To overcome signal blockage and extend coverage to both indoor and outdoor users, simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) are employed. In addition, Non-Orthogonal Multiple Access (NOMA) is integrated to improve spectral efficiency and support multiple-user access. Given this setup, it is essential to investigate the Physical Layer Security (PLS) performance of STAR-RIS-assisted NOMA systems, particularly with multiple eavesdroppers. To evaluate the signals’ interception risks, we derive closed-form expressions for the Secrecy Outage Probability (SOP) and the Intercept Probability (IP) for both system setups at the near users and colluding/non-colluding eavesdroppers.1

In this paper, we develop a secure cell-free massive multiple-input multiple-output (CF-mMIMO) communication system assisted by a passive reconfigurable intelligent surface (RIS). In this model, multiple mobile unmanned aerial vehicle access points (UAV-APs) are tasked with communicating with multiple legitimate users (UEs), while multiple jammers are in charge of sending interference signals to UEs. This work proposes a unique framework to maximize the average sum secrecy rate (ASSR) by jointly optimizing the UAV’s trajectory, total transmit power at UAV-APs, and RIS phase shift. Ensemble global optimization (EGO) is implemented followed by deep learning (DL) techniques for optimization with low computational complexity. In particular, we train a deep neural network offline, to optimize the RIS phase shift, UAV-AP trajectory, and UAV-AP transmission powers online. A real-time optimization capability of the trained model is assessed online with low computational complexity. The numerical results show the performance analysis of the best neural network for real-time optimization. Furthermore, numerical results demonstrate that passive RIS technology and increased UAV-AP speed significantly enhance the performance of the cell-free communication system.

Efficient resource management in the Centralized Unit User Plane (CU-UP) of 5G and beyond networks is essential to support the diverse and dynamic demands of next-generation wireless Internet of Things (IoT) applications. In this paper, we present a novel autoscaling technique that leverages the capabilities of the xApp and rApp features of the near-Real-Time RAN Intelligent Controller (near-RT RIC) and non-Real-Time RAN Intelligent Controller (non-RT RIC) to achieve sub-millisecond latency and broadband capabilities necessary for Ultra-Reliable Low-Latency Communications (URLLC) and Enhanced Mobile BroadBand (eMBB) slicing use cases in the RAN. The proposed RICStar leverages three QoS prediction models: latency prediction, throughput estimation, and provisioning latency models to execute optimal capacity adjustment decisions based on the output of a time series traffic forecasting model. Our evaluation uses a publicly available dataset representative of eMBB and URLLC traffic where the autoscaler adjusts the capacity units available for each slice. Our results indicate that RICStar achieves better QoS and cost performance compared to canonical autoscaling techniques. This improvement is achieved by closely tracking traffic demand trends and proactively autoscaling CU-UP resources. These findings underscore the benefits of predictive autoscaling in mitigating network degradation caused by under-provisioning while reducing costs for Communication Service Providers (CSPs). As a result, CSPs can readily support emerging applications such as tele-medicine, connected vehicles, and AR/VR with greater capacity and reliability.

As edge computing devices for Internet of Things (IoT) applications become more capable, the demand for data driven deep neural network (DNN) based algorithms grows. However, this leads to increased model vulnerability to adversarial attack. The Visual Internet of Things (VIoT), a subdomain of IoT, has great need for methods to improve robustness of algorithms to such attacks, along with person Re-Identification (Re-ID) through 3-dimensional (3-D) skeleton-based gait-analysis. For other domains, either adversarial training or synthetic data augmentation has shown to improve model performance in benign and adversarial attack cases. However, a combined approach that leverages both for robust classification in IoT has yet to be studied for 3-D skeleton based Re-ID. Additionally, assessment of 3-D skeleton-based Re-ID model vulnerability to adversarial attacks along with methods to improve robustness against such attacks are critical literature gaps. This work seeks to address these gaps by proposing an adversarial attack tolerant computational framework for 3-D skeleton-based Re-ID. Our approach, for the first time in the literature, leverages both synthetic data augmentation using a Generative Adversarial Network (GAN) and adversarial training through transfer learning of 3-D skeleton data on a Convolutional Neural Network (CNN) for person Re-ID. We evaluate the robustness of our method against seven popular adversarial attacks across four unique Re-ID 3-D skeleton datasets. We demonstrate how our method responds to varying amounts of real data and study the viability of our method for deployment on edge devices. Our findings show the proposed approach improves test accuracy in both the benign and adversarial case. Our method is deployable to small and inexpensive edge devices popular in IoT like the Raspberry Pi microcomputer.

Autonomous Vehicles (AVs) rely extensively on GPS signals for navigation, exposing them to a wide range of GPS spoofing attacks, from simplistic signal manipulation to sophisticated, coordinated falsification. Existing detection and mitigation solutions, both conventional and AI-based, face several critical limitations: they struggle to adapt to novel or evolving GPS spoofing strategies, rely on shallow or handcrafted features that fail to capture the semantic complexity of signal distortions, and often lack scalability, as they are typically designed for isolated scenarios and cannot be readily extended to heterogeneous AV fleets. In this study, we introduce a novel framework that integrates multiple Lightweight Language Models (LightLMs), including BERT, RoBERTa, DistilBERT, and TinyBERT, with Reinforcement Learning (RL) algorithms such as Q-Learning, Deep Q-Network, Advantage Actor-Critic, and Proximal Policy Optimization, to improve detection and mitigation of GPS spoofing. The LightLMs are used to convert structured GPS-related features into enriched state embeddings, which serve as input to the RL agent. These embeddings provide semantically meaningful representations that help the agent recognize complex spoofing behaviors and apply mitigation strategies. To train and evaluate the proposed models, we build a Python-based simulation environment that emulates multiple spoofing scenarios and integrates LightLM-driven state inputs. Experimental results across the datasets used show that RL models enhanced with LightLM-generated state representations significantly outperform their baseline counterparts in detection accuracy, mitigation efficiency, and response time. The results also demonstrate the proposed approach’s scalability, generalizability, and operational reliability for secure AV navigation.

Cell-free massive multiple-input multiple-output (mMIMO) systems distribute a large number of access points (APs) over a wide geographic region, improving coverage, capacity, and user experiences notably. Similarly, reconfigurable intelligent surface (RIS) allows a significant leap forward in signal quality and energy efficiency by adding additional flexibility and intelligent control over the propagation environment of wireless systems. However, optimal resource allocation in RIS-integrated cell-free mMIMO systems faces substantial challenges due to the presence of a large number of APs and passive reflecting elements (PREs) at RIS while simultaneously providing network coverage to aerial and ground users. To efficiently navigate the high-dimensional optimization space, this paper proposes a novel framework for optimal resource allocation in RIS-assisted cell-free mMIMO systems employing deep learning (DL) techniques. In particular, we train a deep neural network offline to optimize the transmit powers at APs and the phase shift of PREs at RIS. The trained model is tested online to perform real-time optimization with low computational complexity. Simulation results demonstrate the effectiveness of the proposed data-driven resource allocation scheme while comparing it with the state-of-the-art baseline scheme.

Generative Adversarial Networks (GANs) are powerful tools for generating synthetic data, making them highly valuable in data-driven research by addressing data scarcity/imbalance in real-world datasets. However, GANs often suffer from mode collapse, where the generator produces a limited range of outputs, failing to capture the full diversity of the target distribution. While the Wasserstein GAN (WGAN) improves training stability and mitigates mode collapse compared to traditional GANs, it is not entirely immune to this issue. In this paper, we propose an Entropy-Augmented WGAN (E-WGAN), which incorporates an additional entropy-based information into the generator's loss function to explicitly promote diversity in generated samples. By maximizing the entropy of the generator's output distribution, we aim to enhance the diversity of WGAN-generated data without compromising quality. Our results show that E-WGAN effectively increases the diversity of generated samples while preserving the stability and high quality of the outputs seen in standard WGANs. The proposed method offers promising results to further mitigating mode collapse and enhancing the generative capabilities of WGANs.

In this paper, we design a physical (PHY)-layer digital twin model, called PHY-Layer twin by leveraging ray-tracing (RT) and deep learning (DL) for a reconfigurable intelligent surface (RIS) assisted cell-free wireless communication system. Specifically, a recurrent neural network (RNN) driven low-complexity resource allocation scheme is studied to optimize system resources by interfacing with the proposed PHY-Layer twin. The proposed PHY-Layer twin-assisted resource allocation scheme jointly learns the correlation between channel gains obtained from RT and the real-world environment, and it allocates optimal transmit power for access points (APs) of the cell-free wireless networks. Numerical results demonstrate that the proposed scheme outperforms the related state-of-the-art approaches while operating with lower computational overhead.

Recognizing inattentive students can be particularly challenging on online learning platforms such as Zoom. The rapid growth of virtual education has created an urgent need for reliable and interpretable methods to detect learner engagement. Although several models have been proposed, many struggle to capture engagement accurately and often misclassify inattentive students as engaged. To overcome this limitation, we propose a predictive framework that detects engagement by combining visual behavioral cues, such as head movement and facial expressions. The proposed approach combines the Swin Transformer architecture with head pose estimation through a feature-level fusion strategy to capture global context and directional attention cues. We validate our framework using the publicly available dataset named ‘Student Engagement Dataset’ and benchmark performance gains across standard metrics, including accuracy. The findings demonstrate that our approach outperforms current state-of-the-art methods, increasing accuracy from 94% to 99%. This study highlights the Capability of transformer-based models to enhance engagement detection systems in virtual education environments.

Recent advances in computing, storage, embedded systems, wireless communication, and networking technologies have led to the Internet of Things (IoT) where smart things/devices are connected to the Internet and generate big data. These massive number of connectivity in IoT and big data flow has led to an expansion of the attack surfaces for cyber adversaries. Recently, there have been significant research for developing network intrusion detection systems (NIDSs) to detect malicious activities by using deep learning. However, the effectiveness of deep learning is limited in network security due to the significant role of the heterogeneous structured datasets that are primarily organized in a tabular format, representing a challenge for deep learning models without such structured datasets. Next, imbalanced data further hinders deep learning models’ ability to learn malicious behavior, impeding accurate intrusion detection. Furthermore, recent research has indicated that learning without domain knowledge can lead to overfitting and poor performance on independent datasets. To address these aforementioned challenges, we leveraged the gradient boosting machines (GBMs) named XGBoost combined with rough set theory. For performance evaluation and effectiveness of our proposed approach, we present experimental results using various datasets from different network environments and the results have demonstrated that the proposed approach significantly outperforms the state-of-the-art NIDSs (where accuracy, precision, F1-score, and recall are higher by about 7%, 3%, 8%, and 2%, respectively, compared to other related approaches).

The emergence of the Internet of Things (IoT) has revolutionized service automation, enabling the development of smart applications. However, the vast interconnectivity of IoT devices not only produces large volumes of data but also creates multiple potential attack surfaces. While Machine Learning (ML) offers insights from IoT data, inherent data privacy and security challenges hinder its effective utilization. Federated Learning (FL) offers privacy-preserving ML for distributed edge devices. Nevertheless, the susceptibility to attacks poses a threat to the integrity of IoT data impacting ML for IoT services and applications. To tackle this challenge and identify IoT devices compromised by attacks like label-flipped data, this article introduces an innovative defense mechanism modeled after the human immune system. Analogous to ‘B’ cells, which detect viruses within the human body, the Reinforcement Learning (RL) agent identifies malicious IoT nodes that participate in federated learning enabled IoT. Subsequently, the FL server, similar to ‘T’ cells in Human immune systems eliminate/destroy infected cells, quarantines/discards the malicious IoT nodes (that are FL clients) and their reported parameters. Like ‘B’ cells and ‘T’ cells work together to defend the human body against infections and diseases, RL agent and FL server work together to defend/secure FL enabled IoT from compromised/malicious IoT devices. Specifically, with the help of Deep Reinforcement Learning (DRL), the RL agent continually monitors model updates from participating IoT nodes during training phase to find malicious nodes and then to isolate or remove those malicious nodes (i.e., parameters) at FL server while aggregating parameters for the global model. The effectiveness of the proposed approach is demonstrated through experiments, where RL agent detects malicious/compromised IoT nodes and FL server discards the parameters from such malicious/compromised IoT nodes. We evaluate our proposed approach using numerical results obtained from experiments where we observe that our approach outperforms the existing state-of-the-art approaches in terms of detection rate, error, and accuracy for enhancing IoT security in FL enabled IoT.

The rapid adoption of cloud storage for healthcare data brings enhanced accessibility and streamlined data management, but it also introduces significant security and privacy challenges. This paper presents a multi-layered framework designed to securely manage and protect sensitive healthcare data in cloud environments. The proposed approach leverages encryption for securing data, combined with Deterministic Additive Noise (DAN) to enhance privacy during data storage and retrieval. Additionally, differential privacy mechanisms are employed to safeguard patient data during query processing, preventing unauthorized access while maintaining data utility. Key management is handled via a Hardware Security Module (HSM) to ensure the secure generation, storage, and usage of cryptographic keys. The framework also integrates secure authentication methods, including multi-factor authentication for both healthcare providers and patients, enabling secure data access and retrieval. This comprehensive approach strengthens the Confidentiality, Integrity, and Availability (CIA) of healthcare data in cloud environments, ensuring compliance with stringent privacy regulations such as HIPAA.

A Convolutional Neural Network (CNN) relies on intricate pixel correlations to predict objects within images, demonstrating improved performance when identifying objects alongside commonly co-occurring objects. For instance, a helicopter can be more accurately predicted when it appears with an aircraft carrier compared to when it stands alone. Influence on detecting an object due to the presence or absence of another object can be regarded as a form of contextual bias. This phenomenon poses a significant challenge in object detection. Addressing this bias is crucial. Our study explores correlation-aware prediction techniques to disentangle and leverage connections between objects. We propose CART-OD, a novel object detection approach that enhances model performance by adjusting the confidence scores of correlated objects during inference. By strategically intervening during prediction, CART-OD targets instances where commonly co-occurring objects appear separately, effectively mitigating the influence of object correlation. The adjustment in real-time makes the approach computationally efficient and scalable to handle large-scale datasets. Results indicate that CART-OD significantly enhances precision, recall, and mean average precision (mAP) in detecting objects that appear without their most correlated counterparts, underscoring the potential of correlation-aware strategies in object detection tasks.

The attention mechanism is a core module in today’s well-established models like Transformers and Graph Attention Networks (GAT). The attention coefficients needed typically requires heavy computational resources for stability and better predictions. This requirement limits the size of the sequence or graph a model can learn from, leading to separating connected graphs or sequences to ease training. In this paper, we introduce an attention mechanism that leverages Bilinear forms to handle sequences of arbitrary length, offering greater flexibility and scalability. This improvement not only optimizes computational resources but also offers better model performance. Furthermore, our Bilinear Attention Mechanism (BAM) can capture more complex relationships between nodes for less parameters count.

This paper explores the potential of Dynamic Model Selection (DMS) combined with asynchronous Federated Learning (FL) to enhance military devices within the Internet of Battlefield Things (IoBT). While previous studies have primarily focused on synchronous FL, we argue this approach has limitations. Hence, this study addresses this research gap by investigating asynchronous FL and its advantages over synchronous methods. Our main contribution lies in developing a dynamic client selection algorithm that optimizes the utility function, incorporating communication delay or latency, available computational resources, and model accuracy. This algorithm ensures clients are selected based on their potential contribution to the global model’s performance, thereby improving the efficiency and effectiveness of the training process. Through experimentation with standard datasets (MNIST, CIFAR-10) and our custom dataset (Common Objects in Battlefield (COBA)) tailored for battlefield scenarios, we demonstrate the effectiveness of asynchronous FL and dynamic client selection. By addressing this critical research gap and providing empirical evidence, our study advances the use of FL for battlefield decision-making, significantly contributes to developing IoBT, and offers practical insights for military applications.

Deep reinforcement learning (DRL) has shown remarkable success in tackling complex tasks by learning representations directly from raw data. However, as DRL agents become increasingly sophisticated, they often face challenges in adapting to new tasks or environments due to their monolithic structure. In this paper, we prove that by decomposing the agent into modular components, we enable more efficient adaptation as well as more efficient than that of the singular DRL agent on tackling complex tasks. Overall, our work contributes to advancing the field of DRL by introducing a flexible and scalable framework for agent decomposition. By enabling the creation of modular agents, we empower DRL systems to efficiently adapt to changing environments, learn new tasks with minimal intervention, and achieve higher levels of performance and versatility.

Electronic Health Records (EHR) are well-known for their extensive capabilities in storing pertinent patient information across various specializations. However, EHRs are part of a broader ecosystem of applications that support the medical treatment process. This ecosystem, although somewhat centralized, does not have the interoperability that many assume. The main issue is that current triage models are not personalized to each patient. Context matters in a medical emergency situation especially when resources are low. In this research, we propose a possible solution by applying state of the art techniques in order to develop a clinical knowledge graph that contains relevant data used for triage optimization. We utilize a fine-tuned named entity recognition model (NER) to extract 41 label entity categories from previous medical records. Additionally, we employed prompt engineering utilizing a large language text generation model with medical knowledge to generate relationships. This resulted in a sum of 1,429 relationship type categories and approximately 999 entity nodes were created with 2,387 relationships.

