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With 5G networks on the rise, it becomes more and more important to grant researchers access to tools that allow for development and experimentation in the field of 5G transmission. Healthcare can benefit greatly from these developments. In this paper a real-time transmission technique is described and tested that, if implemented, allows wearable devices to transmit multiple streams of data on various frequencies. These tests will be used to explain how this presented platform works, what drawbacks and benefits exist with the proposed scheme, and how to further develop the solution of real-time transmission of sensitive data, such as substance-use data, at higher frequencies. 

Significant growth of internet applications in recent years has raised a lot of challenges to networks. One of the important applications is smart and connected health (SCH), which utilizes sensing, communication networks and artificial intelligent (AI) techniques to offer healthcare services to the users. In SCH applications, Wireless Body Area Networks (WBANs) consisting of a group of Lightweight and wearable devices designed for use within the proximity of the human body, is a key infrastructure. In this short paper, we discussed the possibility of exploring AI techniques for WBANs to improve network performance and enhance health services. In addition, we present the literature review of AI driven networks for SCH, its related challenges and future directions. 

Node Authentication and Key Distribution are two tightly correlated security tasks for a secure Body Area Networks (BAN) system. Handling them separately may cause many practical issues. Based on the recent advances on node authentication and (shared) key distribution (including key generation), we propose a new integrated method to securely and efficiently conduct the two tasks. We build a system model with the consideration of passive and active attacks and solve some security risks. One of performance metric, key generation rate is significantly improved in our method. We implement and verify the proposed methods on two test beds. The experimental result demonstrates the effectiveness and efficiency of our proposal. 

Securely growing or de-growing nodes is a mandatory requirement to manage Wireless Body Area Networks (WBANs). This requirement raises significant challenges in node authentication, backward node authentication, initial node configuration, and node de-growth. Unlike the traditional approaches using pre-stored secrets or relying on special authentication hardware, we explore the characteristics of WBAN and wireless signal to develop an efficient scheme for adding/removing WBAN node securely and effectively. The major idea of the proposed scheme is to construct a 'virtual' dual-antennae proximity detection system by fully utilizing the existing legitimate nodes and the behavior of human body. We built a system prototype on wireless devices and verified our scheme through experiments. In addition, a data mining (clustering) algorithm is also applied to successfully detect newly joined legitimate node and identify potential attackers.