To meet the imperative for sustainable low-carbon wireless communications, integrating distributed renewable energy sources with base stations is essential. However, there is often a mismatch between the energy generated by renewable sources and the energy required by base stations. To address this issue, optimization strategies such as traffic offloading and energy sharing are commonly employed. This paper introduces a spatial model based on stochastic geometry, mapping the spatial distribution of base stations and users, and quantifying the probability of coverage in cellular networks powered by renewable energy with energy-sharing and traffic offloading capabilities. Complementing this spatial analysis, we apply queuing theory to represent the base station energy status as a Markov chain, leading to a nuanced carbon emissions model post energy-sharing. Furthermore, a new metric called carbon efficiency is defined for accurately capturing the trade off between carbon emissions and performance of cellular networks. Simulations show that there is an optimal traffic offloading probability that can minimize carbon emissions in cellular networks while sacrificing the expected ergodic rate of users. These insights offer a foundation for developing optimization strategies that elevate the carbon efficiency of renewable-energy-driven cellular networks.

Most researchers focus on designing accurate crowd counting models with heavy parameters and computations but ignore the resource burden during the model deployment. A real-world scenario demands an efficient counting model with low-latency and high-performance. Knowledge distillation provides an elegant way to transfer knowledge from a complicated teacher model to a compact student model while maintaining accuracy. However, the student model receives the wrong guidance with the supervision of the teacher model due to the inaccurate information understood by the teacher in some cases. In this paper, we propose a dual-knowledge distillation (DKD) framework, which aims to reduce the side effects of the teacher model and transfer hierarchical knowledge to obtain a more efficient counting model. First, the student model is initialized with global information transferred by the teacher model via adaptive perspectives. Then, the self-knowledge distillation forces the student model to learn the knowledge by itself, based on intermediate feature maps and target map. Specifically, the optimal transport distance is utilized to measure the difference of feature maps between the teacher and the student to perform the distribution alignment of the counting area. Extensive experiments are conducted on four challenging datasets, demonstrating the superiority of DKD. When there are only approximately 6% of the parameters and computations from the original models, the student model achieves a faster and more accurate counting performance as the teacher model even surpasses it.

The emergence of mobile edge computing (MEC) technology has deployed edge clouds with strong computing capabilities closer to Internet of Thing (IoT) devices, which can effectively meet the demands for computing power and latency. However, in addition to the stringent latency requirements, more and more emerging IoT applications also have higher standards for the freshness and timeliness of collected information. In order to ensure the freshness and high-information value in IoT system, we propose an Age of Information (AoI)-based optimization strategy for computation offloading and transmission scheduling. The strategy considers the AoI during the transmission phase and the execution phase, respectively, under the constraints of delay and remaining energy. Then, a joint optimization model is established based on the comprehensive benefits of AoI and computation rate. To address the strong coupling between the offloading decision and the transmission decision, the original optimization problem is divided into two stages. By the use of the deep deterministic policy gradient (DDPG) algorithm and the dueling double deep$Q$network (D3QN) algorithm, the solution is obtained in terms of the offloading decision and transmission scheduling decision, respectively. The proposed joint optimization strategy considers the impact of the transmission decision on the offloading decision and is adaptable to the dynamic changes in the channel connection between the edge cloud and the user due to user mobility. Experimental results show that compared with other offloading and transmission strategies, the proposed approach has higher overall system revenue and lower AoI.

The papers in this special section focus on the use of artificial intelligence (AI)-enabled technologies to address human wellness. As the COVID-19 pandemic took hold over the last several years, there was an urgent demand to pay more attention to psychological health for human wellness by providing methods and means of sensing psychological parameters, emotional care and mental disorder patient monitoring, especially during these difficult times. With the aid of wearable computing technology and artificial intelligence, emotion and mental disorder detections are available through sensing and analyzing psychological parameters. Discusses the use of AI-based patient monitoring and the ability to monitor human wellness via remote sensing technologies. The papers in this issue provide a snapshot of some of the latest research advances on the research and application of Small Things and Big Data, knowledge discovery and knowledge representation for the combination towards biomedical and health informatics.