As the demand for medical services grows, the need for an efficient care process becomes increasingly urgent. Recent events have highlighted that the demad for services exceeds the available supply. In response, the focus of heaht information technology has shifted toward advanced clinical decision support systems (CDSS) to addess the critical challenges facing the medical industry. However, many healthcare organiztions lack robust clinical systems and tools. This research presents a comprehensive study of current industry applications, its related works, and provides insights into optimizing current state of the art machine learning (ML) CDSS with newer technologies, such as generative artificial intelligence-based knowledge graphs. This paper offers an overview of CDSS, details the patient treatment process and triage methods, explores the capabilities of knowledge graphs in healthcare, and underscores the need to incorporate machine learning into these processes to increase intelligence, robustness, and personalization.

Recently, Unmanned Aerial Systems (UAS) have become heavily reliant on communication, navigation, and other critical components such as sensors and actuators, which are essential for operations in both civilian and defense applications. However, this increasing reliance makes UAS more vulnerable to attacks and faults, posing rising threats. While there have been many advances in UAS security, a significant number of studies have proposed artificial intelligence (AI)-enhanced solutions to address these challenges. Yet, no research has explored the potential of generative AI (GenAI) in this domain. GenAI stands out due to its ability to detect and prevent cyberattacks by continuously learning and adapting to new threats and vulnerabilities. In this paper, we propose DroneDefGANt, a GenAI-based approach that combines the capabilities of generative adversarial networks (GAN) and transformer models. The DroneDefGANt is designed to detect both external UAS attacks like GPS spoofing and jamming, and internal attacks such as actuator faults. Through evaluations using synthetic datasets, DroneDefGANt surpassed various conventional AI models, demonstrating superior accuracy and robustness, particularly in the presence of Gaussian noise.

Advancements in wireless communication technolo-gies provide information interaction among vehicles, humans, and roadside infrastructure. As a result, the size and volume of content that must be streamed in real-time have grown rapidly, resulting in congested data traffic at content servers and a degradation in the user experience. Hence, to address these issues, an efficient Sliding Exponential Window-Kalman Filter (SEW-KF) and Weight Normalized ReLU-Memristor-like Activation Function-Long Short Term Memory (WNormRMAF-LSTM) based proactive content caching framework is proposed using Asynchronous Federated Learning (AFL). Primarily, the number of vehicles on the road is initialized and clustered using the Taylor Kernelized-Affinity Propagation Clustering (TK-APC) technique. Then, the cache vehicle is selected using HenonChebyshev Cosine-Honey Badger Optimization (HMCo-HBO). Next, the historical data (global model) from the AFL is given to cluster members by Cache Vehicle (CV) and trained using WNormRMAF -LSTM. After training the global model, each cluster member transfers the trained model to a CV, which in turn transfers to Road Side Unit (RSU). In RSU, content popularity prediction takes place using SEW-KF. After popularity prediction, more and less popular content is placed in Macro Base Station (MBS), whereas the popular content is placed in the RSU using Greedy Algorithm (GA). At last, data accessing takes place, where the availability of the data is checked and forwarded to the user. Finally, the results of the proposed method are compared to those of traditional algorithms.

Networked Autonomous Systems (AS) represent a networking paradigm where nodes autonomously make decisions, operating independently or collaboratively to achieve network goals. Unlike traditional networks, networked AS emphasizes decentralized control, enabling nodes to adapt dynamically without relying heavily on centralized controllers. This autonomy promotes agility, scalability, and resilience, making networked AS applicable to various domains like IoT and wireless communications. However, this architecture is vulnerable to various threats, including Advanced Persistent Threat (APT), which exploits vulnerabilities leading to resource depletion and hindering legitimate network operations. APTs, particularly prevalent in IoT-enabled networked AS, pose a significant risk by overwhelming network resources and disrupting services. To address these challenges, this paper introduces a new neural network-based framework tailored to detecting APT in networked AS environments. The proposed framework leverages generative adversarial networks and Deep Learning (DL) to accurately identify malicious activities in networked AS. We evaluated our framework using the newly released dataset TON_IoT, which contains a comprehensive variety of APT and addresses gaps in existing datasets in the context of AS. We obtained a significant improvement in APT detection by comparing DL techniques such as Conventional Neural Networks, Gated Recurrent Units, Long Short-Term Memory, Multilayer Perceptron (MLP), and combining MLP with Auto-Encoder (MLP+AE). The proposed framework demonstrates important accuracy and robustness by analyzing the selected DL techniques’ performance as influenced by variations in the training set size.

In this paper, we present SleepWalker, a few-shot fine-tuning approach for conditional text-to-motion generation. Our key innovation is extending contrastive learning, previously applied to image diffusion, to the new domain of text-to-motion synthesis. By binding subject representations to a text-conditioned diffusion model, SleepWalker enables personalized 3D human motion generation directly from natural language descriptions using just 15-25 examples. We demonstrate state-of-the-art performance on standardized metrics and benchmarks. The accessible fine-tuning method advances conditional motion synthesis, unlocking new creative possibilities in AR/VR, gaming, robotics, and human-computer interaction.

Adversarial fine-tuning has shown promise in improving model robustness against test-time perturbations, but its effectiveness in mitigating backdoor attacks in language models remains understudied. This work investigates the use of adversarial fine-tuning techniques and deprogramming, specifically focusing on basic fine-tuning, fine-tuning with paraphrasing, and fine-tuning with retokenization, to mitigate backdoor attacks in language models. We evaluate the effectiveness of these techniques by measuring the accuracy of our models with and without the context of the original query. Our approach extends the idea of supervised fine-tuning (SFT) and incorporates adaptors from the parameter-efficient fine-tuning (PEFT) framework to efficiently fine-tune our models while minimizing computational resources. While supervised fine-tuning alone can alleviate backdoor triggers to some extent, our results demonstrate that combining SFT with paraphrasing and retokenization techniques can achieve even more effective mitigation. In particular, adversarial fine-tuning with paraphrasing significantly reduces the success rate of backdoor attacks in language models.

Considering the Activity and Event Network model, also known as AEN, a recent knowledge graph model adept at handling the uncertain and dynamic nature of network activities, this paper proposes an unsupervised anomaly detection system, whose technique is foster around graph clustering within the AEN framework. The goal is to recognize and adapt to normal behaviour across different varying time periods while establishing the baseline behaviours for each cluster. The effectiveness of our proposed system in identifying the anomalies resulted in a false positive rate of 0.85% and a detection rate of 81 %, assessed using the CIC 2018 IDS dataset.

Intelligent techniques, including artificial intelligence and deep learning, normally perform on complete data without missing data. Multiple imputation is indispensable for addressing missing data resulting in unbiased estimates and dealing with uncertainty by providing more valid results. Most state-of-the-art techniques focus on high-missing rates (around 50%–60%) and short missing gaps, while imputation for extreme missing gaps and missing rates is an important challenge for multivariate time-series data generated through the Internet of Things (IoT). Hence, we propose an lightweight-window-portion-based multiple imputation (LWPMI) based on multivariate variables, correlation, data fusion, regression, and multiple imputations. We conduct extensive experiments by generating extreme missing gaps and high-missing rates ranging from 10% to 90% on data generated by sensors. We also investigate different sets of feature to examine how LWPMI works when features have high, weak, or a mixture of high and weak correlation. All the obtained results prove LWPMI outperforms baseline techniques in preserving pattern, structure, and trend in both 90% extreme missing gap and missing rates.

With the rise of smart vehicles, an intelligent transportation system intelligent transport system (ITS) learning from the tremendous volume of the data generated by the distributed vehicles is becoming a reality. blockchain (BC)-based federated learning (FL) facilities a highly secure and trustless collaborative learning framework; however, it could be inefficient for the time-sensitive services of the Internet of Vehicles (IoV). In this regard, this study investigates information dissemination delay in BC-based FL for IoV using a small world network-based peer selection strategy. Additionally, we also incorporate IoV specific informed decision for peer selection to facilitate even faster dissemination of information in the network and make BC-based system efficient. Network topology of vehicles is represented using a graph approach and based on that the information dissemination delay is formulated. The delay is compared between scenarios when peer selection strategy uses informed decision and that does not use informed decision. The performance of the proposed approach is evaluated through the graph analysis which demonstrates that the inclusion of informed decision reduces the information dissemination delay remarkably.

Recent years have witnessed the increasing popularity and dramatic progress of contactless sensing technologies, which are able to conduct remote signal acquisition without body contact. Both the physical signs and the physiological parameters can be acquired with contactless sensing. This paper introduces popular contactless sensing technologies, explores their application scenarios, and delves into the underlying theoretical principles. It comprehensively reviews the open datasets released in this field, encompassing collection scenarios, sample counts, data formats, and volunteer information. The performance baseline, typical work, and accessible links are also furnished. In addition, it includes discussions on the primary challenges and potential solutions in the context of contactless sensing with open datasets. Finally, suggestions for establishing a high-quality dataset are also given to the community.

With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and Quality-of-Service (QoS) standards. Recent developments in federated learning (FL) have made it possible to train complex machine-learned models in a distributed manner and have become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this article, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unraveling the complexities of designing reliable and scalable FL models. This article outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of FL, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. Recent literature has shown that FL models are robust and generalize well to new data, which is essential for medical applications. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state of the art and identifying open problems and future research directions.

Artificial Intelligence of Things (AIoT) represents a collaborative fusion of artificial intelligence (AI) and the Internet of Things (IoT). AIoT systems enable real-time data acquisition through IoT sensors and conduct intelligent data analysis tasks across the entire spectrum from terminal to edge to cloud, creating a dynamic and empowering ecosystem. However, the evolving landscape of AIoT faces a perplexing challenge: how to effectively sense the geographically diverse and highly variable environment, accurately gather massive and diverse IoT data with varying value density, and intelligently integrate multisource data to deliver real-time, intelligent, and high-quality applications.

Fault tolerance, energy management, and quality of service (QoS) are essential aspects for the design of real-time embedded systems. In this work, we focus on exploring methods that can simultaneously address the above three critical issues under standby-sparing. The standby-sparing mechanism adopts a dual-processor architecture in which each processor plays the role of the backup for the other one dynamically. In this way, it can provide fault tolerance subject to both permanent and transient faults. Due to its duplicate executions of the real-time jobs/tasks, the energy consumption of a standby-sparing system could be quite high. With the purpose of reducing energy under standby-sparing, we proposed three novel scheduling schemes: The first one is for (1, 1)-constrained tasks, and the second one and the third one (which can be combined into an integrated approach to maximize the overall energy reduction) are for general ( m,k )-constrained tasks that require that among any k consecutive jobs of a task no more than ( k - m ) out of them could miss their deadlines. Through extensive evaluations and performance analysis, our results demonstrate that compared with the existing research, the proposed techniques can reduce energy by up to 11% for (1, 1)-constrained tasks and 25% for general ( m,k )-constrained tasks while assuring ( m,k )-constraints and fault tolerance as well as providing better user perceived QoS levels under standby-sparing.

Lane detection is an important task in the field of automatic driving. Since lane lines usually have complex topologies and exist in various complex scenes (e.g., damaged lanes, severe occlusion, etc.), lane detection remains challenging. In this work, we propose a Polynomial Global Attention Network (PGA-Net) for lane detection, which is an end-to-end model for mining global road information and predicting lanes shape parameter formulas simultaneously. We model lane shape with cubic polynomial function and use the transformer-based DETR model to introduce the context information of lanes and roads to better regress the lane parameters. For polynomial curve modeling, we propose Mean Curvature Loss (MCL) to constrain the curvature of the predicted lanes, thereby enhancing the quality of curve lanes prediction. In addition, we design an improved supervision strategy to eliminate information bias between our parametric prediction methods and the labeling methods of lane datasets. Our method achieves state-of-the-art performance on two popular benchmarks (TuSimple and LLAMAS) and a most challenging benchmark (CULane), while exhibiting accelerated speed (>140fps on 3090 GPU, 28.9% improvement in average) and lightweight model size (https://github.com/qklee-lz/PGA-Net.

For real-time embedded systems, QoS (Quality of Service), fault tolerance, and energy budget constraint are among the primary design concerns. In this research, we investigate the problem of energy constrained standby-sparing for both periodic and aperiodic tasks in a weakly hard real-time environment. The standby-sparing systems adopt a primary processor and a spare processor to provide fault tolerance for both permanent and transient faults. For such kind of systems, we firstly propose several novel standby-sparing schemes for the periodic tasks which can ensure the system feasibility under tighter energy budget constraint than the traditional ones. Then based on them integrated approachs for both periodic and aperiodic tasks are proposed to minimize the aperiodic response time whilst achieving better energy and QoS performance under the given energy budget constraint. The evaluation results demonstrated that the proposed techniques significantly outperformed the existing state-of-the-art approaches in terms of feasibility and system performance while ensuring QoS and fault tolerance under the given energy budget constraint.

Vehicular networks with Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V21) communications can benefit from Mobile Edge Computing (MEC) while sharing the computation abilities amongst vehicles. In this paper, we proposed an effective vehicle scheduling centered on Cumulative Distribution-Satin Bowerbird Optimization (CD-SBO) along with Xavier- Convolutional Neural Network (X-CNN) that leverages the vehicles' cache memory. In the proposed model, initially, regarding the data at-tained from the roadside unit (RSU), the range of vehicles on the road is initialized. After that, the vehicles are clustered using the Kullback-Leibler Divergence-K-Means Algorithm (KLD-KMA) model, which assigns RSU as a cluster of centroids. Then, to avoid data collision and uneven delay, the vehicles' request is balanced by using the CD-SBO algorithm. Next, the optimized requests are transferred to the cloud server through the gateway and Software Defined Network (SDN). Further, in the cloud server, data is decomposed into smaller components to enhance the cache storage. Then, the decomposed data is fed into the X-CNN classifier, which extracts the features from the Decomposed data and schedules the vehicles based on the extracted feature vectors. Finally, the results are forwarded to the targeted vehicles efficiently. The performance of the proposed method is better enhanced compared to the existing approaches.

In this paper, we propose an approach to find the link performance of vehicle mobility and the antenna orientation in the THz band. We use the Von Mises-Fisher to find the PDF of the movement of vehicle assimilated to a 3-D oriented random walk and Cauchy PDF for the joint distribution of diffusion coefficient and the number of turns during the random walk. These PDFs are useful in finding two specific performances of the vehicular network link at the vehicle behavioral level. The performances as a function of speeds are analyzed. Our results provide better trigger points for beam hand-off compared to the exiting approaches.

Unmanned aerial systems (UAS) and unmanned aerial vehicles (UAVs) have been utilized in numerous civilian and military applications. However, with the increasing deployment of UAS, several cybersecurity challenges have emerged, particularly vulnerabilities in GPS transmissions. GPS jamming attacks pose a significant threat to UAS, resulting in performance degradation and capability loss. To address this challenge, we propose a novel approach for detecting jamming attacks on GPS transmissions in UAS. The proposed approach combines Naive Bayes and Multi-Layer Perceptron algorithms to offer efficient and precise detection of GPS jamming attacks. We evaluated the proposed approach’s performance against various machine learning (ML) models using a synthetic dataset. The experimental results demonstrate the effectiveness of the proposed approach in detecting jamming attacks in GPS transmissions, achieving better accuracy than other ML models under several jammers. Index Terms---AI, ML, UAS/UAV, GPS Jamming Attacks.

The credibility of information on social media platforms are a necessity for ensuring that the content conveyed is trustworthy and exerts a positive influence on social media users. The spread of misinformation, such as rumors or fake news, is undesirable given that many people get the news and information first on their favorite social platforms and could be misled by such unauthentic information. One of the social media platforms that have been extensively used in such deceptive activities is Twitter. The automated detection of misinformation and preventing its spread on Twitter is a challenging task. One of the solutions is to determine the credibility of users and the content they generate through automated computing solutions. In this paper, we proposed a new mechanism to assess a user's credibility on Twitter to determine whether a user who shares a tweet is believable or trustworthy. This mechanism has been done by assigning different weights to the user's profile attributes (such as account creation date, Is the account verified or not, and account description ) and social attributes (such as the number of followers and following) to compute the user credibility score. This score is then used to enhance the detection model of rumor and unverified information applicable to Twitter using the Bidirectional long-short-term memory (BiLSTM) approach. Experimental results showed that the model can detect rumors with an accuracy of 0.86.

