Cloud storage-based secure big data analytics mechanism for drone-assisted healthcare 5.0 data fusion system

Abstract

Drone-assisted healthcare 5.0 data fusion system is the amalgamation of some cutting-edge technologies, like the Internet of Things (IoT), blockchain, robotics, drones or Internet of Drones (IoD), big data, artificial intelligence (AI), and cloud computing. It offers a range of various medical services, like healthcare decision making, patient remote monitoring and tracking, operating virtual clinics, patient shifting through smart ambulances, ambient supported living, self-illness management, reminders of treatment, compliance, and adherence. Since the devices communicate through the Internet, the potential attackers have some chances to launch different attacks on the drone-assisted healthcare 5.0 data fusion system. Under these circumstances, the healthcare data of the system may be revealed to unauthorized parties, or it may be updated in an unauthorized way. Therefore, to mitigate these issues, a cloud storage-based secure big data analytics mechanism for drone-assisted healthcare 5.0 data fusion system is proposed in the paper (in short, CSDM-DHF). Due to the deployed security mechanism, the proposed CSDM-DHF is capable enough to provide security to multi-sensor data fusion process. The provided security analysis proves the robustness of CSDM-DHF against various potential attacks. Moreover, the formal security verification of CSDM-DHF is also provided through the well-known scyther software validation tool. A comparative analysis of the existing schemes shows that the proposed CSDM-DHF outperforms other schemes. Finally, a practical demonstration of CSDM-DHF is provided, and essential performance parameters are computed and analyzed.