Cyber-Physical Systems (CPS) integrated with Cognitive Radio Sensor Networks (CRSNs) present significant opportunities for intelligent IoT applications but also faces substantial challenges in achieving optimal energy efficiency while maintaining seamless performance. The resource-limited and heterogeneous characteristics of CPS-enabled CRSNs creates a complex computational and communication for real-time 6G applications. To address these challenges, we propose a novel hybrid machine learning framework integrating Random Forest (RF) and Neural Networks (NN) specifically designed for CPS-oriented CRSNs. The hybrid model improves classification accuracy and energy optimization by incorporating RF predictions as additional input features for NN by leveraging the complementary strengths of both techniques. The proposed framework is evaluated and compared with existing machine learning approaches including Support Vector Machines (SVM), Decision Trees (DT), and K-Nearest Neighbors (KNN) in simulated CPS environments using synthetic datasets. The proposed work’s results demonstrate that the hybrid model consistently outperforms traditional methods in terms of accuracy, energy consumption, and classification reliability, validating its effectiveness for energy-efficient communication in CPS-driven CRSN applications across diverse IoT scenarios.

Cognitive Radio Sensor Networks (CRSNs) have emerged as a critical enabler for IoT-enabled healthcare by dynamically optimizing spectrum use in real-time, yet their adaptability introduces vulnerabilities to internal and external attacks. This paper presents a novelintent-based trust management frameworkthat ensures security, reliability, and efficiency in CRSN deployments within sensitive healthcare scenarios. The proposed model integrates multi-dimensional trust metrics—behavioral, data-driven, and historical—combined with adaptive weighting and dynamic trust updates to fortify cooperative spectrum sensing and isolate malicious nodes. A comprehensive system model encompassing threats such as spectrum sensing data falsification (SSDF), Sybil, and impersonation attacks is mathematically formulated and validated through extensive simulations. Quantitative evaluations demonstrate significant performance improvements over existing trust and reputation management schemes. The proposed framework achieves an average detection probability (PD) exceeding 0.9 with a 40% reduction in convergence time, while lowering the false alarm rate (PFA) to below 0.05. The energy efficiency is enhanced by approximately 18% compared to hierarchical or Bayesian TRM models, owing to selective trust updates and exclusion of unreliable nodes. Moreover, network trust stabilization occurs 25–30% faster under adversarial conditions, ensuring continuous reliability in health-critical data transmission. These findings underline the feasibility and scalability of the intent-driven approach for building secure, adaptive, and resource-efficient CRSNs for IoT-based healthcare systems.

This research introduces the Density-Clustering based Aggregation for Personalized Federated Learning (DCPFL) algorithm, which utilizes DBSCAN clustering to enhance model accuracy in AI-enabled aerial and edge computing contexts, particularly for UAVs. The DCPFL framework promotes model sharing among clients, fostering the development of personalized and optimized models. DBSCAN is beneficial in automatically determining cluster numbers using EPS neighborhoods and MinPts, with parameter optimization achieved through cross-experimental analysis. We further refined the model exchange mechanism by integrating a moving average prediction model to optimize the timing of these exchanges. Tests conducted on three public datasets covering two different machine learning tasks show that DCPFL surpasses existing methods, offering greater accuracy and enhanced adaptability in varied data environments. Implementing this algorithm in UAV networks leverages AI capabilities in aerial and edge computing to efficiently balance personalized modeling requirements with high performance, showcasing its potential to push federated learning forward in complex and dynamic settings.

In the era of Industrial Internet of Things (IIoT) 5.0, recognizing emotions through speech plays a crucial role in creating advanced and emotionally intelligent systems for better human-machine interactions (HMI) for various IoT applications. These systems are especially valuable in smart manufacturing environments and their respective applications. The proposed research showcase the challenge of improving speech quality for reliable speech emotion recognition (SER) in noisy industrial settings by introducing an Improved Modulation Spectral Subtraction (IMSS) method. The IMSS technique enhances traditional Analysis Modification Synthesis (AMS) frameworks with refined processing in the modulation domain, utilizing advanced noise estimation approaches like the Minimum Statistics (MS) method. To recognize emotions, the study employs a machine learning algorithm based on a convolutional neural network (CNN). The proposed algorithm processes the enhanced speech signals to accurately detect emotional states in speech. The combination of the IMSS method with the CNN model ensures that emotional details in speech are retained, which is essential for effective SER. The proposed technique significantly improves speech clarity and quality, evaluated through objective measures such as the Perceptual Evaluation of Speech Quality (PESQ). The experimental results show notable improvements in speech quality, with an average 14.91% increase in PESQ scores for input signal-to-noise ratios (SNRs) between 0 and 15 dB, along with a 63% reduction in Log Spectral Distance. These findings highlight the method’s effectiveness under various noisy conditions. Spectrogram analysis further demonstrates the IMSS method’s ability to enhance the accuracy and reliability of SER, which is reinforced by the strong performance of the CNN in classification tasks. By optimizing the modulation frame duration to 128 milliseconds, the work approach provides a valuable contribution to adaptive and safety-oriented IIoT 5.0 applications.