Implantable medical devices (IMDs) necessitate secure and energy-efficient communication systems to safeguard patient data and maintain device functionality. In this paper, our primary focus is on examining the impact of varying channel models on the success of Physical Layer Key Generation (PLKG) for Implantable Medical Devices (IMDs), whilst also introducing an energy-aware approach. We utilize the unique properties of double$\kappa-\mu$Shadowed fading channels, a generalized model allowing us to cover a range of special cases, including Nakagami-m, Rician, and Rayleigh fading. We apply a moving average filter to improve the stability of received signal strength (RSS) measurements and employ an adaptive threshold for quantization. To reconcile the generated keys, we adapt the Cascade protocol, ensuring its lightweight operations. Energy awareness is a paramount feature in our PLKG system, achieved by limiting reconciliation attempts to prevent rapid battery depletion. Through rigorous simulations, we present a comprehensive analysis of the performance of our energy-aware PLKG system under various fading conditions

Digital Twin, the Internet of Things, and Machine Learning have the potential to redefine our imagination of innovation and impact every sector in the future. A digital twin is a virtual replica of a physical object that facilitates real-time data collection from IoT sensors and uses machine learning/artificial intelligence to analyze and predict data. In this paper, we investigate the use case of federated machine learning for analyzing data on digital twins. Since digital twins capture data about their physical counterparts, they may contain private and sensitive information. This poses challenges in performing machine learning tasks on digital twins due to potential data privacy leakage threats. The motivation behind this research is to explore data ownership and privacy issues in the context of the vast amounts of data available in digital twins and to apply federated learning as a potential solution to address these challenges. We investigated how federated learning can enhance the predictive capability of digital twins by promoting knowledge sharing while maintaining privacy. Our findings indicate that federated learning is an effective approach for facilitating learning between twins and can significantly reduce communication overhead in comparison to training on physical edge devices. Additionally, we show that the convergence rate of federated learning on digital twins improves due to the greater amount of training data available on digital twins compared to the baseline federated learning approach.

Non-orthogonal-multiple-access (NOMA) technique has been considered to fulfill the exponential increase in wireless devices, data traffic, and access demand. Still, NOMA-aided systems are subject to wireless attacks such as eavesdropping. As a solution to encounter such menace, physical layer security (PLS) has been widely adopted to protect the networks from an information theoretic perspective. In this paper, we analyze the PLS of a downlink NOMA-aided network while employing a decode-and-forward (DF) relaying system. We assume the generalized Fisher-Snedecor composite fading$\mathcal{F}$, wherein Nakagami-m, Rayleigh, and one-sided Gaussian are special cases. Under those assumptions, we derive the closed-form expressions of the outage probability (OP), ergodic capacity, average secrecy capacity (ASC), and upper bound approximations. Our results are validated with Monte Carlo simulations, where they indicated that the power allocation ratio in NOMA and the fading parameters have a major impact on the system's robustness against the eavesdropping attack.

As a result of the popularity and flexibility of using credit cards, the increment of credit card fraud trans-actions is growing and becoming a significant security issue that threatens cardholders, merchants, and financial institutions. Therefore, this paper proposes a predictive framework to improve credit card fraud detection by deeply analyzing cardholders' behavior, including spending, traveling, and shopping peak days behavior. Both profile approaches are used, the owner approach (cardholder spending behavior analysis) and the operation approach (transaction authentication and location). Moreover, our novelty method is applied to estimate the transaction's authentication score, which helps to distinguish between original and fraudulent transactions more accurately. In addition, traditional machine learning, ensemble learning, and deep neural network algorithms are applied to our predictive framework. The performance metrics used to evaluate our classifier models are accuracy, precision, recall, and f1-score. The recall metric got our attention because misidentifying fraudulent transactions will cause critical financial losses for all parties. Hence, the results show the effectiveness of our proposed framework and the improvement of the classification performance and prediction accuracy for all our classifier models due to its efficiency in understanding cardholder history and her/his transactions historical.

Scene understanding in a contested battlefield is one of the very difficult tasks for detecting and identifying threats. In a complex battlefield, multiple autonomous robots for multi-domain operations are likely to track the activities of the same threat/objects leading to inefficient and redundant tasks. To address this problem, we propose a novel and effective object clustering framework that takes into account the position and depth of objects scattered in the scene. This framework enables the robot to focus solely on the objects of interest. Our system model incorporates YOLOv5 object detection model (ODM) and a pre-trained depth estimation model (DEM), whose outputs are fed into the KMeans Clustering (KM C) model. To train YOLOv5, we create our own battlefield dataset entitled “Common Objects in Battlefield (COBA)” which is split into a training dataset and a validation dataset using our customized object-aware stratified 7-fold cross-validation technique. We present the creation of minimum optimal clusters assuming the minimum number of available battlefield robots. These clusters exhibit accurate relative distances among objects, further validating the effectiveness of our approach for depth and object-aware clustering for scene understanding. Overall, our proposed approach facilitates efficient work division among robots, enhances decision-making capabilities, and helps to improve situational awareness in battlefield scenarios.

Federated Learning (FL) is a distributed machine learning technique that enables multiple devices or systems to collaboratively train a distributed model without sharing raw data. This is particularly useful for the Internet of Battlefield Things (IoBT) as it enhances the performance and capabilities of military devices through distributed training of models without compromising data privacy or security for involved parties. In this paper, we delve into the potential of FL to enhance military devices within the IoBT. Distinguishing our work from other works in this line of research, we introduce a tailored battlefield dataset tailored specifically for battlefield scenarios. Our approach combines both synchronous and asynchronous FL techniques, demonstrating their practicality in the context of the IoBT using this custom dataset. Our study not only explores the benefits and challenges of employing FL in battlefield scenarios but also highlights the capability of asynchronous FL to achieve comparable accuracy to synchronous FL while significantly reducing resource consumption. By addressing these critical aspects, our research advances the use of FL in military applications and contributes to the ongoing development of secure and efficient systems for the IoBT.

With the increase of medical illnesses and the varying treatment processes, medical professional are having difficulty to supply the increasing demand for medical services. In clinical and urgent settings, patient triage is believed to be similar in a emergency settings, however, it does not amount. Prior to a diagnosis, the medical staff will conduct an initial assessment in order to determine urgency for further evaluation. However, this requires a plethora of time for research in order to provide a proper triage classification. In this paper, we aim to automate patient triage utilizing a new concept of automated machine learning (AutoML). This concepts allows for faster streamline of comparing, modeling, and selecting features in order to acquire the best model for predicting a Simple Triage and Rapid Treatment (START) category. An efficient pipeline that is dynamic in training and evaluating patient data will allow for a robust system. This research utilized 15 different classification models that can be compared for selecting the best model. In this research, we also added the formula for means arterial pressure in order to have some relevance between the systolic and diastolic blood pressure. The best model selected was the Extra Trees Classifier comprised of the following result performance metrics: Accuracy (61%), F1 score (61%), Recall (61%), and Area Under Curve (AUC) (77%).

The rapid expansion of IoT brings unmatched convenience and connectivity, but it also raises significant security concerns. The prioritization of functionality over security in IoT devices exposes vulnerabilities like default credentials, outdated components, and insecure interfaces. To mitigate risks and combat cyberattacks effectively, it is crucial to identify and isolate compromised IoT infrastructures. In this paper, we present a curated dataset for IoT security research, which combines 40 recent IoT behavior datasets using class balancing and feature reduction techniques. This curated dataset serves as a valuable resource for future research in the field. Additionally, we compare machine learning techniques to detect compromised IoT devices, leveraging preprocessed and SMOTE-balanced network data. Our ensemble model surpasses other methods, achieving an impressive up to 98 percent F1-score, thus highlighting its efficacy in predicting compromised IoT devices and emphasizing the significance of our dataset and methodology contributions.

Network-in-box (NIB) architectures are designed to improve communication information sharing in an ad hoc manner with limited infrastructure support. These architectures are interoperable, and hence it is capable of providing services based on sixth-generation (6G) communication technologies. Resource allocation for massive machine-type communications in the 6G platform aided for NIB architectures is challenging due to the terahertz and high throughput features. Then it comes to resource allocation, and the most difficult part is figuring out what capacity is to check the availability of work on a project is difficult to determine. In this manuscript, the attuned slicing-dependent concurrent resource allocation (AS-CRA) method is formulated for improving the service reliability of the 6G users in NIB architecture. Learning assisted slicing and concurrent resource allocation process is jointly exploited in this proposed method to improve the users’ service reliability. The output of the learning process is useful in classifying resource allocation and user mapping irrespective of the limited NIB infrastructure support. Virtualization and concurrency in resource allocation are balanced based on the user capacity and network blocking rate to achieve optimality in service responses. The performance of the proposed resource allocation method is verified using simulations, and the performance is verified using the metrics capacity 89.726%, latency 81.32%, resource utilization rate 0.963%, response ratio 92.309, and blocking rate 0.047%.

The seamless integration of sensors and smart communication technologies has led to the development of various supporting systems for financial technology (FinTech). The emergence of the next-generation Internet of Things (Nx-IoT) for FinTech applications enhances the customer satisfaction ratio. The main research challenge for FinTech applications is to analyze the incoming tasks at the edge of the networks with minimum delay and power consumption while increasing the prediction accuracy. Motivated by the above-mentioned challenge, in this article, we develop a ranked-based service deployment strategy and an artificial intelligence technique for financial data analysis at edge networks. Initially, a risk-based task classification strategy has been developed for classifying the incoming financial tasks and providing the importance to the risk-based task for meeting users’ satisfaction ratio. Besides that, an efficient service deployment strategy is developed using$Hall's$theorem to assign the ranked-based financial data to the suitable edge or cloud servers with minimum delay and power consumption. Finally, the standard support vector machines (SVMs) algorithm is used at edge networks for analyzing the financial data with higher accuracy. The experimental results demonstrate the effectiveness of the proposed strategy and SVM model at edge networks over the baseline algorithms and classification models, respectively.

With the emerging communication and computation technologies, the Internet of Vehicles (IoV) has become a new paradigm that enables vehicles to communicate with roadside units (RSUs) as well as with other vehicles to collect or exchange information. However, the inherent characteristics of IoV, such as the high mobility of the vehicle and the limited storage capacity of the edge nodes, cause numerous difficulties in developing a caching scheme. Therefore, in this article, a novel improved multinomial recurrent neural network (MRNN) classifier and Caesar combined key-based elliptic curve cryptography (2CK-ECC) algorithm are proposed to predict the vehicular mobility, security, and content caching for the IoV. Initially, the vehicles are checked for registration. Afterward, vehicle login and vehicle authentication take place. Then, mobility prediction is carried out for the authenticated vehicles using the MRNN Classifier. After that, vehicle clustering is done via the novel variance-balanced iterative reducing and clustering using hierarchies (VBIRCHs) technique. Next, the relay vehicles (RVs) are selected using the novel Harris self-avoiding hawks optimization (HSAHO). Furthermore, the data contents are divided into chunks and saved in the cache memory. From the cache memory, data is transferred to the RSU. Finally, a novel 2CK-ECC algorithm is investigated for secure data transmission. The experimental outcomes demonstrate that the proposed technique outperforms the existing baseline approaches.

Although the Internet of Things (IoT) incorporates millions of heterogeneous devices to provide advanced intelligent services and has greatly impacted our lives over time, it has a huge blind spot since its design favors connectivity over security. Myriad efforts have been made to secure it, but it is still one of the most lucrative and often an easy target for attackers. IoT devices remain at higher risk of attack due to their intrinsic properties which include but are not limited to extreme heterogeneity, mostly plug-and-play nature, computational limitations, improper patch management, unnecessary open ports, default or no security credentials, and extensive use of reusable open-source software. To address these security concerns we need to thoroughly understand IoT devices’ vulnerabilities, associated attacks, and how criminal services can abuse these devices. In this paper, we present recent advances in IoT security vulnerabilities, criminal services by empirically identifying major vulnerable IoT devices and cyber attacks exploiting them by cyber criminals. Additionally, we present mapping of vulnerabilities, criminal services, attacks, and potential solutions against such vulnerabilities and attacks. We have also presented different approaches in a tabular form for side by side comparison.

The papers in this special issue focus on advanced wearable sensor technologies for monitoring and disease prediction. The seamless integration of sensor technologies with the smart healthcare infrastructure has leveraged the sensing and communication capabilities to monitor patient’s health parameters remotely through various wearable/medical sensors. Advanced sensor technologies enable various types of smart healthcare applications, including diagnosing the symptomatic/ asymptomatic patients’ health condition, health symptoms forecasting, disease prediction and analysis, and ontologybased recommendation. Advancements in wearable sensors and communication technologies (6G/5G and-beyond) enable the design of smart healthcare frameworks and efficiently analyzing the sensing parameters. Besides that, advanced AI-enabled technologies, including machine learning and deep learning algorithms, come into play to analyze the sensed data at remote computing devices for disease prediction and diagnosis. This special issue focus on discussions and insights into the latest advancements and technologies pertaining to these technologies.

The Internet of Things (IoT) is delivering solutions with improved efficiency and security, and providing better productivity in manufacturing, retail, and other sectors. Maritime Transportation Systems (MTSs) is currently adopting the IoT to move toward a digitalized, data-driven world with increased efficiency and lower costs, and creating new revenue opportunities. Integration of the IoT also enables real-time tracking of shipments, improved efficiency in cargo handling, pre-emptive maintenance, route optimization, reduced fuel consumption, and improved safety in maritime transportation systems. With IoT technology expanding and evolving rapidly, more applications are predicted to assist and improve all aspects of MTSs, making them hassle-free and safe.

Intelligent Transportation Systems (ITSs) rely on environmental information for communication, navigation, and driving assistance. This technology-based interconnected vehicular network provides a wide range of support for heterogeneous real-time applications. However, the network relies on secure and robust information for providing driving application support, which is defaced at times due to fraudulent devices and information. In this article, Graph-Transient Security Method (GTSM) is proposed for improving the cybersecurity features of ITS. The proposed method uses a trusted graph model for identifying reliable infrastructure and neighboring units in communication. The units are verified based on the information exchanged for identifying the frauds through the mutual trust sharing paradigm. In the mutual trust sharing process, fastened classifier learning is employed for assessing the trust of the communicating vehicles and infrastructure units. Based on the output of the classifier learning, a connected trust-based transportation network is constructed. This helps to replace, transform the connected scenario depending on the cybersecurity requirement. The proposed method improves sharing rate by 11.4% and detection ratio by 7.84% and reduces down-time and latency by 11.53% and 10.7% in different sharing intervals.

Unmanned aerial vehicles (UAVs) have increasingly shown to be useful in civilian applications (such as agriculture, public safety, surveillance) and mission critical military applications. Despite the growth in popularity and applications, UAVs have also been used for malicious purposes. In such instances, their timely detection and identification has garnished rising interest from government, industry and academia. While much work has been done for detecting UAVs, there still exist limitations related to the impact of extreme environmental conditions and big dataset requirements. This paper proposes a novel ensemble deep learning framework that has hybrid synthetic and deep features to detect unauthorized or malicious UAVs by using acoustic, image/video and wireless radio frequency (RF) signals for robust UAV detection and classification. We present the performance evaluation of the proposed approach using numerical results obtained from experiments using acoustic, image/video and wireless RF signals. The proposed approach outperforms the existing related approaches for detecting malicious UAVs.

The prevalent use of offensive content in social media has become an important reason for concern for online platforms (customer service chat-boxes, social media platforms, etc). Classifying offensive and hate-speech content in online settings is an essential task in many applications that needs to be addressed accordingly. However, online text from online platforms can contain code-switching, a combination of more than one language. The non-availability of labeled code-switched data for low-resourced code-switching combinations adds difficulty to this problem. To overcome this, we release a real-world dataset containing around 10k samples for testing for three language combinations en-fr, en-es, and en-de, and a synthetic code-switched textual dataset containing ~30k samples for training In this paper, we describe the process for gathering the human-generated data and our algorithm for creating synthetic code-switched offensive content data. We also introduce the results of a keyword classification baseline and a multi-lingual transformer-based classification model.

In this paper, we aim at tackling the security performances of dual-hop mixed free-space optical (FSO)/radio frequency (RF) cooperative with the non-orthogonal multiple access (NOMA) technique. Particularly, we consider a downlink communication scenario, where a base station (BS) is commu-nicating with remote users in the existence of an eavesdropping attack. The hop from the BS to the relaying system is considered an FSO link, where the atmospheric turbulences are described by Málaga-$\mathcal{M}$. Besides, the RF links between the relay and users/attacker are assumed to follow the Double$\kappa-\mu$Shadowed fading since it unifies a wide set of special-case fading models. Moreover, we derive the closed-form expressions of the outage probability at the 1stlegitimate user and at the attacker, as well as the secrecy outage probability (SOP) in terms of the multivariate Fox-H function. Finally, we investigate the impact of the RF fading parameters, path-loss, power ratio, and pointing error on the secrecy performance.

Mixed radio frequency-free space optical (RF-FSO) systems are widely investigated and employed in network architectures due to their high data rate capabilities and large coverage areas. Although, the FSO beam is very sensitive to blockages such as building which leads to severe penetration loss. Thereby, in such case scenarios, it is more convenient to avoid the blockage by adding a third FSO hop to reach the destination and the signal pathway becomes a triple-hop RF-FSO-FSO system. Moreover, we need to have a more generalized RF fading model to obtain performance metrics that cover multiple signal propagation mediums. Inspired by that, we consider an uplink triple-hop RF-FSO-FSO communication scenario, where the RF link is modeled with the Double Shadowed$\kappa-\mu$fading while the FSO links are subject to Málaga-$\mathcal{M}$with pointing errors. Furthermore, we derive the closed-from expressions of the outage probability (OP) and the ergodic capacity in terms of the multivariate Fox-H function.

