Automatic modulation recognition (AMR) is a key enabler for intelligent spectrum utilization in 5G-and-beyond wireless systems, requiring both high classification accuracy and low computational complexity. This paper proposes a lightweight hybrid deep learning framework, termed CBLGNet, that integrates convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and gated recurrent units (GRU) for efficient AMR from raw in-phase and quadrature (I/Q) samples. The CNN extracts compact spatial representations, while the BiLSTM–GRU structure captures bidirectional temporal dependencies with reduced parameter complexity. Unlike existing hybrid models that rely on deep recurrent stacks or heavy dense layers, the proposed architecture achieves effective feature fusion with a compact parameter budget. Evaluations on the RML2016.10a and RML2016.10b datasets demonstrate that CBLGNet achieves 93.39% classification accuracy, outperforming several state-of-the-art AMR methods while maintaining low computational cost.

The challenge of fraud detection, especially in credit card transactions, continues to grow as fraudsters adapt to increasingly sophisticated tactics. Traditional methods, including rule-based systems, are often limited by high false positives and poor adaptability to new fraud patterns. This study introduces a cutting-edge approach using an attention-based ensemble framework that synergizes the strengths of convolutional neural networks (CNNs), graph neural networks (GNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) models. A key innovation of this model is its confidence-driven gating mechanism, which dynamically combines the most reliable prediction, ensuring both accuracy and transparency. By incorporating dependent ordered weighted averaging (DOWA) and induced ordered weighted averaging (IOWA) operators, the model effectively integrates outputs from various classifiers, improving robustness. Additionally, SHAP-based feature selection reveals the most influential variables, providing deeper interpretability. Extensive evaluations across three diverse benchmark datasets demonstrate the superior performance of this framework, surpassing individual classifiers and existing ensemble models in both balanced and highly imbalanced settings. This research paves the way for more reliable and transparent fraud detection systems, capable of adapting to evolving fraud tactics in real-world financial environments.

Despite progress in global healthcare systems, the utilization of domestic healthcare facilities remains limited in several regions, with a considerable proportion of people pursuing treatment overseas. This development highlights the necessity for systematic incorporation of patient-centered input, an essential element for enhancing accountability, transparency, and quality in local healthcare. Our research seeks to address this deficiency by establishing a system that collects and analyzes patient feedback to inform and improve healthcare policies and practices, particularly in areas with elevated demand for medical services. We offer an effective platform for viewing reviews from different hospitals, especially in places where people routinely visit for medical services. Therefore, we built our primary "Dhaka Private Hospitals Review Dataset," considering gathering and evaluating patient opinions methodically. We further employ transformer-based generative adversarial learning to evaluate sentiment analysis using knowledge distillation (KD) to boost model efficiency. Our proposed GANBERT architecture includes two optimized student models gated recurrent unit-based Contextualized BERT (GC-BERT) and LSTM-based Contextualized BERT (LC-BERT) with enhanced generators and discriminators. Our GC-BERT enhances execution time by 1.27% to 24.27%, while LC-BERT improves by 14.13% to 23.30%, showing superior advancements compared to other contemporary models. Each model with reductions ranging from 82.50% to 99.99% parameters, making them lightweight and efficient compared to other teacher models in the KD process. Instead of using contextual word representations which demand more space and complexity for reviewing patient feedback, we utilize the single static pretrained and low-dimensional word embedding space approach integrating student models.

In the evolving landscape of wireless communications for 5G, 6G, and beyond, the deployment of unmanned aerial vehicles (UAVs) has emerged as a groundbreaking strategy to expand coverage areas due to their flexibility and ease of deployment. Simultaneously, reflecting intelligent surfaces (RISs) have introduced a transformative paradigm aimed at improving key performance metrics, such as average sum-rate and energy efficiency (EE). The seamless integration of advanced technologies, including UAVs, RIS, and nonorthogonal multiple access (NOMA), presents a promising avenue for significantly boosting the performance and efficiency of next-generation communication systems. This study investigates EE maximization for two scenarios in a NOMA-enabled mmWave network: 1) multi-UAV-mounted base stations (BSs) and 2) multi-UAV-mounted distributed RIS. In both cases, each UAV serves a NOMA cluster with imperfect successive interference cancellation (SIC), capturing the impact of hardware impairments in real-world NOMA systems. For each scenario, an optimization problem is formulated to maximize EE by jointly optimizing the beamforming matrix, phase shift matrix, NOMA power allocation, and UAV 3-D placement. The nonconvex problems are tackled using both model-based and model-free deep reinforcement learning (DRL) algorithms under constraints, such as minimum Quality of Service (QoS), beamforming and phase shift limits, and UAV trajectory constraints. The simulation results demonstrate that the proposed DRL algorithms significantly enhance spectral efficiency (SE) and EE, showcasing their suitability for 6G communication systems. Furthermore, a comparative analysis with orthogonal multiple access (OMA) and spatial-division multiple access (SDMA) confirms that NOMA outperforms both techniques, achieving substantial gains in efficiency and performance.

Brain tumor detection has advanced significantly with the development of deep learning technology. Although multimodal data, such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), has potential advantages in diagnostics, most existing studies rely solely on a single modality. This is because common fusion methods may lead to the loss of critical information when attempting multimodal fusion. Therefore, effectively integrating multimodal data has become a significant challenge. Additionally, medical image analysis requires large amounts of annotated data, and labeling images is a resource-intensive task that demands experienced professionals to spend a considerable amount of time. To address these challenges, this paper introduces a new unsupervised learning framework named Double-SimCLR. This framework builds on the foundation of contrastive learning and features a dual-branch structure, enabling direct and simultaneous processing of MRI and CT images for multimodal feature fusion. Given the "weak feature" characteristics of CT images (e.g., low soft tissue contrast and low resolution), we incorporated adaptive weight masking technology to enhance CT feature extraction. Moreover, we introduced a multimodal attention mechanism, which ensures that the model focuses on salient information, thereby elevating the precision and robustness of brain tumor detection. Even without substantial labeled data, experimental results demonstrate that Double-SimCLR achieves 93.458% accuracy, 92.463% precision, and a 93.058% F1-score, outperforming state-of-the-art (SOTA) models by 2.871%, 2.643%, and 3.098%, respectively.