The emerging field of smart healthcare has identified emotion detection as a key component in improving patient care, diagnostics, and therapeutic interventions. This paper introduces an innovative approach to emotion detection within the healthcare domain by integrating a Convolutional Neural Network (CNN) with a Maximum A Posterior (MAP) estimator prepared for Magnitude-Squared Spectrum (MSS) analysis. The effectiveness of CNN’s advanced feature extraction capabilities with the statistical strength of MAP estimation offers a promising avenue for interpreting complex physiological signals. The proposed methodology aims to accurately discern and quantify emotional states, thus contributing to the personalization and effectiveness of healthcare services. To validate the efficacy of this approach, the work conducted extensive experiments on a diverse data set composed of physiological signals, demonstrating that the proposed model outperforms existing limitations in emotion recognition tasks. The integration of MSS into CNN frameworks, added with MAP estimation, provides a significant improvement in the detection and analysis of emotions, resulting in more responsive and intelligent healthcare systems. This proposed paper not only presents a novel methodological contribution, but also demonstrates the groundwork for future research toward the intersection of emotional intelligence and healthcare technology.

This comprehensive abstract of the special issue presents an extensive array of collections of research studies that focus on the integration of state-of-the-art technologies and methodologies to advance healthcare services in smart cities through Internet of Things (IoT) applications. The studies explore innovative solutions across multiple aspects of healthcare, including privacy preservation, telemedicine, smart healthcare systems, security, abnormality detection, functional assessment, feature selection, health monitoring, diagnostics, medical vehicle routing, behavioral patterns discovery, federated learning, and personalized healthcare.

Abstract The production rate of crops is significantly declining due to natural disasters, animal interventions and plant diseases. Internet of things (IoT) and wireless sensor networks are widely applied in crop field monitoring systems to observe the quality of each plant and the field. This work proposes IoT based crop field protection system (ICFPS) that monitors and protects the crop fields from animal intrusions. This proposed system uses ultrasonic sensors, hyperspectral cameras, voice recorded buzzers and other agriculture sensors to protect the entire crop field. This system uses numerous sensor nodes and cameras for gathering field objects (images and environmental objects). The proposed ICFPS creates deep learning techniques such as recurrent convolutional neural networks (RCNN) and recurrent generative adversarial neural networks (RGAN) for feature extraction, disease detection and field data monitoring practices. This proposed work develops a smart city‐based agriculture system using cognitive learning approaches. This proposed system analyses crop field data and provide automatic alerts regarding animal interferences and crop diseases. Moreover, the cognitive smart crop field system observes various field conditions which support for good production rate. In this system, sensors and camera‐enabled agriculture drones are coordinated with each other to collect the field data regularly. At the same time, the proposed work trains the RCNN and RGAN units using effective crop field datasets to attain realistic decisions within minimal time intervals. The experiment details and results show the proposed ICFPS works with 8%–10% of more classification accuracy than existing systems.

Data security and resource allocation are two important terms associated with the Internet of Things (IoT). This recent technical evolution has made its mark in industrial applications making the network more flexible and computation friendly through connecting all the devices. As a subset of IoT, the framework of Industrial IoT (IIoT) is based on the huge number of nodes with the continuous process of multiple works at a time. Due to this, multiobjective network, interference in the path always becomes the reason for the loss of network resources as well as the security of data becomes vulnerable. In most of the previous works, dedicated channel states are considered for fixed resources which remains a major issue of IIoT network flexibility along with security. In this article, both the problems are incorporated by calculating the channel security and using convolutional neural network (CNN) optimal channel state extracted for different applications. This results as a fast system with proper utilization of resources and validated with mathematical analysis and simulations.

