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.

The Internet of Things (IoT) is a constantly expanding system connecting countless devices for seamless data collection and exchange. This has transformed decision-making with data-driven insights across different domains. However, challenges arise concerning security and computational limitations. To strengthen IoT against cyber threats and optimize resource usage, combining quantum computing (QC) with machine learning (ML) is a promising approach. ML enables computers to learn from data and detect patterns without explicit programming. By leveraging ML algorithms, vast datasets from IoT devices can be analyzed, identifying anomalies and forecasting potential security breaches. Yet, conventional ML algorithms may need help with the complexity and scale of IoT data. QC, based on quantum mechanics, offers unparalleled computational speed and scale. Quantum ML algorithms can quickly analyze IoT datasets, identifying patterns and potential threats. This study examines the ideas behind ML, QC, and their potential collaboration within IoT networks. The research focuses on the possibility of improving the security of IoT networks by integrating QC approaches with ML. It also addresses the challenges and limitations of integrating ML and QC in the context of IoT networks. These obstacles include hardware constraints, algorithm complexities, and the need for specialized knowledge.

This article presents an optimized and decentralized resource allocation approach aimed at maximizing the system throughput and energy efficiency of device-to-device (D2D) communication. The proposed scheme modifies the meta-heuristic whale optimization algorithm (WOA) by blending the differential evolution (DE) technique in the WOA’s exploration phase to offer intelligence and reduce the computational overburden. The hybrid WOA (DE+WOA) serves as a physical layer access control that efficiently finds the optimal cellular users (CUs) and D2D users (DUs) based on their channel conditions. The proposed access control acts as a restrictive filter, where only optimal CU-DUs can participate in resource allocation tasks. Furthermore, a dataset has been prepared using the optimal CUs-DUs channel conditions from the hybrid WOA to serve as input for the federated learning (FL)-based resource allocation. We utilized statistical tests (e.g., Spearman’s test) to analyze the generated dataset’s non-independent and identically distributed (non-IID) characteristics, thus providing generalization in the AI training. Allowing only the optimal CUs and DUs (from hybrid WOA) in the FL-based resource allocation substantially reduces the computational cost of AI training and improves energy efficiency. In the FL-based resource allocation, we used a sequential convolutional neural network (CNN) trained on the aforementioned dataset to provide proactive resource allocation decisions. Furthermore, we used momentum-based weight aggregation in the FL to reduce the computational burden on the central server. The proposed scheme is assessed by utilizing different standard metrics, such as training accuracy (98.93%), training time, overall system throughput (35.62 Mbps), energy efficiency (96.42 bits/joule), and resource fairness.

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.

Conventional machine learning involves training models on centralized storage locations such as servers or databases, which pose security and privacy threats. Federated learning (FL) overcomes such limitations through collaborative global model training instead of centralized processing without sharing the private data in raw format, ensuring end-to-end integrity and confidentiality and protecting clients’ privacy. However, security issues such as local data leakage through shared gradients are a potential threat in FL. An attacker may forge sensitive information from the client’s local model updates and corrupt the aggregated global model result without detection, resulting in clients’ privacy vulnerability. This paper introduces SeFL, a privacy-preserving FL framework empowered by aggregator-oblivious (AO) techniques to ensure secure model aggregation between clients and a server. The aggregation server protects the masked gradients without revealing local data privacy. SeFL introduces a dynamic client management system which is robust against client dropout. Security analysis shows that SeFL can prevent the privacy-preserving requirements in FL. A thorough experiment is performed with various deep learning architectures using diverse datasets to evaluate the proposed framework. Results show that SeFL achieves comparable training accuracy with FedAvg and low computation & communication overhead when implementing the proposed privacy-preserving scheme.

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 recent times data breaches in various sectors of industry have become a common threat. It has become very crucial to secure patient data in the health industry. The upcoming Healthcare 4.0 techniques can play an important role in this. We have implemented these techniques in our proposed model to protect personalised health information of the individual health profiles of the patient using blockchain. The proposed model is also a recommending system with the aim to offer relevant advice to the patients to keep a check on the various health parameters like blood pressure, body temperature, blood sugar etc.

The Internet of Medical Things (IoMT) revolution-izes healthcare by integrating medical devices and systems with the internet. However, the vast amounts of sensitive medical data in IoMT networks pose significant security and privacy concerns. Traditional security measures often fall short in identifying the malicious data attacks within the IoMT ecosystems. This paper introduces an AI-based non-malicious data classification scheme based on blockchain. We applied and evaluated the machine learning (ML) classifiers, such as support vector machine (SVM), random forest (RF), and K-Nearest neighbor (KNN). We evaluated the proposed framework based on various performance metrics that includes accuracy, precision, recall, and F1 score. The accuracy using SVM obtained 75.3%, RF is 75.9%, and K-NN is 84.3%. The results shows that KNN performs better than other models hy the factor of 9%.

The expansion of intelligent transportation systems (ITS) is driven by the demand for cutting-edge cyber-physical systems, applications prioritizing comfort, and specialized services designed for utilization in smart vehicles. The internet of vehicles (IoV), a crucial element of ITS, supports data communication like vehicle-to-vehicle (V2V) and vehicle-to-anything (V2X). Ensuring the safety and security of passengers relies heavily on effective inter-vehicle communication. The network layer's routing protocols enable efficient data communication within the IoV, addressing its dynamic network topology. Existing literature has introduced various routing protocols, including topology-based, position-based, cluster-based, broadcast-based, and hybrid protocols, to navigate the dynamic network topology of IoV. The performance evaluation of these protocols considers parameters like security, throughput, packet delivery ratio, jitter, delay, and overhead. However, these protocols are susceptible to malicious attacks that compromise the integrity, confidentiality, and availability of network messages, potentially leading to accidents and degrading the system's performance. In this context, we propose a novel secure routing protocol called secure optimized link state routing protocol SecOLSR, which employs symmetric secret key exchange for connected vehicles, aiming to mitigate the aforementioned challenges. Our paper presents a comprehensive security analysis of SecOLsr in the IoV network and assesses its performance in terms of quality-of-service (QoS) parameters alongside existing state-of-the-art protocols.