Vapor loss and molecular absorption make the transmission distance in sub-Terahertz bands a challenge, especially in mobile statues such as UAVs communication. The molecular absorption element is an essential part of the path loss in THz communication channel modeling that cannot be neglected. Along this direction, we investigated the UAV trajectories in sub-THz band. To maximize the secrecy rate of the UAVs communication, an optimization problem has been proposed to jointly optimize the trajectory and transmit power. To enhance the obtained average secrecy rate, MIMO communication and a cooperative UAV jammer strategy were used in this paper. Also, analysis and simulations results were presented to show the performance of UAV-ground communication at THz communications. Finally, Secrecy Outage Probability was obtained for each UAV trajectories in different flight periods to examine the performance of physical layer security added to the UAV-ground communication at sub-THz communication.

Unmanned Aerial Vehicles (UAV) network has been explored widely in recent years. The deployment of small to medium-scale UAVs are being considered for several real-time applications such as monitoring, surveillance, search and rescue services. Throughput in the communication model plays a vital role in improvising available capacity and balancing the data traffic loads. Often UAVs carry real-time sensitive data which are vulnerable to cyber-attacks and the UAVs’ wireless communication networks become more vulnerable to various forms of cyber-attacks. In this paper, we propose a secure machine learning-based approach to maximize the throughput of a Software Defined Network (SDN) controller for better UAV communications and security. The proposed approach consist of three steps, a) optimal placement and user association of UAVs using Genetic algorithm b) SDN controller placement using shortest path via single connected graph and c) creation and detection of DDoS attack using Feedforward neural network classifier. The best position of UAVs as base stations is obtained from the energy’s estimation of previous base station and users. After UAVs optimal placement, the availability of network capacity under SDN controller is done by finding the shortest path using bellman ford algorithm. It intends to balance the loads and generate traffic-free data transmission. Additionally, detection of DDoS attacks is also studied. The combination of two feature reduction techniques, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are employed to find the features relevant to DDoS attacks. These features are then fed into the Feedforward neural network classifier that classifies the normal and abnormal network traffic data of UAVs. The proposed approach is evaluated using simulations. Experimental results show high efficiency and security measures in SDN-based UAV networks.

Anomaly-based machine learning-enabled intrusion detection systems (AML-IDSs) show low performance and prediction accuracy while detecting intrusions in the Internet of Things (IoT) than that of deep learning-based intrusion detection systems (DL-IDSs). In particular, AML-IDS that employ low complexity models for IoT, such as the principal component machine (PCA) method and the one-class support vector machine (1-SVM) method, are inefficient in detecting intrusions when compared to DL-IDS with the two-class neural network (2-NN) method. PCA and 1-SVM AML-IDS suffer from low detection rates compared to DL-IDS. The size of the data set and the number of features or variants in the data set may influence how well PCA and 1-SVM AML-IDS perform compared to DL-IDS. We attribute the low performance and prediction accuracy of the AML-IDS model to an imbalanced data set, a low similarity index between the training data and testing data, and the use of a single-learner model. The intrinsic limitations of the single-learner model have a direct impact on the accuracy of an intelligent IDS. Also, the dissimilarity between testing data and training data leads to an increasingly high rate of false positives (FPs) in AML-IDS than DL-IDS, which have low false alarms and high predictability. In this article, we examine the use of optimization techniques to enhance the performance of single-learner AML-IDS, such as PCA and 1-SVM AML-IDS models for building efficient, scalable, and distributed intelligent IDS for detecting intrusions in IoT. We evaluate these AML-IDS models by tuning hyperparameters and ensemble learning optimization techniques using the Microsoft Azure ML Studio (AMLS) platform and two data sets containing malicious and benign IoT and industrial IoT (IIoT) network traffic. Furthermore, we present a comparative analysis of AML-IDS models for IoT regarding their performance and predictability.

The recent evolution of the Internet of Things in the healthcare sector has attained substantial recognition from the government and industry. Healthcare data accumulated from diverse sources are stored by health service providers which is useful for patient diagnosis as well as for research for pivotal analysis. However, healthcare data contains sensitive information of an individual that needs to be protected. The sensitive health information of an individual contains multiple attributes and the correlation of this information may lead to a privacy breach. In more complex scenarios, such type of data is expected to be released periodically and dynamically, the privacy breach of individuals becomes imminent due to the presence of personal data. In this article, a fuzzy logic-based intelligent privacy-aware algorithm is proposed to protect the individual’s privacy with multiple sensitive attributes in a dynamic data release scenario. Our formal modeling and analysis show that the proposed approach offers a robust privacy guarantee while releasing health-related data. Furthermore, the empirical evaluation exhibits that the proposed model outperforms the state-of-the-art approaches in terms of information loss and query accuracy.

Decentralized paradigm in the field of cybersecurity and machine learning (ML) for the emerging Internet of Things (IoT) has gained a lot of attention from the government, academia, and industries in recent years. Federated cybersecurity (FC) is regarded as a revolutionary concept to make the IoT safer and more efficient in the future. This emerging concept has the potential of detecting security threats, taking countermeasures, and limiting the spreading of threats over the IoT network system efficiently. An objective of cybersecurity is achieved by forming the federation of the learned and shared model on top of various participants. Federated learning (FL), which is regarded as a privacy-aware ML model, is particularly useful to secure the vulnerable IoT environment. In this article, we start with background and comparison of centralized learning, distributed on-site learning, and FL, which is then followed by a survey of the application of FL to cybersecurity for IoT. This survey primarily focuses on the security aspect but it also discusses several approaches that address the performance issues (e.g., accuracy, latency, resource constraint, and others) associated with FL, which may impact the security and overall performance of the IoT. To anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts, challenges, and research trends in this area. With this article, readers can have a more thorough understanding of FL for cybersecurity as well as cybersecurity for FL, different security attacks, and countermeasures.

Solving linear programming (LP) and nonlinear programming (NLP) problems is momentous because of their wide applications in real-life problems. There is no unified way to find the global optimum for NLPs. But on the other hand, the simplex algorithm, as the dominating methodology for LPs for several decades moves only on the boundary (vertices) and ignores the vast majority of the feasible region in the process of searching. In this article, we study two gradient-based methodologies that explore the whole feasible region, which guarantee faster convergence rates for both LP and NLP optimization problems including IoT problems such as Software-Defined Internet of Vehicles (SDIoV) and vehicular ad hoc networks (VANETs). The gradient-simplex algorithm (GSA) for LPs, which moves inside the feasible region in the gradient direction at first to reduce the search space and then explores the reduced boundary to find an optimal solution. The evolutionary-gradient algorithm (EGA), on the other hand, is for NLPs and uses an evolutionary population to estimate gradients by evolving to find better solutions in steps. Based on extensive simulations, the obtained numerical results show that both approaches provide efficient solutions and outperform the state-of-the-art methods on optimization problems with large feasible spaces. Comparative results of applying the GSA on SDIoV and VANETs with different sizes are included.

Cities have circumvented privacy norms and deployed sensors to track vehicles via toll transponders (like E-Zpass tags). The ethical problems regarding these practices have been highlighted by various privacy advocacy groups. The industry, however, has yet to implement a standard privacy protection regime to protect users’ data. Furthermore, existing risk management models do not adequately address user-controlled data-sharing requirements. In this article, we consider the challenges of protecting private data in the Internet of Vehicles (IoV) and mobile edge networks. Specifically, we present a privacy risk reduction model for electronic toll transponder data. We seek to preserve driver privacy while contributing to intelligent transportation infrastructure congestion automation schemes. We thus propose TollsOnly, a fully homomorphic encryption protocol. TollsOnly is expected to be a postquantum privacy preservation scheme. It enables users to share specific data with smart cities via blockchain technology. TollsOnly protects driver privacy in compliance with the European General Data Protection Regulation (GDPR) and the California Consumer Privacy Act.

The sixth-generation (6G) communication technology provides a high level of interoperability through terahertz data transfer and latency-less service sharing. Due to its interoperable nature, the integration of heterogeneous networks, such as the Internet of Things (IoT) and cloud radio access networks (CRANs), is performed at ease. This integration is managed using software-defined networks (SDNs) for managing the Quality of Service (QoS) experience of the users, irrespective of the application. This manuscript proposes the service virtualization and flow management framework (SVFMF) for the reliable utilization of resources in the 6G-cloud environment. The imbalance in a service request and response due to overloaded and idle virtual resources is addressed in this framework. For this purpose, this framework endorses service virtualization and user allocation modules for mitigating the drawbacks of imbalanced service allocations. Linear decision making of the service virtualization process helps to reduce the computation and service discovery by identifying overloaded services and performing a reallocation. The purpose of user allocation is to distribute the service requests to the idle service providers to reduce the prolonged wait time of the increasing user requests. The performance of the proposed framework is verified using experimental analyses, for the metrics service discovery and computation time, service failure ratio, and flows. The reliability of SVFMF is proved by varying the density of users, virtual machines, service requests, and user allocation per virtual machine, respectively.

Cyberthreats are continually evolving and growing in numbers and extreme complexities with the increasing connectivity of the Internet of Things (IoT). Existing cyber-defense tools seem not to deter the number of successful cyberattacks reported worldwide. If defense tools are not seldom, why does thecyber-chasetrend favor bad actors? Although cyber-defense tools monitor and try to diffuse intrusion attempts, research shows the required agility speed against evolving threats is way too slow. One of the reasons is that many intrusion detection tools focus on anomaly alerts’ accuracy, assuming that preobserved attacks and subsequent security patches are adequate. Well, that is not the case. In fact, there is a need for techniques that go beyond intrusion accuracy against specific vulnerabilities to the prediction of cyber-defense performance for improved proactivity. This article proposes a combination of cyberattack projection and cyber-defense agility estimation to dynamically but reliably augur intrusion detection performance. Since cyber-security is buffeted with many unknown parameters and rapidly changing trends, we apply a machine-learning (ML)-based hidden Markov model (HMM) to predict intrusion detection agility. HMM is best known for robust prediction of temporal relationships mid noise and training brevity corroborating our high prediction accuracy on three major open-source network intrusion detection systems, namely, Zeek, OSSEC, and Suricata. Specifically, we present a novel approach for combined projection, prediction, and cyber-visualization to enable precise agility analysis of cyber defense. We also evaluate the performance of the developed approach using numerical results.

Advances in artificial intelligence (AI) and wireless technology are driving forward the large deployment of interconnected smart technologies that constitute cyber–physical systems (CPSs) and Internet of Things (IoT) for many commercial and military applications. CPS is characterized by communication, computing, and control engineering based on a large volume of data originating from various devices, plants, sensors, etc. Wireless technologies have enabled the ease of networking and communications for both CPS and IoT, by providing massive and critical connectivity and control mechanisms. However, they are prone to challenges, such as low latency, throughput, and scheduling. Recent research trends focus on how to intelligently use data from CPS units to enhance wireless connectivity in CPS. AI tools, particularly AI systems and machine learning (ML) algorithms, have been widely applied in the literature to develop efficient schemes for wireless CPS/IoT. This article presents a review on the role of AI in wireless networking for CPS and IoT. In particular, we focus on ML paradigms, such as transfer learning (TL), distributed learning, and federated learning, that have evolved as building blocks for the utilization of large data for learning, adaptation, and predictions in CPS and IoT systems that leverage wireless networking. Furthermore, we also highlight challenges faced by current and future wireless networks pertaining to CPS/IoT, which are yet to be addressed.

The Ever-Increasing evolution of technologies in communication, artificial intelligence (AI), manufacturing, etc., is promoting a new wave of industrial revolution. Industrial Internet of Things (IIoT) has been considered as a critical stimulator for both science and economics by amounts of countries.

The emergence of advanced technologies has triggered a sweeping digital transformation in the industrial ecosystem. The cutting-edge technologies (like, Internet of things, big data, artificial intelligence, drones, cyber-physical systems, and augmented reality, and computer vision) are key enablers of this industrial revolution. Industry 4.0 has reshaped the conventional manufacturing and production processes into automated operations and workflows. This industrial transition is fueled by advanced computing (cloud and edge computing), analytic (big data analytics and computational analytics), intelligent (machine and deep learning), and communication (programmable and intelligent networks) infrastructure and technologies. The collection, aggregation, analysis, and processing of big data generated from the industrial periphery (like manufacturing equipment and maintenance systems) enable real-time decision-making and autonomous opportunities. However, the voluminous size, variability, and frequency of this data bring a wide array of disputes and oppositions in the resource-limited Industrial systems. Moreover, the continuous decision-making workflow in production and manufacturing segments increases the sharing of data across different functions, systems, and organizational boundaries. For this reason, cloud computing and big data technologies (Hadoop and Map-Reduce) can improve the anticipated response and reaction times. Industry 4.0 will lead toward more devices enriched with embedded computing platforms which boost the capabilities of the overall workflow. But, this also leads towards an increased communication and interaction between these devices which can end up in various challenges for the underlying network infrastructure. However, the conventional communication protocols may end up in various performance bottlenecks which in turn can increase the threat from different kinds of attacks and security challenges. Concluding the above discussion, the industrial ecosystem would rely on two entities: (1) users or infrastructure (physical world) and (2) cloud-enabled algorithms and autonomous systems (virtual world) that are connected through advanced and autonomous communication technologies. The driving force behind the success of these industrial ecosystems relies on the efficient gathering/collection, analysis, and storage of data generated by smart devices and sensors. Under this domain, big data analytics is set to be driving predictive manufacturing and provide timely detection of anomalies and system failures to predict product quality. In this way, big data is bound to play a prominent role in driving the industrial ecosystem. Even more, the only reason for this concern is not limited to the volume of data but the major concern is the contribution of this data for the design and implementation of efficient industrial processes and policies. The interpretation and understanding of the available data help to the design of efficient processes and policies related to industrial systems. The focus of this special issue is to present novel and seminal contributions around the important issues and challenges related to big data management and analytics for industrial 4.0 ecosystems. It provides ground-breaking research from academia and industry, that emphasizes the novel solutions, applications, tools, software, and algorithms designed to handle the industrial big data. A substantial number of submissions were received for the special issue. The papers were reviewed by at least three reviewers and underwent a rigorous two rounds of reviews. After the completion of the peer review process, we have accepted 10 seminal contributions related to big data analytics for Industry 4.0. All the accepted papers either discuss the recent solutions related to big data analytics or proposes an innovative way of handling big data across diverse infrastructure deployments. The outline of these contributions discussed below. The first paper titled "An Efficient Scheme for Secure Feature Location using Data Fusion and Data Mining in IOT Environment" by Balaji et al.1 proposes a secure feature location approach based on data fusion and data mining to overcome the challenges of the existing textual and dynamic approaches. The first step in this approach involves the removal of repeated test cases followed by the selection of important attributes. The artificial flora optimization algorithm was used to remove the repeated test cases. After this, the Caesar Cipher-RSA algorithm was used to encrypt the selected attributes, and thereafter a score value was assigned to them. This score value acts as an input to the K-mean algorithm to normalize it using the min-max approach. The evaluation results show that the proposed approach is superior in comparison to existing variants. The second paper titled "Data Dimensionality Reduction Techniques for Industry 4.0: Research Results, Challenges, and Future Research Directions" by Chhikara et al.2 provides a comprehensive survey on dimensionality reduction techniques. This survey discussed various data dimensionality techniques, analyzed them, and provided a thorough comparison based on different factors and parameters. The survey provided an understanding of the applicability of dimensionality reduction techniques in group or stand-alone use cases. The third paper titled "Deep-Q Learning-based Heterogeneous Earliest Finish Time Scheduling Algorithm for Scientific Workflows in Cloud" by Kaur et al.3 proposes a workflow scheduling approach wherein a deep-Q learning mechanism is used. This deep-Q mechanism was based on a heterogeneous earliest-finish-time algorithm was designed to amalgamate the deep learning approach with the heuristic approach for task scheduling. The evaluations were performed on a workflow simulator and the results depict the superiority of the proposed approach in contrast to the existing algorithms in terms of makespan and speed. The fourth paper titled "A multi-domain VNE algorithm based on multi-objective optimization for IoD architecture in Industry 4.0" by Zhang et al.4 proposes a multidomain virtual network embedding algorithm to improve the performance and reduce the computational delay. This algorithm is based on centralized hierarchical architecture and avoids local optimum by improvising the particle swarm optimization algorithm to include a genetic variation factor. However, as the problem is composed of multiple objectives, the proposed work simplifies the same by decomposing it into a single-objective problem using a weighted summation method. According to the obtained results, the proposed approach converges to an optimal solution quickly. Further, a candidate selection algorithm was proposed to reduce the cost associated with mapping. In this algorithm, the physical domain calculates the mapping cost for all nodes and selects the one with the lowest mapping cost. The results show the efficiency of the proposed approach in terms of delay, cost, and several other performance indicators. The fifth paper titled "A Community-based Hierarchical User Authentication Scheme for Industry 4.0" by Sinha et al.5 proposes a community-based hierarchical approach that is used to decide the way to provide access rights of the smart end devices to the users in the Industry 4.0 ecosystem. The hierarchical structure helps to ensure that only the legitimate users get access rights after clearing the multilevel authorization process. This approach also ensures identity leakage as the legitimate parties coordinate closely with each other for the authentication process. The validation shows that the proposed approach is susceptible to various types of attacks. The sixth paper titled "PSSCC: Provably Secure Communication Framework for Crowdsourced Industrial Internet of Things Environments" by Dharminder et al.6 proposes an identity-based signcryption method in the provably secure communication framework. During signcryption, the end-user performs pairing-free computation that proves to be computationally efficient. Based on the modified bilinear Diffie-Hellman inversion and strong Diffie-Hellman problems, the framework is proved to be secure in the Industrial Internet of Things environment. The evaluation was performed based on communication and computation cost and the results look very promising. The seventh paper titled "Applying Artificial Bee Colony Algorithm to the Multi-depot Vehicle Routing Problem" by Gu et al.7 utilizes an artificial bee colony algorithm in multidepot Vehicle Routing Problem to manage the vehicular routes among multiple depots in an optimized and time-efficient manner. Initially, the multidepot Vehicle Routing Problem is decomposed single-depot problem using depot clustering. After this, a modified artificial bee colony algorithm is used to generate solutions for each depot. In the end, a coevolution strategy is proposed to realize a complete solution to the multi-depot Vehicle Routing Problem. The proposed algorithm was validated through extensive experiments and the results were compared with greedy and genetic algorithms based on different parameters. The results depict a performance enhancement to the tune 70% over the greedy algorithm and 3% over the genetic algorithm. The eighth paper titled "An IoT-enabled Decision Support System for Circular Economy Business Model" by Mboli et al.8 proposes a decision support system based on the Internet of Things for a circular economy business model. This system is based on an ontological model that effectively allows to predict, track, and monitor the residual value of the product. This allows businesses to utilize circularity decisions complemented by a semantic decision support system to create a first of its kind, semantic ontological model. The proposed model was validated based on real-world use case scenario to understand viability and applicability. The ninth paper titled "Security Analytics for Real-Time Forecasting of Cyberattacks" by Javed et al.9 proposes a pattern identification framework for cyberthreats. After identification of the cyber patterns a forecasting model suggests the pattern of growth in an emerging network threat. This framework predicts the maximum threat intensity and its occurrence over the period thereby suggesting the likelihood of maximum intensity. The framework involves four steps, (1) continuous activity monitoring, (2) behavior forecasting, (3) estimating the intensity of a potential cyberattack, and (4) predicting the potential risk of cyber attacks over a predefined time window. The validation depicts an average lead time of 1.75 h good enough to limit the potential impact of the attack. The tenth paper titled "An Efficient Hadoop based Brain Tumor Detection Framework using Big Data Analytic" by Chahal et al.10 proposes a brain tumor segmentation approach based on a hybrid weighted fuzzy mechanism. This approach works in tandem with the Matlab Distributed Computing Server and Hadoop to fuzify the pixel values to create meaningful clusters of large data. The approach is validated based on huge MR brain data across clusters of varying sized DICOM datasets using hybrid fuzzy clustering in MapReduce on Hadoop. The experiments performed compared the read, write, and processing time on each node. The outcomes show an elevation in the read and write operation time with an increase in the data size to multinode. The processing time comes out to be 35 min and 3.4 min on single and three-node clusters, respectively. Further increasing the data size to 7.3 GB, the proposed approach process the data in 235.4 min and 2085.2 min for the three-node cluster and single node, respectively. We hope that the seminal research contributions and findings presented in this special issue would benefit the readers to enhance their knowledge base and encourage them to work on various aspects of big data analytics. We express our sincere gratitude and thanks to the editor-in-chief for allowing us to organize this special issue. The support from the editorial office staff was excellent and we thank them for the same. We are also thankful to all the authors who submitted their ideas and finding in this special issue and made it possible, and to the reviewers for their thoughtful and critical suggestions to improve the quality of the submitted papers.

