Modern quantum computing technologies endanger the security mechanisms of RSA and ECC used to protect IoT-based healthcare systems. This research introduces a privacy-protecting healthcare data infrastructure that uses post-quantum lattice cryptography along with the Paillier scheme in order to secure IoT exchange and protected data processing. Vital signs of IoT devices reach a central server through a secure system which also allows data encryption and processing outside the server. Our simulation models a medical institution containing 200 IoT devices to test the framework’s operation regarding encryption delays together with aggregated data precision and quantum resistance capabilities. Experiment results demonstrate that the framework raises quantum breach resistance from R ≈ 0.985 to R′≈ 0.3. Future work will focus on real-time optimization to improve the system performance despite the existing performance bottlenecks from Paillier’s encryption.

Intrusion detection in self-regulating manufacturing requires light yet precise modes to do real-time threat prevention. The performance of TinyML, 1D-CNN, GRU, and LSTM models is tested based on different metrics in this research. TinyML works better than all models with 97% accuracy and log-loss as low as 1.2, which is an indication of confident predictions. One of the significant aspects of TinyML is improved accuracy, and decreased sensitivity, with increased false negative and false positive rates also making it more useful in resource-constrained areas. For natural language generation tasks, Mixtral 8x7B 32768 shows the highest BLEU score of 0.60 and METEOR score of 0.86, reflecting its high similarity with reference outputs. It also scores the highest in ROUGE-1 (0.77) and ROUGE-2 (0.75), providing high-quality phrase-level recall. These results confirm that TinyML is the most effective for intrusion detection, while Mixtral 8x7B 32768 excels in text generation tasks.

This paper introduces a secure federated learning (FL) system aimed at detecting anomalies in the communication of maritime autonomous surface ships (MASS). The model effectively distinguishes between normal and anomalous nano-traffic while safeguarding data privacy among distributed clients. To bolster security, Quantum Key Distribution (QKD) protocols, such as BB84 and E91, are utilized for encrypted key exchange during ship-to-ship and ship-to-ground interactions. The suggested framework demonstrates consistent performance enhancement, achieving individual client accuracies of 0.77, 0.79, and 0.81, with the global model reaching an accuracy of 0.83. Moreover, the global model’s loss significantly reduces from 0.92 to 0.55 across five rounds, indicating successful convergence. These findings reinforce the framework’s efficacy in providing secure, decentralized anomaly detection within maritime networks. Future research will focus on incorporating neuromorphic architectures with FL to improve real-time performance in MASS communication.

Telesurgery is a new medical technology wherein the surgeon is situated remotely and operates through computers on surgically interactive robotic equipment connected through high speed communications networks. Optical fibers are necessary for this process as they have low latency and high capacity for realtime control of the process and exchange of data. Nevertheless, transmission is vulnerable to disruption by optical fiber faults which is disruptive to surgery safety and accuracy. This paper introduces TeleOps framework to enhance communication over optical fibers and fault management in telesurgery systems. Deep Learning (DL) models like Feedforward Neural Network (FFNN), 1D Convolutional Neural Network (1D-CNN), and Recurrent Neural Network (RNN) were implemented to detect and classify faults in Optical Time-Domain Reflectometer (OTDR) trace sequences. The RNN with Adam optimization achieved the highest detection accuracy. The proposed TeleOps framework also includes a blockchain-based smart contract to ensure transparent and encrypted tracking of faults, decentralized storage, and entity management in terms of surgical outcomes. This dual strategy provides reliable communication and efficient fault monitoring to minimize downtime of the remote surgical action. Thus the proposed TeleOps framework promises to enable safer and more effective solutions for remote healthcare by significantly enhancing the security and reliability of telesurgery systems.

The study develops an analysis framework built with deep learning techniques that extensively tests various architectures of modern Convolutional Neural Network (CNN) structures. The ResNet-18 model demonstrated the most successful implementation by reaching a training accuracy of 97.56% and validation accuracy of 86.92% at the same time. The corresponding training and validation losses amounted to 0.0754 and 0.6071, respectively. The LIME and Grad-CAM techniques of the XAI, and Occlusion were used to enhance the transparency of the model while providing components to better understand model decisions. The combination of accurate CNN models with interpretability tools produces successful explainable and robust classification in real-world application scenarios.

As new and upcoming technological advancements emerge, the threat of malware will continue to diversify and increase. Malware poses a serious threat to privacy and security of critical data. Some malware do not collect data but use the device's resources for crypto-mining and other activities. Thus, new technology for evading and detecting malware continues to grow and advance. Artificial Intelligence (AI) is one such field that has helped tackle this problem with great precision. Most of the existing solutions use Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and algorithms to make more accurate predictions. However, their solutions are not optimized due to generalized training and higher latency due to large model structures. Inspired by the aforementioned challenge, this paper proposes a distributed learning approach called FedShield to solve the said problem using a Windows Malware dataset. Federated Learning (FL) is one such algorithm that helps to train models distributively, ensuring data security, privacy, scalability, and diversification. Each client model has an ANN that communicates with the global model to update its model via a blockchain layer. The FL model achieves an accuracy of 96 % with 13 clients on the unseen data. Furthermore, the blockchain layer also stores the malicious files in order to make them tamper-proof and secure. The proposed FedShield system is evaluated by comparing it with pre-existing models. The transaction and execution costs in the blockchain for each function are recorded. This approach can help various anti-malware softwares to improve their products.

The emergence of smart healthcare has transformed the delivery of medical services. It increased the reliability issues related to telesurgery system. Robotic technologies integrate to achieve surgical precision in countless medical fields. But, this increased connectivity has raised concerns about vulnerability to denial of service (DoS) attacks that can disrupt operations and affect users' safety. In this paper, we propose an explainable machine learning (ML) framework for detecting DoS attacks in telesurgery environments. Our framework utilizes several ML algorithms, including K-Nearest Neighbors (KNN), Decision Tree (DT), XGBoost (XGB), and Logistic Regression (LR), to analyze network traffic and distinguish between legitimate and malicious signals. Here, XGB achieves the highest accuracy at 0.8967, making it a good choicefor this application. We also used the concepts of Explainable AI (XAI) such as SHAP and LIME to enhance decision-making process of our models. We aim to build trust among healthcare providers and ensures the safe conduct of robotic telesurgery operations and smart healthcare solutions by improving the interpretation of research and providing information on performance metrics such as accuracy, precision, recall, F1 scores, and ROC curves.

