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.

The Internet of Things (IoT) has shown its presence in applications that require monitoring extreme environments, such as wildfires, military operations, and coastal areas, among others. In these applications, the IoT nodes are deployed in hazardous terrains where humanistic access is hard or not possible. Hence, to ensure reliable data transmission in these applications, novel routing protocols need to be designed due to the multihop nature of communication possessed by the deployed nodes. Currently, most of the routing protocols utilized by IoT nodes follow traditional approaches, which creates congestion and contention in the network. As a result, the network performance is degraded in terms of various communication metrics. To address this problem and improve the communication statistics in extreme environments, we propose a deep-$Q$-learning-enable-destination-sequenced distance-vector (DQL-DSDV) framework. DQL-DSDV focuses on selecting the next hop during communication. Initially, the DSDV protocol updates routing information for connected nodes. This information is subsequently utilized by the deep-$Q$-learning (DQL) algorithm to compute the next hop count. This computation is based on reward functions, known as$Q$-values, which are conceptualized as the distance between connected nodes by taking into account the traffic flow. These distinguishing operational features of DQL and DSDV ensure that DQL-DSDV minimizes the packet lost ratio, congestion, end-to-end delay, and communication cost with improved Quality of Service (QoS). During simulations, we observed significant improvement in these performance metrics, in the presence of the existing schemes. Despite that, we checked the computation complexity of the proposed approach with existing protocols, which demonstrated noteworthy outcomes just like the other metrics.

The technology of Unmanned Aerial Vehicles (UAVs) has sparked a revolution in numerous Internet of Things (IoT) applications, such as flood monitoring, wildfire monitoring, coastal area surveillance, intelligent transportation, and classified military operations, etc. This technology offers several advantages when used as a flying base station to enhance the communication metrics of an employed IoT appplication. However, as an integrated technology (UAV-assisted IoT applications), it suffers from many challenges, and security is one of the foremost concerns. Considering that, in this paper, we proposed a hybrid lightweight key exchange authentication model for UAV-assisted IoT applications to resolve the device-to-device (D2D) authentication and data privacy issues in these networks. The proposed model employs five different security parameters named registration, authentication, authorization, accounting, and cache wash and update (R3ACWU) in coordination with a hash function. The network architecture consists of UAVs, IoT devices, and micro base stations, followed by base stations, authentication servers, and service providers (SP). In this framework, we introduce a concept known as ‘dead time’, a specific time period after which each device’s cache memory is cleared and updated. This practice not only enhances the security of the devices in use but also reduces computational and memory overhead by eliminating the records of devices that haven’t participated in the communication process within the specified time frame. Results statistics of our lightweight R3ACWU authentication scheme exhibit notable improvement corresponded to the present authentication schemes in terms of comparative parameters.

Unmanned aerial vehicles (UAVs) as an intermediary can offer an efficient and useful communication paradigm for different Internet of Things (IoT) applications. Following the operational capabilities of IoTs, this emerging technology could be extremely helpful in the area, where human access is not possible. Because IoT devices are employed in an infrastructure-less environment, where they communicate with each other via the wireless medium to share accumulated data in network topological order. However, the unstructured deployment with wireless and dynamic communication make them disclosed to various security threats, which need to be addressed for their efficient results. Therefore, the primary objective of this work is to present a comprehensive survey of the theoretical literature associated with security concerns of this emerging technology from 2015-to-2022. To follow up this, we have overviewed different security threats of UAV-aided IoT applications followed by their countermeasures techniques to identify the current challenges and requirements of this emerging technology paradigm that must be addressed by researchers, enterprise market, and industry stakeholders. In light of underscored constrains, we have highlighted the open security challenges that could be assumed a move forward step toward setting the future research insights. By doing this, we set a preface for the answer to a question, why this paper is needed in the presence of published review articles. For novelty and uniqueness, we have performed a comparative analysis section-wise with rival papers to demonstrate that how this paper is different from them.