Search Results (309)

Land use and land cover (LULC) in Northern Bangladesh have undergone substantial transformations due to both anthropogenic and natural drivers. This study examines historical LULC changes (1990–2022) and projects future trends for 2030 and 2054 using remote sensing and the Cellular Automata-Artificial Neural Network (CA-ANN) model. Multi-temporal Landsat imagery was classified with 80.75–86.23% accuracy (Kappa: 0.75–0.81). Model validation comparing simulated and actual 2014 data yielded 79.98% accuracy, indicating a reasonably good performance given the region’s rapidly evolving and heterogeneous landscape. The results reveal a significant decline in waterbodies, which is projected to shrink by 34.4% by 2054, alongside a 1.21% reduction in cropland raising serious environmental and food security concerns. Vegetation, after an initial massive decrease (1990–2014), increased (2014–2022) due to different forms of agroforestry practices and is expected to increase by 4.64% by 2054. While the model demonstrated fair predictive power, its moderate accuracy highlights challenges in forecasting LULC in areas characterized by informal urbanization, seasonal land shifts, and riverbank erosion. These dynamics limit prediction reliability and reflect the region’s ecological vulnerability. The findings call for urgent policy action particularly afforestation, water resource management, and integrated land use planning to ensure environmental sustainability and resilience in this climate-sensitive area. Full article

The idea of multi-keyword ranked search over encrypted cloud data has attracted considerable attention in recent studies, as it allows users to securely and efficiently retrieve highly relevant results. Traditional methods improve search efficiency by incorporating the K-means clustering algorithm. However, when applied to large-scale datasets, K-means can become computationally expensive. This paper introduces a multi-keyword ranked search method, SAO-KRS, which leverages the snow ablation optimizer (SAO) to enhance clustering performance. The approach begins with principal component analysis (PCA) to reduce the dimensionality of high-dimensional data, followed by clustering the reduced data using SAO, which reduces clustering overhead massively. By incorporating a heuristic best-first search algorithm over index trees, the scheme achieves reduced computational cost with high retrieval accuracy. In the best-case scenario, the proposed method achieves up to 21 times faster clustering and 2.7 times faster searching compared to the traditional K-means approach. Extensive experimental results verify that this method significantly improves clustering efficiency while ensuring both search speed and accuracy. Full article

In recent years, cutting-edge technologies, such as artificial intelligence (AI), blockchain, and digital twin (DT), have revolutionized the healthcare sector by enhancing public health and treatment quality through precise diagnosis, preventive measures, and real-time care capabilities. Despite these advancements, the massive amount of generated biomedical data puts substantial challenges associated with information security, privacy, and scalability. Applying blockchain in healthcare-based digital twins ensures data integrity, immutability, consistency, and security, making it a critical component in addressing these challenges. Federated learning (FL) has also emerged as a promising AI technique to enhance privacy and enable decentralized data processing. This paper investigates the integration of digital twin concepts with blockchain and FL in the healthcare domain, focusing on their architecture and applications. It also explores platforms and solutions that leverage these technologies for secure and scalable medical implementations. A case study on federated learning for electroencephalogram (EEG) signal classification is presented, demonstrating its potential as a diagnostic tool for brain activity analysis and neurological disorder detection. Finally, we highlight the key challenges, emerging opportunities, and future directions in advancing healthcare digital twins with blockchain and federated learning, paving the way for a more intelligent, secure, and privacy-preserving medical ecosystem. Full article

Pompeii is widely known for its tragic past. In 79 A.D., a massive eruption of Mount Vesuvius buried the city and its inhabitants under volcanic ash. Lost for centuries, it was rediscovered in 1748 when the Bourbon monarchs initiated excavations, marking the beginning of systematic digs. Since then, Pompeii has gained worldwide recognition for its archeological wonders. Despite centuries of looting and damage, it remains a breathtaking site. With millions of visitors annually, the Pompeii Archeological Park is the one most visited site in Italy. Managing such a vast and complex heritage site requires significant effort to ensure both visitor safety and the preservation of its fragile structures. Accessibility is also crucial, particularly for individuals with disabilities and staff responsible for site management. To address these challenges, integrated systems and advanced technologies like the Internet of Things/Everything (IoT/IoE) can provide innovative solutions. These technologies connect people, smart devices (such as mobile terminals, sensors, and wearables), and data to optimize security, safety, and site management. This paper presents a security/safety IoT/IoE-based system for security, safety, management, and visitor services at the Pompeii Archeological Park. Full article

