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.

Millimeter-wave (mmWave) radar is becoming an important tool for contactless health monitoring because it can sense very small chest motions while preserving privacy by avoiding visual imagery. Existing surveys on radar- or RF-based vital sign monitoring either focus mainly on classical radar architectures and signal processing, provide broad RF sensing overviews in which mmWave healthcare is treated only briefly, or catalog machine learning models without clearly linking them to mmWave propagation, hardware constraints, datasets, and clinical evaluation practices. Because of these gaps, we believe the existing surveys do not provide a holistic, accurate picture of this technology. To address this, we present a comprehensive survey of AI-enabled mmWave radar for contactless health monitoring, covering the literature from 2015 to 2025. The objective of this work is to provide a clear, top-down understanding of the full sensing and inference pipeline by connecting the physical foundations of mmWave propagation and frequency-modulated continuous wave (FMCW) radar modeling with modern AI-based algorithms. We first summarize mmWave propagation, FMCW waveform and array design, and micromotion modeling, with emphasis on design choices that affect vital sign accuracy and robustness. To do this, we introduce a unified physics-to-intelligence framework that connects sensing configurations, subject scenarios, signal-processing and feature-representation pipelines, and the evolution of AI algorithms such as CNNs, LSTMs, transformers, self-supervised learning, and physics-guided networks. In parallel, we consolidate the scarce public mmWave FMCW datasets, together with windowing protocols and evaluation metrics, and highlight how limited dataset availability and heterogeneous benchmark practices continue to prevent fair comparison, reproducibility, and clinical translation. Building on this view, we discuss major challenges such as domain generalization, motion and interference, model interpretability and trust, privacy, multimodal fusion, and edge deployment, and we outline a practical roadmap for designing mmWave health monitoring systems that are robust across environments, efficient on embedded platforms, and aligned with clinical workflows. The survey is intended to serve researchers working at the intersection of wireless communications, sensing, and AI, both as a reference and a design guide for next-generation contactless health-monitoring applications.

The literature repeatedly reports that the unique nature of individual brainwave patterns makes them suitable for identification and authentication, because they are difficult to replicate or forge. Therefore, many researchers have utilized brainwaves for authentication by training traditional deep learning and machine learning models. However, the internal decision processes of these black-box models have not been evaluated in terms of biases, overfitting, large training data requirements, and handling complex data structures, which keep them in a fuzzy state. To address these limitations, a smart system is needed to be develop that could be capable of making the authentication process user-friendly, robust, and reliable. In this paper, we present a deep reinforcement learning-based biometric authentication framework known as "BrainAuth" for personal identification using the gamma ($\gamma$) and beta ($\beta$) brainwaves. This approach improves the accuracy of authentication by using the (i) Dyna framework and a dual estimation technique. Both these technique helps to maintain the integrity of brainwave patterns, which are needed for authentication and understanding of spoofing activities. (ii) We also introduce a layered structure architecture in the proposed model to reduce the time needed for exploration using two deep neural networks. These networks work together to handle the complex data while making decisions in delay sensitive environment. (iii) We evaluate the model on seen and unseen data to verify its robustness. During analysis, the model achieved an equal error rate (EER) of $\approx$ 0.07% for seen data and $\approx$ 0.15% for unseen data, respectively. Furthermore, the analysis metrics such as true positive (TP), false positive (FP), true negative (TN), and false negative (FN) followed by false acceptance rate (FAR), false rejection rate (FRR), true acceptance rate (TAR) revealed significant improvements compared to existing schemes.

Current Retrieval-Augmented Generation systems use uniform processing, causing inefficiency as simple queries consume resources similar to complex multi-hop tasks. We present SymRAG, a framework that introduces adaptive query routing via real-time complexity and load assessment to select symbolic, neural, or hybrid pathways. SymRAG’s neuro-symbolic approach adjusts computational pathways based on both query characteristics and system load, enabling efficient resource allocation across diverse query types. By combining linguistic and structural query properties with system load metrics, SymRAG allocates resources proportional to reasoning requirements. Evaluated on 2,000 queries across HotpotQA (multi-hop reasoning) and DROP (discrete reasoning) using Llama-3.2-3B and Mistral-7B models, SymRAG achieves competitive accuracy (97.6–100.0% exact match) with efficient resource utilization (3.6–6.2% CPU utilization, 0.985–3.165s processing). Disabling adaptive routing increases processing time by 169–1151%, showing its significance for complex models. These results suggest adaptive computation strategies are more sustainable and scalable for hybrid AI systems that use dynamic routing and neuro-symbolic frameworks.

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.