The growing use of smart technologies in homes has changed the way we manage power consumption. In this paper, CHEFS an innovative framework is proposed for smart home power consumption analysis, using Explainable AI (XAI) to improve clarity and accuracy with prediction and continuous decision-making by edge computing. Devices like the Internet of Things (IoT) and smart meters are used in smart homes to observe energy usage precisely. Such devices are also used for cutting energy costs and reducing environmental impacts. They often lack clarity in decision-making which makes it difficult, whereas the traditional machine learning models offer accurate predictions. To show this, we use XAI methods like SHAP and LIME to get a better understanding of the power usage patterns and forecasts. Edge computing reduces delays and bandwidth usage while also enhancing response time. This approach enhances clarity and dependability in Artificial Intelligence (AI) models providing reliable insights into household power consumption. A comprehensive evaluation helps create a more transparent and effective system for creating smarter energy solutions.

With the advent of technology, the transfer of sensitive information has become more prone to misuse, especially through unsecured platforms like social media. To address this challenge, UAV communication, particularly drones, has emerged as an alternative for document delivery in highly sensitive areas. But these devices can also get vulnerable to attacks which can cause a serious issue when the information is sensitive. Therefore, we propose a UAV-based secure document delivery framework that leverages AI models to detect potential attacks on the UAVs. The system ensures security throughout the document transfer process by evaluating various drone parameters. We employ the Decision Tree Classifier, which uses entropy to classify the potential threats. The result of the classification is used to guide the decision-making process for a secure delivery process. We further compare the various models based on metrics like accuracy, precision, recall, and$\mathbf{F - 1}$score, demonstrating the effectiveness of our framework, which enhances the security of UAV-based communication systems.

This paper proposes a new deep learning (DL) model for the detection of infant sleep disorders specific to Confusional Arousals (CA), Leg Restlessness (LR), and Sleep Apnea (SA) in 5G smart homes and e-Healthcare systems integration. The proposed framework, CHILD, consists of four critical layers: specific applications such as Infant Monitoring, Sensor and Data Acquisition, Artificial Intelligence systems and e-Healthcare and Smart Home systems. The smart home part increases the effectiveness of real-time environmental detection, the e-Healthcare system helps to provide convenient communication with doctors. The sleep disorder categorization problem is solved using an enhanced deep learning framework involving LSTM and GRU algorithms; the data are from sensors installed in the smart home., we obtained the 88% accuracy of LSTM model in consideration of the home automation and intelligent e-Healthcare system to enhance infant health and response actions.

Telesurgery is transforming healthcare by enabling surgeons to perform operations remotely through robotic systems connected to high-speed networks. The reliability and safety of these procedures depend on seamless communication, often secured using Proof-of-Stake (PoS) blockchain technology to ensure data integrity and validate transactions. However, malicious nodes within PoS blockchain networks pose significant risks by introducing delays, invalidating legitimate transactions, or colluding to compromise the system. This paper proposes a Deep Learning (DL) based framework to detect malicious nodes in PoS-based blockchain applications, ensuring secure and reliable operations. Using a dataset of node activity, DL models—LSTM, 1D-CNN, and FFNN—were trained with optimizers including Adam, Nadam, and RMSprop. Among these, the LSTM model with RMSprop achieved the highest detection accuracy of 87.37%. The framework enhances security by enabling real-time malicious node detection and communication monitoring, addressing key challenges in blockchain integrity and operational precision, ultimately ensuring the security and reliability of blockchain-integrated telesurgical systems.

As industrial robotic systems have become more integral to modern manufacturing, ensuring their cybersecurity and operational efficiency is a crucial part. This paper suggests a multi-layered framework aimed at improving the cybersecurity and operational efficiency of industrial robots. It consists of three layers: the Robot Data Acquisition Layer, which verifies and pre-processes sensor data for safe transmission; the Cybersecurity and Threat Detection Layer, which utilizes AI to classify data as malicious or non-malicious and identify specific types of attacks for targeted countermeasures; and the Data Translation and Execution Layer, which transforms safe data into actionable commands for smooth robot operation. Three models were tested for prediction tasks namely Long Short Term Memory (LSTM), Gated Recurrent Units (GRU) and 1-Dimensional Convolutional Neural Networks (1-D CNN). Using metrics like precision, recall, accuracy, and F1-score the proposed framework performance was measured with an overall accuracy of 97.87% from the experiments. This not only showed an excellent mitigation property against cyberattacks but also robustly improved the data integrity which therefore solved the security threats in industrial automation.

The rapid growth of online financial transactions has made fraud detection a critical priority, especially with evolving fraud strategies that evade traditional systems. This paper presents a novel and secure fraud detection framework integrating Deep Learning (DL), Federated Learning (FL), and Quantum Encryption Communication (QEC). Our approach ensures high fraud detection accuracy while maintaining user data privacy and secure communication. We implemented a 3-layer GRU model using the MOON algorithm under the FL paradigm and achieved a global accuracy of 97.47%, outperforming traditional models like LSTM, 1D-CNN and XGBoost. To secure model parameter exchange between clients and server, we evaluated two entanglement-based Quantum Key Distribution (QKD) protocols — BBM92 and MDI-QKD. Experimental results revealed BBM92 to be more stable and suitable for integration with FL, demonstrating superior average secret key rate (SKR) and lower quantum bit error rate (QBER). The proposed system effectively combines accuracy, privacy, and quantum security, making it a scalable solution for real-world fraud detection.

The transportation system has seen rapid evolution with the coming of Industry 4.0. Self-driving cars and intelligent traffic control have replaced manually driven vehicles and the conventional traffic management system. To enhance their performance, these entities continuously need to exchange data amongst themselves regarding their position, speed, direction, obstacles on the road, traffic signal state and weather conditions. This data can be intercepted by any malicious eavesdropper and the entire smart transportation system can be compromised. To ensure secure and reliable communication among these autonomous vehicles and road infrastructure quantum communication is used. In the proposed Q-ShielD framework, two such quantum protocols are implemented, namely BB84 Quantum Key Distribution (QKD) and the Continuous Variable quantum protocol. The two protocols differ in the way they send the transmitted data. It was found that the Continuous Variable QKD performed better over BB84 in terms of Key Generation Rate, Quantum Bit Error Rate, and Distance covered in regular weather conditions.

