Intelligent transportation networks are increasingly integrating autonomous aerial vehicles (AAVs) to enable services essential for modernizing industries like transportation, logistics, and search and rescue. A significant beneficiary is the Internet of Connected Vehicles (IoCVs), where AAVs play a transformative role in supporting next-generation cellular networks. This paper addresses the challenge of minimizing the cost of delivering content to vehicles on road segments with congested traffic or insufficient infrastructure. Vehicles entering these segments request content from a AAV-hosted library that updates dynamically based on item popularity. Each vehicle submits a request, requiring the AAV to compute an optimal trajectory to maximize operational utility. Given the AAV’s limited energy, we aim to develop an energy-efficient solution. The problem is framed as a joint optimization of caching decisions, AAV trajectory planning, and radio resource allocation, formulated as a mixed integer non-linear programming (MINLP) problem. The environment’s complexity, with random vehicle arrivals and fluctuating content, makes traditional optimization techniques inadequate. To address this, we reformulate the problem as a constrained Markov decision process (CMDP) and employ a primal-dual proximal policy optimization (PPO) algorithm, enhanced by a multi-scale generative transformer (MGFormer) for improved speed and accuracy over traditional deep neural networks (DNNs). Simulation results demonstrate that the proposed framework reduces service costs by up to 30% and achieves robust performance even under high-density traffic and Doppler shifts, validating its superiority over traditional Primal-dual PPO.

The purpose of this research is to develop and evaluate an adaptive cooperative spectrum-sharing framework for Hybrid Satellite-Terrestrial Networks (HSTNs), incorporating realistic hardware impairments and flexible Amplify-and-Forward (AAF) and Decode-and-Forward (DAF) relaying to enhance reliability, spectral efficiency, and power allocation. This study employs a mathematical and simulation-based approach to evaluate Overlay Cognitive Hybrid Satellite-Terrestrial Networks (OCHSTNs) under realistic hardware impairments and fading conditions. Adaptive Relay Protocol (ARP) is analyzed for both AAF and DAF schemes, incorporating Shadowed-Rician and Nakagami-m channels with Additive Gaussian Noise. Closed-form Outage Probability (OP) expressions for primary and secondary networks are derived, and Monte-Carlo simulations validate analytical results. The method also examines Relay Cooperation Ceiling (RCC) and Data Link Ceilings (DLC) to assess network reliability and spectrum-sharing efficiency. The research demonstrates that ARP significantly improves outage performance and spectral efficiency in HSTNs under realistic hardware impairments. DAF relaying exhibits superior resilience compared to AAF, maintaining higher data rates and reliability. Critical ceiling effects, including RCC and DLC, are identified, highlighting performance limitations at high thresholds. Flexible relaying protocols effectively mitigate Hardware Deficiencies (HDs), ensuring robust primary and secondary network operation while optimizing power allocation and spectrum sharing. The study provides practical guidance for designing resilient hybrid satellite-terrestrial networks, showing that adaptive DAF relaying and flexible spectrum-sharing strategies enhance reliability, mitigate hardware impairments, and optimize network efficiency.

Wastewater treatment plants (WWTPs) are among the most energy-intensive components of urban infrastructure and bear strict regulatory responsibilities for wastewater quality. These dual challenges, minimizing energy consumption and maintaining environmental compliance, are deeply interrelated and must be managed simultaneously to achieve sustainable plant operation. This study proposes a framework that comprises two customized components. The first component employs a voting ensemble model based on transformer architecture to predict energy consumption. It processes heterogeneous feature domains — including hydraulic, wastewater, and climatic variables — through parallel attention-driven streams. The outputs from these streams are then aggregated using a weighted voting mechanism to produce the final prediction. Second, a multitask Bidirectional Gated Recurrent Unit (Bi-GRU) forecasts wastewater quality indicators concurrently (ammonia, Biochemical Oxygen Demand (BOD), and Chemical Oxygen Demand (COD)), capturing shared temporal dependencies and reducing model complexity. A hybrid preprocessing strategy is applied, incorporating domain-aware outlier detection (z-score and Interquartile Range (IQR)), K-Nearest Neighbors (KNN) Imputation, and feature selection using Extreme Gradient Boosting (XGBoost). Experimental results showed that. The voting ensemble model achieved the best results for energy consumption prediction with 31.61 of Root Mean Squared Error (RMSE). The multitask Bi-GRU achieved the best results for wastewater quality indicators with RMSE at 6.1689, 48.0323, and 88.2214 for ammonia, BOD, and COD, respectively. This work is among the first to integrate transformer ensembles and multitask learning in a unified WWTP forecasting system. Simultaneously addressing energy efficiency and water quality assurance, this offers a practical, scalable, and intelligent decision-support tool for sustainable wastewater management.

Early detection is critical to effectively and efficiently managing chronic obstructive pulmonary disease (COPD) and improving patient outcomes. To enhance early COPD detection, this article presents a new multimodal neuro-fuzzy framework called “FuzzyGuard.” FuzzyGuard uses ensemble learning on various datasets, such as computed tomography (CT) scans and audio recordings of coughs and lung sounds, to guarantee comprehensive analysis and accurate diagnosis. The neuro-fuzzy random vector functional link (RVFL) is used in FuzzyGuard for clinical relevance evaluation and early COPD prediction. FuzzyGuard flexibility is increased by using RVFL to pick hyperparameter tuning parameters, including learning rate$(\eta)$, momentum$(\mu)$, the number of epochs, and regularization coefficient. Using ensemble deep learning techniques, the FuzzyGuard-based framework extracts discriminating features from the chest diagnostic images and sputum samples from cough, as well as lung sound samples for COPD classification using an RVFL network, initialized with a random vector. FuzzyGuard’s excellent accuracy is demonstrated by the assessment, which yielded rates of 99.97% (CT-scan model), 96.98% (cough-based model), and 98.65% (output of FuzzyGuard) for early diagnosis of COPD based on early weighted sum-fusion approaches applied to cough and chest X-ray data. FuzzyGuard outperforms established benchmarks, marking a substantial leap in the diagnosis of COPD, and offers better patient treatment and respiratory health outcomes.

