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The profound fusion of reconfigurable intelligent surfaces (RIS) with non-orthogonal multiple access (NOMA) holds the potential to substantially boost the data transmission rates within the Internet-of-Medical-Things (IoMT). However, a notable concern arises from the utilization of successive interference cancellation (SIC) technology by NOMA users in the IoMT networks, which raises privacy challenges when dealing with a substantial number of NOMA users within the IoMT ecosystem. This paper introduces a groundbreaking approach leveraging RIS to significantly enhance the security of private users within RIS-aided NOMA networks, specifically tailored for IoMT applications. Our core objective is to maximize the systems secure rate for private users, achieved by a simultaneous optimization of both transmit beamforming and reflecting vectors. Acknowledging the inherent non-convex nature of the optimization problem at hand, we strategically decompose it into two distinct subproblems: the optimization of beamforming strategies and the intricate optimization of RIS reflection mechanisms. Leveraging the established successive convex approximation (SCA) methodology, we adeptly transform these challenging subproblems into tractable and convex forms. Addressing these complex challenges, we introduce a highly efficient algorithm grounded in an alternating optimization paradigm, enabling us to iteratively address the subproblems in a coordinated, synergistic manner. Our simulation results unequivocally showcase substantial gains in secrecy performance afforded by our proposed scheme.

Energy efficiency is regards as an important problem in energy-limited wireless medical sensor networks owing to the energy shortage. On the other hand, full-duplex is considered as the promising technology for the future wireless consumer medical sensor networks that provides higher transmission rate. In this paper, we discussed a secure energy-efficient beamformer design that was impaired by channel state information (CSI) for two-way multi-input multi-output (MIMO) wireless medical sensor networks in smart healthcare, enabling simultaneous wireless data and power communication. We consider the norm-bounded channel error model and pose a new worse case maximization problem in terms of secure energy efficiency, aiming to promote the secure rate and reduce the networks power consumption, subject to meeting several reality constraints such as the full-duplex transmitter’s power constraint and the minimum harvested energy constrains. To handle the non-convex problem, we firstly examine the relationship between rate and weighted mean square error (WMSE), then utilize the sign-definiteness and S-procedure lemma to eliminate the impact of channel errors. Furthermore, an iterative green secure beamformer design algorithm is developed. The numerical results show that our proposed algorithm has the better performance compared with the conventional schemes in terms of secure-energy-efficiency. Specifically, our proposed algorithm improves secrecy energy efficiency by 57.14%, as compared to the half-duplex secure transmission scheme.