In today’s world, the safety of children is of utmost importance due to numerous compelling factors, for example, accidents and injuries. In this fast-paced lifestyle of the new age parents, they might not always be able to accompany their children everywhere therefore the need for the tracking of the child including safety considerations and also the parent’s desire to stay connected with their child and in the absence of the parents the guardian of the child can look up for the safety of the child. This paper presents an implementation of the smart bag for toddlers which is built using blockchain technology and language solidity that ensures the tracking facility of the child. blockchain is the decentralized ledger technology that provides transparency and security between the networks. Therefore we have created a digital contract known as the smart contract on blockchain technology named a smart bag for toddlers. A smart contract is a digital agreement that is signed and stored on the blockchain and executes automatically when its terms and conditions are met.

Unmanned Aerial Vehicles (UAV) are highly versatile systems with applications expanding in various fields, for instance, Surveillance, Reconnaissance, Disaster Response, Agriculture, and many more. Although UAVs have many important advantages over conventional manned aircraft, such as cheaper operating costs and a lower risk for human pilots, their vulnerability to Radio Frequency (RF)-jamming poses a substantial risk to navigation and communication systems by disrupting signals. UAVs depend on remote control and autonomous operations. These attacks are concerning in many application areas. Communication and navigation play crucial roles in these autonomous systems. This paper aims to explore different deep-learning algorithms for the detection of RF-jamming attacks in UAVs. A comparison analysis is conducted to evaluate five distinct architectures using conventional evaluation metrics criteria comprising accuracy, precision, recall, confusion matrices, and the area under the ROC curve. These comparative analyses helped in selecting an accurate architecture for the definition, RNN architecture gave an impressive accuracy of 93% and demonstrated a superior performance than other architectures. This paper aims to strengthen RF-jamming detection systems in UAVs to enhance their safety and operational reliability in several scenarios.

In dynamic defense operations, satellite imagery has a major role in offering crucial insights into geographical landscapes and possible dangers. Satellite imagery provides an aerial view of the battlefield environment, which helps strategically improve decision-making. The proposed framework makes use of deep learning techniques to segment satellite images into particular regions. The image is segmented into different classes, such as land, buildings, roads, vegetation, and water. The combination of the segmentation model with a comprehensive situational awareness framework allows military personnel in analyzing terrain, identification of infrastructure, and detection of anomalies. This helps improve operational effectiveness and rapid response in dynamic battlefield environments.

Analog signal transmission has always been a crucial broadcasting technique, particularly in the early days of television. Even with the development of digital technologies, analog signals remain significant, particularly in locations remote from transmission towers. However, analog transmissions are susceptible to damage from noise and interference, which can reduce the signal-to-noise ratio (SNR). This SignalStats explores machine learning-based statistical analysis along with techniques like Adequacy Tweak (AM) and Recurrence Tweak (FM) to optimize interference control in analog stations. A multitude of factors, such as air quality, topography, and transmitter distance, influence signal quality. The project emphasizes data collection and preprocessing approaches in order to enable spatial analysis and visualization to understand station distribution and service provider dominance. Moreover, statistical analysis is used to assess the effectiveness of the signal, channel usage, and ERP. The SignalStats findings provide valuable insights for developing interference control tactics, which in turn improves the efficiency of analog communication networks in the face of rapidly changing technological environments.

Integration of the Internet of Things (IoT), intelligent sensor networks, and patient-centric modules has successfully revitalized the way we pursue healthcare services. Seamless online doctor-patient communication facilities have vanished the partial line between traditional physical on-site treatment and current remote monitoring. In the Healthcare 4.0 environment, patients can connect with doctors via video conference, send audio transcripts as responses, and share pictorial or text-based vital information. One of the major concerns related to remote healthcare treatment applications is the efficient utilization of networks for data transmission. To mitigate this paramount challenge, we propose SDN care. It is a Deep Learning(DL)-based SDN-enabled network classification approach to facilitate seamless and secured communication between doctors and patients in the Healthcare 4.0 ecosystem. SDN care relies on one-dimensional Convolutional Neural Network (CNN) architecture to efficiently classify the type of data under communication and make adjustments in SDN parameters. The latency, bandwidth, jitter, etc., are adjusted based on prediction from the CNN model for effective utilization. Proposed SDN-care is further compared with Artificial Neural Network (ANN), and it also has been examined using different types of optimizers. The performance evaluation of SDN-care has been done through various metrics such as accuracy, loss convergence, precision, recall, f1 score, Receiver Operating Characteristic (ROC) curve, precision-recall trade-off curve and compared with different optimizers such as Adam, SGD, RMSprop, and Adadelta. Thus, SDN care introduces significant advancements in SDN-enabled remote patient-doctor communication environments using various modes of data exchange.

Parkinson’s disease (PD) is a neurodegenerative disease which is the second most common neurological disease. Early diagnosis of PD poses significant challenges as in earlier stages of PD, symptoms can’t be clinically recognized. This paper presents a framework called XSH-ParK with integrated deep learning (DL) models and XAI techniques to assist in early PD diagnosis using MRI scans. Pre-trained models VGG16, InceptionV3, ResNet50 and a custom CNN are used to analyze the NTUA dataset, which consists of MRI scans of 78 individuals. Through rigorous evaluation considering accuracy, precision, recall, and F1-score metrics, it is evident that the fine-tuned VGG16 model achieves the highest efficiency with an accuracy rate of 97.56% in the XSH-ParK framework. Additionally, LIME and integrated gradient are the XAI methods used on the top-performing VGG16 model to provide transparent and interpretable diagnostic insights, Enabling healthcare professionals to understand the reasons behind the models’ decisions.

This paper introduces a novel telehealth communication system, designed to enhance the security and integrity of medical data exchange. In the rapidly evolving digital healthcare landscape, the protection of sensitive patient information is paramount. To address this, our system uniquely combines quantum cryptography, specifically the BB84 protocol, with blockchain technology, offering a dual-layered security framework. The Quantum Layer, underpinned by the BB84 protocol, establishes quantum-secure communication channels, effectively encrypting data exchanges between patients, doctors, and hospitals. This layer guarantees that medical information remains confidential and safe from potential quantum-level eavesdropping threats. The subsequent Blockchain Layer further strengthens the system by storing these encrypted communications in an immutable blockchain ledger. This approach not only secures the data against unauthorized alterations but also provides a transparent and permanent record of all transactions, thereby enhancing the auditability of medical communications.

