A lightweight and secure sensing model for body area networks in the Internet of Things in biological warfare applications

Abstract

In the era of communicational technologies, body area networks (BAN) provide useful and applicable devices for monitoring soldiers' health, of paramount importance for the governments and countries' security. However, BANs face two main challenges of security and energy, due to limitations in energy, memory, and process of the utilized sensors. Despite the developments of many solutions to tackle the stated challenges, the simultaneous consideration of energy and security has been overlooked in the majority of them. Besides, in some of the recommended solutions, the energy consumption is not optimum, resulting from discarding information content in data sampling. In addition, another group of the current methods is not practical, not guaranteeing security, given the high computational costs and the entropic inefficiency of encryption keys. Therefore, in the present study, a lightweight and secure sensing model is recommended for BANs, applicable in biological warfare. In the proposed model, the data are sampled according to their entropies, then compressed and encrypted simultaneously considering their sparsity levels, aiming at reducing energy consumption and boosting security. To synchronize two functions of compression and encryption, a measurement matrix, a type of sub-Gaussian matrix, is used. Furthermore, the biological data associated with the under-care soldier is encrypted using a key, guaranteeing the secure transfer of data to the smart healthcare center. In this process, the key is generated from the time interval calculated from the heart rate of the under-care soldier. The simulation data reveal that the proposed sensing model causes a reduction in energy consumption while providing security, compared to the previous methods.