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.

Summary With the advent and popularity of electric vehicles (EVs), the intelligent transportation system has adopted them as an alternative to fossil fuel or gasoline vehicles owing to their benefits of reduced greenhouse gas emissions. However, it becomes critical to schedule the vast EVs at the charging station (CS) efficiently while maintaining security, privacy, and optimality during the energy trading. Therefore, we propose a blockchain and zero‐sum game theory‐based energy trading scheme, that is, DEVS, for optimal and secure EV charging at the CS. We incorporate the Interplanetary File System with the blockchain to ameliorate the cost‐efficiency and reliability of EV energy trading. Furthermore, we implement the zero‐sum game theory between players EV and CS to yield an optimal payoff based on pure and mixed strategies during the energy trading procedure. Moreover, we deploy and execute the smart contract (SC) of the blockchain‐based proposed DEVS scheme in the Remix integrated development environment to secure EV energy trading transactions. Additionally, the security of the proposed DEVS scheme is checked and analyzed using a fuzzing‐based Echidna tool for protected energy trading. Finally, the performance evaluation of the proposed DEVS scheme is analyzed using various standard metrics such as profit comparison based on strategies, energy price comparison, CS profit comparison for pure strategy (based on arrival time), convergence comparison, and profit (EV and CS) at saddle point and mixed strategy to avail preserved and optimal energy trading for EVs.

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%.

Integration of device-to-device (D2D) communication has gained significant attention within cellular networks as a means to enhance their capacity, coverage, and performance. Despite these advantages, D2D communication encounters various challenges, such as high interference, resource allocation, energy efficiency, and security. In this paper, we investigate the problem associated with resource allocation in D2D communication underlying cellular networks. The existing resource allocation schemes (e.g., game theory and graph theory) do not offer an access control mechanism, due to which the existing schemes are computationally intensive and do not converge to offer a global optimum solution. Toward this goal, we proposed a whale optimization algorithm(WOA)-based access control scheme to enhance the performance of the resource allocation scheme in D2D communication. In WOA, we created a signal-to-interference-plus-noise ratio (SINR)-based objective function that iteratively discovers the best D2D users, allowing them to participate in the resource allocation process. Moreover, for resource allocation, we adopted the Munkres algorithm, which allows only optimized D2D users (from WOA) to reuse the resources of cellular users (CUs). In the proposed work, WOA acts as an access control scheme that optimally finds the best D2D users and only allows them to reuse cellular resources in the Munkres resource assignment problem. Simulation results show that the proposed scheme significantly improves the system’s throughput compared to other existing algorithms. Moreover, other evaluation parameters, such as convergence rate, fairness, WOA update positions, and execution time, show the outperformance of the proposed scheme.

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.

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.

Abstract Parkinson's disease (PD) is a neurological disorder of the central nervous system that causes difficulty in movement, often including tremors and rigidity. Early detection of PD can prevent symptoms up to a certain age and increase life expectancy. For this purpose, we have used brain images from magnetic resonance imaging (MRI) technique. A deeper level of feature detection in MRI can identify biomarkers that can be used to know how the disease spreads, leading to a cure in the future. With these motives, we have presented two novel approaches using deep learning (DL) techniques. 2D and 3D convolution neural networks (CNN) are used, which are trained on MRI scans in the axial plane. The dataset was constructed using images from Parkinson's progression markers initiative (PPMI). The four pre‐processing techniques used in this article are bias field correction, histogram matching, Z‐score normalization, and image resizing. Pre‐processing techniques were essential inaccurate training models. Every class prediction done by the model would have taken multiple features into account across multiple layers of the brain and not relied on a single or few important features, making DL a powerful concept. A total of 318 MRI scans were used to train and test a 2D CNN and a 3D CNN model. We have compared the models' results using different evaluation parameters such as accuracy, loss, confusion matrix, receiver operating characteristic (ROC) curve, and precision‐recall (PR) curve. The 3D model learned key features from the data and was able to classify the test data with 88.9% accuracy with 0.86 area under curve (AUC). In contrast, the 2D model achieved a mediocre accuracy of 72.22% with 0.50 AUC. This shows that the 3D model is more accurate and reliable than the 2D model.

Nonorthogonal multiple access (NOMA) with a grant-free access has received a lot of attention due to its support to massive machine-type communication (mMTC) devices. The devices in grant-free systems are allowed to transmit information without undergoing an authentication process. Therefore, in such systems, the base station needs to distinguish between active and nonactive devices, called the active user detection (AUD) process. This process is challenging as the active device needs to be detected from the received signals that are superimposed. Furthermore, the identification of the Internet of Things (IoT) devices from these signals also poses a great challenge, which could help allocate resources in future generation communication systems. Motivated from the aforementioned facts, this article proposes a device detection and identification (DDI) architecture for joint AUD and IoT device identification from the received superimposed signals. The architecture extracts the Fourier patterns as the representative feature vector, which results in an improved detection and identification process. Experimental results show that the architecture not only outperforms the conventional schemes and deep neural network-based approaches in terms of success probability for the AUD task but also yields lower computational complexity. The evaluation of the DDI architecture for IoT device identification problems has also been performed and compared to various shallow learning methods to prove its efficacy.