The papers in this special section focus on recent advances in fuzzy-based intelligent Internet of Things and cyber-physical systems. The rapid development of real-time Internet of Things (IoT) applications including smart grids, smart city, and intelligent transport networks generate a tremendous amount of data from massively distributed sources, which require high computing and communication demand that frequently exceeds the users’ requirements. Furthermore, many emerging IoT applications including remote surgery, machine monitoring and control, fault detection, and healthcare generate delay-sensitive tasks, which require timely processing with minimum delay. Besides that, cyber-physical systems (CPS) integrate computing and communication capabilities with monitoring and control of entities in the physical world. These systems are usually composed of a set of networked agents, including sensors, actuators, control processing units, and communication devices. All the critical infrastructures are also a part of the cyber-physical ecosystem to enable smart and connected environments.

The Internet of Medical Things (IoMT) is an arising trend that provides a significant amount of efficient and effective services for patients as well as healthcare professionals for the treatment of disparate diseases. The IoMT has numerous benefits; however, the security issue still persists as a challenge. The lack of security awareness among novice IoMT users and the risk of several intermediary attacks for accessing health information severely endanger the use of IoMT. In this article, rooted elliptic curve cryptography with Vigenère cipher (RECC-VC) centered security amelioration on the IoMT is proposed for enhancing security. First, this work utilizes the exponential K-anonymity algorithm for privacy preservation. Second, a new improved Elman neural network (IENN) is proposed for analyzing the sensitivity level of data. The Gaussian mutated chimp optimization is employed for weight updating in this IENN. Finally, a novel RECC-VC is proposed for securely uploading the data to the cloud server. Additionally, data are stored in the cloud server using blockchain technology. In experimental analysis, the proposed methodologies attain better results than the prevailing methods. The proposed IENN model achieves an accuracy of 96% and is validated against state-of-the-art methods. Also, the proposed RECC-VC attains 98% of the security level.

Rrecently, communication technologies have been advanced rapidly to guarantee large bandwidth with low latency and reduced overhead. In addition, many new technologies are in pursuit to ensure guaranteed communication and services such as 5th and 6th generation (5G/6G) services and technologies, future Internet architectures, edge computing techniques, edge cloud-based architectures, services virtualization, and so forth. The prime objective of these new technologies is to make user-produced information easily accessible to the users on the go. Hence, these technologies play a vital role in forms of wireless networks. However, in this Special Issue, we solely focus on the new solutions, services, communication techniques, and architectures to provide efficient information communication to the vehicular networks.

Cooperative intelligent and autonomous transportation systems rely on intelligent sensing, computing, and actuating technologies for unmanned freight and public movements. The information gained from neighbors and communication infrastructures provides efficient actuation for safe and sustained transportation. This article resolves traffic data management congestion using sixth-generation (6G) communication and computing techniques. Terahertz and machine-type communications are exploited for swift information exchange, bypassing the congestion effects. Congestion occurs when demand for road space exceeds supply. This proposal incorporates prediction-based learning to compute the feasibility of handling traffic information and cooperative intelligent transportation. This model is named Congestion-aware Pre-predictive Data Allocation (CPPDA). The traffic flows causing congestion in the data exchange process are predicted for re-allocation and independent channel utilization. In this learning, the pre-predicted instances are updated with the actual identified utilization-to-congestion rate. Therefore, the congestion-causing channels for sensing are identified with ease, reducing the outage. The outage is examined for a basic inter-vehicle data link. Through the optimal allocation of channels for actuation, cloud-aided resources are utilized to a maximum level, leveraging infrastructure support. In addition to an outage of 10.83%, the response time of 14.75%, congestion factor of 8.2%, computational overhead of 6.4%, and information gain factors of 6.86% are analyzed through a comparative study.

Wireless virtualization has emerged as one of the efficient ways to overcome numerous wireless networking challenges such as resource sharing by allowing multiple concurrent virtual wireless networks having run on shared physical wireless infrastructure. Spectrum sharing in wireless networks focuses on increasing the usage efficiency of radio frequency (RF) spectrum with its increasing demand. To reduce uncertainties that accompany the spectrum sharing process caused by RF sensing, database-based spectrum sharing allows opportunistic spectrum access through occupancy information query of a spectrum database. In this article, we propose a 2-level hierarchical spectrum allocation framework for multiple service-based virtual networks (VN) by leasing wireless resources from Wireless Infrastructure Providers (WIPs). The framework leverages the software defined network (SDN)-enabled virtualization where network parameters are configured based on the operating environment on the fly. This article focuses on the competition between multiple VNs to get best RF channels to serve their users while eliminating the need for one-to-one contracts between VNs and WIPs. We develop a mathematical model for the spectrum allocation scheme which allows heterogeneous network configurations with better economic benefit. Using network segments formed based on available sharable resources, the allocation scheme is localized which makes the scalability of this scheme possible. A trust-based security mechanism is also proposed for early detection and prevention of security attacks to maintain the availability attribute of the resource allocation process. Simulation results show that spectrum sharing is efficient, and assures service availability to perceived legitimate VN users.

Wireless virtualization is becoming a popular solution to resource sharing problems integral in providing access to future wireless networking applications by enabling access to the same physical wireless infrastructure by multiple virtual networks concurrently. This paper investigates cyber deception using game theory model for defining the deceptive interactions between a cyber attacker and the defender. We consider both the naive attacker who is not aware of the defender's strategy, and the rational (powerful) attacker who knows the defense strategy. In the Stackelberg game, the defender controls the observable configuration of the network when probed by an attacker. We show that the process for selecting an optimal defense strategy against a naive attacker is NP-hard. We also proposed an algorithm for computing optimal strategy for a naive attacker and a greedy based algorithm for selecting the optimal strategy against a powerful attacker. Results from extensive simulation runs show the scalability and effectiveness of the proposed approaches based on the run time in finding the optimum defense strategy as well as minimizing the attacker's utility are better than the approaches in state of the art literature.

Financially sustainable solution is the need to migrate legacy IPv4 network infrastructure into latest networking paradigms viz. Software-defined networking (SDN) and the Internet protocol version 6 (IPv6) addressing. For Internet service provider (ISP) networks, IP routers are either to be upgraded or replaced with new device to transform the network into Software-defined IPv6 (SoDIP6) networks. In this paper, we classify the network routers require its hardware/firmware either to be upgraded or replaced using adaptive neuro fuzzy inference system (ANFIS), a well-known intelligent approach used in classification, prediction, and estimation. Device specification details with end-of-life and end-of-support information are stored in the knowledge base (KB) system. Device performance parameters, for example average CPU usage, throughput, and memory capacity are being extracted in real time using simple network management protocol (SNMP) and mapped with information obtained from the KB for input to ANFIS. The experimental results show that proposed approach is more accurate and optimal that assists service providers for smooth transitioning to SoDIP6 networks with optimum cost of migration.

Edge Computing (EC) has seen a continuous rise in its popularity as it provides a solution to the latency and communication issues associated with edge devices transferring data to remote servers. EC achieves this by bringing the cloud closer to edge devices. Even though EC does an excellent job of solving the latency and communication issues, it does not solve the privacy issues associated with users transferring personal data to the nearby edge server. Federated Learning (FL) is an approach that was introduced to solve the privacy issues associated with data transfers to distant servers. FL attempts to resolve this issue by bringing the code to the data, which goes against the traditional way of sending the data to remote servers. In FL, the data stays on the source device, and a Machine Learning (ML) model used to train the local data is brought to the end device instead. End devices train the ML model using local data and then send the model updates back to the server for aggregation. However, this process of asking random devices to train a model using its local data has potential risks such as a participant poisoning the model using malicious data for training to produce bogus parameters. In this paper, an approach to mitigate data poisoning attacks in a federated learning setting is investigated. The application of the approach is highlighted, and the practical and secure nature of this approach is illustrated as well using numerical results.

Vehicular cyber physical systems (VCPS) will play a vital role in the quest to develop intelligent transportation systems (ITS) and smart cities around the world. Consequently, researchers in academia, industry and government continue to leverage on emerging technologies like software defined networking (SDN), blockchain, cloud computing and machine learning (ML) to improve the overall efficiency of these intelligent systems. Recently, mobile edge computing (MEC) has been used to enhance content caching and efficient resource allocation and therefore advance the development of data-intensive and delay-constrained applications that improve the driving experience in VCPS. Security and privacy concerns that endanger the safety of lives and infrastructure necessitate the need to use federated learning (FL), a distributed ML algorithm that employs learning at the edge to ensure that data remains at the different vehicles and thus enhance greater efficiency. In this paper therefore, we propose the use of FL, together with differential privacy to improve the resiliency of VCPS to adversarial attacks in connected vehicles.

Dynamic wireless resource allocation is one of the challenging problem in wireless networks to offer services with high data rate, high spectral efficiency, minimum energy and low latency when there is a high demand of limited wireless resources. To address this problem, we investigate an efficient wireless resource allocation scheme using fully connected convolutional neural network (CNN) with long time short memory (LSTM) algorithm for predicting demand of mobile virtual network operators (MVNOs) to sublease spectrum from wireless infrastructure providers (WIPs) to serve MVNOs' users. WIPs sublease wireless resources to MVNOs on the fly and MVNOs act like independent wireless service providers for their end users. While maximizing their payoffs and resource utilization, it is necessary for WIPs and MVNOs to allocate wireless resources adaptively to meet users' requirements. We present both formal mathematical analysis and numerical results to support our claims. Numerical results show that the WIP and MVNO make informed decisions based on prediction for wireless resources in order to maximum their payoffs when users change their requirements. Furthermore, performance comparison results show that the proposed approach outperforms the existing approaches in terms of payoffs, energy and delay.

Whether intentionally or not, many individuals in society express some form of prejudice or bias in their thinking and communications. Prejudice can be expressed in a multitude of ways and may be directed towards anything in which people can have an opinion. What makes prejudiced/biased thinking different and potentially more dangerous than having an opinion is that they are not always based on one's personal experiences and therefore may be misinformed. With the rise of social media platforms in the last 20 years or so, people have more ways than ever to express their opinions and share their voices. This paper defines an approach to using Twitter, one of the most popular modern-day social media platforms, to analyze an individual's tweets for the purposes of identifying potential biases. Using sentiment analysis along with other natural language processing tools, a score of potential bias of an individual user is calculated. This score can provide insight to the degree and/or frequency at which a person can be expected to exhibit biased behavior or make biased statements and is only meant to serve as a reference.

Data falsification attack in Vehicular Ad hoc Networks (VANET) for the Internet of Vehicles (IoV) is achieved by corrupting the data exchanged between nodes with false information. Data is the most valuable asset these days from which many analyses and results can be drawn out. But the privacy concern raised by users has become the greatest hindrance in performing data analysis. In IoV, misbehavior detection can be performed by creating a machine learning model from basic safety message (BSM) dataset of vehicles. We propose a privacy-preserving misbehavior detecting system for IoV using Federated Machine Learning. Vehicles in VANET for IoV are given the initial dull model to locally train using their own local data. On doing this we get a collective smart model that can classify Position Falsification attack in VANET using the data generated by each vehicle. All this is done without actually sharing the data with any third party to perform analysis. In this paper, we compare the performance of the attack detection model trained by using a federated and central approach. This training method trains the model on a different kind of position falsification attack by using local BSM data generated on each vehicle.

Machine Learning (ML) is on the verge of transitioning from centralized, distributed to federated learning (FL) due to the inherent privacy-preserving framework by FL. FL enables collaborative learning among participants just by exchanging updated model parameters with the FL server while keeping training data local in the end devices which is suitable for vehicular communications. However, this learning framework makes the life of the server difficult to detect the malicious behavior of participants. Malicious model updates from participants may affect the accuracy of the learning model considerably and consequently may cause severe consequences in the Internet of Vehicles (IoV) environment. To address this issue, we propose a novel approach to apply Shiryaev's quickest change detection (QCD) technique in the FL realm. QCD is applied to detect abnormal changes in statistical properties of model parameters in FL as quickly as possible. We apply QCD on the server-side in two ways. First, QCD is applied to detect a change in the statistical properties over the model parameters sent by the participating devices. Second, QCD is applied to the history of aggregated FL model parameters. The first approach facilitates identifying malicious clients which can be eliminated in future learning activities. The other approach assists the server to roll back to an earlier version of the model in case of identifying the anomaly in the aggregated FL parameters. As QCD is applied on the server-side, it does not add any computation overhead on the client-side as well as communication overheard during transmission. These two approaches are evaluated with the help of numerical results.

Wireless vehicular network (WVN) is exponentially gaining attention from industries and researchers since it is the keystone of intelligent transportation systems (ITS). Despite the sophisticated features and services that it can offer, it is susceptible to networking attacks such as eavesdropping threats where the confidential transmitted signal could be overheard by a malicious entity. In this paper, we intend to study the physical layer security (PLS) where we consider the eavesdropping attack for vehicle-to-infrastructure (V2I) communications. We analyze the average secrecy capacity, under different scenarios by comparing the performances of employing the decode-and-forward (DF) relay, the amplify-and-forward fixed gain (AFFG) relay, and the intelligent reflecting surface (IRS). Actually, this comparison investigates the efficiency of IRS comparing to the traditional relaying systems, since it was introduced as a novel paradigm in wireless technology with highly promising potential, especially in 5G and 6G.

