Research Projects

The security risks are inevitable in wireless transmission due to its broadcast nature. With the exponential evolution in healthcare technology solutions, a huge amount of medical data is being shared over wireless media among its sensors and wireless devices. Therefore, the security vulnerabilities become more challenging due to the broadcasting nature of the radio propagation over which the patient’s personal health information is being shared. The legitimate information can be intercepted by an eavesdropper in order to get the patient’s personal medical data. Thus, secrecy and latency become major quality of service (QoS) parameters to optimize the performance of such personal and delay critical applications. Generally, secure transmission is obtained with the help of complex cryptographic techniques using secret keys. The commonly used digital key-based cryptographic schemes are not sufficient to provide secure transmission in existing and future generation wireless networks. The major drawback of key-based cryptographic techniques is that the detection of compromised security keys is not easy to implement, since the basic physical characteristics of communication devices and users are not taken into consideration. As a complementary approach, physical layer security (PLS) has emerged as a potential candidate for next-generation wireless networks, which can be developed without using complex upper-layer algorithms. Such keyless secure techniques are based on an information-theoretic approach that utilizes the intrinsic characteristics of the wireless medium. However, due to the large scale of 5G networks, along with its increasing complexity and heterogeneity, it is difficult to achieve high secrecy and other quality of service requirements altogether. Thanks to the recent advancement in artificial intelligence (AI) that allows us to resolve the challenges in this domain. The basic application of AI and machine learning is to use big data analytics as a tool to improve the overall network performance. Recently, Huawei investigated the AI-based next-generation wireless networks. The objective of this proposal is to fill the gaps between physical layer security and AI for the advancement of secrecy in health monitoring applications. Specifically, a learning-based secure system with cooperative relaying is considered, where a legitimate transmission from a medical device to a healthcare monitor takes place in the presence of an eavesdropper. For such systems, for the first time, the learning-based physical layer security techniques will be investigated to enhance the security of the wireless health monitoring systems. For the prototype development, software-defined radio will be used. The proposed research can be applied in developing many state-of-the-art applications in the various areas of IoT applications such as Smart Homes/Cities, Biomedical Applications, Logistics, etc.