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.

Smart grid (SG) systems necessitate secure demand response management (DRM) schemes for real-time decisions making to increase the effectiveness and stability of SG systems along with data security. Motivated from the aforementioned discussion, in this article, we propose Q-SDRM, a secure DRM scheme for home energy management (HEM) using reinforcement learning (RL) and ethereum blockchain (EBC) to facilitate energy consumption reduction and decrease energy costs. In cooperation with RL,$Q$-learning is adopted to make optimal price decisions using Markov decision process (MDP) to reduce energy consumption, which benefits both consumers and utility providers. Then, Q-SDRM uses ethereum smart-contract (ESC) to deal with data security issues and incorporate with off-chain storage interplanetary file system (IPFS) that handles data storage costs issue. Experimental results reveal the effectiveness of the proposed Q-SDRM scheme, which significantly reduces energy consumption and energy cost. The proposed scheme also provides secure access to energy data in real time compared with state-of-the-art approaches regarding different evaluation metrics, such as scalability, overall energy cost, and data storage cost.

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.

This paper proposes a generic scheme VaCoChain, that fuses blockchain (BC) and unmanned aerial vehicles (UAVs) underlying fifth-generation (5G) communication services for timely vaccine distribution during novel coronavirus (COVID-19) and future pandemics. The scheme offers 5G-tactile internet (5G-TI) based services for UAV communication networks (UAVCN) monitored through ground controller stations (GCs). 5G-TI enabled UAVCN supports real-time dense connectivity at ultra-low round-trip time (RTT) latency of [Formula: see text] and high availability of 99.99999%. Thus, it can support resilient vaccine distributions in a phased manner at government-designated nodal centers (NCs) with reduced round trip delays from vaccine production warehouses (VPW). Further, UAVCNs ensure minimizes human intervention and controls vaccine health conditions due to shorter trip times. Once vaccines are supplied at NCs warehouses, then the BC ensures timestamped documentation of vaccinated persons with chronology, auditability, and transparency of supply-chain checkpoints from VPW to NCs. Through smart contracts (SCs), priority groups can be formed for vaccination based on age, healthcare workers, and general commodities. In the simulation, for UAV efficacy, we have compared the scheme against fourth-generation (4G)-assisted long term evolution-advanced (LTE-A), orthogonal frequency division multiplexing (OFDM) channels, and traditional logistics for round-trip time (RTT) latency, logistics, and communication costs. In the BC setup, we have compared the scheme against the existing 5G-TI delivery scheme (Gupta et al.) for processing latency, packet losses, and transaction time. For example, in communication costs, the proposed scheme achieves an average improvement of 9.13 for block meta-information. For 4000 transactions, the proposed scheme has a communication latency of 16 s compared to 36 s. The packet loss is significantly reduced to 2.5% using 5G-TI compared to 16% in 4G-LTE-A. The proposed scheme has a computation cost of 1.6 ms and a communication cost of 157 bytes, which indicates the scheme efficacy against conventional approaches.

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.

People need to breathe, and so do other living beings, including plants and animals. It is impossible to overlook the impact of air pollution on nature, human well-being, and concerned countries' economies. Monitoring of air pollution and future predictions of air quality have lately displayed a vital concern. There is a need to predict the air quality index with high accuracy; on a real-time basis to prevent people from health issues caused by air pollution. With the help of Unmanned Aerial Vehicle's onboard sensors, we can collect air quality data easily. The paper proposes a distributed and decentralized Federated Learning approach within a UAV swarm. The accumulated data by the sensors are used as an input to the Long Short Term Memory (LSTM) model. Each UAV used its locally gathered data to train a model before transmitting the local model to the central base station. The central base station creates a master model by combining all the UAV's local model weights of the participating UAVs in the FL process and transmits it to all UAV s in the subsequent cycles. The effectiveness of the proposed model is evaluated with other machine learning models using various evaluation metrics using test data from the capital city of India, i.e., Delhi.

Vehicular ad hoc Networks have emerged as a viable alternative for enabling user applications on moving vehicles. However, maintaining acceptable levels of Quality of Service and message latency still remains a challenging task. Several solutions have been proposed for improving performance of these networks. Clustering has been considered as one of the important mechanism that structures vehicles into organize groups. However, high deployment overheads and lack of security are the major bottlenecks hindering its deployment. Software defined networking has been emerged as a promising solution on account of its characterstics such as dynamic access control and scalabilty. In view of this, TruClu: a trust based clustering mechanism that creates vehicular clusters for a Software Defined Vehicular Network is proposed. Cluster formation and cluster head selection in TruClu is based on vehicular mobility and trust value that alleviates the drawbacks of traditional clustering and also enabling trust based communication in the network. The performance of TruClu is evaluated through extensive simulations and obtained results indicate the comparable performance of the proposed scheme in terms of standard performance parameters.

