As the living standards improve and the health consciousness enhances, the healthcare industry has become a hot spot in nowadays society and some health monitoring systems emerge one after another in recent years. However, the mostly existing systems only focus on the logic reasoning but ignore the factor of the user's emotion, which is regarded as an important factor to impact human health. In this paper, we design a system for big data application in emotion-aware healthcare (BDAEH), which pays attention to both the logic reasoning and the emotion computing. Meanwhile, the SDN the and 5G technologies are adopted in the BDAHE system to improve the resource utilization and the overall network performance of the system. The BDAEH system includes the following functions: healthcare data collection, healthcare data transmission, healthcare data storage, healthcare data analysis, and human-machine interaction. The healthcare data are generated by wearable devices or sensing-less sensors, and these healthcare data are regarded as the foundation to expand a series of data processing. The healthcare data transmission is performed through leveraging the SDN and the 5G technologies. In the data center, the related technologies based on cloud computing are utilized to store and analyze healthcare data, which obtains both the emotion and the health state of the users, and the relation between the emotion and the illness. Finally, the BDAEH system returns the analysis result to the users or the doctors for further treatment schemes or rehabilitation advice. The presented system is expected to validly improve the healthcare services by considering the emotion factor.

Social development and technological advances have enabled the Internet of Vehicles (IoV) to combine the social factors to form a new intelligent transportation system: Social IoV (SIoV). The emergence of SIoV helps to find new traffic management solutions of the serious problems caused by the ever-increasing traffic flow. In this paper, we propose an algorithm called social vehicle route selection (SVRS) to reduce traffic congestion and achieve the purpose of traffic flow control. First, a social clustering method for SIoV is designed by utilizing both historical and current driving information. Then we use game evolution to calculate the optimal route for vehicles, and prove the vehicle route selection game is a potential game and its strategy selection converges to Nash equilibrium. Extensive simulations are carried out to evaluate the SVRS with several performance criteria. Our analysis and simulation results demonstrate that SVRS algorithm can achieve high performance in clustering the vehicles and reducing traffic congestion.

In the foreseeable future, the rapid growth of devices in the Internet of Things (IoT) will make it difficult for 5G networks to ensure sufficient network resources. 6G technology has attracted increasing attention, bringing new design concepts to the dynamic real-time resource allocation. The resource requirements of devices are usually variable, so a dynamic resource allocation method is needed to ensure the smooth execution of tasks. Therefore, this article first designs a 6G-enabled massive IoT architecture that supports dynamic resource allocation. Then, a dynamic nested neural network is constructed, which adjusts the nested learning model structure online to meet training requirements of dynamic resource allocation. An AI-driven collaborative dynamic resource allocation (ACDRA) algorithm is proposed based on the nested neural network combined with Markov decision process training for 6G-enabled massive IoT. Extensive simulations have been carried out to evaluate ACDRA in terms of several performance criteria, including resource hit rate and decision delay time. The results validated that ACDRA improves the average resource hit rate by about 8% and reduces the average decision delay time by about 7% compared with three reference existing algorithms.

Video transmission is an indispensable component of most applications related to the mobile cloud networks (MCNs). However, because of the complexity of the communication environment and the limitation of resources, attempts to develop an effective solution for video transmission in the MCN face certain difficulties. In this paper, we propose a novel green video transmission (GVT) algorithm that uses video clustering and channel assignment to assist in video transmission. A video clustering model is designed based on game theory to classify the different video parts stored in mobile devices. Using the results of video clustering, the GVT algorithm provides the function of channel assignment, and its assignment process depends on the content of the video to improve channel utilization in the MCN. Extensive simulations are carried out to evaluate the GVT with several performance criteria. Our analysis and simulations show that the proposed GTV demonstrates a superior video transmission performance compared with the existing methods.

In this letter, we consider a non-orthogonal multiple access (NOMA) network with a half-duplex (HD) decode-and-forward (DF) relay to improve physical layer security (PLS) of two users. Two legitimate users of U1 and U2 are far from the multi-antenna base station (BS). Thus, a relay with multi-antenna is used to enhance communication and obtain diversity gain. In the first stage, the BS transmits the superimposed signal to the relay via NOMA. Meanwhile, U1 generates the jamming signal to the eavesdropper (Eve). In the second stage, the relay forwards the signal to two users while the BS transmits the jamming signal to disrupt the eavesdropping. It is assumed that the Eve has strong eavesdropping ability. Thus, we need to introduce jamming to guarantee security. Furthermore, we use U1 instead of a full-duplex (FD) relay to send the jamming signal in the first stage to avoid self-interference. To measure the secrecy performance, a closed-form expression of secrecy outage probability (SOP) is derived for the two users. Simulation results are provided to verify the correctness of the proposed scheme.