Recently, the field of healthcare has experienced remarkable technological advancements. However, a considerable challenge persists in providing state-of-the-art healthcare services to individuals in tribal and remote areas. To solve health issues in remote areas, this article introduces a federated learning framework for the early diagnosis of multivariate pulmonary diseases based on cough (voice) samples collected from tribal regions. The proposed framework performs model training on connected local devices, including those living in remote or tribal regions with limited connectivity to centralized servers. The proposed framework ensures the early diagnosis of multivariate lung diseases, such as chronic obstructive pulmonary disease (COPD), to obtain accurate prediction using ensemble learning techniques. The framework applies a convolutional neural network (CNN) to extract discriminatory features from generated spectrograms of cough (voice) samples to classify COPD and non-COPD (healthy) using transfer learning techniques. The proposed framework demonstrates the efficacy of our proposed framework in minimizing resource utilization and model complexity, achieving an impressive accuracy of up to 98.62% with reduced communication rounds and latency, thus facilitating the early diagnosis of COPD. The prototype model of the proposed framework offers an alternative way of a fast diagnosis of respiratory diseases based on cough samples collected from tribal people, which is used with the support of pulmonary and tuberculosis experts (doctors and professionals) for statistical analysis of critical cases of COPD for early diagnosis.

Satellite-terrestrial integrated networks have recently gained substantial interest due to their exceptional coverage, lower transmission delay, robust storage, and computing power. However, existing task offloading schemes often fail to effectively manage task dependencies, resulting in incorrect execution sequences, increased end-to-end delay, and excessive energy consumption. To address these challenges, we propose a dependency-aware task offloading framework for jointly optimizing delay and energy consumption in satellite-terrestrial collaborative networks with mobile-edge computing (MEC). First, we construct a directed acyclic graph (DAG) based dependency-aware task offloading framework aimed at reducing delay and energy consumption. Second, to reduce the frequency of low Earth orbit (LEO) satellite access, we design a cluster head selection strategy (CHSS), which leverages DAG-based task dependencies to optimize the association between Internet of Things (IoT) devices and LEO satellites. Finally, we formulate system delay and energy consumption as a cost-minimization problem, modeling it as a Markov decision process (MDP). We also propose a novel hybrid deep reinforcement learning (DRL) algorithm to effectively handle DAG structures and optimize task offloading decisions, thereby minimizing the total cost. Extensive simulation results confirm the effectiveness of the proposed method, demonstrating that the proposed algorithm significantly outperforms others by reducing system delay by 18.07% and decreasing energy consumption by 21.15% on average, respectively.

Efficient monitoring of carbon capture and storage (CCS) systems heavily relies on sensor data. However, sensors are susceptible to potential multiple faults, leading to performance degradation and posing a risk of catastrophic failures. Timely detection of sensor faults in CCS systems is crucial for safe and efficient carbon dioxide (CO2) pipeline operation. This paper addresses the challenge of diagnosing multiple sensor faults in CO2 pipelines by introducing a novel approach based on partial-distributed particle filter (PDPF). The novel distributed-filtering framework aims to reduce the computational complexity while identifying multiple faults in highly nonlinear systems. The proposed PDPF architecture comprises a collection of linear local filters and a nonlinear main filter. More specifically, the algorithm segregates nonlinear computations from local filters and assigns them to the main filter. The main filter handles the time updates involving all nonlinear computations associated with the nonlinear system, while the parallel linear local filters, each equipped with a distinct subset of sensor measurements, perform the measurement updates and combine their estimates via information fusion. As for fault detection and isolation, each local filter utilizes a novel kernel density estimation (KDE)-based approach that analyzes the consistency between model predictions and observed behavior, enabling the identification of sensor faults. Compared to existing methods, this approach reduces the computational requirements and is well-suited for highly nonlinear systems experiencing multiple sensor faults. Additionally, performance assessment via numerical simulations confirms its effectiveness and superiority in comparison to stateof-the-art alternative methods.

The capacity of Dataspace enables the distribution of heterogeneous data from several sources and domains and has attracted attention for resolving data integration challenges. Drone data sharing faces challenges such as protecting privacy and security, building trust and dependability, controlling latency and scalability, facilitating real-time data processing, and preserving the caliber of shared models. Therefore, sixth-generation (6G) networks provide high throughput and low latency to improve drone operations; security issues are exacerbated by the sensitive nature of shared data and the lack of centralized monitoring. To address the challenges, this paper presents a conceptual framework for a Dataspace in the Sky to enable secure and efficient drone data-sharing within 6G networks in the transition from Industry 4.0 to Industry 5.0 . The Dataspace in the Sky integrates Federated Learning (FL), a decentralized Machine Learning (ML) approach that enhances security and privacy by sharing models instead of raw data, facilitating effective drone collaboration. However, the quality of shared local models often suffers due to inconsistent data contributions and unreliable recording mechanisms, which can undermine the performance of FL. To tackle the challenges, the framework employs blockchain (BC) to decentralize and secure the Dataspace, ensuring the integrity of contribution records and improving the reliability of shared models. Dataspace in the Sky empowered decentralized data sharing which addresses latency issues by decentralizing decision-making and enhances trust and reliability by leveraging immutable and transparent BC mechanisms. The robustness of Dataspace in the Sky solution is not only secures drone-sharing operations in 6G environments but enables the development of citizen-friendly mobility services, expanding opportunities across smart environments.