Increased vehicular flow on roads along with proposed deployment of autonomous vehicles has necessitated the need for accurate traffic forecasting so as to achieve effective route guidance, traffic management, public safety and congestion avoidance. Although a number of traffic forecasting algorithms have been proposed but most of these algorithms perform short term traffic predictions. However future vehicular systems also defined as intelligent VANETs will require a hybrid traffic forecasting model that predicts the vehicular traffic for varying values of time. This paper proposes a time varying forecasting model that predicts vehicular flow by utilizing Long Short-Term Memory (LSTM) and Convolutional Neural Network(CNN). The model is based on large-scale, network-wide traffic with spatio-temporal features. The temporal features learned by LSTM and spatial features learned by CNNs from the matrices are further fused with external factors to derive the final forecast. Model has been implemented on the traffic data set of Chandigarh city in India, mapped onto three two-dimensional matrices of time and space. The predicted information is then forwarded by the vehicle to all the other vehicles in their vicinity using vehicular adhoc networks. Experimental results indicate that the proposed model performs significantly better than other state-of-the-art models in terms of accuracy and efficiency.

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.

With the advent of the Internet, the gaming industry has grown tremendously in business, which also raises concerns for cheating and unfair gameplay. In this paper, we propose a novel approach (GiNA) using Blockchain technology to address a few problems with online Peer-to-Peer (P2P) games. GiNA uses two different data packet transfer schemes to ensure the security and authenticity of the data packet sent and received by game clients. More sensitive data uses a Smart contract-based ON-CHAIN data packet transfer solution and less sensitive data uses an OFF-CHAIN data packet transfer solution with end-to-end encryption for data security. A marketplace where peers can buy and sell purchasable assets with the help of Gicoins. Gicoins is a stable token with compliance with the ERC 20 token of Etheruem Blockchain. Later, a low cost and low bandwidth utilization data storage solution is proposed for storing data in a decentralized and distributed manner. Results show that the performance of the proposed approach GiNA is better in comparison to the traditional approaches with parameters such as latency, scalability, packet loss percentage, Blockchain (BC) performance, and data storage comparison.

Financial institutions have made lives easier for a lot of individuals and organizations that would earlier use to face capital shortage now and then. Therefore, it becomes necessary to make the financial systems more reliable, secure, time-conserving, and cost-effective. Although several approaches have already been proposed, all of these tend to fail on at least one of the key features, i.e., trust. Motivated by this, in this paper, we propose DeLend, an Ethereum blockchain-based peer-to-peer (P2P) lending system. In DeLend, the problems of security, trust, and reliability have been solved with the help of Ethereum-based smart contracts (SCs). To make the system middlemen-free and much more cost-effective, we use the interplanetary file system (IPFS) protocol as a data storage. Through extensive simulation, we show that DeLend requires less bandwidth, which makes it a suitable enabling technology for the next generation of cellular networks, i.e. 6G. Finally, DeLend’s performance evaluation demonstrates its efficacy compared to traditional lending schemes.

The rapid growth and advancement of technology in industries provide a better quality of services to the end-user in the industrial Internet of Things (IIoT). The digital twin (DT) is an innovative technology recently developed in Industry 4.0 to provide a virtual representation of physical components, products, or equipment such as computer numerical control (CNC) machines. It can be used to run simulations before manufacturing. However, traditional DT platforms lack data privacy, traceability, immutability, authentication of stakeholders. Moreover, manual prediction of the wearing of the tool condition of the CNC machine is challenging. Motivated from these gaps, in this paper, we propose a six-layered architecture for DT of CNC, which predicts CNC tool wear detection using a novel ensemble technique based soft voted prediction model consisting of XGBoost, random forest, and AdaBoost models. The proposed architecture also incorporates the public Ethereum blockchain (BC) to maintain the aforementioned issues of authentication, traceability, and transparency through constraints and automation programmed into the smart contracts (SC) developed. We evaluate the proposed scheme’s performance through simulation and compare it with other traditional approaches concerning several performance parameters (accuracy, F1-score, precision, and recall). The result shows that the proposed approach outperforms the traditional approaches on these same performance parameters such as accuracy, F1-score, precision, and recall.

The COVID-19 pandemic has adversely affected the lives of millions of people worldwide. With an alarming increase in COVID-19 cases, it is important to detect and diagnose COVID-19 in its early stages to prevent its spread. To diagnose remote patients, the Internet can be useful for accessing data of that patient. But, the Internet has also had issues related to data security, reliability, and privacy. Motivated by these challenges, in this paper, we propose a Blockchain (BC) based COVID-19 detection scheme (BCovX) for fast and reliable diagnosis of COVID-19 using chest X-Ray (CXR) images. For fast and accurate detection of COVID-19 using CXR, BCovX consists of a Convolutional Neural Network (CNN) model, using which a patient can be diagnosed for COVID-19 remotely. CNNs have performed successfully in medical imaging classification. BCovX provides reliable and secure data access and exchange using BC and smart contracts (SC). To solve issues related to data storage and its associated cost, the InterPlanetary File System (IPFS) protocol is used to store medical data. We also present a real-time SC developed in Solidity to govern the transaction between the patient and the doctor. The SC has been compiled and deployed on Remix Integrated Development Environment (IDE). Finally, we have evaluated the performance of BCovX with traditional schemes in terms of storage cost, bandwidth requirements, and accuracy of the CNN model.

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.

Unmanned aerial vehicles (UAV) can provide efficient and effective solutions for the development of smart cities. They have been widely used in civilian and military applications, such as data acquisition, data dissemination, audio and video surveillance, aerial photography, crop surveys, and real-time medical care. The network communication and security challenges in UAV networks are explored by research organizations across the globe, but still, many challenges remain unsolved like sensitive and end-user-related applications. Moreover, traditional UAV communication is not adequate to deal with the high mobility and dynamic features of UAV. So, there is a need for an efficient and secure network of UAV as they have been widely used in hostile environments. Motivated from the aforementioned facts, in this article, we present a broad survey on the architecture, requirements, and use cases of 6G technology. It also presents a solution taxonomy based on the applications of UAV communication. Based on the findings from the survey, we present a blockchain-envisioned security solution and 6G-enabled network connectivity in UAV communication. A summary of future research directions for the integration of blockchain and 6G technology in UAV communications is also presented. Then, we present a case study of a blockchain-envisioned UAV communication using 6G networks to secure Industry 4.0 applications.

