Sentiment analysis, or more specifically, the integration of IoMT into healthcare systems, requires frameworks that must adapt at runtime with high precision. Most existing methods have several limitations of either latency or accuracy issues, and therefore perform less effectively in dynamic scenarios. This study aims to address these challenges by proposing EmoSyn, a novel framework that incorporates Emotion Wave Modulation (EWM), Neuro-Cognitive Language Dynamics (NCLD), and the Sentient IoMT Interaction Protocol (SIP). EWM generates dynamic emotional waveforms using high-dimensional feature vectors and kernelized mappings, while NCLD employs synthetic neural mappings and adaptive linguistic modeling to capture semantic transitions. SIP facilitates real-time IoMT recalibration through bidirectional sentiment-driven feedback. Implemented using mathematical frameworks and a custom Emotion-Aware Predictive Synthesis Algorithm (EAPSA), EmoSyn ensures precise sentiment interpretation and efficient IoMT interactions. The framework was evaluated using MOSEI and MIMIC-III datasets in a Python-based simulation environment. The results showed EmoSyn achieving 91% precision for MOSEI and 86% for MIMIC-III, with average latencies of 29 ms and 27 ms.

Vehicular networks face increasing challenges to ensure security, privacy, and efficiency in dynamic communication environments.Current solutions often lack adaptability to evolving threats and efficient mechanisms for preserving privacy and reducing computational overhead.This study proposes PrivNet, a framework that integrates generative AI with advanced cryptographic and trust mechanisms to address these limitations.The framework comprises the Quantum-Augmented Holographic Cryptographic System (QAHCS) for dynamic and secure key generation, the Neural Overlap Privacy System (NOPS) for adaptive pseudonym morphing and entropy-driven identity obfuscation, and the Self-Supervised Generative Anomaly Detection (SS-GAI) module for real-time threat modeling and counter-anomaly injection.The system also incorporates Hyperledger Mesh for energy-efficient and secure transaction validation.Simulations were performed using NSL-KDD, CICIDS2017, and Car-Hacking / VeReMi datasets for 300 minutes.The results demonstrate a 12% improvement in detection accuracy, a 23% improvement in energy efficiency, and a 22% reduction in resource utilization.

With the exponential growth of Internet of Things (IoT) devices, edge computing has emerged as a vital paradigm for localized data processing, low-latency communication, and efficient resource utilization. However, routing in IoT-based edge networks remains challenging due to dynamic topologies, constrained energy resources, and growing security threats. To tackle these challenges, this paper proposes QLB-IoT, a novel framework that integrates Q-learning-based intelligent routing with blockchain-assisted trust management to optimize energy consumption while ensuring secure and adaptive data transmission. The proposed method enables IoT devices to autonomously learn energy-efficient routing paths based on real-time network parameters such as residual energy and link distance. The Q-learning mechanism minimizes route flapping and promotes adaptive decision-making. Simultaneously, blockchain and digital signatures establish a decentralized trust layer that authenticates devices and prevents attacks like blackhole and Sybil. Compared with Open Shortest Path First (OSPF), Q‑Routing, Deep Reinforcement Learning-based control framework for traffic engineering (DRL‑TE), and Intelligent Edge Network Routing (ENIR), exhibits an extended network lifetime to ≈ 1300 rounds (2.4× OSPF and 8% beyond ENIR), increases throughput by 18–30%, and reduces average latency by half, while minimizing packet loss by ≈ 40% and increasing the likelihood of quality of service (QoS) compliance by up to 10%. These improvements stem from a real-time exploration–exploitation mechanism that dynamically reroutes traffic away from energy-depleted nodes, and from the immutable ledger that prevents malicious route manipulation without centralized oversight. By combining adaptive, energy-aware routing with verifiable trust, QLB-IoT delivers a scalable and resilient solution for mission-critical IoT-edge deployments.