Digital Twins are virtual representations of physical assets and systems that rely on effective Data Management to integrate, process, and analyze diverse data sources. This article comprehensively examines Data Management challenges, architectures, techniques, and applications in the context of Digital Twins. It explores key issues such as data heterogeneity, quality assurance, scalability, security, and interoperability. The paper outlines architectural approaches like centralized, distributed, cloud-based, and blockchain solutions and Data Management techniques for modeling, integration, fusion, quality management, and visualization. Domain-specific considerations across manufacturing, smart cities, healthcare, and other sectors are discussed. Finally, open research challenges related to standards, real-time data processing, intelligent Data Management, and ethical aspects are highlighted. By synthesizing the state-of-the-art, this review serves as a valuable reference for developing robust Data Management strategies that enable Digital Twin deployments.

Unmanned aerial vehicles (UAVs) in beyond 5G (B5G) are crucial for revolutionizing various industries, including surveillance, agriculture, and logistics, by enabling high-speed data transfer, ultra-low latency communication, and ultra-reliable connectivity. However, integrating UAVs into B5G networks poses various security and privacy concerns. These risks encompass the possibility of unauthorized access, breaches of data, and cyber-physical attacks which jeopardize the integrity, confidentiality, and availability of UAV operations. Moreover, UAVs in B5G networks are also at high risk from the application of machine learning (ML)-based attacks by exploiting vulnerabilities in ML models, leading to adversarial manipulation, data poisoning and model evasion techniques, which can compromise the integrity of UAV operations, lead to navigation errors, and expose sensitive data collected by UAVs. Considering the aforementioned security and privacy concerns, this review article presents emerging security and privacy solutions for UAVs in B5G networks. Firstly, We introduce the essential background of integrating UAVs into B5G networks and discuss the advantages and security challenges which the emerging integrated network architecture have. Then, we proceed to analyze and examine the security and privacy landscape by including threats and requirements of UAVs in B5G networks. Based on these threats and requirements, solutions from physical layer security (PLS), blockchain (BC), federated learning (FL) and post-quantum cryptography (PQC) are discussed and explored in details. Moreover, potential future research directions are discussed in details as open research issues.

Wireless Power Transfer (WPT) is a disruptive technology that allows wireless energy provisioning for energy-limited IoT devices, thus decreasing the over-reliance on batteries and wires. WPT could replace conventional energy provisioning (e.g., energy harvesting) and expand to be deployed in many of our daily-life applications, including but not limited to healthcare, transportation, automation, and smart cities. As a new rising technology, WPT has attracted many researchers from academia and industry about WPT technologies and wireless charging scheduling algorithms. Therefore, in this paper, we review the most recent studies related to WPT, including classifications, advantages, disadvantages, and main domains of application. Furthermore, we review the recently designed wireless charging scheduling algorithms (schemes) for wireless sensor networks. Our study provides a detailed survey of wireless charging scheduling schemes covering the main scheme classifications, evaluation metrics, application domains, advantages, and disadvantages of each charging scheme. We further summarize trends and opportunities for applying WPT at some intersections.

This paper reviews three main aspects of authentication protocols of Internet of Things (IoT): classifications and limitations, current trends, and opportunities. First, we explore the significance of IoT authentication protocols in ensuring secure communication and the protection of transmitted and received data, focusing on the classifications and associated limitations. Second, we discuss the latest developments and trends, such as using blockchain technology and machine learning to enhance authentication protocols. Third, we highlight the future opportunities, including the development of human-centric authentication designs and improved platform interoperability. At the end of this paper, we provided some insights gained for the new researcher, offering analyses of the trends and challenges in this field, giving recommendations for improving IoT authentication protocols, and emphasizing the need for further research and cooperation to develop advanced security solutions.

Recently, a plethora of promising green energy provisioning technologies has been discussed in the orientation of prolonging the lifetime of energy-limited devices (e.g., sensor nodes). Wireless rechargeable sensor networks (WRSNs) have emerged among other fields that could greatly benefit from such technologies. Such an ad-hoc network comprises a base station(s) and multiple sensor nodes, which are primarily deployed in harsh environments, meeting the requirements of transmitting, receiving, collecting, and processing data. Unlike existing works, this survey paper focuses on energy provisioning technologies within the context of WRSNs by reviewing two interrelated domains. First, we introduce various energy provisioning techniques and their associated challenges, including conventional energy harvesting methods (e.g., solar, thermal, and mechanical). We highlight wireless power transfer (WPT) as one of the most applicable technologies for WRSNs, covering both radiative and non-radiative WPT. Additionally, we present radio frequency (RF) energy harvesting, including simultaneous wireless information and power transfer (SWIPT) and wireless powered communication networks (WPCNs), as well as backscatter communications. Furthermore, we compare hybrid energy harvesting techniques (e.g., solar-RF, vibro-acoustic, solar-thermal, etc.). Second, we introduce the fundamentals of wireless charging, reviewing various charger types (static and mobile), charging policies (including full and partial charging), charging modes (offline and online), and charging schemes (periodic and on-demand). We also present the collaborative charging mechanisms. Additionally, we address several key challenges facing WRSNs, such as energy consumption, multi-charger coordination, dynamic network recharging, monitoring & security threats, vehicle-to-vehicle (V2V) charging, and hybrid WRSNs Finally, we highlight trends and future directions for integrating advanced artificial intelligence (AI) technologies into WRSNs.