Lately people are using social media for sharing their thoughts, insights about different topics and issues. The main aim of social media is to connect users and update their statuses/thoughts. One of the most used online social networking sites to share information is Twitter with roughly 330 million uses globally and 48.35 million US users where they share their opinions and thoughts. Recently, the world faced a serious pandemic, Corona virus disease (COVID-19) outbreak and the World Health Organization (WHO) declared the virus as a global health emergency. The COVID-19 started in late December of 2019 in Wuhan City, Hubei Province, China. Around the world during this time, individuals use social media to share their opinions about the pandemic. Because of the lack of information about the virus, people switched to micro-blogging platforms such as Twitter. In this study, we utilize natural language processing (NLP) techniques for opinion mining to extract negative and positive sentiments/tweets on COVID-19. We investigate NLP based sentiment analysis using Recurrent Neural Network (RNN) model with Long-Short Term Memory networks (LSTMs). Predicted sentiment using LSTM-RNN, which gives high accuracy, can be used to educate people about the virus.

The concept of intelligent transportation systems (ITS) is considered to be a highly promising area of research due to its diversity of unique features. It is based mainly on the wireless vehicular network (WVN), where vehicles can perform sophisticated services such as sharing real-time safety information. To ensure high-quality service, WVN needs to solve the security challenges like eavesdropping, where malicious entities try to intercept the confidential transmitted signal. In this paper, we are going to provide a security scheme under the Double kappa-mu Shadowed fading. Our solution is based on the use of a friendly jammer that will transmit an artificial noise (AN) to jam the attacker’s link and decrease its eavesdropping performances. To evaluate the efficiency of our solution, we investigated the outage probability for two special cases: Nakagami-m and Rician shadowed while taking into consideration the density of the blockage and the shadowing effects. We also studied the average secrecy capacity via deriving closed-form expressions of the ergodic capacity at the legitimate receiver and the attacker for the special case: Nakagami-m fading distribution.

The immense vulnerabilities within network protocols have led to prevalent spoofing attacks. Security measures such as cryptographic authentication, router level filtering, and other anti-spoofing methods are promising yet inadequate. In fact, research shows almost 50% of existing autonomous systems can be "hijacked" and used as spurious network nodes. Spoofing is falsified node identification of a trusted node and subsequent delivery of imposter data packets. Correctly identifying a packets’ travel path has been widely used to ascertain node positions and flag unexpected locations as spoofing agents. Challenges and inadequacies arise in analyzing the copious dynamic possible packet paths. We propose a practical anti-spoofing technique when routing is asymmetric or multi-path that contrast network connections from trusted nodes after considering all possible routing alternatives, compared to a network path under scrutiny. We subtly perform obscured intrusion detection through hopcounts clustering via IP header’s time-to-live field and special representative nearest captain nodes that differentiate features of valid paths. While many proposed anti-spoofing solutions only analyze static traffic to give ≈ 90% efficacy, we consider all alternatives that deviate from accurate features to achieve improved efficacy of 94.2%.

Wireless communications networks are an integral part of intelligent systems that enhance the automation of various activities and operations embarked by humans. For example, the development of intelligent devices imbued with sensors leverages emerging technologies such as machine learning (ML) and artificial intelligence (AI), which have proven to enhance military operations through communication, control, intelligence gathering, and situational awareness. However, growing concerns in cybersecurity imply that attackers are always seeking to take advantage of the widened attack surface to launch adversarial attacks which compromise the activities of legitimate users. To address this challenge, we leverage on deep learning (DL) and the principle of cyber-deception to propose a method for defending wireless networks from the activities of jammers. Specifically, we use DL to regulate the power allocated to users and the channel they use to communicate, thereby luring jammers into attacking designated channels that are considered to guarantee maximum damage when attacked. Furthermore, by directing its energy towards the attack on a specific channel, other channels are freed up for actual transmission, ensuring secure communication. Through simulations and experiments carried out, we conclude that this approach enhances security in wireless communication systems.

The number of connected smart devices has been increasing exponentially for different Internet-of-Things (IoT) applications. Security has been a long run challenge in the IoT systems which has many attack vectors, security flaws and vulnerabilities. Securing billions of connected devices in IoT is a must task to realize the full potential of IoT applications. Recently, researchers have proposed many security solutions for IoT. Machine learning has been proposed as one of the emerging solutions for IoT security and reinforcement learning (RL) is gaining more popularity for securing IoT systems. RL, unlike other machine learning techniques, can learn the environment by having minimum information about the parameters to be learned. It solves the optimization problem by interacting with the environment adapting the parameters on the fly. In this article, we present an comprehensive survey of different types of cyberattacks against different IoT systems and then we present RL and deep RL-based security solutions to combat those different types of attacks in different IoT systems. Furthermore, we present the RL for securing CPS systems (i.e., IoT with feedback and control), such as smart grid and smart transportation system. The recent important attacks and countermeasures using RL in IoT are also summarized in the form of tables. With this article, readers can have a more thorough understanding of IoT security attacks and countermeasures using RL, as well as research trends in this area.

The potential for the wide-scale acceptance of the Industrial IoT is limited by a lack of automation, real-time monitoring, and connectedness. However, the future communication trend towards 5G is expected to bring greater benefits to IIoT infrastructures in terms of high-speed transmission and ultra-low latency. Furthermore, with emerging techniques such as millimeter-wave (mmWave), massive multiple-input multiple-output (MIMO), and machine-to-machine (M2M) communications, the coupling of IIoT and 5G will advance profoundly. Despite these advantages, 5G-envisioned IIoT ecosystems are expected to face other potential concerns such as trust, security, and privacy. Apart from this, the challenges related to data storage and processing and computational complexities will also draw significant attention. To address the above-mentioned challenges, it's important to analyze data in real-time. In this direction, transfer learning (TL) can be a revolutionary breakthrough. TL fosters greater explorations and experimentations, leading to innovations and greater productivity.

Internet of Vehicles (IoV) in 5G is regarded as a backbone for intelligent transportation system in smart city, where vehicles are expected to communicate with drivers, with road-side wireless infrastructure, with other vehicles, with traffic signals and different city infrastructure using vehicle-to-vehicle (V2V) and/or vehicle-to-infrastructure (V2I) communications. In IoV, the network topology changes based on drivers' destination, intent or vehicles' movements and road structure on which the vehicles travel. In IoV, vehicles are assumed to be equipped with computing devices to process data, storage devices to store data and communication devices to communicate with other vehicles or with roadside infrastructure (RSI). It is vital to authenticate data in IoV to make sure that legitimate data is being propagated in IoV. Thus, security stands as a vital factor in IoV. The existing literature contains some limitations for robust security in IoV such as high delay introduced by security algorithms, security without privacy, unreliable security and reduced overall communication efficiency. To address these issues, this paper proposes the Elliptic Curve Cryptography (ECC) based Ant Colony Optimization Ad hoc On-demand Distance Vector (ACO-AODV) routing protocol which avoids suspicious vehicles during message dissemination in IoV. Specifically, our proposed protocol comprises three components: i) certificate authority (CA) which maps vehicle's publicly available info such as number plates with cryptographic keys using ECC; ii) malicious vehicle (MV) detection algorithm which works based on trust level calculated using status message interactions; and iii) secure optimal path selection in an adaptive manner based on the intent of communications using ACO-AODV that avoids malicious vehicles. Experimental results illustrate that the proposed approach provides better results than the existing approaches.

In the Internet of Vehicles (IoV), vehicles communicate wirelessly with other vehicles, sensors, pedestrians, and roadside units. IoV is aimed at improving road safety, driving comfort, and traffic efficiency. However, IoV is exposed to a range of threats to security and privacy. The presence of dishonest and misbehaving peers in the system is of a major concern, which may put lives in danger. Thus, establishing trust among these probable untrusted vehicles is one of the most significant challenges of such a network. The critical pitfalls of existing and traditional mechanisms are scalability, a single point of failure, maintaining the quality of service, verification, and revocation and dealing with sparsity, consistency, availability, efficiency, robustness, privacy concerns are some of the biggest challenges to be addressed. Blockchain technology, with its great success in applications like cryptocurrencies and smart contracts, is considered as one of the potential candidates to build trust in IoV. In this paper, we propose a blockchain-based decentralized trust management scheme using smart contracts. Specifically, we introduce the concept of blockchain sharding for reducing the load on the main blockchain and increasing the transaction throughput. Our proposal has two key contributions: blockchain to maintain and update reliable and consistent trust values across the network and incentive scheme to encourage peers to perform well. We also conduct extensive experiments, which demonstrate the implementation feasibility of proposed mechanisms in the real world.

‘‘Knowledge is power” is an old adage that has been found to be true in today’s information age. Knowledge is derived from having access to information. The ability to gather information from large volumes of data has become an issue of relative importance. Big Data Analytics (BDA) is the term coined by researchers to describe the art of processing, storing and gathering large amounts of data for future examination. Data is being produced at an alarming rate. The rapid growth of the Internet, Internet of Things (IoT) and other technological advances are the main culprits behind this sustained growth. The data generated is a reflection of the environment it is produced out of, thus we can use the data we get out of systems to figure out the inner workings of that system. This has become an important feature in cybersecurity where the goal is to protect assets. Furthermore, the growing value of data has made big data a high value target. In this paper, we explore recent research works in cybersecurity in relation to big data. We highlight how big data is protected and how big data can also be used as a tool for cybersecurity. We summarize recent works in the form of tables and have presented trends, open research challenges and problems. With this paper, readers can have a more thorough understanding of cybersecurity in the big data era, as well as research trends and open challenges in this active research area.

Wireless virtualization has emerged as a technology of interest to overcome resource sharing problem by allowing multiple virtual networks to access the same physical wireless infrastructure concurrently based on service level agreements (SLAs). The main focus so far has been on improving the efficiency of wireless resource sharing process. From a security perspective, this technology and its variations have not commanded as much attention partly due to the early state of wireless virtualization development and the undefined nature of potential attacks. In this paper, we present a Deceptor-in-the-Middle (DitM) cyber-deception mechanism that leverages the wireless virtualization architecture to enable virtual network operators to learn about the interest and nature of attacks without the attacker being aware and also not compromising the security and quality of service requirements of the virtual network users. We also propose a detection mechanism for identifying the presence of an attacker and redirecting to deception virtual networks. The performance of the proposed approach is evaluated using numerical results obtained from Monte Carlo simulations. The numerical results signify that the proposed DitM results in higher throughput than that of without the deceptor. We also analyzed the DitM using different metrics including the probability of miss detection and false alarm.

Named Data Network's (NDN) data-centric approach makes it a suitable solution in a networking scenario where there are connectivity issues as a result of the dynamism of the network. Coupling of this ability with the blockchain's well-documented immutable trustworthy-distributed ledger feature, the union of blockchain and NDN in an Internet-of-Battlefield-Things (IoBT) setting could prove to be the ideal alliance that would guarantee data exchanged in an IoBT environment is trusted and less susceptible to cyber-attacks and packet losses. Various blockchain technologies, however, require that each node has a ledger that stores information or transactions in a chain of blocks. This poses an issue as nodes in an IoBT setting have varying computing and storage resources. Moreover, most of the nodes in the IoT/IoBT network are plagued with limited resources. As such, there needs to be an approach that ensures that the limited resources of these nodes are efficiently utilized. In this paper, we investigate an approach that merges blockchain and NDN to efficiently utilize the resources of these resource-constrained nodes by only storing relevant information on each node's ledger. Furthermore, we propose a sharding technique called an Interest Group and introduce a novel consensus mechanism called Proof of Common Interest. Performance of the proposed approach is evaluated using numerical results.

The assumption that technology makes the world a better place translates to naive optimism with every successful cyber-attack. Research has seen an uptake in cyber-security visualization to complement weaknesses in traditional cyber-defense systems. However, due to the onerous task of positively handling cyber-attack “attribution”, only a few of these solutions scale geographical referenced networks. Many real-world systems can be expressed as networks consisting of nodes connected by edges, thus this paper explores connectivity modeled in a geographically referenced network, to visualize cyber-attack effects on the technical space (i.e., computing assets). The paper also demonstrates effects of defense strategies handling illegal connections.

Wireless virtualization is an emerging approach to utilize the limited wireless resources effectively to support massive number of wireless connections and devices for different applications like mobile cyber physical system (CPS) and Internet of Things (IoT).With increasing use of mobile CPS and IoT, there will be an exponential rise in the demand for wireless services. Wireless virtualization is regarded as an important alternative to meet the demands of different applications, where wireless services are provided to end users (in CPS or IoT) via mobile virtual network operators (MVNOs) that are created adaptively using data about users and their demands. In this paper, we propose adaptive data-driven wireless virtualization by creating MVNOs, where the cloud based controller assembles the right set of physical base stations and RF slices to meet the quality of service (QoS) constraints of the users by using mobility prediction and QoS requirements of the users. Numerical results obtained from simulations show that the proposed data driven approach outperforms the static approach for wireless virtualization.

With wireless virtualization, Wireless Infrastructure Providers (WIPs) are able to sublease out RF spectrum to multiple Wireless Virtual Network Operators (WVNO) who in turn offer services to their customers while sharing the same physical infrastructure. WVNOs are capable of leasing through a reservation process which may be accompanied by some strict guarantees, usually discouraging overbooking through certain penalties. On a global scale, it is important for WIPs to also be able to proactively reserve spectrum resources for consumer usage based on informed estimates. As part of the educated estimation, predictions are made from data of previous spectrum allocations and harmonized with aggregation of crowd-sourced data for events in a bid to reduce the probability of overbooking. The data aggregation effort relies on the the reliability of workers to generate highly accurate results using a community-based aggregation model. Also in this paper, a novel spectrum reservation prediction algorithm, namely Volume-conditioned Spectrum Selective Moving Average (VSSMA) is proposed using the trend similarity of spectrum allocation. The simulation results show that the relative mean error of the VSSMA algorithm is much lower than the Exponential Weighted Moving Average (EWMA) algorithm which is widely used now. We validate the desirable properties of the proposed approach through theoretical analysis, as well as simulations.

For real-time embedded systems, QoS (Quality of Service), fault tolerance, and energy budget constraint are among the primary design concerns. In this research, we investigate the problem of energy constrained standby-sparing for weakly hard real-time systems. The standby-sparing systems adopt a primary processor and a spare processor to provide fault tolerance for both permanent and transient faults. With the purpose of ensuring the feasibility of such kind of systems, we firstly present two novel scheduling schemes for the standby-sparing systems with tighter energy budget constraint than the traditional ones. Then based on them a hybrid approach is proposed to achieve better performance. The evaluation results demonstrated that the proposed techniques significantly outperformed the existing state of the art approaches in terms of feasibility and system performance while ensuring QoS and fault tolerance under given energy budget constraint.

In this article, we design, develop, and evaluate a wireless virtualization (WiVi) architecture for wireless networking for emerging Internet of Things (IoT) or 5G network applications by using a three-layer game theory model. WiVi is regarded as an emerging network paradigm to support different network service requirements (delay sensitive, bandwidth hungry, high data rate, etc.) for different IoT applications. In the proposed three-layer game model, interactions among three entities: 1) wireless resource providers (WRPs); 2) mobile virtual network operators (MVNOs); and 3) their subscribed wireless users (i.e., IoT devices), are formulated as strategies to optimize their respective utilities. The WiVi enables WRPs (also known as layer-1 leaders in the three-layer game) to sublease their wireless resources to MVNOs (also known as layer-2 leaders in the three-layer game) through RF slicing and adaptively setting their prices for subleasing. The MVNOs set the optimal competitive prices to attract more end-users/IoT devices (also known as followers in the three-layer game) to maximize their utilities. The end-users (IoT devices) maximize their data rates (i.e., utilities) by meeting the imposed quality of service (QoS) requirements and budget constraints. We investigate formal analysis of the uniqueness and existence of the equilibrium point of the three-layer game. Performance is evaluated using simulation results. Results show that the proposed three-layer game has unique and optimal equilibrium game. The numerical results show maximized utilities for WRPs, MVNOs, and IoT devices/users.

Industrial Internet is applicable across a broad industrial spectrum including manufacturing, aviation, road and rail transport, power, oil and gas, healthcare, smart cities and buildings. Some of the major impacts of the Industrial Internet include the development of new and innovative services and products, which in turn also has economic benefits. The purpose of this special issue is to bring together research studies proposing novel techniques, algorithms, models, and solutions to address challenges such as interoperability, security, and privacy associated with Industrial Internet, blockchain and Cyber-physical systems. We accepted seven articles after two review rounds consisting of three reviews from experts in the areas. The special issue contains seven articles organized in the following categories. 1) Secure searching for edge-cloud assisted industrial Internet of Things (IoT) devices. 2) Privacy protection framework for mobile crowdsensing in Industrial Internet of Things (IIoT). 3) Content privacy for autonomous vehicles in cyberphysical system (CPS). 4) Delegated Proof of Stake (DPoS) consensus mechanism in blockchain. 5) Balancing privacy and accountability for industrial mortgage management. 6) Performance and security in wireless blockchain networks. 7) False data injection attacks in networked control systems.

