of patient care and reduce healthcare costs. A major challenge for these systems is how the Cloud of Things can handle the data generated from billions of connected IoT devices. Edge computing infrastructure offers a promising solution by operating as a middle layer between the IoT devices and cloud computing. The Edge of Things (EoT) can offer small-scale real-time computing and storage capabilities that ensures low latency and optimal utilization of the IoT resources. However, the EoT has privacy-preservation issues, which is a significant concern for the healthcare systems that contain sensitive patient data. This paper introduces a novel EoT computing framework for secure and smart healthcare surveillance services. Fully homomorphic encryption preserves data privacy and is stored and processed within an EoT framework. A distributed approach for clustering-based techniques is developed for the proposed EoT framework with the scalability to aggregate and analyze the large-scale and heterogeneous data in the distributed EoT devices independently before it is sent to the cloud. We demonstrate the proposed framework by evaluating a case study for the patient biosignal data. Our framework rapidly accelerates the analysis response time and performance of the encrypted data processing while preserving a high level of analysis accuracy and data privacy.