Autonomous Transportation Systems (ATS) make the transportation system transition from “passive transportation” to “autonomous service”. The wireless nature of communication in ATS presents significant cybersecurity challenges. Conventional intelligent vehicular intrusion detection methods may not suffice in situations where vehicular data is produced at an unprecedented scale and diverse cybersecurity threats are launched. Therefore, there is a demand for the creation of advanced intelligent vehicular intrusion detection systems that can effectively manage potential cyberattacks within ATS. Toward this end, this paper proposes QnGSA (hypercube driven graph self-attention intelligent vehicular intrusion detection model) in ATS, a novel intrusion detection model that helps to protect both the vehicles and the data they transmit, preventing disruptions to services, theft of sensitive information, and potential harm to passengers or cargo. QnGSA not only proposes a construction method of association graph by introducing hypercube and semi-supervised K-means++ clustering algorithm (QnSSKM). But also, QnGSA self-extracts the graph structural information of hypercube, and uses the graph self-attention mechanism to aggregate node features and obtain more accurate representation. Furthermore, this paper uses Graph Attention Network classifier to correlate the learned node representation with the fault category, and uses Softmax function to map the node representation to the probability distribution of different categories. The category with the highest probability is selected as the prediction label of the node, so as to realize the intelligent vehicular intrusion detection. Experiments results show that our proposed QnGSA method achieves the best results compared with state-of-the-art methods in terms of accuracy, macro precision/recall/F1.

Ultrareliable low-latency communication (URLLC) is essential to facilitate mission-critical applications like the Industrial Internet of Things (IIoT). In order to improve communication efficiency, URLLC adopts short-packet signals to transmit the delay-sensitive control information among the Internet of Things (IoT) devices in IIoT. However, IoT adoption is hindered by the security vulnerability of URLLC due to the openness of wireless medium. Therefore, in this article, we focus on developing a secure and energy-efficient URLLC signal transmission scheme for mission-critical IIoT. In this regard, we formulate an optimization problem for maximizing the secure energy efficiency of the IIoT system under the constraints like URLLC Quality of Service (QoS), decoding error probability, and intercept probability of the eavesdroppers. For that, we adopt the physical-layer security (PLS) enhancement techniques in this work because it can provide low-complex security solutions for URLLC. Specifically, average secrecy throughput is used to measure the PLS performance of the URLLC-IIoT system. The optimization problem is solved iteratively by maximizing the secrecy throughput and minimizing the average power allocation per device. Meanwhile, a joint optimization of blocklength and pilot signal length is proposed to maximize the average secrecy throughput of the system. The nonasymptotic closed-form expression of the average secrecy throughput and the decoding error probability at the legitimate receiver have been derived to make the numerical evaluation tractable. Using the Lagrange multiplier technique with the Karush–Kuhn–Tucker (KKT) conditions we obtain the optimal power allocation for the maximization of the secure energy efficiency. The extensive simulation and numerical results validate the theoretical approximations presented in the work and demonstrate the effectiveness of the proposed method for improving the secure energy efficiency of IIoT.

The Augmented Intelligence of Things (AIoT) is an emerging technology that combines augmented intelligence with the Internet of Things (IoT) to facilitate advanced decision-making processes. In this paper, we focus on the detection of vehicle trajectory anomalies in a vehicle-road collaboration system by AIoT, aiming to improve the traffic safety and road operation efficiency. We transmit collaboration data collected by sensors to an IoT server, which enables the effective data analysis for vehicle trajectory information. We propose a self-supervised learning augmented intelligence algorithm to achieve precise and efficient detection of trajectory anomalies. First, we models the traffic road network as a topology graph. Subsequently, we sample the relevant subgraph contexts for each target node through a random walk algorithm. And the subgraphs with higher intimacy scores are selected as the contextual background to be input along with the target node. After that, the anomaly score of each target node is computed through the generative learning module and the contrastive learning module. To evaluate the effectiveness of our anomaly detection approach, we initially conduct pre-training of the model using four widely utilized graph machine learning datasets. The experimental results reveal that our approach surpasses previous methods in the accuracy of identifying graph anomaly nodes. In addition, we carry out our approach on two real traffic datasets with high accuracies of 86.47% and 85.2%, respectively. This result demonstrates the effectiveness of our proposed approach in detecting trajectory anomalies in real traffic scenarios.

Ensuring the stability and security of unmanned aerial vehicle (UAV) communication, especially during long-distance missions, is essential for safeguarding against potential attacks. Large-scale UAV communication faces challenges including eavesdropping threat, data tampering, replay threat and man-in-the-middle threat. We propose a security information transmission solution based on reinforcement learning and location confusion algorithm (RLPC-SIT) to achieve a secure data transmission between UAVs. First, we leverage the principles of reinforcement learning to identify the most stable transmission routes. Secondly, we employ location confusion techniques to blur each location of the transmitting UAV with respect to other UAVs. Furthermore, we utilize the concept of message authentication to encrypt the transmitted data, thus making it inaccessible to malicious nodes and preventing forgery. The results of our theoretical analysis and simulation-based experiments indicate that our approach outperforms other security schemes.