Large language models deliver strong performance but at the cost of high energy use and carbon emissions, limiting their accessibility. Federated Learning (FL) addresses energy efficiency by utilizing distributed data sources, coordinating learning from diverse, energy-constrained clients, and allowing the training process to occur without the need for large centralized datasets. This paper evaluates LLM training using various FL client selection strategies and a centralized approach, integrating Parameter-Efficient Fine-Tuning (PEFT). The assessment focuses on carbon emissions, energy consumption, and model performance, providing insights into the environmental impact of different training methods and identifying sustainable practices. Results show that centralized training achieves the highest accuracy but lacks privacy and scalability, whereas FL methods like FedFull+LoRa provide comparable accuracy but incur higher energy consumption and emissions. FedProx+LoRa and FedNorm+LoRa balance energy efficiency and accuracy. We also extended our FedSustain client selection strategy by incorporating real-time renewable-energy signals from the Electricity Maps API alongside on-device energy buffers. On an IMDB/DistilBERT benchmark, FedSustain matches leading FL baselines in both accuracy and convergence speed while reducing total energy use by up to 38% and CO$_{2}$emissions by up to 46%. Furthermore, to address the limitations of the current client selection strategy, we present an enhanced strategy for future evaluation on more complex tasks and larger models. These results highlight the potential of energy-aware client selection to make large-model training more sustainable.

Wireless Sensor Networks (WSNs) are used to sense and monitor physical conditions in various services and applications. However, there are a number of challenges in deploying WSNs, especially those pertaining to energy replenishment. Using the current solutions, when a significant number of sensors need to replenish their energy, this would be costly in terms of time, efforts and resources. Thus, this paper aims to solve this problem by efficiently deploying wireless power transfer technologies and scheduling Mobile Charging Vehicles (MCVs) in WRSN. The proposed method deploys multi-criteria decision-making (i.e., Analytical Hierarchy Process (AHP)) to schedule the charging tasks. To the best of our knowledge, this paper is the first to depend solely on AHP in MCVs scheduling. The paper demonstrates the validity of the proposed method by illustrating that the matrices that are created are within the accepted values of consistency ratio. In addition, the paper proposes a method of partitioning the values of our criteria to avoid the problem of different criteria having different measurement units. Unlike existing works, the paper aims to schedule an MCV for charging based on both the distance and residual energy of the sensor. The proposed method exhibits superiority in terms of the average remaining energy available in the system, having the shortest queue length, shorter MCV response time, shorter charging duration, and shorter queue waiting time against the state-of-the-art methods. Our study paves the way for next generation efficient charging and MCV scheduling.

Medical healthcare centers are envisioned as a promising paradigm to handle vast data for various disease diagnoses using artificial intelligence. Traditional Machine Learning algorithms have been used for years, putting the sensitivity of patients' medical data privacy at risk. Collaborative data training, where multiple hospitals (nodes) train and share encrypted federated models, solves the issue of data leakage and unites resources of small and large hospitals from distant areas. This study introduces an innovative framework that leverages blockchain-based Federated Learning to identify 15 distinct lung diseases, ensuring the preservation of privacy and security. The proposed model has been trained on the NIH Chest Ray dataset (112 120 X-Ray images), tested, and evaluated, achieving test accuracy of 92.86%, a latency of 43.518625 ms, and a throughput of 10034017 bytes/s. Furthermore, we expose our framework blockchain to stringent empirical tests against leading cyber threats to evaluate its robustness. With resilience metrics consistently nearing 87% against three evaluated cyberattacks, the proposed framework demonstrates significant robustness and potential for healthcare applications. To the best of our knowledge, this is the first paper on the practical implementation of blockchain-empowered FL with such data and several diseases, including multiple disease coexistence detection.

This paper studies distributed data dissemination in multiple mobile sinks wireless sensor networks. Previous studies employed separated paths to disseminate data packets from a given source to a given set of mobile sinks independently, which exhausts the constrained resources of the network. In this paper, we explore how the merged paths mechanism could rationalize utilizing network resources. To do so, we propose a protocol named Merged Path, which is implemented in four steps in a distributed manner. First, the bifurcation points (i.e., where the path is branched into multiple sub-branches) are discovered. Second, we developed a Discrete Cumulative Clustering algorithm (DCC) to divide the sinks into disjoint clusters at each bifurcation point. Third, we propose a Diagonal Virtual Line (DVL) structure to delegate the communication between thehigh-tierand low-tier nodes. Last, on top of DVL and DCC, we propose an opportunistic metric that captures multiple network-layer attributes to disseminate the data packet to the sinks through multiple branches. The simulation results showed that about 50% of the network energy could be saved by merging the paths versus the separate paths, considering an area of interest application with 20 mobile nodes each carrying a sink.

The dramatic recent increase of the smart Internet of Everything (IoE) in Industry 4.0 has significantly\nincreased energy consumption, carbon emissions, and global warming. IoE applications in Industry\n4.0 face many challenges, including energy efficiency, heterogeneity, security, interoperability, and\ncentralization. Therefore, Industry 4.0 in Beyond the Sixth-Generation (6G) networks demands moving\nto sustainable, green IoE and identifying efficient and emerging technologies to overcome sustainability\nchallenges. Many advanced technologies and strategies efficiently solve issues by enhancing\nconnectivity, interoperability, security, decentralization, and reliability. Greening IoE is a promising\napproach that focuses on improving energy efficiency, providing a high Quality of Service (QoS), and\nreducing carbon emissions to enhance the quality of life at a low cost. This survey provides a comprehensive\noverview of how advanced technologies can contribute to green IoE in the 6G network of\nIndustry 4.0 applications. This survey provides a comprehensive overview of advanced technologies,\nincluding Blockchain, Digital Twins (DTs), Unmanned Aerial Vehicles (UAVs, a.k.a. drones), and\nMachine Learning (ML), to improve connectivity, QoS, and energy efficiency for green IoE in 6G\nnetworks. We evaluate the capability of each technology in greening IoE in Industry 4.0 applications\nand analyze the challenges and opportunities to make IoE greener using the discussed technologies.

