Federated learning (FL) offers distributed machine learning on edge devices. However, the FL model raises privacy concerns. Various techniques, such as homomorphic encryption (HE), differential privacy, and multiparty cooperation, are used to address the privacy issues of the FL model. Among them, HE ensures greater security and privacy since end-to-end encryption maintains data privacy throughout the computation process. Compared with other privacy-preserving techniques, HE does not require the establishment of a trusted environment or protocol among multiple parties and does not involve any artificial noise that can impair system performance. Unfortunately, it suffers from efficiency overhead when applied to privacy-preserving FL (PPFL). Some existing surveys on PPFL discuss the generic construction and organization of PPFL from the perspective of practical HE deployment in PPFL. However, none of them covers the efficiency optimization of HE when applied to PPFL. This article conducts a comprehensive review of the efficiency optimization of HE when applied to PPFL. First, we review general optimization strategies and discuss their limitations when applied directly to HE-based PPFL. Second, an overview of algorithmic, hardware, and hybrid optimizations is provided, along with a discussion of their adaptation. Additionally, we provide a detailed taxonomy of optimizations. Finally, we suggest future HE-based PPFL research directions.

We are on the threshold of the explosive growth in the global Internet-of-Things (IoT) market. Comparing with the legacy human-centric applications, machine type communication scenarios exhibit totally different characteristics, such as low throughput, delay insensitivity, occasional transmission, and deep coverage. Meanwhile, it also requires the terminal devices to be cheap enough and sustain long battery life. These demands hasten the prosperity of low power wide area (LPWA) technologies. Narrowband IoT (NB-IoT) is the newest Long Term Evolution (LTE) specification ratified by the third generation partner project as one of the LPWA solutions to achieve the objectives of super coverage, low power, low cost, and massive connection. Working in the licensed frequency band, it is designed to reuse and coexist with the existing LTE cellular networks, which endows it with outstanding advantages in decreasing network installation cost and minimizing product time-to-market. In this backdrop, it has been extensively regarded as one of the most promising technologies toward the IoT landscape. However, as a new LTE standard, there are still a lot of challenges that need to overcome. This paper surveys its evolutions, technologies, and issues, spanning from performance analysis, design optimization, combination with other leading technologies, to implementation and application. The goal is to deliver a holistic understanding for the emerging wireless communication system, in helping to spur further research in accelerating the broad use of NB-IoT.

The next-generation of wireless network 6G is envisioned to achieve seamless global full coverage, and provide ultra-high-speed and extreme low delay network to connect the Internet of Everything (IoE) on the Earth. To this end, as the space-air-ground-underwater integrated network is a solid foundation of 6G, it is critical to develop reliable and robust cross-boundary communication techniques to connect the sky and the ocean. However, due to the harsh environment-induced channel complexity of different transmission media, penetrating through the air/water interface seamlessly faces significant challenges. In this paper, we present a comprehensive review of recent progress of air/water cross-boundary communications, and group the existing works into three categories, such as 1) optical direct communication, 2) relay-based communication, and 3) non-optical direct communication, and then summarize them respectively. The three types of cross-boundary approaches have their own pros and cons which are suitable for different application scenarios. Among them, the optical direct communication has made tremendous progress recently. However, the volatile wavy surface and depth-temperature-dependent parameters make it a great challenge to build robust communication links. Moreover, the misalignment caused by the dynamics of the channel severely degrades the performance and limits their practical applications. To tackle these challenges, we summarize the complex channel characteristics, analyze the causes of the misalignment and provide potential solutions. In the end of this paper, we also discuss future research directions and hope to inspire expanding research on the air/water cross-boundary communications to further propel towards the space-air-ground-ocean integrated network in 6G.

The ocean covers nearly two-thirds of the surface on the Earth, and there has been great interest in developing underwater wireless sensor networks (UWSNs) to help us explore the ocean realm. A great deal of efforts have been devoted to it, and significant progress has been made since the beginning of 2000s. However, most of the networks are isolatedly developed currently, inherently hardware-based and application-oriented with inflexible closed-form architectures, which are difficult to reconfigure, reprogram and evolve. They also lack the capability in sharing resources, and are far from service-oriented networks. These limitations impair their capacity for wide range of applications. To further propel the development of UWSNs, next-generation UWSNs have been proposed recently, which are robust, flexible, adaptive, programmable, support resource-sharing feature and are easy to manage and evolve. Moreover, a number of novel software-defined techniques and paradigms, such as software-defined radio (SDR), cognitive acoustic radio (CAR), network function virtualization (NFV), software-defined networking (SDN), Internet of underwater things (IoUTs) and sensor-cloud, have been emerging. These software-defined technologies have the capability of softwarizing network resources, and then redefining them to satisfy diverse application requirements, improve resource utilization efficiency and simplify network management. Consequently, these evolving technologies are envisioned as critical building blocks and major driving forces, which will transform conventional UWSNs towards software-based, programmable, user-customizable and service-oriented next-generation UWSNs. In this paper, we provide a comprehensive review of existing works on implementing these techniques, and also present discussions for future research. We hope to inspire more active research on these areas and take a step further towards realizing next-generation UWSNs.

In the modern world, wearable smart devices are continuously used to monitor people’s health. This study aims to develop an automatic mental stress detection system for researchers based on Electrocardiogram (ECG) signals from smart T-shirts using machine learning classifiers. We used 20 subjects, including 10 from mental stress (after twelve hours of continuous work in the laboratory) and 10 from normal (after completing the sleep or without any work). We also applied three scoring techniques: Chalder Fatigue Scale (CFS), Specific Fatigue Scale (SFS), Depression, Anxiety, and Stress Scale (DASS), to confirm the mental stress. The total duration of ECG recording was 1800 min, including 1200 min during mental stress and 600 min during normal. We calculated two types of features, such as demographic and extracted by ECG signal. In addition, we used Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Logistic Regression (LR) to classify the intra-subject (mental stress and normal) and inter-subject classification. The DT leave-one-out model has better performance in terms of recall (93.30%), specificity (96.70%), precision (94.40%), accuracy (93.30%), and F1 (93.50%) in the intra-subject classification. Additionally, The classification accuracy of the system in classifying inter-subjects is 94.10% when using a DT classifier. However, our findings suggest that the wearable smart T-shirt based on the DT classifier may be used in big data applications and health monitoring. Mental stress can lead to mitochondrial dysfunction, oxidative stress, blood pressure, cardiovascular disease, and various health problems. Therefore, real-time ECG signals help assess cardiovascular and related risk factors in the initial stage based on machine learning techniques.