The significant increase in the number of Internet of Things (IoT) devices in various domains requires robust and adaptive authentication mechanisms. Existing methods often fail to address the dynamic and heterogeneous nature of the IoT ecosystem, resulting in significant security vulnerabilities. This paper presents a context-aware LLM-enhanced authentication framework (CLAF-IoT) that dynamically adjusts authentication protocols based on real-time environmental and user-specific contexts. Using the advanced contextual understanding and generation capabilities of Large Language Models (LLMs), the proposed framework enhances both security and usability in highly dynamic IoT environments. Key components include environmental context sensing, user behavior analysis, adaptive authentication protocols, real-time threat detection, and federated learning integration for continuous improvement and privacy preservation. Experimental evaluations demonstrate that CLAF-IoT achieves higher authentication accuracy in different scenarios, 11.11% false acceptance rate and 9.09% false rejection rate.

Integration of deep learning-based edge computing into industrial processes has enabled intelligent automation in the Industrial Internet of Things (IIoT). However, the deployment of deep learning inference models in embedded edge clusters remains a challenge due to energy constraints, communication latency, and computational limitations. This paper proposes an energy-aware task allocation framework for optimizing deep learning inference in IIoT environments utilizing a Embedded cluster with both Wi-Fi and Ethernet-based communication setups. We have implemented and evaluated the performance of four classical deep learning models (MobileNet SSD, Tiny YOLO, EfficientDet Lite, and Faster R-CNN) on the edge cluster and analyze their execution time, energy consumption, and network efficiency. The framework provides a scalable and energy-efficient solution for deploying deep learning inference on resource-constrained edge computing platforms. The is ongoing development for the Embedded-Edge test beds and Future work will explore heterogeneous edge device integration and 5G-enabled computing for further performance enhancements. Experimental results demonstrate that Ethernet-based communication improves task execution speed by 15-20% and reduces energy consumption by 12-18% compared to Wi-Fi. Additionally, a fault-tolerant task reallocation mechanism ensures uninterrupted operation in case of node failures. The findings suggest that efficient task scheduling and network optimization strategies can significantly enhance the real-time processing capability of IIoT applications on Embedded edge also.

The beginning of cellular communication (next-gen, such as 5G and 6G) promises an extreme leap in connectivity, introducing intelligent, adaptive solutions that integrate communication, artificial intelligence, and emerging technologies. Our approach combines reinforcement learning with Medium Access Control (MAC) protocols to dynamically optimize resource allocation and enhance network performance. In this work, we explore the integration of the adaptive frame size adjusting approach similar to the IEEE 802.1CB to ensure the efficient handling of seamless redundancy. The proposed solutions are validated through simulation, ensuring robustness and real-world applicability. Results indicate significant improvements in redundancy rate detection and delay in the network. This work contributes to achieving intelligent, adaptive, and seamless connectivity in the next generation of communication systems.

The recent outbreak of a novel coronavirus, named COVID-19 by the World Health Organization (WHO) has pushed the global economy and humanity into a disaster. In their attempt to control this pandemic, the governments of all the countries have imposed a nationwide lockdown. Although the lockdown may have assisted in limiting the spread of the disease, it has brutally affected the country, unsettling complete value-chains of most important industries. The impact of the COVID-19 is devastating on the economy. Therefore, this study has reported about the impact of COVID-19 epidemic on various industrial sectors. In this regard, the authors have chosen six different industrial sectors such as automobile, energy and power, agriculture, education, travel and tourism and consumer electronics, and so on. This study will be helpful for the policymakers and government authorities to take necessary measures, strategies and economic policies to overcome the challenges encountered in different sectors due to the present pandemic.

Abstract Automatic modulation classification is a task that is essentially required in many intelligent communication systems such as fibre‐optic, next‐generation 5G or 6G systems, cognitive radio as well as multimedia internet‐of‐things networks etc. Deep learning (DL) is a representation learning method that takes raw data and finds representations for different tasks such as classification and detection. DL techniques like Convolutional Neural Networks (CNNs) have a strong potential to process and analyse large chunks of data. In this work, we considered the problem of multiclass (eight classes) classification of modulated signals, which are, Binary Phase Shift Keying, Quadrature Phase Shift Keying, 16 and 64 Quadrature Amplitude Modulation corrupted by Additive White Gaussian Noise, Rician and Rayleigh fading channels using 3D‐CNN architectures in both frequency and spatial domains while deploying three approaches for data augmentation, which are, random zoomed in/out, random shift and random weak Gaussian blurring augmentation techniques with a cross‐validation (CV) based hyperparameter selection statistical approach. Simulation results testify the performance of 10‐fold CV without augmentation in the spatial domain to be the best while the worst performing method happens to be 10‐fold CV without augmentation in the frequency domain and we found learning in the spatial domain to be better than learning in the frequency domain.

