Today, ensuring work safety is considered to be one of the top priorities for various industries. Workplace injuries, illnesses, and deaths often entail substantial production and financial losses, governmental checks, series of dismissals, and loss of reputation. Wearable devices are one of the technologies that flourished with the fourth industrial revolution or Industry 4.0, allowing employers to monitor and maintain safety at workplaces. The purpose of this article is to systematize knowledge in the field of industrial wearables’ safety to assess the relevance of their use in enterprises as the technology maintaining occupational safety, to correlate the benefits and costs of their implementation, and, by identifying research gaps, to outline promising directions for future work in this area. We categorize industrial wearable functions into four classes (monitoring, supporting, training, and tracking) and provide a classification of the metrics collected by wearables to better understand the potential role of wearable technology in preserving workplace safety. Furthermore, we discuss key communication technologies and localization techniques utilized in wearable-based work safety solutions. Finally, we analyze the main challenges that need to be addressed to further enable and support the use of wearable devices for industrial work safety.

Public safety agencies have been working on the modernization of their communication networks and the enhancement of their mission-critical capabilities with novel technologies and applications. As part of these efforts, migrating from traditional land mobile radio (LMR) systems toward cellular-enabled, next-generation, mission-critical networks is at the top of these agencies’ agendas. In this paper, we provide an overview of cellular technologies ratified by the 3rd Generation Partnership Project (3GPP) to enable next-generation public safety networks. On top of using wireless communication technologies, emergency first responders need to be equipped with advanced devices to develop situational awareness. Therefore, we introduce the concept of the Internet of Life-Saving Things (IoLST) and focus on the role of wearable devices—more precisely, cellular-enabled wearables, in creating new solutions for enhanced public safety operations. Finally, we conduct a performance evaluation of wearable-based, mission-critical applications. So far, most of the mission-critical service evaluations target latency performance without taking into account reliability requirements. In our evaluation, we examine the impact of device- and application-related parameters on the latency and the reliability performance. We also identify major future considerations for better support of the studied requirements in next-generation public safety networks.

Automated systems have been seamlessly integrated into several industries as part of their industrial automation processes. Employing automated systems, such as autonomous vehicles, allows industries to increase productivity, benefit from a wide range of technologies and capabilities, and improve workplace safety. So far, most of the existing systems consider utilizing one type of autonomous vehicle. In this work, we propose a collaboration of different types of unmanned vehicles in maritime offshore scenarios. Providing high capacity, extended coverage, and better quality of services, autonomous collaborative systems can enable emerging maritime use cases, such as remote monitoring and navigation assistance. Motivated by these potential benefits, we propose the deployment of an Unmanned Surface Vehicle (USV) and an Unmanned Aerial Vehicle (UAV) in an autonomous collaborative communication system. Specifically, we design high-speed, directional communication links between a terrestrial control station and the two unmanned vehicles. Using measurement and simulation results, we evaluate the performance of the designed links in different communication scenarios and we show the benefits of employing multiple autonomous vehicles in the proposed communication system.

Extending the existing near-shore terrestrial infrastructure with non-terrestrial network capabilities helps maritime operators alleviate the high costs of communication and meet the requirements imposed by time-sensitive applications. Recognizing that the deployment of terrestrial and non-terrestrial networks necessitates selecting from the available wireless backhaul solutions, which have dissimilar data transmission costs and communication link qualities, it is essential to propose an appropriate backhaul selection policy. Specifically, in this letter, we coin a backhaul selection policy that manages the inherent trade-off between data transmission expenses and timely throughput guarantees for maritime communications. We formulate the backhaul selection problem as a Markov decision process and show that the proposed solution is not only more cost-efficient, but also satisfies the timely throughput requirements in contrast to currently used greedy strategies.

Contemporary research has revealed a limitation in the Uplink (UL) Buffer Status Report (BSR) scheduling procedure - its reliance on outdated information. In addition, a significant limitation in current BSR implementations lies in their inflexibility. The 3rd Generation Partnership Project (3GPP) specifications constrain BSR periodicities to certain quantized values based on Quality of Service (QoS) requirements for various applications. For instance, applications demanding low latency may require very small BSR periodicities, resulting in substantial overhead due to frequent BSR reports. This may result in the wastage of network resources in case of a low BSR periodicity setting. Alternatively, a high BSR periodicity setting may lead packets to wait more at the user buffer and thus result in higher packet latencies. To address these limitations, we propose a framework that predicts time intervals between packet arrivals and subsequently adjusts the BSR periodicity according to the predicted traffic arrivals. The simulation results demonstrate that the proposed Machine Learning (ML)-aided BSR reporting provides flexibility in BSR periodicity adapted to the intensity of traffic arrival and converges to optimal periodicity depending on the mean traffic arrival rate.