In smart communities, Electric vehicles (EVs) have grown in popularity as a key component of the energy ecosystem where the focus has turned to the generation of clean, sustainable energy. The integration of EVs, charging stations (CS), and smart grids (SG), however, poses significant challenges in terms of energy trading (ET) optimization and profit maximization. Next, trust is another challenge in the ET ecosystem among the communicating entities (EVs, CS, and SG) to buy and sell energy. Recent studies have overlooked the fact of ET among CS and SG, and mostly have focused on ET by EVs. However, at peak loads, SG may experience bottlenecks in energy dissipation, and thus excess energy collected by CS from EVs might be traded to SG to manage loads during peak times. So, we propose a framework, StackGrid, that leverages the capabilities of Vehicle-to-Grid (V2G) systems over a blockchain network. We design a Stackelberg game between CS and SG for profit maximization of both parties and to obtain optimal payoff equilibria. The framework is powered over the 5G ultra-reliable low latency communications (uRLLC) service for real-time ET response and data exchange. To address blockchain scalability concerns, we incorporate Interplanetary File Systems (IPFS) as local off-chain ledgers, where only meta-information is stored on-chain to handle blockchain scaling issue. The framework is evaluated on metrics like 5G service latency, optimal payoff scenario, attack probability, and node throughput. The obtained results indicate StackGrid viability in real ET setups, with benefits for sustainable and efficient energy management.

The rise of vehicular networks has ushered in the era of vehicle-to-everything (V2X) communication aimed at bolstering driving safety. A pivotal aspect of V2X communication is its role in facilitating emergency warning systems. This study focuses on the propagation of emergency messages and the provisioning of emergency services through V2X communication while considering the efficient allocation of resources necessary for effective vehicle communication. The primary objective of resource allocation within the context of Cellular-Vehicle-to-Everything (C-V2X) is to optimize the utilization of available resources, amplify system capacity, and address the diverse communication requisites within the confines of system constraints. A notable challenge in C-V2X resource allocation resides in the judicious allocation of spectrum resources and broadcast opportunities to V2X users. Therefore, we present NeuroEvolution of Augmenting Topologies (NEAT)-based efficient channel allocation within the C-V2X framework. Our approach aims to maximize the sum rate and throughput of emergency service vehicles (ESV) while ensuring the attainment of a minimum threshold throughput for other vehicles. Subsequently, we conduct a comparative analysis between the average sum rate achieved by the NEAT algorithm and a random resource allocation scheme. Furthermore, we undertake a comparative assessment of the time complexity of NEAT in contrast to other state-of-the-art techniques employed for channel allocation. These techniques encompass the graph matching algorithm, the Hungarian, and the brute force method. Our proposed C-V2X model demonstrates superior performance across various evaluation metrics compared to various alternative algorithms.

The advent of healthcare 4.0 has catalyzed a paradigm shift in medical practices, ushering in innovative approaches such as telesurgery, a groundbreaking method for remote patient surgery and monitoring. This transformative technique extends beyond traditional surgeries, finding application in dermatological procedures. The success of telesurgery in skin-related surgeries hinges on accurate and efficient skin cancer detection using dermoscopic images. Recognizing the inherent complexities in interpreting deep learning models, especially in the context of healthcare, Explainable Artificial Intelligence (X-AI) becomes imperative. In this context, we propose a novel CNN-powered X-AI mechanism i.e., X-CaD, tailored for skin cancer detection in telesurgery environments, leveraging ResNet and MobileNet for feature extraction. To enhance interpretability and bridge the gap between model predictions and clinical decision-making, we employ X-AI techniques such as Local Interpretable Model-agnostic Explanations (LIME) and Integrated Gradient (IG). LIME provides granular insights into model predictions, elucidating decision-making processes, while IG offers a comprehensive view of feature attributions. X-CaD relies on the synergistic integration of advanced CNN architectures, i.e. ResNet and MobileNet along with X-AI techniques to identify skin cancer accurately and provide clinicians with clear insights. The effective impact of X-CaD is evaluated through the observed loss and accuracy values for the DL models, and heat map outputs for X-AI. This represents a significant advancement in the integration of state-of-the-art technology and healthcare, offering a dependable telesurgery solution for the diagnosis of skin cancer in surgical procedures.

With the changing lifestyle of people, they are getting more prone to various diseases, some of which might prove to be dangerous and can cause serious health problems. The chances of getting brain tumors are increasing, with new alarming cases being found every day, and the detection and classification for further surgery are very crucial and should be done without due. Therefore, we propose here a Deep Learning (DL) CNN-based brain tumor classification framework that helps in analyzing the image and classifying it in its respective type. In addition, we integrate the blockchain technology to securely store the data, make it easily accessible at the time of need, and prevent it from malware practices. We have used the IPFS protocol in order to make our framework cost-efficient. The results of various DL models are compared and a CNN-based framework is proposed. The CNN-based model demonstrated an exceptional performance by achieving an accuracy of 97.91%.

The energy market has witnessed the drastic penetration of on-road electric vehicles (EVs), which serve as a robust and energy-efficient solution for mitigating the high cost, high energy consumption, and environmental pollution challenges of fossil-fuel vehicles. However, the huge energy demand of EVs needs to be coordinated for charging based on the available charging infrastructure, considering the critical factors of fluctuating prices and distance corresponding to the charging station (CS) and energy demand of the EVs. Therefore, we propose an Artificial Intelligence (AI)-based EV charging allocation scheme by performing the CS selection considering the EVs state of charge (SoC). We have applied the neural network model to predict the EV SoC to check their battery level for scheduling purpose. Further, we have formulated various CS selection scenarios based on the price and distance by assigning priority to the scenarios to enable efficient EV scheduling. Finally, the proposed scheme is evaluated considering various performance metrics such as loss curve, MAE curve, and SoC prediction plot in which adam optimizer yields the best result for the neural network model.