Federated learning (FL) has emerged as a promising paradigm for enabling collaborative training of machine learning models while preserving data privacy. However, the massive heterogeneity of data and devices, communication constraints, and security threats pose significant challenges to its practical implementation. This paper provides a system review of the state-of-the-art techniques and future research directions in FL, with a focus on addressing these challenges in resource-constrained environments by a cloud–edge–end collaboration FL architecture. We first introduce the foundations of cloud–edge–end collaboration and FL. We then discuss the key technical challenges. Next, we delve into the pillars of trustworthy AI in the federated context, covering robustness, fairness, and explainability. We propose a dimension reconstruction of trusted AI and analyze the foundations of each trustworthiness pillar. Furthermore, we present a lightweight FL framework for resource-constrained edge–end devices, analyzing the core contradictions and proposing optimization paradigms. Finally, we highlight advanced topics and future research directions to provide valuable insights into the field. Full article

As digital infrastructure continues to expand, networks, web services, and Internet of Things (IoT) devices become increasingly vulnerable to distributed denial of service (DDoS) attacks. Remarkably, IoT devices have become attracted to DDoS attacks due to their common deployment and limited applied security measures. Therefore, attackers take advantage of the growing number of unsecured IoT devices to reflect massive traffic that overwhelms networks and disrupts necessary services, making protection of IoT devices against DDoS attacks a major concern for organizations and administrators. In this paper, the effectiveness of supervised machine learning (ML) classification and deep learning (DL) algorithms in detecting DDoS attacks on IoT networks was investigated by conducting an extensive analysis of network traffic dataset (legitimate and malicious). The performance of the models and data quality improved when emphasizing the impact of feature selection and data pre-processing approaches. Five machine learning models were evaluated by utilizing the Edge-IIoTset dataset: Random Forest (RF), Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and K-Nearest Neighbors (KNN) with multiple K values, and Convolutional Neural Network (CNN). Findings revealed that the RF model outperformed other models by delivering optimal detection speed and remarkable performance across all evaluation metrics, while KNN (K = 7) emerged as the most efficient model in terms of training time. Full article

Rapid urbanization and the rising demand for sustainable living have encouraged the growth of smart cities, which incorporate innovative technologies to ameliorate environmental sustainability, optimize resource management, and improve living standards. The convergence of blockchain (BC) technology and the Internet of Things (IoT) presents transformative convenience for managing smart cities and achieving sustainability goals. In fact, blockchain technology combined with IoT devices provides a decentralized, transparent, and safe framework for managing massive volumes of data produced by networked sensors and systems. By guaranteeing accountability, minimizing fraud, and maximizing resource use, blockchain not only facilitates the smooth operation of smart city infrastructures but also encourages sustainable habits. The various uses of blockchain technology in smart city management and its contribution to sustainability objectives are examined in this study. Through an examination of important domains like energy distribution, waste management, transportation systems, healthcare, and governance, the research shows how blockchain promotes effective data exchange and data security, builds stakeholder trust, and makes it possible to establish decentralized organizations to improve decision-making. Full article