The abnormal growth of malignant or nonmalignant tissues in the brain causes long-term damage to the brain. Magnetic resonance imaging (MRI) is one of the most common methods of detecting brain tumors. To determine whether a patient has a brain tumor, MRI filters are physically examined by experts after they are received. It is possible for MRI images examined by different specialists to produce inconsistent results since professionals formulate evaluations differently. Furthermore, merely identifying a tumor is not enough. To begin treatment as soon as possible, it is equally important to determine the type of tumor the patient has. In this paper, we consider the multiclass classification of brain tumors since significant work has been done on binary classification. In order to detect tumors faster, more unbiased, and reliably, we investigated the performance of several deep learning (DL) architectures including Visual Geometry Group 16 (VGG16), InceptionV3, VGG19, ResNet50, InceptionResNetV2, and Xception. Following this, we propose a transfer learning(TL) based multiclass classification model called IVX16 based on the three best-performing TL models. We use a dataset consisting of a total of 3264 images. Through extensive experiments, we achieve peak accuracy of 95.11%, 93.88%, 94.19%, 93.88%, 93.58%, 94.5%, and 96.94% for VGG16, InceptionV3, VGG19, ResNet50, InceptionResNetV2, Xception, and IVX16, respectively. Furthermore, we use Explainable AI to evaluate the performance and validity of each DL model and implement recently introduced Vison Transformer (ViT) models and compare their obtained output with the TL and ensemble model.

To ensure effective communication and monitoring, Flying Ad-hoc Networks (FANETs) with limited energy must always work together. The purpose of this paper is to introduce a Trusted Dynamic Leader Selection for FANETs (TDLS-FANET), which aims to designate a leader drone that will guide other drones. Using a trust-based mechanism, the proposed approach discerns reliable neighboring Unmanned Aerial Vehicles (UAVs) while identifying and isolating potential malicious UAVs. According to the proposed scheme, trust score is calculated based on three parameters including quality of service (QoS), social trust, and fitness score. A number of parameters are taken into account when calculating the direct trust between neighboring UAVs, including the delay, packet delivery ratio, and signal strength. Indirect trust is derived from recommendation trust. Based on the residual energy and average distance of drones that are closer to the base station (BS), a fitness score is calculated. On the basis of their final trust scores, the system forms an efficient classification of clusters. TDLS-FANET can also select dynamic leaders among several drones based on their physical constraints at varying intervals. To assess performance characteristics such as energy consumption, packet delivery ratio, and transmission delay of the proposed TDLS-FANET with the existing trust based models, simulation results demonstrate that the proposed TDLS-FANET reduces delay by 24%, lowers energy consumption by 19%, and increases packet delivery rates by more than 30%. It can limit delay by up to 41% in extreme situations. In FANETs, the proposed method improves accuracy and adaptability by selecting the dynamic leader drone efficiently. As a result, the performance metrics of TDLS-FANET are superior to those of existing systems.

Artificial Intelligence of Things (AIoT), as a fusion of artificial intelligence (AI) and Internet of Things (IoT), has become a new trend to realize the intelligentization of industry 4.0 and the data privacy and security is the key to its successful implementation. To enhance data privacy protection, the federated learning has been introduced in AIoT, which allows participants to jointly train AI models without sharing private data. However, in federated learning, malicious participants might provide malicious models by launching the poisoning attack, which will jeopardize the convergence and accuracy of the global model. To solve this problem, we propose a malicious model detection mechanism based on the isolation forest (iforest), named D2MIF, for the federated learning-empowered AIoT. In D2MIF, an iforest is constructed to compute the malicious score for each model uploaded by the corresponding participant, and then, the models will be filtered if their malicious scores are higher than the threshold, which is dynamically adjusted using reinforcement learning (RL). The validation experiment is conducted on two public data sets Mnist and Fashion_Mnist. The experimental results show that the proposed D2MIF can effectively detect malicious models and significantly improve the global model accuracy in federated learning-empowered AIoT.

The ability to detect which wireless devices are belonging to the same person from Wi-Fi access point (AP) enables many potential Internet-of-Things (IoT) applications, including continuous authentication and user-oriented devices isolation. The existing cryptographic-based solutions are not suitable for IoT devices with limited power and computing capabilities. The development of electronics and chip technology makes it possible to deploy machine learning (ML) algorithms on APs. In this article, we propose an on-body device clustering (OBDC) scheme. First, the OBDC extracts the trajectory and gait patterns from wireless signals when the user is moving. Second, it utilizes a hierarchical clustering algorithm to measure the similarity of wireless signal patterns between devices. Finally, if the devices are clustered into the same cluster, they are considered to be carried by the same person. Our real-world experimental results show that the devices from about 90% of users can be clustered correctly, while maintaining the devices from only 0.7% of users may be clustered into the same cluster with others’ devices incorrectly.

The deep integration of the Internet of Things (IoT) and the medical industry has given birth to the Internet of Medical Things (IoMT). In IoMT, physicians treat a patient's disease by analyzing patient data collected through mobile devices with the assistance of an artificial intelligence (AI)-empowered systems. However, the traditional AI technologies may lead to the leakage of patient privacy data due to its own design flaws. As a privacy-preserving federated learning (FL) can generate a global disease diagnosis model through multiparty collaboration. However, FL is still unable to resist inference attacks. In this article, to address such problems, we propose a privacy-enhanced disease diagnosis mechanism using FL for IoMT. Specifically, we first reconstruct medical data through a variational autoencoder and add differential privacy noise to it to resist inference attacks. These data are then used to train local disease diagnosis models, thereby preserving patients' privacy. Furthermore, to encourage participation in FL, we propose an incentive mechanism to provide corresponding rewards to participants. Experiments are conducted on the arrhythmia database Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH). The experimental results show that the proposed mechanism reduces the probability of reconstructing patient medical data while ensuring high-precision heart disease diagnosis.