In the last few years, we have witnessed the cache localization as one of the most challenging problems for device-to-device (D2D) communication in the IoT environment. It has been a major performance bottleneck due to cache localization problem in D2D communication as there are advancements in cellular technology, especially in 5G base stations (BSs) deployment around the globe. It is due to the fact that with an increase in the enormous amount of the number of users and devices, there has been an increase in the demands of service availability within a fraction of seconds by the end users. It results in an increase in burden on the existing network infrastructure with respect to Quality of Service (QoS) and Quality of Experience (QoE) provisions to the end users and service providers. However, caching the most popular content on the user equipments (UE's) can resolve the aforementioned problems. Motivated from these facts, in this article, we propose a model to address the problem of the cache localization decision making. In the proposed scheme, first, we collected the data set traces to predict the cache locations. Then, we predicted the locations where the user can cache the most accessed content using machine learning classification models. The classification models used in the proposed solution are decision tree and random forest. The metrics used for evaluation of the results obtained are access delay and energy consumption of the UEs. On comparing the proposal with the other existing state-of-the-art models, we observed that the random forest model yields higher accuracy as compared to other existing models. Also, we have observed that the access delay is maximum at the user's end when contents are shared with the gateway.

In modern decentralized Internet-of-Things (IoT)-based sensor communications, pseudonoise-diffusion oracles are heavily investigated as random oracles for data exchange among peer nodes. As these oracles are generated through algorithmic processes, they pass the standard random tests for finite and bounded intervals only. This ensures a false sense of privacy and confidentiality in exchange through open protocol IoT-stacks in public channels, i.e., Internet. Recently, blockchain (BC)-envisioned random sequences as input oracles are proposed about financial applications, and windfall games like roulette, poker, and lottery. These random inputs exhibit fairness, and nondeterminism in SC executions termed as probabilistic smart contracts (PSCs). However, the IoT-enabled PSC process might be controlled and forged through humans, machines, and bot-nodes through physical and computational methods. Moreover, dishonest entities like contract owners, players, and miners can co-ordinate together to form collusion attacks during consensus to propagate false updates, which ensures forged block additions by miners in BC. Motivated by these facts, in this article, we propose a BC-envisioned IoT-enabled PSC scheme,SaNkhyA, which is executed in three phases. In the first phase, the scheme eliminates colluding dishonest miners through the proposed miner selection algorithm. Then, in the second phase, the elected miners agree through the proposed consensus protocol to generate a stream of random bits. In the third phase, the generated random bit-stream is split through random splitters and fed as input oracles to the proposed PSC among participating entities. In simulation, the scheme ensures a trust probability of 0.38 even at 85% collusion among miners and has an average block processing delay of 1.3 s compared to serial approaches, where the block processing delay is 5.6 s, thereby exhibiting improved scalability. The overall computation and communication cost is 28.48 ms, and 101 bytes, respectively, that indicates the efficacy of the proposed scheme compared to the traditional schemes.

In the recent years, the blockchain (BC) technology has been used in various applications ranging from financial sector to healthcare sector. Moreover, developing BC-based solutions for these applications has been an area of interest among the academia and industry professionals. Diamond has huge potential to become an investment asset, but there are some issues, which hinder the progress of the diamond industry, such as provenance, supply chain traceability, involvement of third party in the verification process, and reliability of transactions. BC seems to be a promising technology, which bridges the gap between the diamond industry and the burgeoning financial markets. Individuals are always confident that the crystals they bought are legitimate and that stolen property can be handed back to the legitimate owner easily. Motivated from these facts, in this article, we surveyed the adoption of BC in the diamond industry, and also present pros and cons of this integration. Then, we discuss issues of the diamond industry operations and based on the literature review, we suggest their probable countermeasures. Then, we analyze various open research issues and challenges associated with integrating the BC in the diamond industry. Finally, we present a case study on frameworks, such as Everledger and Tracr, which highlights the real-time challenges of integrating BC in the diamond Industry.

The coronavirus (COVID-19) outbreak has a significant impact on people’s lives, occupations, businesses, and economies globally. The world economic market is experiencing a big shift and the share market has observed crashes day-by-day. Even, the Indian economy has witnessed a slowdown in the current pandemic, and recovery of it is quite difficult. The restrictions and restrain strategies (e.g., lockdown and social distancing) introduced by the government leave many professions and facilities in a dormant state, catalyzing economy downfall. It necessitates to improve economy along with control strategies of COVID-19, which is a challenging task. To handle the above-mentioned issues, this article proposes a novel economy-boosting scheme, i.e.,$\xi $boost, which is a fusion of artificial intelligence (AI) and big data analytics (BDA) integrated with the Internet-of-Things (IoT)-based data communication. Here, a bidirectional long short-term memory (LSTM) model is anticipated for early prediction of total positive cases as well as the economy. Then, it calculates an optimal subsegment of days, in which trade and commerce related restrictions could be reduced to control a sharp decline in the economy. Next, a spark-based pre and post unlock (PPU) analytics is carried out on the rise of COVID-19 cases to validate the intensity of testing in the country and deciding economy-boosting activities. Then, the$\xi $boostscheme is evaluated based on various factors such as prediction accuracy and others while comparing to existing approaches. It facilitates healthy and profitable smart cities by the means to control pandemic with subsequent economy rise.