Summary Recently, unmanned aerial vehicles (UAV) technology has been utilized to monitor and capture images from disasters to analyze, process, and take action in real‐time for a speedy recovery. UAVs represent one of the critical technologies used beyond the fifth generation (B5G) of heterogeneous networks. Due to the sensitive data captured from disaster areas by UAVs, security has become a significant concern. Therefore, effective methods are needed to defend collected data against hackers and fictional activity by untrusted users. Audio encryption technologies can be applied to military communication, multimedia, medical, telemedical, and the internet. The present work introduces a novel Steganographic technique of converting an image to audio using LSB coding with XOR operation, ensuring the audio signal is protected using a key and maintains the excellent quality of the signal regenerated. Python3 is used for the implementation of the proposed technique. The findings show significant improvement in MSE and SSIM contexts.

Reconfigurable intelligent surfaces (RISs) have emerged as a groundbreaking technology, revolutionizing wireless networks with enhanced spectrum and energy efficiency (EE). When integrated with drones, the combination offers ubiquitous deployment services in communication‐constrained areas. However, the limited battery life of drones hampers their performance. To address this, we introduce an innovative energy harvesting (EH), that is, EH‐RIS. EH‐RIS strategically divides passive reflection arrays across geometric space, improving EH and information transformation (IT). Employing a meticulous, exhaustive search algorithm, the resources of the drone‐RIS system are dynamically allocated across time and space to maximize harvested energy while ensuring optimal communication quality. Deep reinforcement learning (DRL) is employed to investigate drone‐RIS performance by intelligently allocating resources for EH and signal reflection. The results demonstrate the effectiveness of the DRL‐based EH‐RIS simultaneous wireless information and power transfer (SWIPT) system, demonstrating enhanced drone‐RIS spectrum‐efficient communication capabilities. Our investigation is summarized in unleashing potential, which shows how DRL and EH‐RIS work together to optimize drone‐RIS for next‐generation wireless networks.

In the era of data-driven healthcare, the amalgamation of blockchain and Federated Learning (FL) introduces a paradigm shift towards secure, collaborative, and patient-centric data-sharing. This paper pioneers the exploration of the conceptual framework and technical synergy of FL and blockchain for decentralized data-sharing, aiming to strike a balance between data utility and privacy. FL, a decentralized machine learning paradigm, enables collaborative AI model training across multiple healthcare institutions without sharing raw patient data. Combined with blockchain, a transparent and immutable ledger, it establishes an ecosystem fostering trust, security, and data integrity. The paper elucidates the technical foundations of FL and blockchain, unravelling their roles in reshaping healthcare data-sharing. The paper vividly illustrates the potential impact of this fusion on patient care. The proposed approach preserves patient privacy while granting healthcare providers and researchers access to diversified datasets, ultimately leading to more accurate models and improved diagnoses. The findings underscore the potential acceleration of medical research, improved treatment outcomes, and patient empowerment through data ownership. The synergy of FL and blockchain envisions a healthcare ecosystem that prioritizes individual privacy and propels advancements in medical science.

By the limitations of storage capacity and computing power, Internet of Things (IoT) devices may prefer to outsource their valuable and sensitive data to cloud storage providers (CSPs) for further analysis, so it is critical to design protocols that can verify the integrity of these data remotely, while preserving the privacy of their owners. This article proposes a novel remote data integrity checking method for IoT, namely, FDUP-remote data integrity checking (RDIC), which achieves fully decentralized, efficient, and unconditionally privacy-preserving checking simultaneously, that is, the proof-checking is performed efficiently on the blockchain by the smart contracts integrated with native C/C++ codes, while the blockchain or any other entity cannot learn any information about the data content, even they have unbounded computing power. Furthermore, it is optimized for low-power IoT devices by greatly reducing the exponentiations of generating homomorphic verifiable tags to be nearly independent of the block size of the outsourced data. To defend against untrusted IoT and CSP, we present strict proofs and analyses in the aspect of correctness, soundness, and unconditionally privacy-preserving. The evaluation of theoretical performance and the prototype system deployed on a blockchain platform indicate that FDUP-RDIC is suitable for real-world IoT applications.

Breakthroughs in Wireless Energy Transfer technologies have revitalized Wireless Rechargeable Sensor Networks. However, how to schedule mobile chargers rationally has been quite a tricky problem. Most of the current work does not consider the variability of scenarios and how many mobile chargers should be scheduled as the most appropriate for each dispatch. At the same time, the focus of most work on the mobile charger scheduling problem has always been on reducing the number of dead nodes, and the most critical metric of network performance, packet arrival rate, is relatively neglected. In this article, we develop a DRL-based Partial Charging algorithm. Based on the number and urgency of charging requests, we classify charging requests into four scenarios. And for each scenario, we design a corresponding request allocation algorithm. Then, a Deep Reinforcement Learning algorithm is employed to train a decision model using environmental information to select which request allocation algorithm is optimal for the current scenario. After the allocation of charging requests is confirmed, to improve the Quality of Service, i.e., the packet arrival rate of the entire network, a partial charging scheduling algorithm is designed to maximize the total charging duration of nodes in the ideal state while ensuring that all charging requests are completed. In addition, we analyze the traffic information of the nodes and use the Analytic Hierarchy Process to determine the importance of the nodes to compensate for the inaccurate estimation of the node’s remaining lifetime in realistic scenarios. Simulation results show that our proposed algorithm outperforms the existing algorithms regarding the number of alive nodes and packet arrival rate.