Summary In recent years, smart healthcare, artificial intelligence (AI)‐aided diagnostics, and automated surgical robots are just a few of the innovations that have emerged and gained popularity with the advent of Healthcare 4.0. Such technologies are powered by machine learning (ML) and deep learning (DL), which are preferable for disease diagnosis, identifying patterns, prescribing treatments, and forecasting diseases like stroke prediction, cancer prediction and so forth. Nevertheless, much data is needed for AI, ML, and DL‐based systems to train effectively and provide the desired outcomes. Further, it raises concerns about data privacy, security, communication overhead, regulatory compliance and so forth. Federated learning (FL) is a technology that protects data security and privacy by limiting data sharing and utilizing model information of distributed systems to enhance performance. However, existing approaches are traditionally verified on pre‐established datasets that fail to capture real‐life applicability. Therefore, this study proposes an AI‐enabled stroke prediction architecture consisting of FL based on the artificial neural network (ANN) model using data from actual stroke cases. This architecture can be implemented on healthcare‐based wearable devices (WD) for real‐time use as it is effective, precise, and computationally affordable. In order to continuously enhance the performance of the global model, the proposed FL‐based architecture aggregates the optimizer weights of many clients using a fifth‐generation (5G) communication channel. Then, the performance of the proposed FL‐based architecture is studied based on multiple parameters such as accuracy, precision, recall, bit error rate, and spectral noise. It outperforms the traditional approaches regarding accuracy, which is 5% to 10% higher.

Abstract Quantum machine learning (QML) is an emerging field that combines the power of quantum computing with machine learning (ML) techniques to solve complex problems. In recent years, QML algorithms have shown tremendous potential in various applications such as image recognition, natural language processing, health care, finance, and drug discovery. QML algorithms aim to reduce computation costs and solve complex problems beyond the scope of classical machine learning algorithms. In this article, we study the performance of two QML algorithms, that is, quantum support vector classifiers (QSVC) and variational quantum classifiers (VQC), for chronic heart disease prediction in Healthcare 4.0. The performance of the two classifiers is assessed using different evaluation metrics like accuracy, precision, recall, and F1 score. The authors concluded the superior performance of QSVC over VQC with an accuracy of 82%.

Over the last decades, the healthcare industry has been revolutionized heavily, especially after the Covid-19 surge. Various artificial intelligence (AI) approaches have also been explored during this era for their applicability in healthcare. However, traditional AI techniques and algorithms are prone to overfitting with minimal robustness to unseen or untrained data. So, there is a need for new techniques which can overcome the issues mentioned earlier. Federated learning (FL) can help design specific AI services for the network of hospitals with less overfitting and more robust modules. However, with the inclusion of FL, the problem related to user privacy is the biggest challenge, making the use of FL in the real world a grand challenge. Most solutions presented in the literature used blockchain technology to mitigate the issues mentioned earlier. However, it prevents third-party systems from penetrating the decision process, but the network devices can access shared data. Moreover, blockchain implementation requires new paradigms and infrastructure with an additional overhead cost. Motivated by these facts, the paper presents a limited access encryption algorithm incorporating FL (LEAF) framework, i.e., an encryption technique that solves privacy issues with the help of edge-enabled AI models. The proposed LEAF framework preserves user privacy and minimizes overhead costs. The authors have evaluated the performance of the LEAF framework using extensive simulations and achieved superior results. The achieved accuracy of the proposed LEAF framework is 3% higher than that of the traditional centralized and FL-based systems without compromising user privacy. In the best scenario, the proposed framework’s encryption process also compresses the data size by 4–5 times.

The emergence of massive automation has transformed Industrial Internet-of-Things (IIoT) to become adaptive, self-healing, and autonomous. In IloT, the increased volume of data traffic has raised questions about the privacy and security of shared sensor data, resource management, the accuracy of trained models, and the authenticity of network traffic in operation. Thus, conventional security paradigms and centralized learning models are outdated to support the IloT operational space. Modern solutions like federated learning (FL) and onion routing (OR) are integrated into IloT to secure and optimize link communication and improve the computational requirements of central model training. Thus, the paper integrates FL and OR in IloT, and presents a framework FedOnion, where federated classifiers are proposed at intermediate OR circuits, which preserves anonymity and privacy of data sharing among nodes in IloT. In this frame-work, an FL-assisted network traffic classification approach is presented for malicious or non-malicious data requests forwarded to the OR network. Malicious requests are discarded at the next onion router, and it prevents the shared key from getting compromised, as the hash is computed at each hop to signify that data is not tampered with. FL-classifiers divide the overall dataset into small segments, which alleviates the computational burden on OR links and improves the detection rate of malicious data requests. The proposed framework's effectiveness is demonstrated on real-world IloT datasets, based on security and computational parameters. The obtained results indicate the practical viability of the scheme for critical industrial setups which paves the way towards a robust and secured industrial future. The proposed framework is assessed using different performance parameters, such as FL MSE$(10^{-9}$at 300 epochs), onion circuit compromisation rate (16%), and 5G modulation scheme.

With the tremendous emergence of the Industrial Internet of Things (IIoTs) in varied industrial sectors, software-defined networking (SDN) has become a prominent paradigm to improve the scalability, latency, and response time of large-scale IIoT networks. However, despite the enhanced network performance of the IIoT networks, efficient and optimal load balancing and distribution is still one of the critical aspects of the SDN paradigm that can overburden the controllers further degrading the system performance. To address the aforementioned issue, we have utilized the game theory approach to enable the distributed, optimal, and efficient load balancing among multiple SDN controllers considering the IIoT systems over a 5G communication network. The proposed approach focuses on balancing the load state, i.e., idle or medium loaded, of the controllers with the help of employed coalition game theory to evenly and optimally distribute the load between controllers by providing them incentives fairly. For that, we have determined two Nash equilibrium using formulated strategies based on the load state of the controllers. Finally, the proposed game theory approach for load balancing is evaluated, considering various performance metrics such as overall convergence utility, packet error rate, and fairness.

Fifth-generation (5G) offers more advanced and promising wireless communication technology as Device-to-device (D2D) communication. It refers to the direct data exchange between two users' equipment in a wireless network without routing their data through the base station. The close proximity of the devices offers a higher data rate with low communication latency and increases spectral efficiency. Despite the advantages mentioned above, there are still some challenges, such as interference, power control, and security, that need to be addressed concerning D2D communication. There exist many game theory-based solutions for efficient resource allocation. However, they face issues when there are many users in the communication environment. Hence, we proposed artificial intelligence (AI) and game theory-based solutions for efficient resource allocation in this paper. Initially, we proposed different machine learning (ML) classifiers, such as isolation forest (IF), support vector machine (SVM), gradient boosting (GB) classifier, K-nearest neighbours (KNN), and Gaussian naive Bayes (GNB) that select best D2D users. Then, we formulate a coalition game that gives efficiently allocates resources to the best-selected D2D users. Further, we considered different performance evaluation parameters, such as accuracy, validation loss, sum rate, and convergence rate. The empirical results represent that the GB classifier achieves the highest accuracy, 98.23%, because it trains faster with the large dataset size, and the coalition game-based approach maximizes the overall system sum rate for efficient resource allocation in D2D communication.