Abstract The hike in the demand for smart cities has gathered the interest of researchers to work on environmental sound classification. Most researchers' goal is to reach the Bayesian optimal error in the field of audio classification. Nonetheless, it is very baffling to interpret meaning from a three‐dimensional audio and this is where different types of spectrograms become effective. Using benchmark spectral features such as mel frequency cepstral coefficients (MFCCs), chromagram, log‐mel spectrogram (LM), and so on audio can be converted into meaningful 2D spectrograms. In this paper, we propose a convolutional neural network (CNN) model, which is fabricated with additive angular margin loss (AAML), large margin cosine loss (LMCL) and a‐softmax loss. These loss functions proposed for face recognition, hold their value in the other fields of study if they are implemented in a systematic manner. The mentioned loss functions are more dominant than conventional softmax loss when it comes to classification task because of its capability to increase intra‐class compactness and inter‐class discrepancy. Thus, with MCAAM‐Net, MCAS‐Net and MCLCM‐Net models, a classification accuracy of 99.60%, 99.43% and 99.37% is achieved on UrbanSound8K dataset respectively without any augmentation. This paper also demonstrates the benefit of stacking features together and the above‐mentioned validation accuracies are achieved after stacking MFCCs and chromagram on the x ‐axis. We also visualized the clusters formed by the embedded vectors of test data for further acknowledgement of our results, after passing it through different proposed models. Finally, we show that the MCAAM‐Net model achieved an accuracy of 99.60% on UrbanSound8K dataset, which outperforms the benchmark models like TSCNN‐DS, ADCNN‐5, ESResNet‐Attention, and so on that are introduced over the recent years.

Surveillance drones, called as unmanned aerial vehicles (UAVs), are aircrafts that are utilized to collect video recordings, still images, or live video of the targets, such as vehicles, people or specific areas. Particularly in battlefield surveillance, there is high possibility of eavesdropping, inserting, modifying or deleting the messages during communications among the deployed drones and ground station server (GSS). This leads to launch several potential attacks by an adversary, such as main-in-middle, impersonation, drones hijacking, replay attacks, etc. Moreover, anonymity and untraceability are two crucial security properties that need to be maintained in battlefield surveillance communication environment. To deal with such a crucial security problem, we propose a new access control protocol for battlefield surveillance in drone-assisted Internet of Things (IoT) environment, called ACPBS-IoT. Through the detailed security analysis using formal and informal (nonmathematical), and also the formal security verification under automated software simulation tool, we show that the proposed ACPBS-IoT can resist several potential attacks needed in a battlefield surveillance scenario. Furthermore, the testbed experiments for various cryptographic primitives have been performed for measuring the execution time. Finally, a detailed comparative study on communication and computational overheads, and security, as well as functionality features, reveals that the proposed ACPBS-IoT provides superior security and more functionality features, and better or comparable overheads than other existing competing access control schemes.

Recently, edge artificial intelligence techniques (e.g., federated edge learning) are emerged to unleash the potential of big data from Internet of Things (IoT). By learning knowledge on local devices, data privacy preserving and Quality of Service (QoS) are guaranteed. Nevertheless, the dilemma between the limited on-device battery capacities and the high energy demands in learning is not resolved. When the on-device battery is exhausted, the edge learning process will have to be interrupted. In this article, we propose a novel wirelessly powered edge intelligence (WPEG) framework, which aims to achieve a stable, robust, and sustainable edge intelligence by energy harvesting (EH) methods. First, we build a permissioned edge blockchain to secure the peer-to-peer (P2P) energy and knowledge sharing in our framework. To maximize edge intelligence efficiency, we then investigate the wirelessly powered multiagent edge learning model and design the optimal edge learning strategy. Moreover, by constructing a two-stage Stackelberg game, the underlying energy-knowledge trading incentive mechanisms are also proposed with the optimal economic incentives and power transmission strategies. Finally, simulation results show that our incentive strategies could optimize the utilities of both parties compared with classic schemes, and our optimal learning design could realize the optimal learning efficiency.

The Industrial Internet of Things (IIoT) is an emerging technology that can promote the development of industrial intelligence, improve production efficiency, and reduce manufacturing costs. However, anomalies of IIoT devices might expose sensitive data about users of high authenticity and validity, resulting in security and privacy threats to the IIoT applications. That suggests the significance of anomaly detection executed by proper authorities. To address these problems, in this paper, we propose a reliable anomaly detection strategy for IIoT using federated learning. Specifically, we apply the federated learning technique to build a universal anomaly detection model with each local model trained by the deep reinforcement learning (DRL) algorithm. Since local data sets are not required during the federated learning, the chance of privacy leakage is reduced. In addition, by introducing privacy leakage degree and action relation to anomaly detection design, we can greatly improve the detection accuracy. The validation experiments indicate that the proposed strategy achieves high throughput, low latency, and high anomaly detection accuracy for privacy preservation in various IIoT scenarios.

By amalgamating recent communication and control technologies, computing and data analytics techniques, and modular manufacturing, Industry 4.0 promotes integrating cyber–physical worlds through cyber–physical systems (CPS) and digital twin (DT) for monitoring, optimization, and prognostics of industrial processes. A DT enables interaction with the digital image of the industrial physical objects/processes to simulate, analyze, and control their real-time operation. DT is rapidly diffusing in numerous industries with the interdisciplinary advances in the industrial Internet of things (IIoT), edge and cloud computing, machine learning, artificial intelligence, and advanced data analytics. However, the existing literature lacks in identifying and discussing the role and requirements of these technologies in DT-enabled industries from the communication and computing perspective. In this article, we first present the functional aspects, appeal, and innovative use of DT in smart industries. Then, we elaborate on this perspective by systematically reviewing and reflecting on recent research trends in next-generation (NextG) wireless technologies (e.g., 5G-and-Beyond networks) and design tools, and current computational intelligence paradigms (e.g., edge and cloud computing-enabled data analytics, federated learning). Moreover, we discuss the DT deployment strategies at different communication layers to meet the monitoring and control requirements of industrial applications. We also outline several key reflections and future research challenges and directions to facilitate industrial DT’s adoption.