Summary Internet of Things (IoT) connects different objects in the physical world to the Internet, and various Internet protocols are used to provide communication services to a large number of these embedded devices termed as smart devices. But, these devices are resource‐constrained, low configured, and have very low power storage capacity, which depends on the services offered by the protocols. For the exchange of information to the end‐users, smart devices communicate through an open channel, such as the Internet, which is not secure enough. Moreover, efficient delivery ratio, secure data forwarding are not achieved because of the enormous amount of data produced by these smart devices and the possibility of security threats. So there is a need to devise a secure and reliable data dissemination scheme for the IoT environment. Motivated from the these facts, this paper presents a systematic review and propose a solution taxonomy for secure data dissemination techniques for various smart IoT‐based applications. This paper also includes a comparison of the state‐of‐the‐art data dissemination techniques used for the Internet of Vehicles (IoVs), Internet of Drones (IoDs), and Internet of Battlefield Things (IoBTs) applications along with their merits and demerits. Finally, the research challenges and possible countermeasures are also discussed in detail, which gives insights to the beginners who want to start work in this emerging area.

The next-generation energy system, i.e., Smart Grid (SG), empowers the real-time transfer of information using advanced metering infrastructure (AMI) and smart meter (SM) between end-consumers and grid. It accelerates various services such as automatic meter reading, time-of-use (TOU) pricing, demand-response management, and many more. Though it has growing security and privacy concerns and the detection of malicious activity is a critical security task that sacrifices the overall Quality-of-Service (QoS) of SG and Quality-of-Experience (QoE) for customers. To address the aforementioned issues, we propose a data analytics Scheme ArMor for malicious activity detection on the blockchain (BC)-based SG system. The ArMor detects data integrity issues in real-time like false data injection attack and SM failure. Here, we proposed a unique ARIMA-based malicious activity detection model and classified the customer. Then, we proposed a Smart Contract (SC)-based incentive mechanism for utility providers handling the malicious activity at their end. It prevents the entry of malicious data into the SG system as transactional data once stored in BC, it is secured using SC. The obtained results are compared against parameters like prediction accuracy, latency, and data storage cost compared to the state-of-the-art approaches to designate the efficacy of the proposed scheme.

In this paper, we propose a framework to generate cricket highlights from broadcasted cricket matches. Generating cricket highlights is a difficult problem, due to the duration and rules of the game. We formulate the highlight generation problem as a key-event initialization and key-event-closure identification problem. We propose an Inverse Hierarchical Framework, which is generic and capable of automatically generating highlights of a broadcasted cricket match. We introduce a novel context-aware approach for event-initialization and a Structural Similarity Index-based approach for event-closure detection. Despite the quality of highlights being a subjective measure we provide an evaluation of our framework by comparing it with official highlights on various metrics. We also perform a user-survey on the generated highlights. The approval of the users and overlap between the generated highlights and official highlights indicate the robustness of our framework.

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.

The development of nanomedical systems through the Internet of Bio-Nano Things (IoBNT) paradigm promotes designing of therapeutic models to facilitate drug transport and delivery. Such systems utilize microbial communities such as bacteria, which act as biosensors for molecular communication. We model the drug transport and delivery system by considering more realistic properties and characteristics of the biosensor community. We devise a Markov Decision Process (MDP) to model the biosensor lifecycle while considering division and death as parameters to regulate the model. This aids in estimating the required number of drug encapsulated biosensors. The proposed model indicates an increase in the number of instances of biosensor-target interactions that would be required for a better understanding of system dynamics. The proposed approach suggests a populace-aware coordination scheme with 3.5% increase in population, along with 20 -50% increase in information delivery. The solution proposed here can be harnessed in designing the number of optimum drug dosages. We show the effectiveness of our model with 90% increase in average biosensor lifetime, while highlighting the increase in the energy utilized in the network.

A huge amount of data is generated from the interaction of various sensors and Internet of Things (IoT) enabled devices used in various smart industrial applications. This enormous amount of data requires fast processing, huge storage capacity, secure dissemination, and aggregation to make it resistant from the attackers. Secure data dissemination for IoT-based applications has been a prominent issue in consideration with the heterogeneity in generated data. Existing secure data dissemination schemes are inadequate to handle secure data distribution. Research communities across the globe are focused on the delivery of the data among the sensor nodes and overlook the difficulty of its secure streaming. Hence, there is a need to validate the performance of secure data distribution schemes for IoT networks using relevant parameters. Motivated from the aforementioned facts, in this study, we perform a comprehensive review on the state-of-the-art techniques, which can verify and validate the performance of data dissemination schemes for IoT networks. We present a solution taxonomy of various verification and validation methods along with their merits and demerits. Finally, recent issues and future directions on verification and validation methods for the secure data distribution in an IoT network is presented.

Security and reliability in data transmission are considered as the challenging concerns in VANET. Software-defined networking (SDN) and network coding (NC) are the two key apprehensions in networking that have caught much attention from both industry and academia in recent years. Decoupling of control plane and data plane in SDN enables the centralized control of the network, providing flexibility to a great extent. On the other hand, NC has shown immense potential for improving robustness and security when deployed on VANET. This article advocates for the design of an architecture which exercises the SDN concept by incorporating NC with multigeneration-mixing (MGM) functionalities to increase reliability as well as security of data transmission in vehicular networks. We design an MGM-based NC protocol for encoding and decoding of data. Moreover, a centralized SDN controller takes charge of authentication of vehicles. Finally, we build a simulation model based on realistic traffic and communication characteristics. The simulation results show that the proposed protocol leveraged by the SDN framework outperforms the conventional NC-based protocol in case of security and reliability. Moreover, the simulation model is exhibited and point-by-point execution assessment is given to demonstrate the scalability and prevalence of the proposed strategy.