M2M communication in the Industrial Internet of Things is still in its infancy as the information exchange between machines is hindered by various modern security challenges and threats. An attacker can leverage the M2M communication by exploiting it with resource exhaustion, data integrity, and injection attacks. In this article, to address the aforementioned security issues, we first employed a long short-term memory based AI model on the edge servers to classify the machines' malicious and nonmalicious message requests and forwarded them to the onion routing (OR) network. Then, to enhance the security and reliability of the conventional OR network, we have associated it with blockchain technology by incorporating two additional fields along with the original message requests, i.e., verifying token and time to live that validates the incoming message requests. Additionally, the OR network, along with blockchain, is simulated inside a discrete simulator, i.e., a shadow simulator. Finally, the performance of the proposed system is evaluated with different performance metrics, such as F1 score, precision, recall, and false-negative rate. The empirical results show that the proposed OR network outperforms the conventional OR in terms of throughput, decryption time (computationally inexpensive), and OR circuit compromised rate.

The advancement in technology has brought to life the concept of Autonomous vehicles (AV). The primary goal of AV is to reduce driving stress and provide comfort to the occupants. Since AVs can drive themselves, it poses a question of passenger security. Furthermore, AVs are connected to an open network like a public Internet to communicate to the outer world, raising security and privacy concerns. Skillful attackers can effortlessly infiltrate the vehicle by injecting malware which can disrupt the regular operation of the entire AV system. A Deep Learning (DL) and Blockchain framework is proposed for AV to resolve the aforementioned security challenges. The network traffic is continuously monitored, and the malware binaries are converted to grey-scale images, which are then classified by Convolutional Neural Network (CNN) employed in the DL model. The CNN architecture, ResNet50V2, has been tested and proves to be efficient in detecting malware with an accuracy of 97.56%.

Drug Discovery is a process by which new potential drugs are discovered and clinically trialed for commercial medicinal purposes. It has several stages of development, where each stage requires a prescribed time for its completion. The stages of drug development are discovery and development, pre-clinical research, clinical development, Food and Drug Administration (FDA) review, and post-market monitoring. The first three stages themselves take nearly 6.5 years. These stages take a huge time in cases where there is an urgent need for a drug. For example, during the COVID-19 pandemic, there was an urgent need for a vaccine. Many research institutes worked$24 \times 7$to develop a vaccine, but it still took a considerable time to get to a bare minimum vaccine. To tackle this problem, we propose DuBloQ, a novel methodology for drug discovery using Q-Learning. Our Q-Learning model consists of a generator and a predictor model. The generator generates a set of Simplified Molecular Input Line Entry System (SMILES) strings and the Predictor predicts its logp values. Based on the logp values, the reward for the generator is provided to improve its performance. We integrate this model with a blockchain User Interface (UI) that ensures security and privacy. We achieved an accuracy of 76.1% for the generator model.

Abstract The next wave in smart transportation is directed towards the design of renewable energy sources that can fuel automobile sector to shift towards the autonomous electric vehicles (AEVs). AEVs are sensor‐driven and driverless that uses artificial intelligence (AI)‐based interactions in Internet‐of‐vehicles (IoV) ecosystems. AEVs can reduce carbon footprints and trade energy with peer AEVs, smart grids (SG), and roadside units (RSUs). It supports green transportation vision. However, the sensor information, energy units, and user data are exchanged through open channels, and thus, are susceptible to various security and privacy attacks. Thus, AEVs can be remotely operated and directed by malicious entities that can propagate false updates to the peer nodes in IoV environment. This can cause the failure of components, congestion, as well as the entire disruption of IoV network. Globally researchers and security analysts have addressed solutions that pertain to specific security requirements, but still, the detection and classification of malicious AEVs is a widely studied topic. Malicious AEVs exhibit an anomaly behavior that differentiates them from normal AEVs, and thereby, the detection of anomalous AEVs and classification of anomaly type is required. Motivated from the aforementioned facts, the survey presents a systematic outlook of AI techniques in anomaly detection of AEVs. A solution taxonomy is proposed based on research gaps in the existing surveys, and the evaluation metrics for AI‐based anomaly detection are discussed. The open challenges and issues in AI deployments are discussed and a case study is presented on anomaly classification through a weighted ensemble technique. Thus, the proposed survey is designed to guide the manufacturing industry, AI practitioners, and researchers worldwide to formulate and design accurate and precise mechanisms to detect anomalies.

Abstract There is a drastic increase in urbanization over the past few years, which requires energy‐efficient and optimized solutions for transportation, governance, quality of life in a smart city among all the citizens. The Internet‐of‐Energy (IoE) ecosystem offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoE applications is increased while IoE devices continue to grow. Therefore, smart city solutions must have the ability to utilize energy and handle the associated challenges efficiently. Moreover, energy Optimization (EO) techniques can be used to reduce energy consumption to meet the sustainability goals in IoE. Different techniques have been proposed for EO in various fields by researchers worldwide. Computing systems also need energy optimization. The energy consumption in the data center, clouds, and blockchain (BC)‐based architectures are a point of concern at the current time. Due to the enormous energy demands of these systems, we cannot take advantage of the latest technologies to their fullest. Due to the emergence of new technologies and some limitations of proposed techniques, we can still not optimize energy usage more than some extent. There is minimal exploration done in energy optimization in BC‐based systems. In this paper, we have proposed a survey on the energy optimization techniques in various systems, including the optimization techniques in BC‐based systems. We have proposed a taxonomy that classifies energy optimization techniques. We have also proposed an energy‐efficient consensus mechanism, Proof‐of‐High Performance optimization (named as PoHPo), for High‐Performance Computing (HPC) based ecosystems. The open issues and challenges are then discussed in EO. The survey intends to propose future directions for industry professionals, green‐energy stakeholders, and researchers worldwide to explore this topic further.