The development and maturity of the fifth-generation (5G) wireless communication technology provides the industrial Internet of Things (IIoT) with ultra-reliable and low-latency communications and massive machine-type communications, and forms a novel IIoT architecture, 5G-IIoT. However, massive data transfer between interconnecting industrial devices also brings new challenges for the 5G-IIoT routing process in terms of latency, load balancing, and data privacy, which affect the development of 5G-IIoT applications. Moreover, the existing research works on IIoT routing mostly focus on the latency and the reliability of the routing, disregarding the privacy security in the routing process. To solve these problems, in this article, we propose a quality of service (QoS) and data privacy-aware routing protocol, named QoSPR, for 5G-IIoT. Specifically, we improve the community detection algorithm info-map to divide the routing area into optimal subdomains, based on which the deep reinforcement learning algorithm is applied to build the gateway deployment model for latency reduction and load-balancing improvement. To eliminate areal differences, while considering the privacy preservation of the routing data, the federated reinforcement learning is applied to obtain the universal gateway deployment model. Then, based on the gateway deployment, the QoS and data privacy-aware routing is accomplished by establishing communications along the load-balancing routes of the minimum latencies. The validation experiment is conducted on real datasets. The experiment results show that as a data privacy-aware routing protocol, the QoSPR can significantly reduce both average latency and maximum latency, while maintaining excellent load balancing in 5G-IIoT.

Future generation vehicles equipped with modern technologies will impose unprecedented computational demand due to the wide adoption of compute-intensive services with stringent latency requirements. The computational capacity of the next generation vehicular networks can be enhanced by incorporating vehicular edge or fog computing paradigm. However, the growing popularity and massive adoption of novel services make the edge resources insufficient. A possible solution to overcome this challenge is to employ the onboard computation resources of close vicinity vehicles that are not resource-constrained along with the edge computing resources for enabling tasks offloading service. In this paper, we investigate the problem of task offloading in a practical vehicular environment considering the mobility of the electric vehicles (EVs). We propose a novel offloading paradigm that enables EVs to offload their resource hungry computational tasks to either a roadside unit (RSU) or the nearby mobile EVs, which have no resource restrictions. Hence, we formulate a non-linear problem (NLP) to minimize the energy consumption subject to the network resources. Then, in order to solve the problem and tackle the issue of high mobility of the EVs, we propose a deep reinforcement learning (DRL) based solution to enable task offloading in EVs by finding the best power level for communication, an optimal assisting EV for EV pairing, and the optimal amount of the computation resources required to execute the task. The proposed solution minimizes the overall energy for the system which is pinnacle for EVs while meeting the requirements posed by the offloaded task. Finally, through simulation results, we demonstrate the performance of the proposed approach, which outperforms the baselines in terms of energy per task consumption.

In the vehicular ad hoc networks (VANETs), minimizing the broadcast storm that arises due to message rebroadcast during emergency message dissemination in extremely mobile environments under sparse or dense networks is a significant challenge. Proper selection of rebroadcasting vehicles guarantees acceptable end-to-end delay, high delivery ratio, and efficient bandwidth utilization. To date, many protocols have been proposed to select an appropriate rebroadcasting vehicles based on vehicle position information only. However, such approaches neglect the fact that both vehicle velocity and position information can be utilized efficiently to alleviate rebroadcast message collisions and control bandwidth consumption. In this work, we present a new broadcast suppression protocol, named, velocity and position-based broadcast suppression for VANETs (VP-CAST), which can work in both sparse and dense network situations. VP-CAST does rely on periodic beacon messages, rather the position and velocity information of broadcasting vehicle are included in a broadcast message. Moreover, the transmission range of broadcasting vehicle is divided into dynamic time slots based on velocity and position information of broadcasting and receiving vehicles.The proposed scheme assigns shorter and dynamic waiting time to the vehicles moving at high velocities and located farther from the sender vehicle that eventually reduces both the message re-transmission delay and the number of rebroadcasting vehicles. The proposed protocol is compared with the DV-CAST in terms of end-to-end delay, message delivery ratio, and message overhead.

Heterogeneous networks (HetNets) are becoming a promising solution for future wireless systems to satisfy the high data rate requirements. This paper introduces a stochastic geometry framework for the analysis of the downlink coverage probability in a multi-tier HetNet consisting of a macro-base station (MBS) operating at sub-6 GHz, millimeter wave (mmWave)-enabled unmanned aerial vehicles (UAVs) operating at 28 GHz, and small BSs operating both at mmWave and THz frequencies. The analytical expressions for the coverage probability for each tier have been derived in the paper. Monte Carlo simulations are then performed to validate the analytical expressions. The effectiveness of the HetNet is analyzed on various performance metrics including association and coverage probabilities for different network parameters. We show that the mmWave and THz-enabled cells provide significant improvement in the achievable data rates because of their high available bandwidths, however, they have a degrading effect on the coverage probability due to their high propagation losses.