Wireless Power Transfer (WPT) is a promising technology that can potentially mitigate the energy provisioning problem for sensor networks. In order to efficiently replenish energy for these battery-powered devices, designing appropriate scheduling and charging path planning algorithms is essential and challenging. Whilst previous studies have tackled this challenge, the conjoint influences of network topology, charging path planning, and energy threshold distribution in Wireless Rechargeable Sensor Networks (WRSNs) are still in their infancy. We mitigate the aforementioned problem by proposing novel algorithmic solutions to efficient sector-based on-demand charging scheduling and path planning. Specifically, we first propose a hexagonal cluster-based deployment of nodes such that finding an NP-Complete Hamiltonian path is feasible. Second, each cluster is divided into multiple sectors and a charging path planning algorithm is implemented to yield a Hamiltonian path, aimed at improving the Mobile Charging Vehicle (MCV) efficiency and charging throughput. Third, we propose an efficient algorithm to calculate theimportanceof nodes to be used for charging duration decision-making and prioritization. Fourth, a non-preemptive dynamic priority scheduling algorithm is proposed for charging tasks’ assignments and scheduling. Finally, extensive simulations have been conducted, revealing the significant advantages of our proposed algorithms in terms of energy efficiency, response time, dead nodes’ density, and queuing processing.

Amidst prevailing healthcare challenges, a dynamic solution emerges, fusing knowledge graph technology, clinical trials optimization, dataspace integration, and AI innovation. This unified approach tackles issues like limited patient insights, suboptimal trial designs, and imprecise treatments. By interlinking diverse data through knowledge graphs, this method illuminates disease trends, therapeutic efficacies, and patient prognoses. AI techniques, especially machine learning, contribute predictive power by unveiling hidden patterns for accurate diagnostics, prognostics, and personalized treatments. This multidisciplinary fusion transforms clinical trials, enhancing comprehensiveness and precision through real-world data analysis and subgroup identification. In reshaping healthcare, this proposition aims to accelerate treatment personalization, elevate therapeutic efficacy, and empower informed medical decisions, encompassing the essence of ’Advancing Healthcare through Innovation: Knowledge Graphs, Clinical Trials, Dataspace, and AI’.

Recently, sparse arrays have received considerable attention owing to their capability of achieving increased degrees of freedom (DoFs) by exploiting the virtual sensors resulting from their difference or sum-difference coarrays. Mutual coupling is another factor that attracts interest to these kinds of arrays. In this paper, both the fundamental criteria of high DoFs and reduced mutual coupling are considered in the design of the proposed array configuration for the direction of arrival (DoA) estimation of non-circular signals. Simulation results are provided to verify the robustness of the proposed array against heavy mutual coupling.

The Metaverse is an emerging concept that aims to integrate the physical and virtual worlds, creating a shared 3D virtual world where users can interact and immerse in new experiences. With the rise of Metaverse-driven Cyber-Physical-Social Systems (CPSSs), integrating drones as a critical technology in the Metaverse has become increasingly important. CPSSs have become proliferating and integral to our daily lives. This paper proposes a conceptual framework for Metaverse-driven drone edge intelligence, which integrates drone-enabled sensing, communication, and computation to enable real-time decision-making in CPSSs. We present a detailed analysis of the challenges and opportunities for integrating drones in the Metaverse and discuss the potential impact of our framework on various application domains. Our work contributes to advancing the Metaverse and CPSSs by providing a novel approach for empowering real-time decision-making and enabling new user experiences through integrating drones and the Metaverse. The proposed framework has the potential to revolutionize the way we approach data-driven decision-making in various industries and applications, including precision agriculture, transportation, emergency response, smart cities, healthcare, manufacturing, and energy.

Recently, innovations in the Internet of Medical Things (IoMT), information and communication technologies, and machine learning (ML) have enabled smart healthcare. Pooling medical data into a centralized storage system to train a robust ML model, on the other hand, poses privacy, ownership, and regulatory challenges. Federated learning (FL) overcomes the prior problems with a centralized aggregator server and a shared global model. However, there are two technical challenges: 1) FL members need to be motivated to contribute their time and effort and 2) the centralized FL server may not accurately aggregate the global model. Therefore, combining the blockchain and FL can overcome these issues and provide high-level security and privacy for smart healthcare in a decentralized fashion. This study integrates two emerging technologies, blockchain and FL, for healthcare. We describe how blockchain-based FL plays a fundamental role in improving competent healthcare, where edge nodes manage the blockchain to avoid a single point of failure, while IoMT devices employ FL to use dispersed clinical data fully. We discuss the benefits and limitations of combining both technologies based on a content analysis approach. We emphasize three main research streams based on a systematic analysis of blockchain-empowered: 1) IoMT; 2) electronic health records (EHRs) and electronic medical records (EMRs) management; and 3) digital healthcare systems (internal consortium/secure alerting). In addition, we present a novel conceptual framework of blockchain-enabled FL for the digital healthcare environment. Finally, we highlight the challenges and future directions of combining blockchain and FL for healthcare applications.

The hierarchical structure-based data dissemination is the most popular technique in mobile sink wireless sensor networks (MS-WSNs). An ingenious virtual structure design combined with a precise routing management strategy is significant to attaining efficient data dissemination in hierarchical approaches. This article proposes a hierarchical protocol called territory-bound data delivery (TBDD) that divides the network into multiple partitions called Regions, and spots the location of the mobile sink (MS) according to these partitions. TBDD dynamically assigns a defined role to each division by adopting the mobility of the sink. Thus, the protocol takes advantage of the sink’s movement and the Regions’ flexible role in balancing energy consumption (EC) throughout the network. A Region is designated as active if it contains the sink node or passive otherwise. By using the territory of the active region as a temporal location of the MS, the proposed protocol hides the local movements (i.e., moves inside the active region) of the sink from the rest of the network. In such a way, regardless of the exact position of the sink, sensed data flows from different network ends to the sink’s temporal location. Therefore, TBDD reduces the query request and response burden employed to get the position of the sink. Besides, TBDD implements a spanning tree to report the location information of the MS. Last, we applied an opportunistic routing technique that captures multiple network criteria to elect packet forwarder nodes. The proposed protocol is mathematically analyzed and experimentally evaluated and shows outstanding performance in terms of the number of hops, EC, delay, network lifetime, and success ratio.

