Internet of Audio Things (IoAuT) is an emerging paradigm that integrates intelligent audio processing, ubiquitous connectivity, and edge–cloud computing resources to enable a network of devices capable of sensing, analyzing, and exchanging sound-based information seamlessly across distributed environments. This technology supports a wide range of applications, ranging from interactive musical performances to intelligent environmental monitoring, that play a significant role in everyday life. Although this technology holds great potential and could be highly useful across many domains in the future, its reliance on real-time audio streaming and distributed sensing introduces several communication and Quality of Service (QoS) challenges, such as latency, bandwidth limitations, packet loss during audio transmission, resource management, and energy-efficient networking, all of which directly affect its usability and scalability. In this paper, we provide the first holistic analysis of IoAuT applications from a QoS and communication perspective. We systematically review artistic, functional, and industrial use cases to explore how performance and sustainability trade-offs shape system design. Unlike prior reviews, our study introduces a unified architecture taxonomy and sustainability-enabled QoS metrics to evaluate network efficiency, interoperability, and energy use. We further examine the roles of edge computing, adaptive streaming, and dynamic resource allocation in achieving reliable and low-latency audio transmission. Finally, the paper highlights open challenges and suggests future research directions to help build IoAuT applications that can scale, work well with other systems, and reduce their impact on the environment.

Cooperative robotic healthcare systems (CRHS) are advanced technologies that enhance medical services by allowing robots to collaborate with healthcare professionals, making clinical practices safer and more efficient. However, for these systems to work efficiently, they need fast and reliable communication and computation, all while managing the limited resources and energy available in robot-embedded sensors. Therefore, this survey focuses on clarifying how various networking and computing decisions impact different aspects of this technology, such as latency, reliability, Quality of Service (QoS), and scalability, etc. We evaluated the recent research on resource allocation, as well as orchestration in edge, fog, and cloud computing, to have a holistic overview of what has been done so far in this field. Moreover, we analyzed communication technologies such as 5G, Ultra-Reliable Low-Latency Communication (URLLC), Time-Sensitive Networking (TSN), Software-Defined Networking (SDN), Network Function Virtualization (NFV), and network slicing to understand their role in RHCS QoS metrics. Our synthesis finds that (i) placing perception/control close to the edge consistently decreases end-to-end delay, (ii) SDN/NFV and time-sensitive networking improve predictable and real-time operation in multi-robot hospital environments; and (iii) learning-based scheduling and offloading often outperform static heuristics in variable workloads. Despite these advancements, we have identified several challenges in the literature, such as limited interoperability between different vendors and a lack of standardized benchmarks for Quality of Service (QoS), etc. Therefore, we conducted a comparative analysis to understand how specific design choices influence the QoS metrics of this technology. In addition, we have proposed potential research directions that address the open challenges to ensure the real deployment of this technology.

In recent years, quantum computing has made significant contributions to many emerging technologies. However, it also poses serious security challenges to these technologies, and one of them is Healthcare Internet of Things (HC-IoT) applications. The devices used in HC-IoT often have limited power, memory, and computational resources, making them especially vulnerable to various cyberattacks. Even a small security breach could cause serious problems, from general system failures to risks that directly affect patients’ diagnoses and treatment. To address this important issue, we review research from 2017 to 2025, examining both the strengths and weaknesses of the technology across various subdomains of the healthcare system. We begin by presenting a taxonomy of healthcare, along with a breakdown of different domains where this technology has been applied or holds potential for future use. This foundation helps establish the motivation and context for the study. Next, we discuss various security threats, considering both the pre-quantum and post-quantum computing eras. Then, we explore existing studies to see what progress has been made and what is still needed. Finally, we point out key security challenges that need more attention from the research community. Lastly, we provide a comparative analysis with existing review articles to address the question of why this article is needed in the presence of published reviews.

The complexity of next-generation industrial cyber-physical systems (NG-ICPSs) is increasing due to the integration of machine-embedded sensors, cyber-infrastructure, and physical processes, which calls for the new intelligent operation mechanisms to achieve system-level objectives. Although NG-ICPS has proliferated in many applications, such as advanced manufacturing, intelligent transportation, smart homes, etc., and achieved remarkable results. But these applications are susceptible to many problems and new security threats are some of them that goes beyond the scope of traditional communication and network security, due to the tight integration of cybers and physical systems. For redressal of this, several traditional authentication and data privacy schemes have been used in the recent past, but somehow, they did not satisfy the need for this emerging technology, due to their complex verification and validation processes. Recently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) enabled authentication and data preservation techniques had shown remarkable results to address the security problems of this technology at the system/client side and server side cost-effectively. Given that, in this article, we present a comprehensive survey of the current literature on NC-ICPS technology security threats and their countermeasures, with a focus on AI, ML, and DL-enabled techniques. We evaluate these techniques by identifying their advantages and disadvantages compared to traditional authentication and data preservation methods. In addition, we discussed the review articles published on this topic to acknowledge their contributions and limitations, because most of them cover a specific part of security concerns of this technology, and unable to present the true picture of all problems under one shallow. Building on this, we addressed the gaps in the literature by highlighting the open security challenges of NG-ICPS technology and suggesting potential future research directions, considering the capabilities of AI, ML, and DL-enabled algorithms. Finally, we compared this article sectionwise with rival review articles to claim its novelty followed by the question of reviewers, editors, students, and readers why this article is needed in the presence of these articles and what are its distinctive factor that makes this article different from them.

Internet of Vehicles (IoV) is growing rapidly with the potential to revolutionize transportation systems. Considering the promising future and potential contributions of IoV’s technology, it has attracted the attention of researchers, industry stakeholders, and potential intruders. However, the IoV’s network topological infrastructure faces several connectivity and communication challenges, along with security issues that are beyond the scope of current literature. Although each aspect and challenge has its own consequences, this work focuses on Physical Layer Security (PhyLaySec) threats, which are the most devastating because they undermine the trust of all stakeholders associated with this technology. In the literature, this topic is bearly focused, which demonstrates that the existing PhyLaySec countermeasures would not be able to counter future security challenges in IoV in terms of vehicle-to-vehicle (V2V) authentication, vehicle-to-infrastructure (V2I) authentication, vehicles-to-everything (V2X) authentication, etc., due to factors such as high vehicle mobility, dynamic network topologies, limited bandwidth, and ultra-fast communication. Therefore, this paper aims to provide a systematic review of state-of-the-art PhyLaySec techniques from 2017 to 2025, with a focus on their strengths and weaknesses. Through our review, we identify key open research questions that require further investigation to enhance the security of IoV’s technologies. Moreover, we highlight potential future research directions that aim to ensure the foolproof security of IoV technology with respect to underlined challenges. Finally, we acknowledge that this is the first paper to comprehensively address the topic of PhyLaySec of IoV technology, which makes it a valuable resource for researchers and professionals working in this field.