This article proposes a blockchain and non-cooperative game theoretic-based secure and optimized data pricing scheme, i.e.,Block-CPS. It aims to secure the data transactions between vehicle owners and customers for rides. It uses the fifth-generation (5G) communication network that offers ultrareliable low-latency communications between vehicle owners and customers. The Interplanetary file system (IPFS) storage protocol used in the proposal reduces the blockchain data storage cost. We then formulated a non-cooperative game-theoretic approach to maximize the profits for vehicle owners and customers. Formulated non-cooperative game is integrated with blockchain to provide security to the Block-CPS. The vulnerability of the developed smart contract is verified and validated using tools like smartcheck and verisol. The performance of Block-CPS is evaluated by comparing it with the traditional approaches using blockchain with 4G and LTE-A networks. The performance evaluation parameters used are system scalability, network latency, data storage cost and its computation, network throughput, profit, communication reliability, and convergence for the optimal payoff between vehicle owners and customers. The performance results shows the Block-CPS outperforms the traditional blockchain-based systems.

This article proposes a SaTya scheme that leverages a blockchain (BC)-based deep learning (DL)-assisted classifier model that forms a trusted chronology in fake news classification. The news collected from newspapers, social handles, and e-mails are web-scrapped, prepossessed, and sent to a proposed Q-global vector for word representations (Q-GloVe) model that captures the fine-grained linguistic semantics in the data. Based on the Q-GloVe output, the data are trained through a proposed bi-directional long short-term memory (Bi-LSTM) model, and the news is classified as real-or-fake news. This reduces the vanishing gradient problem, which optimizes the weights of the model and reduces bias. Once the news is classified, it is stored as a transaction, and the news stakeholders can execute smart contracts (SCs) and trace the news origin. However, only verified trusted news sources are added to the BC network, ensuring credibility in the system. For security evaluation, we propose the associated cost of the Bi-LSTM classifier and propose vulnerability analysis through the smart check tool for potential vulnerabilities. The scheme is compared against discourse-structure analysis, linguistic natural language framework, and entity-based recognition for different performance metrics. The scheme achieves an accuracy of 99.55% compared to 93.62% against discourse structure analysis. Also, it shows an average improvement of 18.76% against other approaches, which indicates its viability against fake-classifier-based models.

In this paper, we propose a dynamic spectrum allocation (DSA) scheme DeepBlocks at the backdrop of sixth-generation (6G) communication networks that address the challenges of fixed spectrum allocations (FSA). The scheme exploits the advantages of deep-Q-network (DQN) and minimizes the search state explosion through a reward-penalty framework. A dynamic allocation of unallocated resource blocks (RBs) to mobile units (MUs) is carried out and once the allocation of RBs is complete, we integrate blockchain (BC) to record the transactional ledgers. The resource usage of MUs is recorded through smart contracts (SCs). We model the proposed scheme as a convex optimization problem, and subproblems are decomposed into a Pareto-optimal solution via Techebyecheff decomposition. In the simulation, we compare our scheme against FSA, and fifth-generation (5G) based DSA schemes like reinforcement learning (RL), deep neural networks (DNN)-based, and duelling DQN based schemes. The comparative analysis of 6G-DQN is modeled in terms of reward formulation, scalability of 6G-DQN-assisted DSA, and profit scenarios of BC-based allocation through intelligent channel control. The scheme proposes significant findings, with the best fit learning rate of 0.0001, and takes 500 episodes to converge to 60 total resource blocks. The servicing latency of the scheme is 272.4 ms, compared to 2010 ms in the duelling DQN approach. In spectrum allocation, an improvement of 26.32% is observed against non-DQN approaches, and 13.57% in the fairness parameter for spectrum allocation due to BC inclusion. The findings present the scheme efficacy for DSA over the aforementioned conventional approaches.

Device-to-device (D2D) communication is the most evolving technology in fifth-generation (5G) networks. It allows direct communication of devices in the proximity-with or without the base station help. This achieves high gain and low latency in communication and improves the network spectral efficiency. Despite the aforementioned challenges, interference and eavesdropping (by a malicious node) on D2D links/pairs are significant challenges. An eavesdropper can decode the information exchanged between the D2D devices. To mitigate the issues mentioned above nonorthogonal multiple access (NOMA) technique is quite helpful even in social scenarios. NOMA mitigates the interference issues in D2D communication, which increases the signal-to-interference-plus-noise ratio (SINR). Further, we used a zero-sum game approach to improve the data security and minimize eavesdropper’s security risk on the D2D pair. It achieves the goal of the D2D transmitter, i.e., maximize the message security. Simulation results prove the proposed zero-sum game-based system’s superiority, considering average network sum rate and average channel secrecy capacity.

Sensor communication in the smart home environment is still in its infancy as the information exchange between sensors is vulnerable to security threats. Many traditional solutions use single-layer or multi-layer (i.e., onion routing protocol) encryption/decryption algorithms. But, in the traditional onion routing protocol, if the directory server is compromised, it may not track the malicious onion nodes within the onion network. It questioned the path anonymity of the onion routing protocol. Motivated by this, we proposed a blockchain and onion routing (OR)-based secure and trusted framework in the paper. The anonymity of the proposed OR network is maintained by storing and tracking the onion nodes threshold values through the blockchain network. A long short-term memory (LSTM) model is also utilized to classify the sensors data requests as malicious and non-malicious. The performance of the proposed system is evaluated with different performance metrics such as F1 score and accuracy. The LSTM model significantly improves the initial detection rate of malicious data requests from smart home sensors. Over these benefits, we considered the entire communication via 6G channel, reducing the overall communication latency. Additionally, the OR network is simulated over the shadow simulator to analyze the OR network’s performance considering parameters such as packet delivery ratio and malicious onion node detection rate.