Abstract The development of the Internet of Things (IoT) technology and their integration in smart cities have changed the way we work and live, and enriched our society. However, IoT technologies present several challenges such as increases in energy consumption, and produces toxic pollution as well as E-waste in smart cities. Smart city applications must be environmentally-friendly, hence require a move towards green IoT. Green IoT leads to an eco-friendly environment, which is more sustainable for smart cities. Therefore, it is essential to address the techniques and strategies for reducing pollution hazards, traffic waste, resource usage, energy consumption, providing public safety, life quality, and sustaining the environment and cost management. This survey focuses on providing a comprehensive review of the techniques and strategies for making cities smarter, sustainable, and eco-friendly. Furthermore, the survey focuses on IoT and its capabilities to merge into aspects of potential to address the needs of smart cities. Finally, we discuss challenges and opportunities for future research in smart city applications.

The integration of drones, the Internet of Things (IoT), and Artificial Intelligence (AI) domains can produce exceptional solutions to today complex problems in smart cities. A drone, which essentially is a data-gathering robot, can access geographical areas that are difficult, unsafe, or even impossible for humans to reach. Besides, communicating amongst themselves, such drones need to be in constant contact with other ground-based agents such as IoT sensors, robots, and humans. In this paper, an intelligent technique is proposed to predict the signal strength from a drone to IoT devices in smart cities in order to maintain the network connectivity, provide the desired quality of service (QoS), and identify the drone coverage area. An artificial neural network (ANN) based efficient and accurate solution is proposed to predict the signal strength from a drone based on several pertinent factors such as drone altitude, path loss, distance, transmitter height, receiver height, transmitted power, and signal frequency. Furthermore, the signal strength estimates are then used to predict the drone flying path. The findings show that the proposed ANN technique has achieved a good agreement with the validation data generated via simulations, yielding determination coefficient$R^2$to be 0.96 and 0.98, for variation in drone altitude and distance from a drone, respectively. Therefore, the proposed ANN technique is reliable, useful, and fast to estimate the signal strength, determine the optimal drone flying path, and predict the next location based on received signal strength.

Topic modelling (TM) has shown significant progress in boosting the effectiveness of image captioning in the last few years. Although important improvements have been shown in previous topic-guided image captioning models, some challenges remain unsolved, such as the independence of the topic predictors and the sentence generators, resulting in ineffective exploitation of semantic information. Also, all the predicted topics or the top-one topic are used throughout the whole captioning task without considering the current time step's linguistic context, which deviates the captioning network to focus on inaccurate image objects. To tackle these challenges, we propose a novel image captioning method consisting of four modules: enhanced topic predictor (ETP), retrieval-based topics re-weighting module (RTR), subsequent topic predictor (STP), and caption generation module. The prediction and generation modules are trained in an end-to-end manner to promote the efficient use of topics by predicting suitable topics at each time step. ETP predicts the topics using the image features, and is enhanced with topic embedding (TE). The RTR is only applied in the testing stage for re-weighting the topics predicted by ETP. In each time step, the STP automatically predicts concise topics subsets to alleviate the diversity of the image topics. Compared with the existing topic-based models, our model can automatically generate more accurate and diverse captions, boosting the explainability of how the topics influence the generated word in each time step. Extensive experiments on the MS-COCO and Flickr30K benchmark datasets show that our method enhances the overall image captioning's performance and the topic prediction task, and outperforms many recent image captioning approaches in terms of the evaluation metrics.

Opportunistic routing (OR) is widely adopted in Wireless Sensor Networks (WSNs) running asynchronous duty-cycled MAC protocols. In conventional routing, where packets are forwarded along predetermined routes, the sender may wait for the receiver to wake up for a long time. To reduce the sender waiting time, the OR protocols allow nodes to select multiple neighbors as the forwarders so that the packets could be forwarded by multi-path. Thus, the forwarders selection algorithm affects network performance seriously. However, an excessive number of forwarders increases the probability that more than one forwarders wake up simultaneously. This will consume more energy since each of them will receive the packet. To address the two issues, a Dynamic Opportunistic Routing protocol using Analytical Hierarchy Process (AHP) and Fuzzy Inference System (FIS) called DORAF is proposed in this paper. DORAF is implemented in three steps. First, multiple criteria (i.e., residual energy, distance, and angle) at the network layer are defined to evaluate the nodes where the importance of these criteria is determined by AHP. Second, the pairwise comparison matrices in AHP are generated by using mathematical functions (i.e., Boltzmann function and Logistic function) and FIS. Third, each node uses AHP and FIS to prioritize its neighbors based on the criteria and selects appropriate ones as the forwarders dynamically in a distributed manner. The experimental results demonstrate that our protocol performs better than other state-of-the-art in terms of network lifetime, energy consumption, and average redundant transmissions.

