A quantum key distribution (QKD) network is proposed to allow QKD protocols to be the infrastructure of the Internet for distributing unconditional security keys instead of existing public-key cryptography based on computationally complex mathematical problems. Numerous countries and research institutes have invested enormous resources to execute correlation studies on QKD networks. Thus, in this study, we surveyed existing QKD network studies and practical field experiments to summarize the research results (e.g., type and architecture of QKD networks, key generating rate, maximum communication distance, and routing protocol). Furthermore, we highlight the three challenges and future research issues in the security of QKD networks and then provide some feasible resolution strategies for these challenges.

This incredibly rapid adoption of Internet of Things (IoT) and e-learning technology, a smart campus provides many innovative applications, such as ubiquitous learning, smart energy, and security services to campus users via numerous IoT devices. However, as more and more IoT devices are integrated and imported, the inadequate campus network resource caused by the sensor data transport and video streaming is also a significant problem. This paper proposes a campus edge computing network in the hardware–software co-design process. The system employs street lighting as the IoT network communication node device. The campus platform integrates campus courses service, regulatory networks, mobile wireless networks, and other computing services. Neural network learning algorithms are employed to analyze the network and compute resource required by each network node operates as a whole network resource allocation service. Moreover, the learning algorithms will be adjusted as the bidirectional IoT communication to avoid inadequate resources with many IoTs service and data streams in the overall campus network service quality. The experimental results show that the proposed mechanism that the edge computing reduces the cloud loading and predicts and adjusts the distribution of the overall network can efficiently allocate resources and maintain load balance.

In recent years, bicycles have quickly become one of the major urban sports. At the same time, with the rise of Internet of Things (IoT), embedded system combine IoT devices are widely used and making computing truly ubiquitous. Although bicycles have various functions, they fail to provide cyclists with sufficient exercise-related information and post-exercise analysis. Therefore, this paper introduced a bicycle record system of ground conditions based on IoT which is combining smartphone and embedded system. The event data recorder comprised two parts, a “smartphone-based event data recorder”and “bicycle-based real-time information feedback system". Using the event data recorder, this paper integrated and provided various types of real-time information for cyclists while they cycled to help them achieve their desired exercise results. After cycling, the cyclists could view cycling-related information through software analysis. This information included cycling routes taken, total cycling distance, and total calories burned. The event data recorder also saved information related to cycling routes, such as acceleration, deceleration, directional changes, and slope changes. By analyzing the recorded information, cyclists not only gained further insight into their exercise results but were also able to share cycling-related information through the Internet, which would benefit cyclists who had not cycled along this route before. By developing the bicycle record system, this paper aimed to provide cyclists with real-time, accurate, and complete information, enabling them to enjoy a consummate cycling environment.

Due to the climate crisis, energy-saving issues and carbon reduction have become the top priority for all countries. Owing to the increasing popularity of advanced metering infrastructure and smart meters, the cost of acquiring data on residential electricity consumption has substantially dropped. This change promotes the analysis of residential electricity consumption, which features both small and complicated consumption behaviors, using machine learning to become an important research topic among various energy saving and carbon reduction measures. The main subtopic of this subject is the identification of abnormal electricity consumption behaviors. At present, anomaly detection is typically realized using models based on low-level features directly collected by sensors and electricity meters. However, due to the significant number of dimensions and a large amount of redundant information in these low-level features, the training efficiency of the model is often low. To overcome this, this study adopts an autoencoder, which is a deep learning technology, to extract the high-level electricity consumption information of residential users to improve the anomaly detection performance of the model. Subsequently, this study trains one-class SVM models for anomaly detection by using the high-level features of five actual residential users to verify the benefits of high-level features.