A Fog computing-based transportation system envisions to reduce energy consumption and communication delay. This paper presents a Fog computing-based scheme for assisting passengers, which involves task offloading and reallocation. We consider a dynamic environment where the passengers frequently change their locations. Additionally, the scheme mitigates the sudden failure of the Fog devices. We employ a game-theoretic approach to determine optimal fractions of a task associated with the passengers to be offloaded among the Fog devices and Cloud. It also supports the reallocation of the allocated fractions of the task. This offloading and reallocation of tasks ensures the execution within a given time constraint and requires minimal execution cost. We also prove the existence of near Nash equilibrium for the allocated fractions of the task on Fog devices. Further, this work covers different possibilities of dependencies among the fractions of the task and corresponding utilities of Fog devices in the dynamic environment. Finally, we present the empirical and real-world evaluations to verify the effectiveness of the proposed scheme in terms of the number of Fog devices, the deadline of the task, and game parameters.

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.

With the expeditious evolution in technology, recent years have witnessed significant growth in passenger assistance applications in the transportation system. Such applications have varying demands for resources and quality of services. This paper presents a Fog computing based transportation system. The system uses multiple Fog devices to provide assistance to the passengers. The passengers and vehicles work as end-users and the Edge devices in the system, respectively. A passenger generates a task and using the Edge device forwards it to the Fog devices for further processing. Selected Fog devices parallel process the fraction of the task, so that the complete task processes within the given time constraint. We use the gamma function based reputation model of Fog devices, which provides the confidence to complete a given task successfully. We present a Knapsack based task offloading algorithm, which helps to fully utilize the resources of the Fog devices. We also present a competitive game model and near Nash Equilibrium solution for estimating the optimal value of the fraction of the task process at Fog devices. Finally, we develop a prototype and present results to investigate the performance of the propose system.

Quality of Service (QoS) provisioning is based on various network management techniques including resource management and medium access control (MAC). Various techniques have been introduced to automate networking decisions, particularly at the MAC layer. Deep reinforcement learning (DRL), as a solution to sequential decision making problems, is a combination of the power of deep learning (DL), to represent and comprehend the world, with reinforcement learning (RL), to understand the environment and act rationally. In this paper, we present a survey on the applications of DRL in QoS provisioning at the MAC layer. First, we present the basic concepts of QoS and DRL. Second, we classify the main challenges in the context of QoS provisioning at the MAC layer, including medium access and data rate control, and resource sharing and scheduling. Third, we review various DRL algorithms employed to support QoS at the MAC layer, by analyzing, comparing, and identifying their pros and cons. Furthermore, we outline a number of important open research problems and suggest some avenues for future research.

With the explosive advancements in smartphone technology, video uploading/downloading has become a routine part of digital social networking. Video contents contain valuable information as more incidents are being recorded now than ever before. In this paper, we present a comprehensive survey on information extraction from video contents and forgery detection. In this context, we review various modern techniques such as computer vision and different machine learning (ML) algorithms including deep learning (DL) proposed for video forgery detection. Furthermore, we discuss the persistent general, resource, legal, and technical challenges, as well as challenges in using DL for the problem at hand, such as the theory behind DL, CV, limited datasets, real-time processing, and the challenges with the emergence of ML techniques used with the Internet of Things (IoT)-based heterogeneous devices. Moreover, this survey presents prominent video analysis products used for video forensics investigation and analysis. In summary, this survey provides a detailed and broader investigation about information extraction and forgery detection in video contents under one umbrella, which was not presented yet to the best of our knowledge.

In this article, we propose a lightweight security scheme for ensuring both information confidentiality and transmission resiliency in the Internet-of-Things (IoT) communication. A single-antenna transmitter communicates with a half-duplex single-antenna receiver in the presence of a sophisticated multiple-antenna-aided passive eavesdropper and a multiple-antenna-assisted hostile jammer (HJ). A low-complexity artificial noise (AN) injection scheme is proposed for drowning out the eavesdropper. Furthermore, for enhancing the resilience against HJ attacks, the legitimate nodes exploit their own local observations of the wireless channel as the source of randomness to agree on shared secret keys. The secret key is utilized for the frequency hopping (FH) sequence of the proposed communication system. We then proceed to derive a new closed-form expression for the achievable secret key rate (SKR) and the ergodic secrecy rate (ESR) for characterizing the secrecy benefits of our proposed scheme, in terms of both information secrecy and transmission resiliency. Moreover, the optimal power sharing between the AN and the message signal is investigated with the objective of enhancing the secrecy rate. Finally, through extensive simulations, we demonstrate that our proposed system model outperforms the state-of-the-art transmission schemes in terms of secrecy and resiliency. Several numerical examples and discussions are also provided to offer further engineering insights.

Massive connectivity and limited energy are main challenges for the beyond 5G (B5G)-enabled massive Internet of Things (IoT) to maintain diversified Qualify of Service (QoS) of the huge number of IoT device users. Motivated by these challenges, this article studies the performance of cooperative simultaneous wireless information and power transfer (SWIPT) nonorthogonal multiple access (NOMA) for massive IoT systems. Under the practical assumption, residual hardware impairments (RHIs) and channel estimation errors (CEEs) are taken into account. The communication between the base station (BS) and two NOMA IoT device users is realized through a direct link and the assistance of multiple relays with finite energy storage capability that can harvest energy from the BS. Aiming at improving the system performance, an optimal relay is selected among K relays by using the partial relay selection (PRS) protocol to forward the received signal to the two NOMA IoT device users, namely, the far user (FU) and near user (NU). To evaluate the system performance, exact analytical expressions for the outage probability (OP) are derived in closed form. In order to get a better understanding of the overall system performance, we further undertake diversity order analyses by deriving asymptotic expressions for the OP in the high signal-to-noise ratio (SNR) regime. In addition, we also investigate the energy efficiency (EE) of the considered system, which is a crucial performance metric in massive IoT systems so that the impact of key system parameters on the performance can be quantified. Finally, the optimal power allocation scheme to maximize the sum rate of the considered system in the high SNR regime is also designed. Numerical results have shown that: 1) hardware impairment parameter has a deleterious effect on system performance while the channel estimation parameter is always beneficial to the OP; 2) the expected performance improvements obtained by the user of PRS protocol are enhanced by increasing the number of relays; and 3) the proposed power allocation scheme can optimize the sum-rate performance of the considered system.