The aim of this study is to investigate into $\mu \mathrm{AFL}$, a non-intrusive, feedback-driven fuzzing framework, evaluated on Cortex M4 embedded systems and Unix platforms, focusing on the STM32F407VE Cortex M4 microcontroller. By leveraging the SEGGER J-Trace Pro for trace collection, it demonstrates $\mu$AFL’s utility beyond its traditional scope, showcasing its efficacy in both embedded and general-purpose computing environments. Our analysis, enriched by juxtaposing $\mu$AFL’s capabilities with traditional AFL, emphasizes the adaptability and effectiveness of fuzzing methodologies in firmware security enhancement. Furthermore, the study provides a deep understanding of fuzzing execution on different hardware, presenting an execution strategy for the STM32F407VE that highlights the framework’s potential in identifying vulnerabilities, evidenced by tests on specific firmware programs such as an LED blinking program integrated with semihosting breakpoints and ETM tracing. The use of uninitialized memory sections and strategically placed break-points offers significant insights into the firmware’s execution flow. The results of our comparative analysis clearly show that $\mu \mathrm{AFL}$ excels at uncovering vulnerabilities, reinforcing the need for evolving fuzzing methodologies to build stronger security systems for embedded devices. This contribution underscores the importance of refining fuzzing techniques to meet the intricate security demands of contemporary computing environments.

In this paper, we focus on addressing the challenges of detecting malicious attacks in networks by designing an advanced Explainable Intrusion Detection System (xIDS). The existing machine learning and deep learning approaches have invisible limitations, such as potential biases in predictions, a lack of interpretability, and the risk of overfitting to training data. These issues can create doubt about their usefulness, and transparency, and decrease the trust of involved stakeholders. To overcome these challenges, we propose an ensemble learning technique called the "EnsembleGuard". This approach uses the predicted outputs of multiple models, including tree-based (LightGBM, GBM, Bagging, XGBoost, CatBoost) and deep learning models such as neural network (LSTM (long short-term memory networks) and GRU (gated recurrent unit), to maintain a balance and achieve trustworthy results. Our work is unique because it combines both tree-based and deep learning models to design an interpretable and explainable meta-model through model distillation. By considering the predictions of all individual models, our neta-model effectively addresses key challenges, and ensures both explainable and reliable results. We evaluate our model using well-known datasets, including UNSW-NB15, NSL-KDD, and CIC-IDS-2017, to assess its reliability against various types of attacks. During analysis, we found that our model outperforms both tree-based models and other comparative approaches when it comes to different kinds of attack scenarios.

Internet of Things (IoT) applications are switching from general to precise in different industries, e.g., healthcare, automation, military, maritime, smart cities, transportation, logistics, and many more. In the healthcare domain, these applications had demonstrated an incredible improvement in patient assessment, monitoring, and prescription, etc., with ease of access through the Internet. Despite its benefits, this technology also offers several security challenges for the research community and healthcare stakeholders, because of its wireless communication and open-area deployment. To explore, patient wearable devices and other networking entities follows unstructured communication format to share their accumulated data in the network, which makes them susceptible to manifold security threats. Considering the significance of these applications, data acquisition, processing, storage, and assessment on client and remote sides need a high standard of secure communication infrastructure. Therefore, security of these applications is one of the major obstacles that prevent their widespread use in different healthcare domains. To discuss different security constraints, in this paper, we present a comprehensive survey of the theoretical literature from 2015-to-2023 to highlight the unresolved security problems of this emerging technology. Based on the evaluated literature pros and cons, we determine the security requirements and challenges of Healthcare-IoT (HC-IoT) applications. Following this, we demonstrate future research directions that could be useful for the researchers and industry stakeholders working in this domain. To demonstrate the uniqueness of this work and claim its contribution, we compare our work section-wise with previously published papers to answer the question of reviewers, editors, students, and readers, why this review article is required in the presence of already published review articles.

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.

The number of affirmed COVID-19 cases showed an enormous increase in the recent past throughout the globe. Keeping in view the catastrophic destruction of this devastating virus, there is a must-need situation to maximize the use of existing healthcare technologies such as the healthcare Internet of Things (H-IoT). In healthcare, patient wearable devices are widely recognized as a dormant technology with enormous capabilities to assess and combat various diseases, e.g., cough, seizure, temperature, heartbeat, and so on. As we know, in the H-IoT, patient-wearable devices are dispersed in an infrastructure-free environment that exposes them to several private and public coercion while accumulating and transmitting high sensitive data over the wireless communication channel. Therefore, security is the main concern of these applications, and thus, the primary focus of this article to outline the limitations and challenges in the present literature from 2019 to 2021, to identify the requirements of H-IoT applications used in the context of COVID-19. Following this, we will move one step ahead to explore the current security techniques adopted in these applications. Consequently, we will identify the network architectural, cryptographic, protocols, and operational security challenges during our study to recommend viable research directions and opportunities, which could be helpful and capable to minimize the network architecture, deployment, and maintenance cost with more productive outcomes.