Device-to-device (D2D) communication is one of the promising technology of the fifth-generation (5G) network. In D2D, the devices are in close proximity to each other communicate directly with or without depending upon the base station (BS), resulting in large gain, low latency, and high energy efficiency. Also, it improves the spectral efficiency by sharing the spectrum resources with cellular mobile users (CMUs). Despite these advantages, co-channel interference and eavesdropping attack on the D2D links are two major challenges. To overcome these issues, we used the power domain non orthogonal multiple access (PDNOMA) techniques with the D2D mobile groups (DMGs) under the social-domain scenario. The successive interference cancellation technique of PD-NOMA in the DMGs mitigate the intra-user and co-channel interference among the D2D receivers (DDRs), resulting in an increase in signal to interference noise ratio (SINR) and better quality of services. Furthermore, to improve the spectral efficiency, and reduce the security risk of the eavesdropper on the DMGs over each resource block (RB) in the presence of dynamic channel environment of imperfect channel state information, we used the coalition game approach. The simulated results show the proposed scheme achieves 5.5% and 27.77% higher sum rate and ensure 8.3% and 41.6% higher information secrecy as compared to first-order algorithm (FOA) and orthogonal frequency division multiple access (OFDMA) schemes.

An Artificial Intelligence (AI)-enabled and blockchain-driven Electronic Health Record (EHR) maintenance system has a tremendous potential to facilitate reliable, secure, and robust storage systems for EHRs. Such an EHR system would also facilitate researchers, doctors, and government authorities to access data for research, perform analytics, and help in making well-informed decisions. The Artificial Neural Network (ANN) is employed to classify the patients as potentially COVID-19 positive and potentially COVID-19 negative based on the clinical reports and reports of CT-scan. The data of potentially COVID-19 positive patients is stored on blockchain employing InterPlanetary File System (IPFS) protocol. The accessibility of EHR can be done by authorized entities post verification and validation of entities. We analyze the performance of various AI-based algorithms employing metrics such as loss curve, accuracy, etc. for the task of predicting the patient’s potential COVID-19 infection. The 6G network significantly mitigates the network latency and reliability issues and also facilitates the real-time transmission of information. The amount of data generated is pretty high amidst this pandemic and so we employed IPFS protocol which suffices to be a cost-effective solution, moreover satisfying all are stringent requirements. At last, we evaluate the network, security, and storage performance of our architecture MedBlock, which outperformed other state-of-the-art systems.

The 6G-based spectrum bands allocation to telecom providers would guarantee ultra peak rates, high availability, and extremely low-latency for various user applications. However, the spectrum allocation still suffers from the limitations of fair allocation process, delays in auction process, and collusive bidding due to inherent centralization. Thus, this paper proposes a scheme, Block6Tel, that integrates blockchain (BC) in 6G-envisioned spectrum allocation to ensure secure and trusted band allocation among telecom providers, and ensure transparency among telecom stakeholders. The scheme operates in two phases. First, a 6G-based protocol stack model is proposed that leverages a cell-free communication infrastructure. Then, in the second phase, a BC-based auction algorithm is proposed for inter-operator spectrum allocation, and resource allocations among service providers are finalized. Finally, smart contracts (SC) are executed among telecom providers as bidders, and government authorities (GA) as auctioneers. Through extensive simulations, we prove the superiority of Block6Tel compared with traditional static allocation approaches, in terms of parameters like- resource utilization, requests overhead, and allocation fairness. The results demonstrate that the proposed scheme outperforms the traditional schemes using various parameters.

The paper proposes a sixth-generation (6G)-enabled cellular vehicle-to-anything (C-V2X)-based scheme, Res6Edge, that supports high-data ingestion rate through artificial intelligence (AI) models at edge nodes, or Edge-AI. Through Edge-AI in 6G supported C-V2X, we address the research gaps of earlier schemes based on fifth-generation (5G) resource orchestration. 6G improves decision analytics and real-time resource sharing among C-V2X ecosystems. The scheme operates in three phases. In the first phase, a layered network model is proposed for V2X communication based on 6G-aggregator and core units. Then, based on the proposed stack, in the second phase, 6G resource allocation is proposed through macro base station (MBS) units. MBS ensures channel gain and reduces energy loss dissipation. Finally, in the third phase, an intelligent edge-AI scheme is formulated based on deep-reinforcement learning (DRL) to support responsive edge-cache and improved learning. The proposed scheme is compared to 5G baseline services in terms of parameters like- throughput, latency, and DRL scheme is compared to random allocation approaches. Through simulations, Res6Edge obtains a V2X user throughput of 43.24 Mbps, compared to 0.7 Mbps for 4 x 108connected ACV sensors. The reduced latency is ≈ 13.84 times of 5G. DRL learning algorithm achieves a satisfaction probability of 0.5 for 500 vehicles, compared to 0.35 using conventional schemes. The obtained results indicate the viability of the proposed scheme.

Abstract From the past few years, Unmanned Aerial Vehicles (UAVs) has proved an immense potential in providing the cost and time‐efficient solutions to the various societal applications such as healthcare, supply chain, and video & surveillance. It has many data security and privacy issues, and researchers across the globe have given many solutions to protect data from cyber‐attacks. Many of them have suggested cryptographic‐based solutions, which is very compute extensive. Very few researchers have suggested Blockchain (BC)‐based solutions, but their solutions may suffer from high data storage cost as well as network latency, reliability, and bandwidth issues. To overcome the above‐mentioned issues, this paper proposed an InterPlanetary File System and BC‐based secure UAV communication scheme over the 6G network. This proposed scheme ensures data security and privacy, reduces data storage cost, and enhances network performance. Then, the research challenges and future directions for further improvement of the proposed system have been presented.

Blockchain technology has emerged to provide immense security solutions and create trust between the stakeholders. In a multi-application scenario, fair resource allocation is complex and challenging. Various Resource Allocation Schemes (RAS) have been proposed by the researchers across the globe, but these solutions are not sufficient to handle the security, trust, latency, and bandwidth issues in the network, which introduces vulnerabilities in the system. Motivated from the aforementioned issues, this paper proposes Block-RAS, a blockchain-based RAS to manage the demand-supply of resources between the users and resource providing companies (RPC) in a secured and trusted environment. Block-RAS provides a highly reliable, low-latency, and bandwidth optimum communication between users and RPC with embedded 6G network infrastructure. In Block-RAS, the security, trust, and transparency are achieved using ethereum blockchain, whereas the cost-effective and optimum bandwidth utilization is achieved using the Interplanetary File System (IPFS). Finally, the performance evaluation of Block-RAS is done by a comparative analysis of the proposed approach with traditional approaches that are dependent on centralized 5G based schemes where the Block-RAS outperforms in terms of delay, packet-loss, blockchain block-size, scalability, and network bandwidth utilization.