Green Cloud-Based Data Aggregation with Privacy for IoMT – Based Healthcare Systems

Abstract

The widespread adoption of Internet of Things (IoT) technology in the healthcare sector, specifically in the context of the Internet of Medical Things (IoMT), has facilitated the interconnection of a multitude of medical sensors and equipment. Nevertheless, there are notable obstacles that persist in the realm of data transmission and security, primarily stemming from the constraints imposed by limited energy supplies. Patients frequently employ various medical devices that wirelessly communicate sensed data to servers, resulting in a significant increase in communication network traffic and concomitant elevated energy usage. The utilization of data aggregation has emerged as a feasible approach to address the issue of energy usage by reducing unnecessary data. Nevertheless, it is imperative to ensure the protection of the gathered data in order to mitigate the risk of illegal access. The gathering and transfer of data in healthcare IoT applications encounter difficulties in safeguarding against a range of attacks that seek unauthorized access to data. The implementation of robust security measures is crucial in order to guarantee that patient-sensitive data can only be accessed by authorized persons. This study aims to fill the current void in healthcare IoT by introducing a new methodology: data aggregation employing particle swarm optimization and differential privacy authentication. The primary aim is to minimize the amount of communication required and the energy consumed, while also guaranteeing the secure and reliable consolidation of healthcare data between medical sensors and cloud servers. The system under consideration utilizes particle swarm optimization as a means of enhancing the efficiency of data aggregation. Additionally, it integrates a differential privacy authentication mechanism in order to strengthen the security measures. The experimental development is conducted utilizing the E-Health Sensor Shield V2.0 platform, renowned for its comprehensive array of security functionalities. The findings of the security analysis indicate that the use of a multi-objective strategy leads to notable improvements in many performance metrics, including end-to-end delay, computational cost, communication overhead, packet loss, packet delivery rate, and throughput. These enhancements are achieved without compromising the robustness of the security features.