With the worldwide large-scale outbreak of COVID-19, the Internet of Medical Things (IoMT), as a new type of Internet of Things (IoT)-based intelligent medical system, is being used for COVID-19 prevention and detection. However, since the widespread use of IoMT will generate a large amount of sensitive information related to patients, it is becoming more and more important yet challenging to ensure data security and privacy of COVID-19 applications in IoMT. The leakage of private information during IoMT data fusion process will cause serious problems and affect people's willingness to contribute data in IoMT. To address these challenges, this article proposes a new privacy-enhanced data fusion strategy (PDFS). The proposed PDFS consists of four important components, i.e., sensitive task classification, task completion assessment, incentive mechanism-based task contract design, and homomorphic encryption-based data fusion. The extensive simulation experiments demonstrate that PDFS can achieve high task classification accuracy, task completion rate, task data reliability and task participation rate, and low average error rate, while improving the privacy protection for data fusion under COVID-19 application environments based on IoMT.

Technological development increases capacity of information systems, which with development of faster data transfer will be able to host variety of new devices. In this article we present electronic modules, infrastructure and fuzzy rules control model with implemented software for new generation home environment. The system is developed for the next IoT level based on 6G network communication standards. Proposed control model is efficient in water flow management, wind shield control, security aspects and carbon dioxide limitation via adaptive ventilation. Developed infrastructure is ready for new 6G communication standard, which will additionally improve efficiency and data flow at end-user devices and local area level.

Opinion summarization is a process to produce concise summaries from a large number of opinionated texts. In this paper, we present a novel deep-learning-based method for the generic opinion-oriented extractive summarization of multi-documents (also known as RDLS). The method comprises sentiment analysis embedding space (SAS), text summarization embedding spaces (TSS) and opinion summarizer module (OSM). SAS employs recurrent neural network (RNN) which is composed by long short-term memory (LSTM) to take advantage of sequential processing and overcome several flaws in traditional methods, where order and information about a word have vanished. Furthermore, it uses sentiment knowledge, sentiment shifter rules and multiple strategies to overcome the existing drawbacks. TSS exploits multiple sources of statistical and linguistic knowledge features to augment word-level embedding and extract a proper set of sentences from multiple documents. TSS also uses the Restricted Boltzmann Machine algorithm to enhance and optimize those features and improve resultant accuracy without losing any important information. OSM consists of two phases: sentence classification and sentence selection which work together to produce a useful summary. Experiment results show that RDLS outperforms other existing methods. Moreover, the ensemble of statistical and linguistic knowledge, sentiment knowledge, sentiment shifter rules and word-embedding model allows RLDS to achieve significant accuracy.

Summary The vast amount of data is key challenge to mine a new scholar that is plausible to be star in the upcoming period. The enormous amount of unstructured data raise every year is infeasible for traditional learning; consequently, we need a high quality of preprocessing technique to expand the performance of traditional learning. We have persuaded a novel approach, Authors classification algorithm using Transfer Learning (ACTL) to learn new task on target area to mine the external knowledge from the source domain. Comprehensive experimental outcomes on real‐world networks showed that ACTL, Node‐based Influence Predicting Stars, Corresponding Authors Mutual Influence based on Predicting Stars, and Specific Topic Domain‐based Predicting Stars enhanced the node classification accuracy as well as predicting rising stars to compared with contemporary baseline methods.

Summary Recently Internet of Things (IoT) is being used in several fields like smart city, agriculture, weather forecasting, smart grids, waste management, etc. Even though IoT has huge potential in several applications, there are some areas for improvement. In the current work, we have concentrated on minimizing the energy consumption of sensors in the IoT network that will lead to an increase in the network lifetime. In this work, to optimize the energy consumption, most appropriate Cluster Head (CH) is chosen in the IoT network. The proposed work makes use of a hybrid metaheuristic algorithm, namely, Whale Optimization Algorithm (WOA) with Simulated Annealing (SA). To select the optimal CH in the clusters of IoT network, several performance metrics such as the number of alive nodes, load, temperature, residual energy, cost function have been used. The proposed approach is then compared with several state‐of‐the‐art optimization algorithms like Artificial Bee Colony algorithm, Genetic Algorithm, Adaptive Gravitational Search algorithm, WOA. The results prove the superiority of the proposed hybrid approach over existing approaches.

As an emerging technology, the industrial Internet of Things (IIoT) can promote the development of industrial intelligence, improve production efficiency, and reduce manufacturing costs. In IIoT, the improvement and progress of industrial production and applications are inseparable from data fusion, a process that realizes the collection, analysis, and processing of the massive IoT data generated by industrial equipment and applications. IIot demands a real-time, effective, and privacy-preserving data fusion process. However, the existing works need to train different learning models for data analysis, which cannot meet real-time requirements in IIoT. Meanwhile, the lack of defense against internal attacks and the difficulty to balance system performance and privacy protection hinder the effectiveness and privacy protection in the data fusion process. To solve the abovementioned problems, in this article, we propose a new transfer learning-based secure data fusion strategy (TSDF) for IIoT. The proposed TSDF consists of three parts, guidance based deep deterministic policy gradient (GDDPG) algorithm for task classification, transfer learning based GDDPG for grouping of task receivers, and a multiblockchain mechanism for privacy preservation. The experiment results show that TSDF can achieve high system throughput and low latency, providing privacy preservation in data fusion under various IIoT application environments.