Ambient Assisted Living (AAL) is becoming a necessity in today's world. It provides care to the elderly patients who are under observation. With the advancements in the technology, the ability of health systems to indulge in the patient's life and remote monitoring has proven useful to prevent catastrophes. Automatic sensing based on sensors and computer vision enabled devices has taken up the field of AAL a notch ahead. Motivated from the aforementioned discussion, in this paper, we propose, a fretwork named as HRIDaaY (an architecture for remote monitoring of the heart rate of a patient) by using a ballistocardiogram sensor and fog computing (FC). We further demonstrate a data compression technique at the fog layer to reduce the bandwidth utilization. Then, a comparison is drawn using alone-Cloud and as fog- cloud combination implementation. Finally, the simulation results demonstrate that HRIDaaY has better accuracy of heart rate monitoring in comparison to the state-of-art schemes.

Few decades ago, wellness management systems were not in the position to give salutary to their users. One of the possible reasons is inefficient resources and minimum technological infrastructure which do not allow a comprehensive structure pertaining to specific user. Sleep apnea is one such problem which is related to the permanent condition that involve stagnation of breathing. Although incurable, it can be minimized by maintaining a healthy lifestyle. Motivated from this, in this paper, we propose a health manager directive system to investigate the precise medical condition of sleep apnea. A recommender system is used which suggests the healthy lifestyle schedule to reduce the apnea severity in a patient. A Probabilistic Markov model (PMM) is used to adhere the activities based on time consumption in different activities performed by the patient. We evaluated the recommendation cycle on three patients to demonstrate the reductions in apnea cycles by indicating sound sleep patterns. Numerical results show that the proposed recommendation System suggest a relative improvement in sleep quality for all patients as compared to pre-existing expensive detection and relief schemes for sleep apnea patients.

To reduce the delay for accessing real-time data access from various applications (healthcare, transportations, virtual reality etc.), there is an exponential increase in the usage of Tactile Internet (TI) technology in recent era. Motivated from this, in this paper, we propose a TI-based random forest (RF) learning algorithm for heart disease predictions. The aim of this paper is to monitor and analyse the human activities for real-time data collection. The proposed approach is an analysis of heart ailments and can be used regularly for the health measure. For this purpose, the RF model is trained to map the collected sensor data features to output normal and abnormal states of the patient suffering from heart disorientation. Moreover, it removes excessive dependence on input values and cover possible alternate paths. Simulated results demonstrate that the proposed approach reduces the average delay and provides less training time in comparison to the pre existing conventional techniques.

Energy management of mobile devices is a crucial issue in fifth generation (5G) network due to their limited battery capacity. Simultaneous Wireless Information and Power Transfer (SWIPT) is an emerging technique which allows mobile devices to harvest energy from radio frequency (RF) signals. Moreover, Non-Orthogonal Multiple Access (NOMA) serves multiple users simultaneously using the same subchannel inter-user interference mitigation. By considering the aforementioned issues, in this paper, we propose a subchannel assignment scheme for SWIPT-NOMA based pico base station/femto base station with macro-cellular networks. The energy-efficient subchannel assignment is a probabilistic mixed non-convex optimization problem by considering imperfect channel state information (CSI). To address this problem, many-to-many matching theory is used in the proposal. Numerical results show that the proposed algorithm performs better in terms of numbers of PUs/FUs, average energy efficiency (EE) of the Picocells/Femtocells, in comparison to the orthogonal frequency division access scheme and conventional NOMA.

In the last few years, there has been an exponential increase in the deployment of 5G-based test beds across the globe with an aim to reduce the latency for accessing various applications. The integration of generic services such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), critical machine-type communication (cMTC), and ultra-reliable low-latency communications (URLLC) can improve the performance of 5G-based applications. This service heterogeneity can be achieved by network slicing for an optimized resource allocation and an emerging technology, Tactile Internet, to achieve low latency, high bandwidth, service availability, and end-to-end security. In this paper, we discuss the application-specific nonorthogonal multiple access (NOMA)-based communication architecture for Tactile Internet which allows nonorthogonal resource sharing from a pool of eMBB, mMTC, cMTC, and URLLC devices to a shared base station. We summarize various variants of NOMA and their suitability for future low latency Tactile-Internet-based applications.

Device-to-device (D2D) two-hop cooperative communication improves the network coverage and throughput to provide the quality of service and quality of experience to the end users. Nonorthogonal multiple access (NOMA) can be used at the D2D transmitter to improve the spectral efficiency of the network. But, two-hop transmission with NOMA suffers from delay and interference from the neighboring nodes. To resolve the aforementioned issues, in this paper, we propose Tactile Internet (TI) driven delay assessment for D2D communication (DIYA) scheme, which works in two phases. In the first phase, a full duplex communication at relays (intermediate nodes) is used to have the first- and second-hop transmission simultaneously in the same time slot. Then, TI-based communication is used at D2D transmitter to increase the speed of transmission. In the second phase, pricing-based three-dimensional (3-D) matching is proposed to improve the throughput of the cell edge users along with the mitigation of cochannel interference. Also, the power of the D2D transmitter is optimized using successive convex approximation with low complexity, which converts the nonconvex optimization problem of subchannel allocation and power control into convex problem. Numerical results demonstrate that DIYA achieves higher throughput with reduced delay in comparison to other existing orthogonal multiple access (OMA) and NOMA-based schemes.