Ensuring security and privacy in the Internet of Things (IoT) while taking into account the resource-constrained nature of IoT devices is challenging. In smart home (SH) IoT applications, remote users (RUs) need to communicate securely with resource-constrained network entities through the public Internet to procure real-time information. While the 6LoWPAN adaptation-layer standard provides resource-efficient IPv6 compatibility to low-power wireless networks, the basic 6LoWPAN design does not include security and privacy features. A resource-efficient authenticated key exchange (AKE) scheme becomes imperative for 6LoWPAN-based resource-constrained networks to render indecipherable communication functionality. This article presents a lightweight user AKE scheme for 6LoWPAN-based SH networks (LAKE-6SH) to achieve authenticity of RUs and establish private session keys between the users and network entities by employing the SHA-256 hash function, exclusive-OR operation, and a simple authenticated encryption primitive. Informal security validation illustrates that LAKE-6SH is protected against different pernicious security attacks. The security is further validated formally through the random oracle model. Moreover, through Scyther validation, it is demonstrated that LAKE-6SH is secure. In addition, it is demonstrated that LAKE-6SH renders better security features aside from its low communication and computational overheads.

This study contributes to our understanding of how the emergence of the COVID-19 pandemic changes the global Banking Financial Services and Insurance (BFSI) landscape. Before the COVID-19 pandemic, BFSIs corporate strategy was solely aligned to the quest for operational efficiency. However, during the ongoing COVID-19 pandemic, global BFSIs are forced to adopt digital transformation in their operations due to a rise in transaction volumes. The ongoing COVID-19 pandemic already triggers holistic innovations concerning the global BFSI's product, process, concept, trend, or idea. Thus, the BFSI cannot survive without efficient and innovative system software for global operations. The study plots the hype cycle to identify relevant technologies to deal with real-world business problems. The hype cycle indicates that the need for advanced data integration is growing and COVID-19 pandemic has already triggered it. The study argues that the incorporation of data integration might be challenging initially for BFSIs but eventually it may result in an efficient model to handle these types of pandemic or unexpected circumstances.

Industrial Internet of Things (IIoT) paves way into Industry 5.0, which incorporates human–machine collaboration, thereby making manufacturing industry efficient. As 5G architecture supports massive IoT connectivity and has higher spectrum efficiency, device-to-device (D2D) communication is favorable at 28 GHz. While transmitting data from sensors to end user through IoT network, interference affects the system. Thus, an efficient resource allocation scheme is needed for minimizing interference and increasing data rate. Here, formulated problem is divided into two subproblems, channel assignment and power optimization in order to lower computational complexity. A partial resource multiplexing scheme is proposed that will allocate channels to available D2D users. Later, power optimization problem is formulated which is determined through Lagrangian dual optimization technique. Dynamic sectorization overcomes issue of increase in user traffic. Stability factor, fairness index (FI), and energy efficiency depict the performance superiority of proposed scheme over existing schemes. Simulation results prove efficacy of proposed system.

In this article, we develop a novel approach for detecting patients’ position using the radial basis function of the neural network. This new approach aims to continuously monitor the patients’ health statistics and real-time prediction, even when they are outside of cellular coverage. Our research is driven by an initiative to innovate a novel healthcare system of significant importance for intelligent and efficient medical services. For example, doctors need to remotely monitor any patient’s health with the provided health statistics derived from data collected from battery-powered Internet of Things sensors. To this end, our proposed method has been quantified with a holistic mathematical analysis and extensive simulations considering realistic network situations. Our results have accredited the efficiency in the prediction of localization for the patients’ position for the anticipated intelligent healthcare system.

In the healthcare research community, Internet of Medical Things (IoMT) is transforming the healthcare system into the world of the future internet. In IoMT enabled Computer aided diagnosis (CAD) system, the Health-related information is stored via the internet, and supportive data is provided to the patients. The development of various smart devices is interconnected via the internet, which helps the patient to communicate with a medical expert using IoMT based remote healthcare system for various life threatening diseases, e.g., brain tumors. Often, the tumors are predecessors to cancers, and the survival rates are very low. So, early detection and classification of tumors can save a lot of lives. IoMT enabled CAD system plays a vital role in solving these problems. Deep learning, a new domain in Machine Learning, has attracted a lot of attention in the last few years. The concept of Convolutional Neural Networks (CNNs) has been widely used in this field. In this paper, we have classified brain tumors into three classes, namely glioma, meningioma and pituitary, using transfer learning model. The features of the brain MRI images are extracted using a pre-trained CNN, i.e. GoogLeNet. The features are then classified using classifiers such as softmax, Support Vector Machine (SVM), and K-Nearest Neighbor (K-NN). The proposed model is trained and tested on CE-MRI Figshare and Harvard medical repository datasets. The experimental results are superior to the other existing models. Performance measures such as accuracy, specificity, and F1 score are examined to evaluate the performances of the proposed model.