Decentralized blockchains have grown into massive and Internet-scale ecosystems, collectively securing hundreds of billions of dollars in value. The complex interplay of technology and economic incentives within blockchain systems creates a delicate balance that is susceptible to significant shifts even from minor changes. This paper underscores the importance of conducting thorough, data-driven studies to monitor and understand the impacts of significant shifts in blockchain systems, particularly focusing on Ethereum’s groundbreaking builder–proposer separation (PBS) as a pivotal innovation reshaping the ecosystem. PBS revolutionizes Ethereum’s block production, entrusting builders with block construction and proposers with validation via blockchain consensus, with significant impacts on Ethereum decentralization, fairness, and security. Our empirical study reveals key insights, including the following: (a) A substantial 261% increase in proposer revenue underscores the effectiveness of PBS in promoting widespread adoption, significantly enhancing block rewards and proposer incomes. (b) The small profits garnered by builders, comprising only a 3.5% share of block rewards, raise concerns that the security assumptions based on builder reputation may introduce new threats to the system. (c) PBS promotes a more equitable distribution of resources among network participants by reducing proposer centralization and preventing centralization trends among builders and relays, thereby significantly enhancing fairness and decentralization in the Ethereum ecosystem. This study provides a comprehensive analysis of the dynamics of Ethereum PBS adoption, exploring its effects on revenue redistribution among various participants and highlighting its implications for the Ethereum ecosystem’s decentralization. Full article

Traditional intellectual property transaction traceability has problems such as information asymmetry, traceability information storage methods relying on centralized databases, and easy tampering of transaction information, etc. A blockchain-based information security mechanism for intellectual property transaction traceability is proposed. Firstly, through the analysis of massive intellectual property transaction case information, the commonality and individuality data are studied, and the structure and scope of data collection requirements for traceability information are established; secondly, the traceability information structure is constructed based on the smart contract and PROV data origin model, the signature verification of traceability information is completed based on the BLS threshold signature of the Dynamic DKG protocol, and the signature process integrates the PROV model and constructs a chained signature structure. The multi-level traceability information verification strategy and process are developed to achieve the security protection of traceability information throughout the entire life cycle of intellectual property transactions. Full article

The Internet of Things has transformed industries, cities, and homes through a vast network of interconnected devices. As the IoT expands, the number of devices is projected to reach tens of billions, generating massive amounts of data. This growth presents significant data storage, management, and security challenges, especially in large-scale deployments such as smart cities and industrial operations. Traditional centralized solutions struggle to handle the high data volume and heterogeneity of IoT data, while ensuring real-time processing and interoperability. This paper presents the design, development, and evaluation of a blockchain framework tailored for the secure storage and management of data generated by IoT devices. Our framework introduces efficient methods for managing, transmitting, and securing data packets within a blockchain-enabled IoT network. The proposed framework uses a gateway node to aggregate multiple data packets into single transactions, increasing throughput, optimizing network bandwidth, reducing latency, simplifying data retrieval, and improving scalability. The results obtained from rigorous analysis and testing of the evaluated scenarios show that the proposed blockchain framework achieves a high level of performance, scalability, and efficiency while ensuring robust security being able to integrate a large number of IoT devices in a flexible manner. Full article

Supervisory control and data acquisition (SCADA) systems are vulnerable to cyberattacks; hence, cybersecurity is a major concern. Hybrid methodologies using advanced machine learning (ML) may increase intrusion detection and system security. The intrusion detection algorithms have little adaptability, high false-positive rates for novel threats, and restricted feature extraction. SCADA systems are subject to sophisticated attacks. This study’s hybrid autoencoder-hybrid ResNet–long short-term memory (LSTM) (HAE–HRL) architecture includes deep feature extraction, anomaly detection, and sequential analysis. This framework uses these three methods to improve threat detection. AI can scan massive amounts of data and find patterns humans and traditional systems miss. The hybrid approach gives defenders an unequal edge. Autoencoders identify anomalies, convolutional neural networks (CNNs) extract features, and hybrid ResNet–LSTM learns temporal patterns. Cyber risks are correctly classified using this method. With SCADA security and intrusion detection, the model may considerably enhance network abnormality and hostile activity detection. According to experimental tests, HAE–HRL reduces false positives and improves detection accuracy, making it a robust cybersecurity solution. Full article

In recent years, with the rapid development of the internet, the accumulation of massive data has significantly propelled the advancement of artificial intelligence. Graphs, as an important data structure for representing relationships between entities, are widely used in various real-world scenarios such as social networks and e-commerce platforms. Graph Neural Networks (GNNs) have emerged as a popular research topic, capable of learning information from neighborhoods and extracting features from graph-structured data to solve tasks like graph classification, node classification, and link prediction. However, the centralized training of GNNs often faces challenges due to data isolation and privacy concerns. Federated Learning (FL) has been proposed as a solution to these issues, allowing multiple users to collaboratively train models without sharing raw data. This paper introduces a privacy-preserving mechanism based on Local Differential Privacy (LDP) to enhance the security of Federated Graph Neural Networks (FedGNNs) against inference attacks while maintaining model performance. Full article