Traffic congestion and accidents are increasing exponentially worldwide. More vehicles are sold every year which leads to more traffic fatalities and congestion. There have been several efforts worldwide for mobile Cyber Physical Systems (CPS) to address a range of problems including traffic congestion, accidents, unnecessary time spent in traffic jams, and overall infotainment by using onboard communicating and computing technologies. However, when we use peer-to-peer network-based communication for mobile CPS, malicious users/vehicles could mislead the mobile CPS by not reporting their true periodic status data to their neighbors on the road. In this paper, we study a data validation and correction approach for resiliency in mobile CPS that uses a diverse set of data for reducing false information. Numerical results obtained from Monte Carlo simulation are used to evaluate the proposed approach. Results show that the proposed approach minimizes the false data in the mobile CPS to enhance the resiliency.

Wireless Virtualization has been widely investigated to improve radio resource utilization in wireless networks. Wireless Virtualization (WiVi) enabled architecture utilizes network slicing and Software Defined Network (SDN) to segment physical resources owned by wireless infrastructure providers (WIPs) into separate virtual resources which can then be allocated to the end users of the network through mobile virtual network operators (MVNO). Along this line, we present two Radio Frequency (RF) slice allocation mechanisms - Renyi Allocation and Optimal Allocation that are both concerned with optimization of RF resource allocation in WiVi framework. For Renyi RF allocation, our study shows that end-user latency can be managed while keeping the utilization of RF slices at an acceptable level; whereas for optimal allocation, our study shows that WIP and MVNOs can adapt during negotiations to maximize the expected payoffs.

The Internet of Things (IoT) and the blockchain are justly regarded as the technology for the future. The blockchain is a Distributed Ledger Technology (DLT) solution which has enormous potential as can be seen in the numerous avenues it has been deployed. Simplistically, it is a decentralized database which can revolutionize the current centralized world we live. IoT is the interconnection of devices with mostly bounded resources. IoT applications are also distributed in nature thereby making it inevitable that the paths of the blockchain and IoT will cross in the future. Blockchain's DLT will eventually play a crucial role in how IoT devices will communicate. The Proof of Work (PoW) mechanism was the original consensus technique introduced in the first blockchain based application (Bitcoin). PoW guaranteed consensus in the network by verifying transactions. However, the PoW had deficiencies. The solving of the PoW puzzle is computationally expensive which has become an impediment in the potential marriage of IoT and blockchain. This predicament arises as IoT devices are plagued with limited resources. In this work, we address this issue by presenting an approach where IoT devices can combine their resources to solve PoW puzzles that they might not have been able to solve on their own. This would ensure a successful merger between the blockchain and the IoT.

Software-defined Internet of Vehicles (SDIoV) has emerged as a promising field of study as it could overcome the shortcomings of traditional vehicular networks, such as offering efficient data transmission and traffic shaping in different vehicular scenarios to satisfy all the requirements of applications on the fly. Although routing solutions are lightly addressed for SDIoV, there are many limitations of routing protocols unaddressed in such environment. More precisely, shortest path routing algorithms are mostly focused in the state of the arts. This paper presents quality of service aware routing algorithm that forwards packets toward the most reliable and connected path to the destination. Particularly, candidate routes should satisfy metrics, such as signal to interference and noise ratio (SINR) constraint and have the highest probability of connectivity. To address these issues, we have formulated a discrete optimization problem to favor the best route among candidate paths and proposed the modified laying chicken algorithm (LCA) that results better results than the traditional approaches. We have mathematically analyzed the probability of connectivity along with the SINR metric. Moreover, a multiscore function based on traffic density and greediness factor is proposed to make intelligent decision at the intersections. Simulation results are used to validate the superiority of the proposed routing approach over the existing solutions.

Performance of wireless networks for Internet of Things (IoT) is suffering from exponential growth of lightweight hand-held and smart IoT devices as well as the data flow in the network. Static assignment of wireless resources is creating bottleneck to the further development of emerging applications, including IoT and cyber-physical systems. Wireless network virtualization through slicing of primary wireless resources is regarded as an emerging approach for enhancing overall network performance to solve the bottleneck problem for those emerging applications. In this paper, we study wireless virtualization where a three-layer game is formulated for wireless infrastructure providers (WIPs), mobile virtual network operators (MVNOs), and IoT devices (or end users). In the proposed three-layer game, players play sequentially to find out their optimal strategies. With the help of a controller, WIPs divide and reconfigure their RF bands to sublease frequency slices to MVNOs based on their service-level-agreement. MVNOs let end users use their subleased frequency slices. For the game, we present a unique optimal solution that facilitates a tradeoff between quality of service of end users, payoffs of MVNOs and payoff of WIPs. We also present a necessary condition and closed form for the existence of an equilibrium in the game. Furthermore, performance of the proposed approach is evaluated using numerical results which show that the payoffs of WIPs and MVNOs are maximized while offering competitive price for the subleased RF spectrum for their users.

Due to ubiquitous presence of wireless connectivity and surge in internet of things (IoT) devices, the next generation wireless network is predicted to face crunch of resources. Wireless virtualization is regarded as a viable approach to enhance resource utilization efficiency by sharing and reusing the physical resources in the form of slices or virtual networks (VNs). Mobile edge computing (MEC), on the other hand, facilitates task offloading as well as fast content delivery by bringing the computation and cache resources to the edge of the network. In this paper, we investigate virtualization of physical infrastructure that is embedded with MEC resources. In particular, this paper presents resource allocation in adaptive virtualized wireless networks with mobile edge computing. First, we investigate how to create VNs for mobile virtual network operators (MVNOs) based upon demand from users. Second, after creating the VN, how the MVNO can allocate resources to its users so that the utility (aka revenue) of the MVNO is maximized while satisfying users' quality of service (QoS). For the first problem, we propose an algorithm that incorporates demanded area of the MVNO, demanded spectrum, and computation and cache resources. Next, we present a collaborative resource allocation for maximizing the utility of MVNO while meeting the QoS requirements in terms of rates of the users. The performance is evaluated through numerical results obtained from Monte Carlo simulations. Numerical results show that the proposed approach gives better results in terms of utility and spectrum utilization efficiency.

For enhancing the utilization of radio frequency (RF) bands, dynamic spectrum access has been considered to be an emerging paradigm where unlicensed secondary users access the licensed RF spectrum opportunistically without causing harmful interference to licensed primary users. In order to access idle channels, secondary users are required either to sense channels or search the geolocation based spectrum database before starting their actual communications. Note that the database searching approach has been proven to be more effective than the channel sensing approach since the sensing based approach results in higher sensing uncertainties. When a large number of secondary users query the spectrum database for finding idle channels at the same time, the spectrum server could be overwhelmed that could result in (unintentional) Denial-of-Service attack. In this paper, we investigate group-query-as- aservice for searching idle channels in database driven dynamic spectrum access where selected secondary users (aka grid leaders who are selected based on the interactive trust levels, location and resources in their grids) query on behalf of the other secondary users (aka grid followers). In this approach, secondary users are associated with the contours created based on same idle channels with the help of spectrum sensors and mobile edge computing (MEC) servers at each base station. The performance of the proposed group-query-as-a-service is evaluated using numerical results obtained from Monte Carlo simulation. We found that the proposed approach results in less delay with increase edge hit rate and higher throughput than the individual query based approach.

Lloret, J.; Ahmed, SH.; Rawat, DB.; Ejaz, W.; Yu, W. (2018). Editorial on Wireless Networking Technologies for Smart Cities. Wireless Communications and Mobile Computing (Online). 2018. doi:10.1155/2018/1865908

In a Delay Tolerant Network (DTN), nodes are intermittently connected. In order to deliver a message from a source node to a destination node, the message is copied and forwarded to an intermediate node when the connection between the nodes is established. The node that receives the message stores it and forwards the copy of it to another node it encounters. The process is repeated until the destination node is encountered or the message's life time expires. PRoPHET (Probabilistic Routing Protocol using History of Encounters and Transitivity) is one of the major DTN routing protocols. However, PRoPHET does not consider energy consumption of nodes. Energy consumption of mobile wireless devices has been a major issue recently as the devices are ubiquitously used. In this paper, we propose an Energy Aware PRoPHET that considers energy of nodes and available free buffer of nodes for storing forwarded messages. We extensively simulated our proposal and have shown that our proposed routing performs far more better than PRoPHET in terms of energy consumption, extension of network life, message delivery probability and overhead ratio.

In deep waters, both the natural acoustic systems (such as marine mammals) and artificial acoustic systems (like underwater sensor networks (UWSNs) and sonar users) use acoustic signals for communication, echolocation, sensing, and detection. This makes the channel spectrum, heavily shared by UWSNs posing several salient features such as narrow bandwidth, long propagation delay, and high packet loss caused by acoustic channel. Flooding of data packets in such environment, therefore, is known to be a more appropriate mechanism. Hence, many researchers proposed flooding-based routing protocols for UWSNs such as VBF and HH-VBF. Nevertheless, these known protocols maximize overhead within network due to their dependency on additional parameters such as routing vector. To deviate this overhead and control the flooding, we previously proposed a Directional Flooding Routing (DFR) protocol, which controls the flooding area based on link quality. However, DFR lacks to deal with dynamic changes under the shores due to fixed system parameters throughout the network communications. In this paper, we therefore propose two new DFR protocols, called QA_DFR_AA (QoS-Aware DFR with Angle Adaption) and QA_DFR_TA (QoS-Aware DFR with Threshold Adaption) to reflect QoS dynamically. In addition, we also aid our current DFR with holding time technique to avoid packet collision and redundant packet transmission. Through NS-2 simulations, we found that our new intelligent DFR (iDFR) and two new versions of DFR outperforms the current version of the DFR.

Wireless virtualization is regarded as an emerging paradigm for enhancing radio frequency (RF) spectrum utilization through slicing of primary wireless resources in heterogeneous networks (HetNets). In this paper, we study the creation of virtual wireless networks through wireless virtualization for mobile virtual network operators (MVNOs) where portion of the primary bandwidth is sliced and leased to the MVNOs. For wireless virtualization, owners of wireless infrastructures in HetNets divide and reconfigure their RF bands to sublease to MVNOs based on their service-level-agreements while keeping enough wireless resources to their own users. Next, we leverage the wireless virtualization to investigate the network sum-capacity and revenue maximization problems for MVNOs where MVNOs offer competitive prices to attract more users for wireless communications. We evaluate the performance of the proposed approach using different metrics such as network sum-capacity, revenue of the MVNOs, competitive price for RF spectrum usage that is based on the spectral capacity of the MVNOs, and the outage probability. Numerical results obtained from simulations show that the sum-capacity of virtual network can be maximized while maximizing their revenue and offering lower/competitive spectrum-usage price for their users.

Large-scale multiple input multiple output (MIMO) wireless system is regarded as a solution to provide high speed connection for exponentially increasing wireless subscriptions for emerging cyber-physical systems (CPSs) and Internet of Things. In order to realize its full potential, there are several challenges to be addressed to achieve high secrecy rate or data rate. In this paper, we analyze outage probability for secrecy rate in MIMO wireless systems in the presence of eavesdroppers and jammers for CPS devices. Our proposed approach takes into account the impact of jammers while finding the best response to minimize the jamming/interfering effect (or to enhance the secrecy rate) and the impact of eavesdropper in secrecy rates of the users. We present formal analysis for secrecy outage probability and interception probability considering Rayleigh fading scenario. The performance is evaluated by using numerical results obtained from Monte Carlo simulations. Numerical results indicate that the system performance is improved significantly when the users adapt their transmit vectors based on their observed interference values. Furthermore, the secrecy outage probability increases with power of jammer and the secrecy capacity decreases when jammer power increases. We observed that the proposed approach outperforms the other existing approaches.

The next generation wireless networks are expected to operate in fully automated fashion to meet the burgeoning capacity demand and to serve users with superior quality of experience. Mobile wireless networks can leverage spatio-temporal information about user and network condition to embed the system with end-to-end visibility and intelligence. Big data analytics has emerged as a promising approach to unearth meaningful insights and to build artificially intelligent models with assistance of machine learning tools. Utilizing aforementioned tools and techniques, this paper contributes in two ways. First, we utilize mobile network data (Big Data)-call detail record-to analyze anomalous behavior of mobile wireless network. For anomaly detection purposes, we use unsupervised clustering techniques namely k-means clustering and hierarchical clustering. We compare the detected anomalies with ground truth information to verify their correctness. From the comparative analysis, we observe that when the network experiences abruptly high (unusual) traffic demand at any location and time, it identifies that as anomaly. This helps in identifying regions of interest in the network for special action such as resource allocation, fault avoidance solution, etc. Second, we train a neural-network-based prediction model with anomalous and anomaly-free data to highlight the effect of anomalies in data while training/building intelligent models. In this phase, we transform our anomalous data to anomaly-free and we observe that the error in prediction, while training the model with anomaly-free data has largely decreased as compared to the case when the model was trained with anomalous data.

Cognitive radio network with spectrum sensing to find idle channels and access them opportunistically is regarded as an emerging technology to deal with spectrum scarcity caused by exclusive licensing to primary systems. Simulation-based studies loose practical relevance due to assumptions and usage of simple models for defining transmission regions, multi-path propagation, and traffic intensity. In this paper, we present an experimental study for near real-time spectrum sensing and opportunistic spectrum access in database-driven cognitive radio networks (nROAR) using national instrument USRP devices in wide-band regime. We present numerical results for evaluating spectrum sensing using adaptive threshold-based joint energy and bandwidth detection. Furthermore, we evaluate the dynamic spectrum access using database-driven quorum-based rendezvous for opportunistic access for admitted unlicensed secondary users in diverse wireless bands. The proposed nROAR architecture addresses challenges related to providing spectrum access which require fast processing of a large number of spectrum-sensing measurements across diverse wireless bands, geography, and time.

Communication in smart grid is regarded as a backbone since the choice of communication technology in smart grid is highly critical to provide secure, reliable and efficient bi-directional information transfer between the smart grid components. However, current communication infrastructures and standards are incapable of meeting the strict requirements of smart grid systems. The dynamic/opportunistic spectrum access in heterogeneous wireless networks is a candidate technology that can be used to reduce the communication expenses (by reusing the existing infrastructures) and to improve overall performance of smart grid. In this paper, we investigate the mathematical model for the dynamic spectrum access and the impact of bandwidth accumulation in smart grid communications to maximize payoff/throughput. We evaluate the proposed approaches through simulations. The numerical results show that the proposed opportunistic spectrum access-based smart grid communication outperforms the static smart grid communications.

Need for Radio Frequency (RF) spectrum is increasing along with increasing wireless subscriptions and devices that is causing scarcity in total available RF spectrum. Opportunistic spectrum access in cognitive radio networks is emerging for maximizing the RF spectrum efficiency where unlicensed Secondary Users (SUs) access idle spectrum bands without causing any harmful interference to licensed Primary Users (PUs). All SUs are required to scan/sense RF spectrum to find idle bands or search for idle bands in a spectrum database not to interfere with PUs while using those idle bands. In this paper, we propose a Real-time Opportunistic Spectrum Access in Cloud-assisted Cognitive Radio Networks (ROAR) architecture where SUs (i.e., USRP wireless devices) are equipped with wide-band (50 MHz - 6 GHz) antenna and GPS units. ROAR uses cloud computing platform for real-time processing of wide-band data since SUs' performance is considerably constrained by their limited power, memory and computational capacity. In ROAR architecture, there are two parts: spectrum sensing to create a database of idle channels and dynamic spectrum access for opportunistic SU communications using idle channels. For the spectrum database, RF sensors scan/sense RF bands to find idle bands and report the geo-location of idle band, channel frequency and time stamp to the database installed in the distributed cloud platform. For opportunistic spectrum access, each SU interested for opportunistic communication queries a spectrum database to find idle channels. Distributed cloud computing is used to find idle channels for the SU where geolocation and other demands (e.g., data rate) are checked to find whether the SU is admissible or not for a given geo-location and time. If the SU is admissible based on the admissibility criteria, spectrum server sends the list of channels available for a given location and time to the SU. Then SU chooses the best suited channel to communicate opportunistically. We evaluate the ROAR architecture using numerical results obtained from extensive experiments.

Dynamic spectrum access is regarded as a backbone for cognitive radio networks where unlicensed secondary users (SUs) access spectrum opportunistically which is licensed to licensed primary users (PUs). SUs are required to identify the idle bands and use them dynamically without creating any harmful interference to PUs. In dynamic spectrum access for peer-to-peer based communications, SU transmitter and its intended SU receiver must use common channel to communicate. Note that the fixed common control channel could not be viable in case of jamming attacks and wideband regime. Thus in this paper, we present a test bed using Universal Software Radio Peripheral (NI USRP) devices. We use Lab View® and MATLAB® scripting extensions to program NI USRP devices for peer-to-peer communications. Once SU transmitter and receiver pair knows idle channels, they use one of the best idle channels to communicate. We study sequential channel scanning and quorum based rendezvous methods without using any common control channels. These two schemes are compared in terms of their performance for finding suitable channel. Test bed is developed to test these schemes. Numerical results are presented obtained from test bed.