Industrial Internet of Things (IIoT) is ushering in huge development opportunities in the era of Industry 4.0. However, there are significant data security and privacy challenges during automatic and real-time data collection, monitoring for industrial applications in IIoT. Data security and privacy in IIoT applications are closely related to the reliability of users, which is determined by user authentication that have been widely used as an effective approach. However, the existing user authentication mechanisms in IIoT suffer from single factor authentication and poor adaptability with the rapid growth of the number of users and the diversity of user categories. To solve the aforementioned issues, this article proposes a novel Authentication mechanism based on Transfer Learning empowered Blockchain, coined ATLB. In ATLB, blockchains are applied to achieve the privacy preservation for industrial applications. In addition, by introducing the transfer learning based authentication mechanism, trustworthy blockchains are built such that the privacy preservation for industrial applications is further enhanced. Specifically, ATLB first employs a guiding deep deterministic policy gradient algorithm to train the user authentication model of a specific region, which is then transferred locally for foreign user authentication or cross-regionally for another region's user authentication such that the model training time is significantly reduced. Experimental results show that the proposed ATLB not only provides accurate authentications for IIoT applications but also achieves high throughput and low latency.

Today, with the worldwide offer and rapid increment in multimedia applications on the web, the demands of users to get them accessed are also increasing prominently. The users in vehicular environment too expect efficient multimedia streaming while travelling on the road. However, the high mobility of vehicles as well as the limited transmission range of infrastructure components in IP based network provides low performance by offering high delay and additional network overhead. To provide better Quality of Experience (QoE) with high performance, Information Centric Networking (ICN) is blended with vehicular environment. Caching the content inside network nodes is inherent feature of ICN with various associated benefits such as low content retrieval delay, less network traffic, path reduction and so on. However, challenges still exists for caching the content due to resource constrained network environment (such as limited cache capacity, node battery) as well as for secure delivery of cached data. To solve these challenges and to enhance network performance, we propose a cooperative caching scheme in hierarchical network architecture that jointly considers cache location as well as combined content popularity and predicted future rating score while making caching decision. The proposed approach uses two layer hierarchical architecture where nodes in edge layer are divided into clusters. The proposed scheme uses modified Weighted Clustering Algorithms (WCA) for selection of cluster heads which are then used to decide cache location. A probability matrix is used to compute content caching probability which considers both popularity and predicted future rating of content. The proposed approach dynamically predict the user's preferences using non-negative matrix factorization (NMF) - a machine learning technique which eventually provides prediction of future rating. Based on the selection of both cache location and content to cache, the proposed scheme can effectively cache the content in the network. Further, to deal with the secure delivery of cached content, this work supports legitimate user authorization at edge nodes. The performance of the proposed scheme is evaluated in MATLAB parallel computing toolkit. The results prove significant caching improvement in terms of cache hit, hop reduction and average delay using our proposed scheme.

Radio-frequency-energy-powered cognitive radio network (RF-CRN) is being taken seriously in Connected Vehicles, especially in 5G network, which can better address the challenges of energy limitation and spectrum scarcity. However, the energy efficiency (EE) of the RF-CRN wherein multiple secondary users (SUs) share the same channel is rarely presented. In this article, we consider a RF-CRN in which SUs first harvest energy from RF signals originating from a primary network (PN) and then utilize the available energy in the battery to transmit data. Since all SUs can access the authorized spectrum for transmission simultaneously, co-frequency interference (Co-FI) occurs among SUs. Given the quality of service (QoS) requirement, our goal is to achieve the maximum EE of the RF-CRN by jointly optimizing transmission time and power control. To this end, a resource allocation scheme referred to as approximate convex policy for co-frequency interference (CO-ACP) is proposed. Specifically, the EE problem is firstly converted into a convex one by CO-ACP. Then, we utilize Frank-Wolfe (FW) and one-dimensional linear programming to obtain the optimal solution. Simulation results demonstrate that a tight lower-bound optimum solution for the non-convex EE maximization can be achieved by CO-ACP. Moreover, the CO-ACP provides meaningful system features, such as the number of SUs, energy harvesting efficiency, and the battery energy state of the SUs under different RF-CRN scenarios, providing a clear reference for future deployment of RF-CRN.

The stringent requirements of wireless multimedia transmission lead to very high radio spectrum solicitation. Although the radio spectrum is considered as a scarce resource, the issue with spectrum availability is not scarcity, but the inefficient utilization. Unique characteristics of cognitive radio (CR) such as flexibility, adaptability, and interoperability, particularly have contributed to it being the optimum technological candidate to alleviate the issue of spectrum scarcity for multimedia communications. However, multimedia communications over CR networks (MCRNs) as a bandwidth-hungry, delay-sensitive, and loss-tolerant service, exposes several severe challenges specially to guarantee quality of service (QoS) and quality of experience (QoE). As a result, to date, different schemes based on source and channel coding, multicast, and distributed streaming, have been examined to improve the QoS/QoE in MCRNs. In this paper, we survey QoS/QoE provisioning schemes in MCRNs. We first discuss the basic concepts of multimedia communication, CRNs, QoS and QoE. Then, we present the advantages of utilizing CR for multimedia services and outline the stringent QoS and QoE requirements in MCRNs. Next, we classify the critical challenges for QoS/QoE provisioning in MCRNs including spectrum sensing, resource allocation management, network fluctuations management, latency management, and energy consumption management. Then, we survey the corresponding feasible solutions for each challenge highlighting performance issues, strengths, and weaknesses. Furthermore, we discuss several important open research problems and provide some avenues for future research.