To characterize the coupling effect between patient flow to access the emergency department (ED) and that to access the inpatient unit (IU), we develop a model with two connected queues: one upstream queue for the patient flow to access the ED and one downstream queue for the patient flow to access the IU. Building on this patient flow model, we employ queueing theory to estimate the average waiting time across patients. Using priority specific wait time targets, we further estimate the necessary number of ED and IU resources. Finally, we investigate how an alternative way of accessing ED (Fast Track) impacts the average waiting time of patients as well as the necessary number of ED/IU resources. This model as well as the analysis on patient flow can help the designer or manager of a hospital make decisions on the allocation of ED/IU resources in a hospital.

The explosive growth of Internet of Things (IoT) has mandated the security of data access. Although authentication methods can enhance network security, their vulnerability to malicious attacks may be a barrier for the wide deployments in IoT scenarios. To address the security issue, we advocate the use of physical-layer security through radio-frequency (RF) fingerprint recognition. Observing that most RF fingerprint recognition methods show a degradation of performance under low signal-to-noise ratio (SNR) environments, we present a dynamic shrinkage learning network (DSLN) to enhance security for IoT applications, particularly in the setting of low SNR. We design a novel dynamic shrinkage threshold for improving the accuracy of recognition under low-SNR environments. Additionally, we design an identity shortcut for reducing the running time of RF fingerprint recognition. In comparison with convolutional neural network (CNN), recurrent neural network (RNN), and a hybrid CNN+RNN network (CRNN), our proposed DSLN yields accuracy improvements of up to 20%. Moreover, DSLN can reduce the running time by up to 60%, indicating its great potential to a real-time IoT system, e.g., an intelligent automotive system.

A device-to-device (D2D) underlying cellular network is pervasive to support various wireless applications. However, due to the dramatic increase of data transmission in the network with limited amount of wireless resource, a few users may be required to temporarily disconnect from the network to avoid the interruption of data transmission in the whole network. A critical issue of determining the user access in D2D underlying networks is the authenticity of channel state information (CSI), and usually, a user with a higher CSI can be allocated a larger amount of wireless resource or have a higher probability of staying in the network. In this paper, we propose a blockchain consensus-based scheme to verify the authenticity of CSI and add the users who intentionally advocate a higher value of CSI into a fraud chain. Also, we consider both the cross-tier interference caused by a mobile user and the presence of a user in the fraud chain to determine the access of a user. The analysis results show that our proposed user access scheme can enhance the network performance by efficiently controlling the use access in mobile applications.

A device-to-device (D2D) assisted cellular network is pervasive to support ubiquitous healthcare applications, since it is expected to bring the significant benefits of improving user throughput, extending the battery life of mobiles, etc. However, D2D and cellular communications in the same network may cause cross-tier interference (CTI) to each other. Also a critical issue of using D2D assisted cellular networks under a healthcare scenario is the electromagnetic interference (EMI) caused by RF transmission, and a high level of EMI may lead to a critical malfunction of medical equipments. In consideration of CTI and EMI, we study the problem of optimizing individual channel rates of the mobile users in different priorities (different levels of emergency) within the Internet of Vehicles for mobile health, and propose an algorithm of controlling the transmit power to solve the above-mentioned problem under a game-theoretical framework. Numerical results show that the proposed algorithm can converge linearly to the optimum, while ensuring an allowable level of EMI on medical equipments.

Wireless technologies are pervasive to support ubiquitous healthcare applications. However, a critical issue of using wireless communications under a healthcare scenario rests at the electromagnetic interference (EMI) caused by RF transmission, and a high level of EMI may lead to a critical malfunction of medical sensors. In view of EMI on medical sensors, we propose a power control algorithm under a noncooperative game theoretic framework to schedule data transmission. Our objective is to ensure that the noncooperative game of power control can achieve a network-level objective-the optimal network capacity, although the wireless users are selfish and only interested in optimizing their own channel capacity. To obtain this objective, we show that our proposed noncooperative game is a potential game and propose the best-response-dynamics algorithm which can ensure that the game strategy of each user is induced to the optimal solution to the problem of network-level optimal capacity. Numerical results illustrate that the proposed algorithm can achieve an enhancement of 8% of network performance than the existing algorithm against the variations of mobile hospital environments.

Mobile edge computing (MEC) is pervasive to support high-speed end-to-end data transmission and computation for 5th generation (5G) mobile communications by offloading computation tasks to the cloud at the edge of a cellular base station. In a cellular network for healthcare applications, we must consider the different priorities of data transmission and computation by referring to the emergency levels of patients. A critical issue of determining the allocation of wireless resources among users is the authenticity of their priorities (i.e., emergency levels). Without authenticating the users' priorities, a user who advocates a higher emergency level can allocate more wireless resources, leading to the insufficient wireless resources to the users who are actually in high priorities. In this paper, we investigate the optimization of end-to-end delay of wireless users in a cellular network with the authenticity of their priorities by blockchain consensus and propose an algorithm of allocating communication and computation resources to minimize the delay of data transmission and computation. The numerical results illustrate that our proposed algorithm is capable of dramatically reducing the end-to-end delay under the various scenarios of healthcare applications.