Cognitive radio network is regarded as an emerging technology to solve 'spectrum scarcity' through dynamic spectrum access to support exponentially increasing wireless subscriptions. However, spectrum sensing and dynamic spectrum sharing in cognitive radio network invite more security attacks making security as one of the main concerns. In this paper, we analyze the performance of the secondary users in terms of physical-layer security in the presence of both eavesdroppers and jammers in cognitive radio networks. In this case, secondary users not only have to compete against eavesdroppers and jammers (who are trying to reduce the secrecy rates of secondary users) but also have to compete with other secondary users to gain access to idle channels to gain high secrecy rates. The main contribution of this work is to investigate game theoretical model to maximize utility of secondary users in the presence of eavesdroppers and jammers. The proposed approach can be particularized to a scenario with eavesdroppers only or jammers only while evaluating the performance of secondary user physical layer security. Performance of the proposed approach is evaluated with the help of numerical results obtained from simulations and the proposed approach outperforms other existing methods. Furthermore, there is sever impact on utilities (secrecy rates) of secondary users when both eavesdroppers and jammers are active in the network.

Transportation Cyber Physical Systems (CPS) are expected to rely on robust wireless communication networks for real-time feedback for controlling these systems. The IEEE 802.11p based Dedicated Short Range Communication (DSRC) standard has been proposed for vehicular communications that has 7 channels. However, these channels could be easily congested resulting in delay and unreliable communications when vehicle density is high. In this paper, we present a cloud-assisted global positioning system (GPS)-driven dynamic spectrum access framework for transportation CPS. To provide reliable communications, we assume that each vehicle is equipped with two transceivers: one transceiver (always connected to the internet using e.g., 4G link) queries spectrum database and/or can serve as a GPS through an application (app), and the other transceiver/radio switches channels and adapts to suitable transmit parameters for vehicular communications to avoid any harmful interference to primary users (PUs). Each vehicle calculates the best route to its destination using GPS and finds the set of idle channels along the route. Furthermore, each vehicle periodically checks the spectrum database throughout the route to get most updated spectrum opportunities. We present performance evaluation of the proposed approach with the help numerical results obtained from simulations.

In this paper, we propose an anti-jamming game for space communication systems where a cognitive jammer reactively senses channels using energy detection and jams the channel using "detect and jam" strategy while the legitimate transmitter-receiver pair uses a joint frequency hopping and power/rate adaptation approach to avoid the impact of the jamming. Jamming and anti-jamming process between legitimate satellite link and a jammer is formulated as a zero-sum game. The proposed game takes into account of the signal propagation delay, detection performance (signal detection and jamming start time) of a jammer, received jamming power from the jammer, and hopping and jamming costs. Performance of the proposed approach is evaluated with the help of simulations and the proposed approach outperforms other existing methods.

The transformation of traditional energy networks to smart grids can assist in revolutionizing the energy industry in terms of reliability, performance and manageability. However, increased connectivity of power grid assets for bidirectional communications presents severe security vulnerabilities. In this letter, we investigate Chi-square detector and cosine similarity matching approaches for attack detection in smart grids where Kalman filter estimation is used to measure any deviation from actual measurements. The cosine similarity matching approach is found to be robust for detecting false data injection attacks as well as other attacks in the smart grids. Once the attack is detected, system can take preventive action and alarm the manager to take preventative action to limit the risk. Numerical results obtained from simulations corroborate our theoretical analysis.

Vehicle-to-Vehicle (V2V) communication in Vehicular Ad hoc Networks (VANETs) is one of the key ingredients in the Intelligent Transportation System (ITS) where vehicles receive relevant traffic information using wireless communications from their peers. Forwarding traffic information to drivers can assist with the tasks of avoiding traffic accidents and related congestion. In this paper, we investigate the effect of association time (a.k.a. connection setup time), relative speed of vehicles, transmission range and message/data size in short range based V2V communications. The analysis is illustrated with the numerical results obtained from simulations.

Radio Frequency (RF) resource allocation in a Cognitive Radio Network (CRN) is considerably constrained by its limited power, memory and computational capacity. With the emergence of cloud computing platforms, CRN has the potential to mitigate these constraints by leveraging the vast storage and computational capacity. In this paper, we proposed a game theoretic approach for resource allocation in cloud-base cognitive radio network. The proposed algorithm leverages the geolocation of secondary users and idle licensed bands to facilitate dynamic spectrum access to secondary users. Furthermore, the active secondary users adapt their transmit power using game theoretic approach in distributed manner based on the network condition in terms of estimated average packet error rate while satisfying the Quality-of-Service (QoS) in terms of signal-to-interference-plus-noise ratio. To control greedy secondary users in distributed power control game, we introduce a manager through a Stackelberg power adaptation game. Simulation results are presented to demonstrate the performance of the proposed radio resource management algorithm.

Vehicular networking is regarded as a backbone for transportation cyber-physical systems (CPS). In this paper, we propose a cloud-assisted dynamic spectrum access in vehicular networks where vehicles search the spectrum database for idle channels. Each vehicle queries spectrum database periodically (every other time/distance unit or so) for a route computed by GPS to find idle channels. When two vehicles are within the communication range, they setup a communication link in a channel and exchange their data with the help of software defined radios. The performance of the proposed approach is evaluated with the help of numerical results obtained from simulations.

In Vehicular Ad hoc NETwork (VANET) safety applications, connectivity among vehicles is important to disseminate the upcoming traffic information (e.g., warning messages generated by a source vehicle that detects an accident) to other following vehicles to notify drivers in a timely manner. Because of the highly dynamic VANET topology and the short transmission range mandated for Dedicated Short Range Communication (DSRC) by Federal Communications Commission (FCC), communication links among vehicles are short-lived especially in sparse vehicular density, making the task of establishing (through connection setup process) and maintaining the communications for fast-moving vehicles difficult. Furthermore, when a fixed transmission range is used in dynamically changing VANET, network could be easily suffered by a “broadcast-storm” in dense vehicular density while vehicles in VANET could be disconnected frequently in sparse vehicular density. In addition, when communication channels are overcrowded in VANET, vehicles should be able to switch from one radio frequency (RF) bands to another to make robust communication using spectrum-agile wireless environment. In this paper, we study the connectivity for spectrum-agile VANET in fading channels. Specifically, connectivity enhancement for vehicle-to-vehicle communications for vehicles traveling in opposite directions and for vehicles traveling in same direction is investigated using mathematical analysis and simulation results. Performance of the proposed approach is evaluated using numerical and simulation results.

Cognitive radio networking is emerging paradigm for future generation wireless networks in which cognitive radio users (also known as secondary users) dynamically access the RF spectrum opportunistically without creating harmful interference to primary users. In this paper, the waiting probability of secondary users in the cognitive radio network is analyzed. We consider a cognitive radio network where multiple secondary users (SUs) contend for spectrum access using time division multiple access over idle primary user (PU) channels. Using queue dynamics as Poisson driven stochastic process, we characterize the waiting probability of secondary users. Generally speaking, in practical systems, secondary users of cognitive radio network would have no knowledge of activities of other users, thus the probability of being idle or contention probabilities of SUs' in cognitive radio network have to be assigned according to the available local information. Our focus is on SUs waiting probability analysis, for which a systematic understanding is lacking. Simulation results show that the use of multiple channels and/or multiple slots leads to significant delay reduction and transmission fairness.

The past decade has witnessed a growing interest in vehicular networking and its panoply of potential applications among vehicles. Olariu and his co-workers [1, 2, 3] have put forth the vision of a new type of cloud, Vehicular Cloud Computing (VCC). It is clear that the VCC concept raise exceptional security and privacy challenges. It is also clear that if the VCC concept is to see a wide adoption and to have a significant societal impact, security and privacy issues need to be addressed. In this paper we are interested to analyze the security challenges and potential privacy threats in VCC. We address some major design issues that will affect the future implementation of VCC and provide a set of security and privacy-protecting protocols. To the best of our knowledge, this is the first paper that deals with security problems in VCC. But we state that few VCC security issues are fundamentally new. Many of the security challenges have received attentions in related fields such as cloud computing and VANETs. On the other hand, we address relatively unique security challenges resulted by features of VCC, e.g. the challenges of authentication of high-mobility vehicles and the complexity of trust relationships among multi-players caused by intermittent short-range communication.

Cognitive radio (CR) technology is an emerging technique for dynamic spectrum access, which attempts to share licensed spectrum with licensed users opportunistically and dynamically to provide high bandwidth and efficient spectrum utilisation. In this study, the authors address the problem of finding combined precoder and power of CRs for dynamic spectrum sharing in interference networks. First, the authors present the precoder and power optimisation problem in spectrum underlay by considering both power and interference constraints where CR users coexist and transmit simultaneously with primary users. Then the authors particularise the problem of precoder and power adaptation to spectrum overlay where CRs operate only in idle frequency bands obtained from spectrum sensing. In the proposed approach, bandwidth use is expressed in terms of a finite set of discrete resources whose use is optimised by the CR transmitters in terms of achievable rate corresponding to individual link under operating constraints that restrict the operation of unlicensed secondary CR transmitters to protect licensed primary user transmissions. The authors formulate the optimal resource allocation for CR links as a constrained optimisation problem and solve using the Lagrange method. The authors illustrate the proposed algorithm with the help of numerical results obtained from simulations.

Future development of Intelligent Transportation Systems (ITS) depends on Vehicular Ad-hoc Networks (VANETs) in which communications will help to improve traffic safety and efficiency through exchanging information among vehicles. As each vehicle cannot be a source of all messages in VANET, most communications depend on the information received from other vehicles. To protect VANET from malicious action, each vehicle must be able to evaluate, decide and react locally on information received from other vehicles. Message verification is more challenging in VANETs since the security and privacy of the participating vehicles, in general, and of the drivers and passengers specifically is of major concern. Each vehicle needs to verify the accuracy of the message and needs to verify that the received message is from a legitimate vehicle. In this paper, we propose a new algorithm to secure vehicular communication with the help of trust measured for the given period using a probabilistic approach. The proposed algorithm secures VANETs against the untrustworthy drivers. The proposed algorithm is illustrated through numerical results obtained from simulations.

In this paper we study distributed transmitter adaptation with limited feedback where a B-bit codebook known at both transmitter and receiver is used in the update process. Specifically, we discuss predictive vector quantization (PVQ) where the transmitter is updated incrementally using the codebook element which results in smallest distortion in sum capacity from among the available 2Bcodebook entries. The performance of the proposed PVQ method is compared with that of random vector quantization (RVQ) with numerical results obtained from simulations.

In this paper, we present a new scheme for dynamic adaptation of transmission power and contention window (CW) size to enhance performance of information dissemination in Vehicular Ad-hoc Networks (VANETs). The proposed scheme incorporates the Enhanced Distributed Channel Access (EDCA) mechanism of 802.11e and uses a joint approach to adapt transmission power at the physical (PHY) layer and quality-of-service (QoS) parameters at the medium access control (MAC) layer. In our scheme, transmission power is adapted based on the estimated local vehicle density to change the transmission range dynamically, while the CW size is adapted according to the instantaneous collision rate to enable service differentiation. In the interest of promoting timely propagation of information, VANET advisories are prioritized according to their urgency and the EDCA mechanism is employed for their dissemination. The performance of the proposed joint adaptation scheme was evaluated using the ns-2 simulator with added EDCA support. Extensive simulations have demonstrated that our scheme features significantly better throughput and lower average end-to-end delay compared with a similar scheme with static parameters.

The past decade has witnessed the confluence of Intelligent Transportation Systems (ITS) and Vehicular Ad hoc Networks (VANET) that promises to revolutionize incident detection and the timely dissemination of traffic-related information to the various interested parties. One of the key components is expected to be a Cooperative Collision Warning System (CCWS). Our main contribution is to derive analytical expressions for key CCWS metrics that rely on mobility information exchanged by various players. The feasibility of CCWS has been demonstrated in previous work; this paper analyzes the mobility parameters and derives the conditional probability of a collision. We begin by proposing a set of fundamental parameters for CCWS: conditional probability of collision, headway distance, driver reaction time, relative velocity and acceleration. Preliminary simulation results have demonstrated the effectiveness of our analytical derivations.

Game theory has emerged as a new mathematical tool in the analysis and design of wireless communication systems, being particularly useful in studying the interactions among adaptive transmitters that attempt to achieve specific objectives without cooperation. In this paper, we present a game-theoretic approach to the problem of joint transmitter adaptation and power control in wireless systems, where users' transmissions are subject to quality-of-service requirements specified in terms of target signal-to-interference-plus-noise ratios (SINRs) and nonideal vector channels between transmitters and receivers are explicitly considered. Our approach is based on application of separable games, which are a specific class of noncooperative games where the players' cost is a separable function of their strategic choices. We formally state a joint codeword and power adaptation game, which is separable, and we study its properties in terms of its subgames, namely, the codeword adaptation subgame and the power adaptation subgame. We investigate the necessary conditions for an optimal Nash equilibrium and show that this corresponds to an ensemble of user codewords and powers, which maximizes the sum capacity of the corresponding multiaccess vector channel model, and for which the specified target SINRs are achieved with minimum transmitted power.

In this paper we present a new approach for joint precoder and power adaptation applicable in multi-user spectrum sharing cognitive radio (CR) networks. In the proposed approach bandwidth use is expressed in terms of a finite set of discrete resources whose use is optimized by the CR transmitters in terms of system sum-rate under operating constraints that restrict the operation of secondary (unlicensed) CR transmitters to protect licensed primary user-transmissions. We formulate the optimal resource allocation for CR users as a constrained optimization problem and illustrate the proposed approach with numerical results obtained from simulations.

One of the notoriously difficult problems in vehicular ad-hoc networks is to ensure that established paths do not break before the end of data transmission. This is a difficult problem because the network topology is changing constantly and the routing links are inherently unstable. Inspired by ticket based probing, we propose a scheme to selects a stable routing path in vehicular network environment. On the basis of stability preference, an optimal path (low-cost, low-delay and high-stability) is considered. Our algorithms consider not only the efficiency of path searching but also the balance of stability, delay, and cost metrics to find the optimal routing path. Several possible paths are searched at the same time. The path selection is based on three types of control packets probing routing paths satisfying stability, delay and cost requirements. Extensive simulations show that the proposed algorithm can tolerate the constantly-changing topology in vehicular ad-hoc networks.

One of the fundamental requirements of a traffic management system is the ability to determine when an incident has occurred so that proper responses can be initiated. Most of the existing automatic incident detection techniques suffer from many limitations including their inability to detect incidents under non dense traffic conditions and generation of many false positive alarms. In this paper, we introduce a novel Bayesian-based approach to enhance the performance of existing techniques specially under non-dense traffic flow through vehicle to readside communications. The proposed technique also offers zero false positive alarms under most situations and can be integrated with any of the current techniques.

In this paper, we propose an algorithm for joint adaptation of transmission power and contention window to improve the performance of vehicular network in a cross layer approach. The high mobility of vehicles in vehicular communication results in the change in topology of the Vehicular Ad-hoc Network (VANET) dynamically, and the communication link between two vehicles might remain active only for short duration of time. In order for VANET to make a connection for long time and to mitigate adverse effects due to high and fixed transmission power, the proposed algorithm adapts transmission power dynamically based on estimated local traffic density. In addition to that, the prioritization of messages according to their urgency is performed for timely propagation of high priority messages to the destination region. In this paper, we incorporate the contention based MAC protocol 802.11e enhanced distributed channel access (EDCA) mechanism to implement a priority-based vehicle-to-vehicle (V2V) communication. Simulation results show that the proposed algorithm is successful in getting better throughput with lower average end-to-end delay than the algorithm with static/default parameters.

In this paper we present a new algorithm for joint codeword and power adaptation in the uplink of code division multiple access (CDMA) systems, in which codewords are adjusted using gradient-descent interference avoidance, and powers are updated to match target signal-to-interference plus noise-ratio (SINR) values. The proposed algorithm is analyzed and illustrated with numerical examples obtained from simulations.

In this paper we present an algorithm for joint codeword and power adaptation applicable in the uplink of a Code Division Multiple Access (CDMA) system in which multipath channels between users and the base station receiver are explicitly considered. The proposed algorithm uses incremental updates for codeword and power that allow a smooth transition of the system to a steady state configuration where specified targets for the signal-to-interference+noise-ratio (SINR) are achieved with minimum powers. The algorithm is illustrated with numerical results obtained from simulations.

The NOTICE architecture is a new concept in vehicular ad-hoc networking (VANET) that aims at providing automated notification of traffic incidents on highways in order to reduce congestion and improve overall traffic safety. The basic component of NOTICE is a system of sensor belts that collect information from passing cars through a wireless radio link. An indicator of performance for the NOTICE system is the incident detection time which depends on various parameters among which we note the vehicle speed, the time required by communicating radios for connection setup and information exchange, the amount of information to be transmitted, or the radio technology employed. In this paper we present a numerical performance study of the NOTICE system with realistic values of these parameters in an attempt to provide some basic specifications for the physical layer of the system.