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.

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.

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 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.

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.