Energy-efficient secure data fusion scheme for IoT based healthcare system

Abstract

The Internet of Medical Things (IoMT) ecosystem is enormous and complicated, with several smart devices constantly transferring large amounts of data. Many IoT-based healthcare devices are used to gather and transmit medical data. Consequently, IoMT faces many challenges related to energy efficiency, security, and privacy-preserving due to heterogeneous data. In IoMT, healthcare devices generate a huge volume of data and transfer it to the server, which is less secure and results in data redundancy. Therefore, there is a requirement for an energy-efficient secure data aggregation scheme as the traditional data aggregation paradigms encountered several issues for safely aggregating and transmitting healthcare data, including higher energy consumption, security, privacy, and vulnerability to attacks. In this paper, a secure data fusion-based data aggregation scheme is proposed, which optimizes energy usage and improves data quality. The proposed method uses extended belief propagation to determine the link quality of the shortest routes between sensor nodes and base station (BS) for data transmission and to reduce energy consumption. Further, the active sensor selection problem is optimized using the Archimedes Optimization Algorithm (AOA), which maximizes the spatial and temporal correlation with the passive sensor nodes to improve data quality. The proposed scheme has been implemented in NS2.35 by generating trace files and extracting results with AWK scripts. The simulation results demonstrate that our method outperforms the other state-of-the-art methods in terms of energy usage, network connectivity, reliability, computational cost, latency, and communication overhead. Thus, the proposed method is an efficient and secure data aggregation scheme for IoT-based healthcare.