Unmanned aerial vehicle (UAV) technology has been developed to establish a mobile-edge computing (MEC) network for the Internet of Things (IoT). In the MEC network, users can reduce their latency by communicating and exchanging data with UAV-based edge servers. Security is a critical issue in a UAV-based IoT since the attackers who attempt to access the network may cause interference and influence the flight of the UAV. In this article, we propose a lightweight RF fingerprinting recognition method in consideration of the limited computing power in UAVs, identifying unauthenticated attackers and refusing their access to IoT. Also, we propose a resource allocation scheme in the secure UAV-based MEC network. By establishing a nonconvex resource optimization problem and decomposing it into a few tractable subproblems, we offer a numerical algorithm for the optimum resource allocation. The analysis results illustrate that our proposed method can reduce energy consumption and running time compared to its benchmark methods.

With the advances of Internet of Things (IoT) technology, the Internet of Battlefield Things (IoBT) has revolutionized the military battlefield. Unlike civilian IoT applications, a few adversarial attackers may sneak into an IoBT and destroy the network infrastructure by generating cyberattacks, e.g., Distributed Denial-of-Service (DDoS) attacks, making the Quality of Service (QoS) of network dramatically decrease. To improve the performance of a network, we propose two schemes of enhancing network QoS: one is user access control with the aid of RF fingerprinting because of small-scaled available signal samples, while the other is the optimization of network performance. Specifically, we address the optimization of network utility with the methods of power allocation and channel allocation. Mathematically, we propose a mixed-integer nonlinear (MIN) problem, which optimizes the allocation of wireless resources. Also, the original optimization problem is decomposed into a power allocation subproblem and a channel allocation subproblem, and both these subproblems are nondeterministic polynomial (NP) hard problems. We propose a few approximate algorithms, which can iteratively converge to the optimum of the original problem in polynomial time. The simulation results show that our proposed algorithms can improve network utility and various network QoS than the other algorithms in an IoBT environment.

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) 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 and vehicle-mounted or wearable medical sensors 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 radio frequency transmission. A high level of EMI may lead to a critical malfunction of medical sensors, and in such a scenario, a few users who are not transmitting emergency data could be required to reduce their transmit power or even temporarily disconnect from the network in order to guarantee the normal operation of medical sensors as well as the transmission of emergency data. In this paper, we propose a joint power and admission control algorithm to schedule the users' transmission of medical data. The objective of this algorithm is to minimize the number of users who are forced to disconnect from the network while keeping the EMI on medical sensors at an acceptable level. We show that a fixed point of proposed algorithm always exists, and at the fixed point, our proposed algorithm can minimize the number of low-priority users who are required to disconnect from the network. Numerical results illustrate that the proposed algorithm can achieve robust performance against the variations of mobile hospital environments.

In TD-LTE-A downlink, user selection is a key technique when the number of users exceeds the amount could be supported by eNodeB at one Transmission Time Interval (TTI). In order to improve the fairness of different users and consider the difference of user traffic in the process of user selection, a Signal to Leakage and Noise-Ratio (SLNR) beamforming based joint user selection and beamforming algorithm is proposed in this paper. Since in TD-LTE-A systems, the traffic can be classified into two types: Guaranteed Bit Rate (GBR) traffic and Non-Guaranteed Bit Rate (Non-GBR) traffic, user satisfaction for GBR and Non-GBR traffic are defined at first, and then, based on the difference of traffic, a user satisfaction based joint user selection and beamforming algorithm is proposed. Simulation results show that compared with conventionally used joint user selection and beamforming algorithm, user satisfaction of GBR traffic can be increased by 300% due to the fact that GBR traffic always has higher priority than Non-GBR traffic. Moreover, user fairness can also be improved in terms of the variance of user satisfaction.

To improve the performance of hospital emergency department (ED), we propose a scheduling strategy to reduce the total time of medical tests in ED for a given number of patients. We model the schedulig strategy as a constraint logic programming problem, and then solve this problem with an open source software: Minizinc. Also we compare the medical test time with the scheduling strategy and that without scheduling, which is viewed as a benchmark for our proposed scheduling strategy. The results show that the scheduling strategy can save 12%–25% of medical test time.

In this paper, we detail the design of a hypertension monitoring system via telecommunication and computer technologies and propose a method for theoretically analyzing its system accuracy. In the design of our system, we add a novel decision support unit, building on a diagnosis standard in medicine, into our monitoring system. The architecture of our system, including this decision support system, is detailed in this paper. In the analysis of our system accuracy, we theoretically evaluate the accuracy of a decision support system by linking the system accuracy with the distribution of sensors' errors. Additionally, we verify our proposed analysis of system accuracy in this paper by comparing its result to that of Monte Carlo simulation.

In this paper, we propose a novel method to estimate the accuracy of a decision support system for hypertension monitoring. The decision support system is designed building on a diagnosis standard in medicine, and one decision made by this system depends on both the blood pressure data gathered by medical sensors and some contexts that are manually entered by clinicians. When analyzing the system accuracy, we take into account both the potential sensors' errors and the errors of entering context. In addition, we propose a novel method for the estimation of system accuracy; this method is motivated by the fact that the traditional method to estimate system accuracy would overestimate the system accuracy (the details would be presented in Section III.C.). Finally, we compare the system accuracy estimated by the proposed method and that estimated by the traditional method. Our study shows that our proposed method can well estimate the system accuracy.

To enhance the capacity of patients supported by wireless in-hospital monitoring systems, a bandwidth allocation scheme for the transmission of medical data in the wireless local area network (WLAN) is proposed. The problem of bandwidth allocation, subject to limited wireless bandwidth, quality of service (QoS) requirements of medical data transmission, as well as electromagnetic interference, is modeled as a non-polynomial (NP) optimization problem. To save the computation time of this NP problem, we propose an accelerated genetic algorithm by dynamically adjusting both the inheritance probability and the mutation probability, and then compare it with other off-the-shelf genetic algorithms. Our study shows that our proposed algorithm can save computation time and attain the same result of bandwidth allocation in comparison with other algorithms.