In the last few years, we have witnessed an exponential increase in the popularity of Internet-enabled smart devices. In this era, various smart devices generate a huge amount of data during computing and communication. However, fixed infrastructure, especially in the dense population, have the issues of coverage and connectivity in this environment. But, 5G technology based femtocell emerges as one of the solutions for the aforementioned issues. Hence, in this paper, we investigate the non-orthogonal multiple access (NOMA) transmission with 5G enabled cognitive femtocell to attain higher spectral efficiency and to maximize the sum rate of femto users (FUs) with guaranteed QoS. A paring algorithm between strong and weak users has been proposed to reduce the NOMA interference between different FUs. To achieve higher data rates, the calculation of sum rate for an even/odd number of FUs in a femtocell is also proposed. Numerical results show that the proposed schemes effectively improve the sum rate of the cognitive femtocell under NOMA transmission to minimize the interference.

With the widespread popularity of low cost sensors, the data collection and processing on real-time from different geographical regions becomes easy. Agriculture is one such areas where sensors can be deployed to get real-time data of different regions. In agriculture field, temperature monitoring, soil moisture monitoring and plants growth monitoring can be obtained using sensor nodes. Obtaining the maximum growth in agricultural domain under optimized resources availability through Precise Irrigation (PI) is called as Precision Agriculture (PA). In this paper, we propose PI-Cloud, which is a sensorcloud based measurement to management (M2M) system, which can be used in an agricultural field to maintain the moisture level of soil above a predefined threshold in real-time. We used a cluster based hierarchical architecture of sensor-cloud, where deployed sensors, called as mobile sensor robot (MSR) can be recharged/replaced as and when required. In order to maintain the real-time monitoring of moisture level of soil, replacement mechanism of energy depleted MSR and cluster heads is also proposed. Management to maintain the PI is done using the control action matrix prepared from sensor cloud. Two novel parameters have been selected as, number of replacements required (NRR) for energy depleted MSR and first replacement analysis (FRA) are used for validation of proposed scheme. The results obtained show that with the availability of energy and recharging of MSR, PI can be done on real-time basis with PICloud based architecture system.

From the last few years, Wireless body area networks (WBANs) have attracted a lot of attention from both academia and industry due to an increase in real-time data capturing and processing for patient monitoring. This has become possible due to the technological advancements in which high computing and communication facilities are available for most of the modern handheld devices. In this environment, computing resources are available close to the proximity of the end users using the most popular technology called as Fog computing (devices used in the fog computing are called as fog devices). Most of the solutions reported in the literature for this purpose have used the traditional cloud-based infrastructure in which there may occur a long delay for getting the response even for data which is not of very huge amount which may cause a performance degradation for most of the implemented solutions (such as for treatment of neurological diseases where a real-time monitoring is required) in this environment. Hence, to cope up these issues, in this paper, we proposed a fog computing based patient monitoring system for ambient assisted living (FAAL). Data traces of the movement of the patients (for neurological diseases) are collected using sensor nodes using body area networks (BANs) and are passed using the fog gateways. To reduce the load on the communication infrastructure, an efficient clustering algorithm for data transmission is also presented in the paper. Performance of the proposed solution has been evaluated using the parameters such as-latency, and data overloading. Results obtained clearly show the superior performance of the proposed scheme as compared to the non-fog computing based environment.

Physical therapy has a lot of importance for the well being and a better quality of living for an elderly patient. One integral constituent of any patient regime is the home-based exercise that a patient works on in a much comfortable environment. Although the benefits are well known, there is a big lag between the exercises prescribed by the therapists and the ones actually done by the patient. There is no cost effective and non-complex methods available to quantify the exercises performed by the patient. In this paper, a study was performed to check the validity and efficiency of a system consisting of a Smart IoT enabled speaker, which contains an orchestrator. Which is speech learning unit, an exercise database at the edge, and connected to the cloud, where the generated reports are stored and transferred for further analysis, if required. We report the efficiency of the system compared to the ratings of a physical therapist, a standard currently being used.

Wireless Sensor Networks (WSNs) have gained a lot of popularity in recent years because these are being used in wide range of applications. A collection of randomly/planned deployed tiny Sensor Nodes (SNs) can perform the task according to the need of a specific application. Utilization of energy of SNs is one of the key issues in these networks as nodes are battery operated and recharge or replacement of battery is a difficult task to be achieved. To address this issue, we propose a Bayesian Coalition Game-based optimized clustering in WSNs. To formulate the game, we propose a new Hybrid Homogeneous LEACH (HHO-LEACH) protocol for SNs in WSNs. We have used the concepts of Learning Automata (LA), and Bayesian Coalition Game (BCG) in which SNs are assumed as the players in the game with dynamic thresholds-based coalition formation among themselves, i.e., coalition among the nodes are formed using distance-based thresholds which makes a partition of the network field. SNs near to base station use direct communication for data transfer with or without single hop to the Base Station(BS) after interacting with the environment. During this process, each player may get a reward, or a penalty with respect to the finite number of actions performed. Performance of the proposed protocol is evaluated using extensive simulations by selecting various evaluation metrics. The results obtained show that proposed coalition game achieved better stability, and network lifetime in comparison to other existing protocols such as LEACH, and DD.