In the realm of fourth-generation industrialization, there will be great demand for a skilled workforceTo produce a skilled workforce, we need sustainable education with quality and equity. Conventional ways of delivering and managing education would not fulfil the demands of the fourth industrial revolution (4IR). Disruptive technologies, such as Internet of Things (IoT), have great potential in revolutionizing the current educational setup. Therefore, this research work aims to present an overview of the capabilities of IoT applications in educational settings. Our research article digs into recent research carried out referring to IoT applications in education and provides a detailed insight into the topic from three different perspectives, i.e., from the perspective of school management, teachers, and learners. The present research explains the ways in which IoT has been applied for the benefit of school managers, teachers, and learners, showcased in the recent literature. The paper also sheds light on bottlenecks for IoT applications and explains security, privacy, scalability, reliability, and dehumanization as main constraints in IoT applications in educational settings.

It is expected that the future intelligent transportation system will be endowed with the sensing ability to cope with the complex road environment. Therefore, the integrated sensing and communications (ISAC) system can complement the development of intelligent transportation. In this work, a novel reconfigurable intelligent surface (RIS)-aided ISAC system is investigated, in which an RIS reflects signals to the vehicle target and user by creating a directional path to enhance sensing and communication performance. We are interested in the joint robust design of transmitted beamformer at the dual-functional radar-communication (DFRC) base station and phase-shift at the RIS to maximize the radar mutual information subject to user achievable rate constraint under imperfect angles knowledge and channel state information (CSI). Specifically, two CSI error models, namely, the bounded and the mixed bounded-moment error models, are considered. Then, a worst-case robust (WCR) beamforming problem, as well as a mixed chance-constrained and worst-case robust (MCWR) beamforming problem, are separately formulated. Furthermore, we develop two efficient methods to convert the formulated semi-infinite constraint problems into feasibility ones, and an alternate optimization framework is proposed to obtain stationary points of the original problems. Simulation results are provided to validate the effectiveness of the proposed transformation methods and solution.

We use 250 billion microcontrollers daily in electronic devices that are capable of running machine learning models inside them. Unfortunately, most of these microcontrollers are highly constrained in terms of computational resources, such as memory usage or clock speed. These are exactly the same resources that play a key role in teaching and running a machine learning model with a basic computer. However, in a microcontroller environment, constrained resources make a critical difference. Therefore, a new paradigm known as tiny machine learning had to be created to meet the constrained requirements of the embedded devices. In this review, we discuss the resource optimization challenges of tiny machine learning and different methods, such as quantization, pruning, and clustering, that can be used to overcome these resource difficulties. Furthermore, we summarize the present state of tiny machine learning frameworks, libraries, development environments, and tools. The benchmarking of tiny machine learning devices is another thing to be concerned about; these same constraints of the microcontrollers and diversity of hardware and software turn to benchmark challenges that must be resolved before it is possible to measure performance differences reliably between embedded devices. We also discuss emerging techniques and approaches to boost and expand the tiny machine learning process and improve data privacy and security. In the end, we form a conclusion about tiny machine learning and its future development.

Unmanned aerial vehicle (UAV)-assisted wireless communication systems are able to provide high-quality services and ubiquitous connectivity for massive Internet of Things (IoT) devices. In this paper, we study the Age of Information (AoI) and energy tradeoff in a system where an employed UAV performs data collection for multiple IoT nodes (INs). Bearing in mind the importance of AoI and energy consumption during the data collection process, we present a multi-objective optimization problem to minimize the AoI and UAV energy consumption. To explore the tradeoff between AoI and energy consumption, we jointly optimize the collection time, the UAV trajectory, and the duration of time slots. Due to the non-convexity of the formulated problem, we divide the main problem into three sub-problems and address them by leveraging successive convex approximation (SCA) and Lagrangian dual methods. Finally, we design a multi-variable fixed algorithm to iteratively solve the three sub-problems. Simulations are carried out to investigate the tradeoff between AoI and UAV energy consumption, revealing that reducing AoI and energy consumption simultaneously is unattainable. Furthermore, the convergence and validity of the proposed algorithm are presented and analyzed.

Driver distraction diverting drivers’ attention to unrelated tasks and decreasing the ability to control vehicles, has aroused widespread concern about driving safety. Previous studies have found that driving performance decreases after distraction and have used vehicle behavioral features to detect distraction. But how brain activity changes while distraction remains unknown. Electroencephalography (EEG), a reliable indicator of brain activities has been widely employed in many fields. However, challenges still exist in mining the distraction information of EEG in realistic driving scenarios with uncertain information. In this paper, we propose a novel framework based on Multi-scale entropy (MSE) in a sliding window and Bidirectional Long Short-term Memory Network (BiLSTM) to explore the distraction information of EEG to detect driver distraction based on multi-modality signals in real traffic. Firstly, MSE with sliding window is implemented to extract the EEG features to determine the distraction position. Statistical analysis of vehicle behavioral data is then performed to validate driving performance indeed changes around distraction position. Finally, we use BiLSTM to detect driver distraction with MSE and other traditional features. Our results show that MSE notably decreases after distraction. Consistent with the result of MSE, driving performance significantly deviates from the normal state after distraction. Besides, BiLSTM performance of MSE outperforms other entropy-based methods and is better than behavioral features. Additionally, the accuracy is improved again after adding MSE feature to behavioral features with a 3% increasement. The proposed framework is useful for mining brain activity information and driver distraction detection applications in realistic driving scenarios.

Recently, integrating satellite networks (e.g. Low-earth-orbit satellite constellation) into the Internet of Things (IoT) ecosystem has emerged as a potential paradigm to provide more reliable, ubiquitous and seamless network services. The LEO satellite networks serves as a key enabler to transform the connectivity across industries and geographical border. Despite the convenience brought from the LEO satellite networks, it arises security concerns, in which the essential one is to secure the communication between the IoT devices and the LEO satellite network. However, some challenges inheriting from the LEO satellite networks need to be considered : 1) the dynamic topology; 2) the resource-constraint satellites; 3) the relative long latency; 4) multiple beams authentication. In particular, the centralized authentication schemes are no longer suitable for the emerging LEO satellite assisted IoT ecosystem. In this paper, we first introduce the architecture of the LEO satellite network assisted IoT ecosystem. Then, we propose an efficient and privacy-preserving blockchain-based authentication scheme. The proposed authentication scheme takes the advantages of certificateless encryption and consortium blockchain to provide lightweight key pair computation without appealing devices’ information and efficient signature querying and verification. In addition, a fast authentication mechanism is implemented in the scheme in order to reduce the time complexity from querying a certain record for the authentication within a satellite among multiple beams. With the analysis of the storage and computation complexity, the performance evaluation demonstrates the effectiveness of the proposed scheme.