No abstract available.

As an information carrier, the Internet of Things (IoT) based on the Internet and sensing equipment makes all physical objects form an interconnected network. The 5th generation mobile networks (5G) technology has many advantages, such as high data rates, reduced latency, energy savings, reduced costs, increased system capacity and large-scale device connectivity, realize the real-time data collection, transmission, analysis, management, and application in the era of global Internet of Everything. In order to quickly respond to people’s daily requirements and provide the smart application based on artificial intelligence technology in various scenarios, the number of IoT devices will further increase. The integration of mobile-edge computing (MEC) and IoT is imperative, especially in industries needing real-time data computing, such as smart home, public security, automobile transportation, smart health, emotion care, etc. As a new form of IoT terminal combining 5G and MEC, wearable device based on intelligent fabrics plays an important role in smart health and emotion care, which is one of the potential development directions of the next generation of intelligent medical and rehabilitation systems.

Depression is the result of a complex interaction of social, psychological and physiological elements. Research into the brain disorders of patients suffering from depression can help doctors to understand the pathogenesis of depression and facilitate its diagnosis and treatment. Functional near-infrared spectroscopy (fNIRS) is a non-invasive approach to the detection of brain functions and activities. In this paper, a comprehensive fNIRS-based depression-processing architecture, including the layers of source, feature and model, is first established to guide the deep modeling for fNIRS. In view of the complexity of depression, we propose a methodology in the time and frequency domains for feature extraction and deep neural networks for depression recognition combined with current research. It is found that compared to non-depression people, patients with depression have a weaker encephalic area connectivity and lower level of activation in the prefrontal lobe during brain activity. Finally, based on raw data, manual features and channel correlations, the AlexNet model shows the best performance, especially in terms of the correlation features and presents an accuracy rate of 0.90 and a precision rate of 0.91, which is higher than ResNet18 and machine-learning algorithms on other data. Therefore, the correlation of brain regions can effectively recognize depression (from cases of non-depression), making it significant for the recognition of brain functions in the clinical diagnosis and treatment of depression.

Driven by the vision of edge computing and the success of rich cognitive services based on artificial intelligence, a new computing paradigm, edge cognitive computing (ECC), is a promising approach that applies cognitive computing at the edge of the network. ECC has the potential to provide the cognition of users and network environmental information, and further to provide elastic cognitive computing services to achieve a higher energy efficiency and a higher Quality of Experience (QoE) compared to edge computing. This article first introduces our architecture of the ECC and then describes its design issues in detail. Moreover, we propose an ECC-based dynamic service migration mechanism to provide insight into how cognitive computing is combined with edge computing. In order to evaluate the proposed mechanism, a practical platform for dynamic service migration is built up, where the services are migrated based on the behavioral cognition of a mobile user. The experimental results show that the proposed ECC architecture has ultra-low latency and a high user experience, while providing better service to the user, saving computing resources, and achieving a high energy efficiency.

Owing to a great expansion of broadband network access in recent years, the collaborative graphical desktop sharing systems (GDSS) have gained a considerable popularity and denote a non-negligible amount of data in today's internet traffic. Much research has been focusing on characterization of traffic load from different types of internet applications (such as Web, VoIP, Video streaming and Peer-to-Peer), while the remote desktop protocols have attracted very little attention, despite the fact that they belong to a group of real-time applications with very strict quality of service requirements. As with other complex interactive applications, a good understanding of user behavior workload is important to the design of GDSS systems. In this paper, we present characterization of user behavior workload for GDSS arrival process and develop models for interarrival time of user's sessions and the session duration. Our results not only provide an insight into users' activities and behavior to the collaborative virtual environments research community but they are also useful in the development of synthetic workloads in performance studies of GDSS systems.