The future advancement of technology in Internet of Things (IoT) paradigm, Wireless Sensor Networks (WSNs) provide sensing services to connect all the devices. In the upper layer of OSI model designing an energy efficient routing protocol in WSN is a challenge, which can ease the work of Multi-access edge computing (MEC) in IoT applications. The advent of 6G is also playing key role for reliable communication between the sensing elements for IoT applications. These two phenomena are significantly influencing for the progress of next generation Intelligent Transportation System (ITS). Therefore, the proposed work presents a novel method of implementing Distributed Artificial Intelligence (DAI) with neural networks for energy efficient routing as well as a fast response for intra-cluster communication of the nodes to overcome the challenges for ITS. Although there exist several works on the inter-cluster energy-efficient network, our work proposes a new way of implementing the hybrid approach of DAI and Self Organizing Map (SOM). The proposed approach proves to be a better solution in terms of overall energy consumption by the network, along with the computational challenges. Further, the work presents mathematical analysis, simulation results and comparison with the conventional techniques for justification.

The birth of beyond 5G (B5G) and emerge of 6G have made personal and industrial operations more reliable, efficient, and profitable, accelerating the development of the next-generation Internet of Things (IoT). We know, one of the most important key performance indicators in 6G is smart network architecture, and in massive IoT applications, energy-efficient ubiquity networks rely mainly on the intelligence and automation for industrial applications. This article addresses the energy consumption problem with a massive IoT system model with dynamic network architecture or clustering using a multiagent system (MAS) for industrial 6G applications. The work uses distributed artificial intelligence (DAI) to cluster the sensor nodes in the system to find the main node and predict its location. The work initially uses the backpropagation neural network (BPNN) and convolutional neural network (CNN), which are, respectively, introduced for optimization. Furthermore, the work analyzes the correlation of mutual clusters to allocate resources to individual nodes in each cluster efficiently. The simulation results show that the proposed method reduces the waste of resources caused by redundant data, improves the energy efficiency of the whole network, along with information preservation.

Optical cloud radio access network (O-CRAN) is an emerging solution for IIoT, where numerous different devices/nodes are networked together. O-CRAN provides pool of shareable computing facility, equipped with hundreds of general-purpose processor (GPP). The GPPs process massive big data exerted by nodes via remote radio heads (RRHs), regarded as RRH-requests, which are bandwidth-intensive and deadline-constrained digitized base-band signals. Computing resource (CR) optimization has been widely investigated in O-CRAN. However, the existing optimizations may not guarantee workload and thermal balance among the active GPPs while satisfying RRH-request's deadline, which are necessary to efficiently leverage virtualization GPP capacity in a manner that provides the greatest uniform CR utilization (CRU). Due to varying network-load a single optimal solution does not exist. Therefore, in this article, we propose a modified-first-fit decreasing (MFFD) algorithm to obtain a suboptimal solution for each time_stage. The MFFD evenly assigns RRH-requests among GPPs that maximizes individual CRU uniformly contrasting with FFD.

Wireless sensor network enhances the classic features of wireless communication with cognitive capabilities for efficient spectrum usage. This work focuses on the dynamic correlation between the secondary users (SUs) based on their statistical behaviour while performing the cooperative communication in cognitive radio sensor network. The proposed approach addresses the problem of uneven and repetitive communication between the SUs in a cooperative communication scenario. The authors’ objective is to use a novel approach based on the Gaussian copula theory and advanced particle swarm optimisation algorithm to analyse the dependencies of time‐varying spectrum sensing behaviour of multiple SUs. Here, time delay in prediction reduces due to the analysis of the dynamic correlation between the time delay in spectrum sensing results for the same set of channels. The simulation results show the performance of the proposed approach outperforming the other well‐known techniques.