Internet of Things (IoT) and named data network (NDN) are innovative technologies to meet up the future Internet requirements. NDN is considered as an enabling approach to improving data dissemination in IoT scenarios. NDN delivers in-network caching, which is the most prominent feature to provide fast data dissemination as compared to Internet protocol (IP)-based communication. The proper integration of caching placement strategies and replacement policies is the most suitable approach to support IoT networks. It can improve multicast communication which minimizes the delay in responding to IoT-based environments. Besides, these approaches are playing a most significant role in increasing the overall performance of NDN-based IoT networks. To this end, in this article, the challenges of NDN-IoT caching are identified with the aim to develop a new hybrid strategy for efficient data delivery. The proposed strategy is comparatively and extensively studied with NDN-IoT caching strategies through an extensive simulation in terms of average latency, cache hit ratio, and average stretch ratio. From the simulation findings, it is observed that the proposed hybrid strategy outperformed to achieve a higher caching performance of NDN-based IoT scenarios.

Wireless sensor networks (WSNs) consist of nodes distributed in the region of interest (ROI) that forward collected data to the sink. The node’s location plays a vital role in data forwarding to enhance network efficiency by reducing the packet drop rate and energy consumption. WSN scenarios, such as tracking, smart cities, and agriculture applications, require location details to accomplish the objective. Assuming a 3D application space, a combination of received signal strength (RSS) and time of arrival (TOA) can be helpful for reliable range estimation of nodes. Notably, the anchor node can minimize localization error for non-line-of-sight (NLOS) signals. We proposed an error minimization protocol for localization of the sensor node, assuming that the anchor node’s location is known prior and can limit the receiving signal in LOS, single, or twice reflection. We start to exploit the sensor node’s geometrical relationship and the anchor node for LOS and NLOS signals and address misclassification. We started initially from the erroneous node position, bound its volume in 3D space, and reduced volume with each iteration following the constraint. Our simulation result outperforms the traditional methods on many occasions, such as boundary volume and computational complexity.

In pursuit of the accomplishment of certain position estimations of targets in outdoor places, finding the locations of the targets in indoor environments has been a significant topic. Exact position estimations of the objects for indoor places have potentials for the enhancement of several emerging Internet-of-Things (IoT) applications, such as smart manufacturing, smart home, public security, social networks, transportation, traveling, marketing applications, and information services lead to a huge demand on the designing of low-cost and high-accuracy localization and navigation solutions. On the other hand, the global positioning system (GPS) technology designed for outdoor positioning applications, is not suitable to indoor positioning systems. Making exact position detection with GPS is a compelling problem for indoor positioning methods. In this study, received signal strength (RSS)-based least-squares triangulation approach that utilizes existing infrastructure, is proposed. By increasing the number of access points (APs) and using line fitting algorithms to the RSS values, the triangulation method improves the certainty of location estimation. The utilization of the existing infrastructure turns the proposed approach into cheaper when compared to existing localization methods which require expensive components. The proposed least-squares lateration algorithm is compared with pure lateration (PL) in terms of accuracy error under different Gaussian noise parameters for varying number of APs and varying dimensions of the measurement area. Usage of the least-square algorithm with line fitting approaches provides significant performance improvements for all cases when it compared with PL.

The Internet of Things (IoT) is growing day by day, and new IoT devices are introduced and interconnected. Due to this rapid growth, IoT faces several issues related to communication in the edge computing network. The critical issue in these networks is the effective edge computing IoT device selection whenever there are several edge nodes to carry information. To overcome this problem, in this paper, we proposed a new framework model named SoftSystem based on the soft set technique that recommends useful IIoT devices. Then, we proposed an algorithm named Softsystemalgo. For the proposed system, three different parameters are selected: IoT Device Security (IDSC), IoT Device Storage (IDST), and IoT Device Communication Speed (IDCS). We also find out the most significant parameters from the given set of parameters. It is evident that our proposed system is effective for the selection of edge computing devices in the IoT network.