Smart Parking has gained so much popularity in recent years due to the increasing number of vehicles in big cities, resulting in traffic congestion in urban areas. Not only on the streets but also in places such as educational institutions, hospitals, commercial activities, special events, and entertainment uses. Finding a free parking lot in these places has evolved difficulty for the drivers. To solve such a problem, governments and researchers tried to find alternative solutions to overcome or mitigate the traffic congestion. Many solutions have been proposed, such as increasing the parking capacities, which takes much time or makes it hard to find a square area in crowded places. Most existing studies, do not consider the cost of deployment, energy, and time-to-market consideration which makes the available systems need further investigation. In this paper, we propose an intelligent parking system prototype that can be useful for the drivers to have a prior knowledge about the available parking lots in the area of interest. Our proposed system involves deploying mmWave Radar sensor nodes in each parking lot to detect the availability of parking vehicle through transmitting radio pulses periodically. The detected information can be sent to the gateway through multi-hop for further statistics and reports. We also give an intensive analysis and study about the challenges and consideration on the mmWave radar design aiming to improve the detection accuracy and avoid false-detection that occurs from objects near to the sensor. To ensure continuous operation and extend sensor life-time, we propose Bluetooth Low Energy BLE-enabled relay-feature as the communication protocol between the nodes.

In this paper, a new type of coprime-array-based structure, named AtCADiS, is proposed to achieve increased degrees of freedom (DoFs) and reduced mutual coupling. The closed-form expressions for the sensor positions and the number of uniform DoFs (uDoFs) of AtCADiS are provided. Specifically, AtCADiS is constructed via two steps. First, we shift the leftmost sensor of tailored coprime array with displaced subarrays (tCADiS) to the right by N. Second, we increase the number of sensors of tCADiS by $\left\lfloor {\frac{M}{2}} \right\rfloor $ that are appropriately placed to connect the positive and negative lags of difference coarray of tCADiS and further improved its uDoFs remarkably. Finally, simulations show that AtCADiS can achieve higher number of uDoFs than the existing coprime-array-based structures by using the same number of physical sensors, which leads to stronger resolution capability and higher direction of arrival (DoA) estimation accuracy. In the presence of mutual coupling, AtCADiS can achieve comparable mutual coupling leakage compared with the existing array structures.

We have made research achievements worth sharing on the complicated topic of text-to-image synthesis. Our analysis of popular articles shows that they often use stacked structures to construct and generate confrontation network models and usually introduce multiple sets of generators and discriminator pairs. The entanglement between different generators affects the quality of the final synthesized image. Some researchers have proposed a single-stage network model to avoid traps between multiple generators, But it lacks the use of unstructured natural language information with different granularity. To correct this serious defect, we propose a multi-granularity feature network MGF-GAN, which plays the role of text information with different granularity based on the advantages of the single-stage network. Specifically, we input the three granularity features of the text, including sentences, aspect words, and single words of text, into different stages of the model through spatial attention and channel attention mechanisms to gradually refine the synthetic image from global and local perspectives. In addition, we reconstruct the loss function based on the contrast concept to stabilize the training and ensure that the visual meaning between the synthesized image and the natural language is consistent. We conducted validity experiments on CUB bird and COCO. The significant effect is sufficient to prove the effectiveness and advancement of our MGF-GAN.

Video Question Answering (VideoQA) is a challenging multimodal task that requires the ability to recognize visual elements and reason relations in spatial and temporal dimensions according to the given video and question. Most existing GNN-based methods model the visual elements in a video as graph structures and reason relations between them. Despite the remarkable results of their work, they neglected that the question also has graph structure dependencies, which can be used to reason about relations between the video and the question. In this work, we propose a multimodal graph reasoning and fusion network that builds three graph neural networks for appearance, motion, and text sequences, respectively, and hierarchically reasons and fuses nodes from different modalities. Our proposed method achieves superior performance to several state-of-the-art methods on three benchmark datasets.

Recently, Unmanned Aerial Vehicles (UAVs) present a promising advanced technology that can enhance people life quality and smartness of cities dramatically and increase overall economic efficiency. UAVs have attained a significant interest in supporting many applications such as surveillance, agriculture, communication, transportation, pollution monitoring, disaster management, public safety, healthcare, and environmental preservation. Industry 4.0 applications are conceived of intelligent things that can automatically and collaboratively improve beyond 5G (B5G). Therefore, the Internet of Things (IoT) is required to ensure collaboration between the vast multitude of things efficiently anywhere in real-world applications that are monitored in real-time. However, many IoT devices consume a significant amount of energy when transmitting the collected data from surrounding environments. Due to a drone's capability to fly closer to IoT, UAV technology plays a vital role in greening IoT by transmitting collected data to achieve a sustainable, reliable, eco-friendly Industry 4.0. This survey presents an overview of the techniques and strategies proposed recently to achieve green IoT using UAVs infrastructure for a reliable and sustainable smart world. This survey is different from other attempts in terms of concept, focus, and discussion. Finally, various use cases, challenges, and opportunities regarding green IoT using UAVs are presented.

Abstract Wireless sensor networks (WSNs) are critically resource‐constrained due to wireless sensor nodes' tiny memory, low processing units, power limitations, and narrow communication bandwidth. The data reduction technique is one of the most widely used techniques to reduce transmitted data over the wireless sensor networks and to minimize the sensor nodes' energy consumption, particularly, the entire network in general. This paper proposes a reliable dual prediction data reduction approach for WSNs. This approach performs data reduction through two phases: the data reduction phase (DRP) and data prediction phase (DPP). The DRP is mainly to decrease the number of transmissions between the sensor node and the sink node, thereby minimizing energy consumption. It also detects faulty data and discards them at the sensor node. The discarded faulty data at the sensor nodes are replaced by estimated values at the sink node to maintain data reliability. DPP runs at the sink node or base station, which works in synchronization with the sensor nodes. This phase is responsible for predicting the non‐transmitted data based on the Kalman filter. The simulation results demonstrate that the proposed approach is efficient and effective in data reduction, data reliability, and energy consumption.