This study proposes a Zero Trust security framework for 6G-enabled Cyber-Physical Systems (CPS), integrating Adaptive Access Control (AAC), end-to-end encryption, and blockchain to enhance security, scalability, and real-time threat detection. As 6G networks facilitate massive device connectivity and low-latency communication, traditional perimeter-based security models are inadequate against evolving cyber threats such as Man-in-the-Middle (MITM) attacks, Distributed Denial-of-Service (DDoS), and data breaches. Zero Trust security eliminates implicit trust by enforcing continuous authentication, strict access control, and real-time anomaly detection to mitigate potential threats dynamically. The proposed framework leverages blockchain technology to ensure tamper-proof data integrity and decentralized authentication, preventing unauthorized modifications to CPS data. Additionally, AI-driven anomaly detection identifies suspicious behavior in real time, optimizing security response mechanisms and reducing false positives. Experimental evaluations demonstrate a 40% reduction in MITM attack success rates, 5.8% improvement in authentication efficiency, and 63.5% lower latency compared to traditional security methods. The framework also achieves high scalability and energy efficiency, maintaining consistent throughput and response times across large-scale CPS deployments. These findings underscore the transformative potential of Zero Trust security in 6G-enabled CPS, particularly in mission-critical applications such as healthcare, smart infrastructure, and industrial automation. By integrating blockchain-based authentication, AI-powered threat detection, and adaptive access control, this research presents a scalable and resource-efficient solution for securing next-generation CPS architectures. Future work will explore quantum-safe cryptography and federated learning to further enhance security, ensuring long-term resilience in highly dynamic network environments. Full article

This study focuses on Fortaleza, the largest metropolis in Brazil’s semi-arid region. Due to recurrent droughts, massive infrastructure like high-density reservoir networks, inter-municipal and interstate water transfer systems, and a seawater desalination plant have been implemented to ensure the city’s water security. To evaluate the quantitative and qualitative impact of introducing these diverse water sources into Fortaleza’s water supply macrosystem, adequate calibration of the operating and demand parameters is required. In this study, the macrosystem was calibrated using the Particle Swarm Optimization (PSO) method based on hourly data from 50 pressure head monitoring points and 40 flow rate monitoring points over two typical operational days. The calibration process involved adjusting the operational rules of typical valves in large-scale Water Distribution Systems (WDS). After parameterization, the calibration presented the following results: R² of 88% for pressure head and 96% for flow rate, with average relative errors of 13% for the pressure head and flow rate. In addition, with NSE values above 0.80 after calibration for the flow rate and pressure head, the PSO method suggests a significant improvement in the simulation model’s performance. These results offer a methodology for calibrating real WDS to simulate various water injection scenarios in the Fortaleza macrosystem. Full article

Recent developments in sensors, wireless communications, and data processing technologies are the main drivers for adopting the Internet of Things (IoT) in healthcare systems. IoT-based healthcare systems can enhance the quality of life significantly and help prevent the occurrence of health problems and epidemics. Deploying IoT-based healthcare on a massive scale raises several issues and challenges. One of the main challenges is the management of the end-to-end network connections of the IoT-based healthcare system. This paper presents a comprehensive survey of smart network management protocols that improve IoT-based healthcare efficiency, ensuring real-time monitoring, secure data transmission, and effective device management. Moreover, a reference architecture has been proposed for the network management of IoT-based smart healthcare systems to ensure the sustainability of service delivery to patients and caregivers. The architecture avoids health-related risks and anomalies by incorporating proper network management techniques and operational requirements pertaining to smart healthcare systems. This paper also discusses architectural implementation insights supported by new technologies such as software-defined networking (SDN) and deep learning (DL). Finally, this paper explores emerging paradigms to advance next-generation network management protocols for future smart healthcare systems. Full article