Cyber-Physical Systems (CPS) connected in the form of Internet of Things (IoT) are vulnerable to various security threats, due to the infrastructure-less deployment of IoT devices. Device-to-Device (D2D) authentication of these networks ensures the integrity, authenticity, and confidentiality of information in the deployed area. The literature suggests different approaches to address security issues in CPS technologies. However, they are mostly based on centralized techniques or specific system deployments with higher cost of computation and communication. It is therefore necessary to develop an effective scheme that can resolve the security problems in CPS technologies of IoT devices. In this paper, a lightweight Hash-MAC-DSDV (Hash Media Access Control Destination Sequence Distance Vector) routing scheme is proposed to resolve authentication issues in CPS technologies, connected in the form of IoT networks. For this purpose, a CPS of IoT devices (multi-WSNs) is developed from the local-chain and public chain, respectively. The proposed scheme ensures D2D authentication by the Hash-MAC-DSDV mutual scheme, where the MAC addresses of individual devices are registered in the first phase and advertised in the network in the second phase. The proposed scheme allows legitimate devices to modify their routing table and unicast the one-way hash authentication mechanism to transfer their captured data from source towards the destination. Our evaluation results demonstrate that Hash-MAC-DSDV outweighs the existing schemes in terms of attack detection, energy consumption and communication metrics.

One of the operational challenges in the Internet of Things (IoT) is load balancing, which is the focus of interest of this article. We propose a three-phase enhancedad hocon-demand distance vector (enhanced-AODV) routing protocol for multiwireless sensor networks (multi-WSNs). The three phases are categorized based on traffic priority, namely: 1) high priority; 2) low priority; and 3) ordinary network traffic. The network architecture is divided into chains, i.e., local and public chains, where the cluster heads (CHs) and base stations (BSs) are used, respectively, to manage the network traffic based on priority information with alternative route allocation. Moreover, our three-phase enhanced-AODV protocol provides traffic categorization with alternatives route allocation to minimize energy consumption and prolong the lifetime of participating devices in the network. The proposed model is implemented in the simulation environment to overview results statistics in terms of network lifetime, prioritize traffic, computation and communication costs, latency, and packet lost ratio (PLR). Findings from the simulation suggest that our scheme achieves 15% improvement in network lifetime, 17% latency, 22% PLR, and approximately 10% in the computation and communication costs of the network, in comparison to three other similar protocols.

In this article, we present a robust channel categorization scheme to fix data privacy and preservation problems in an Industrial Healthcare Internet of Things (IHC-IoT) network. The proposed model categorizes the transmission bandwidth into four independent channels for each device by defining triggering rules with respect to time for reception and transmission of data. Besides, our developed prototype, which is known as high optimal path channel triggering protocol (HOPCTP) ensures data privacy and preservation utilizing minimal network resources in an IHC-IoT network. Furthermore, the HOPCTP prototype enables client-side devices to transmit and receive data with four different independent communication channels following the triggering mechanism to avoid adversary device anticipation in the network. The categorized channels are triggered with a defined time period to change their transmission and reception functionality, which triggers the transmitted data between different channels. Same data transmission via four different channels ensures the confidentiality and integrity of data because if an attacker captures one channel of data, he will not be able to understand the full message. The convalescent communication infrastructure is developed among patient wearable devices followed by cluster heads, micro base station, and macro base station to ensure data privacy and preservation with better communication metrics. In addition, the objective of the HOPCTP prototype is to resolve the data privacy issues in delay-sensitive applications, i.e., IHC-IoT networks. To achieve this, the HOPCTP prototype promotes data preservation and communication in terms of authenticity, congestion, throughput, communication cost, and packet loss ratio. The result statistics of the proposed scheme demonstrate remarkable improvement over the existing schemes for aforementioned comparative metrics.

In this paper, we present a three-byte-based Media Access Control (MAC) protocol to resolve the mutual authentication problem in an Autonomous Internet of Vehicles (AIoV) network. Initially, the network architecture is divided into two chains, i.e. the local and public chain, wherein the local chain the authentication and communication process is controlled by Cluster head (CH), while in the public chain it is controlled by the base station (BS). The proposed paradigm uses the 48-bit MAC address of the vehicle’s embedded sensors for authentication, with the ability to alter the authentication parameters by triggering the last three bytes (24 bits) of the MAC address with a predetermined time interval. Persistent triggering of the last three bytes of an AIoV’s MAC address guarantees its integrity in the network because only legal vehicles are capable of initiating and validating the authentication request with the other vehicles in the network. Initially, the MAC addresses of all AIoVs are registered with the BS in the public chain through the concerned CH. Likewise, the MAC-address triggering of registered AIoVs is carried out in the BS with a defined time period and broadcasted in the public chain, which is further distributed through CHs in the local chain. Most of the computation is supervised by BS and CH in the public and local chains respectively, which minimize the client-side authentication complexity and enhances network efficiency in terms of authentication with 98.3% detection rate, communications, and computing costs, along with 11% improvement in the latency, 15% improvement in packet loss ratio (PLR), and throughput.