Search Results (7,876)

Mobile Radio Frequency Identification (RFID) systems are emerging as a fundamental part of modern smart environments, enabling automatic identification, tracking, and data exchange among different mobile platforms. While these systems are increasingly being adopted, they have a major drawback: an RFID tag has very little computational power, and the wireless communication channels can be attacked by adversaries. Several authentication and key management mechanisms to protect data and provide secure access have been proposed to solve these problems. In this study, we propose a new scheme that improves system security through explicit three-party mutual authentication, epoch-based pseudonym indexing for $O\left(1\right)$ server lookup, and comprehensive resiliency against replay, impersonation, and man-in-the-middle attacks. An in-depth security analysis, along with performance evaluation, substantiates that the proposed protocol improves privacy and resilience without losing compatibility with low-cost RFID tags equipped only to perform lightweight cryptographic functions. This protocol also provides epoch-based unlinkability and is well suited for large-scale deployments, as found in healthcare, logistics, and Internet of Things (IoT) applications. Full article

Leveraging the favorable properties of cryptographic computation, FHE effectively ensures data availability without visibility, thereby holding broad application prospects in cloud computing security and data privacy protection. However, computational efficiency remains a critical bottleneck that constrains its practical deployment and further development. Consequently, research on hardware implementations of FHE has become a major direction in the cryptographic community. This paper first systematically reviews the research progress of FHE schemes, summarizing and analyzing the characteristics of representative FHE schemes. Subsequently, we survey and analyze hardware research progress and optimization techniques from the perspectives of overall accelerator architecture design, polynomial multiplier design, and integer modular multiplier design, highlighting the main advantages, disadvantages, and common features of different hardware structures. Finally, based on an analysis of existing hardware implementation architectures for FHE, this paper presents the potential deficiencies, summarizes and outlines future research directions and development prospects, aiming to further improve the operational performance of FHE hardware implementations. Full article

This article examines the artisanal fisheries of the Basse Casamance region in southern Senegal and investigates how changing governance arrangements shape their capacity to sustain local food security under conditions of socio-ecological change. These fisheries face mounting pressures from climate change, global market integration, shifting governance regimes, and emigration, while also undergoing profound institutional and socio-ecological transformations. Based on three months of ethnographic fieldwork, including participant observation and semi-structured interviews, the study traces the historical evolution of fisheries governance from precolonial common-property arrangements through colonial and postcolonial interventions to contemporary challenges such as industrial overfishing and environmental degradation. The analysis combines theoretical perspectives from New Institutionalism, Political Ecology, and Common-Property Theory to illuminate how local fisheries governance operates within a context of institutional pluralism and external pressures. The findings demonstrate that inland fisheries remain embedded in communal responsibility, local rulemaking, and ecological stewardship, but are increasingly strained by state regulation, market integration, and demographic shifts. Nevertheless, local actors sustain resilience by negotiating institutions and diversifying livelihoods. These adaptive strategies not only mitigate external pressures but also reinforce the role of fisheries as a cornerstone of local food security. By highlighting the resilience of artisanal fisheries as locally grounded governance systems, this study contributes to broader debates on sustainability, food security, and institutional change in West African fisheries. It underscores the need to recognize the enduring importance of community-based governance in managing common-pool resources under conditions of globalization and environmental uncertainty. Full article

Background: Generative artificial intelligence (GenAI) is diffusing among consumers at exceptional speed, yet little is known about how its adoption relates to households’ prior engagement with digital financial services, or about the barriers associated with non-adoption. Methods: Using individual microdata from the 2025 Spanish Survey on ICT Equipment and Use in Households (INE; 14,642 internet users aged 16 and older), we estimate survey-weighted logistic regressions of GenAI adoption on financial digital engagement, digital skills and sociodemographics, with autonomous community fixed effects and cluster-robust standard errors, complemented by average marginal effects, alternative variance estimators, and an analysis of all stated reasons for non-use. Results: GenAI adoption is 37.3% among internet users. Online banking users have 73% higher adjusted odds of adopting GenAI (OR = 1.73; 95% CI 1.53–1.97), and adoption rises monotonically from 18% to 79% across a 0–4 financial digital engagement index, although this gradient is driven mainly by the online banking (extensive) margin. A steep age gradient is present, but the financial digital advantage does not significantly widen with age. Among non-adopters, the most frequently stated reason for non-use is a lack of perceived need (67%); privacy or security concerns are also prominent (43%) and are cited disproportionately by online banking users, women, and older individuals. Conclusions: Prior financial digital engagement is a strong correlate, rather than a demonstrated cause, of consumer GenAI adoption, consistent with experience and facilitating condition constructs in UTAUT2. Among capable non-adopters, for whom access and skills are not the obstacle, privacy- and security-related concerns, rather than a broader deficit of trust in AI, emerge as the most salient barriers to adoption, underscoring these concerns as priorities for consumer-facing AI in financial services. Full article

Background: With the deepening coupling of multi-microgrids (MMGs) and transportation systems in smart cities, maintaining frequency stability under extreme conditions increasingly relies on vehicle-to-grid (V2G) flexibility. However, existing V2G dispatch strategies often overlook the noticeable battery degradation caused by high-frequency regulation and the vulnerability of extensive communication networks to false data injection attacks (FDIAs), while the high-dimensional coordination of EV routing and discharging makes classical algorithms struggle to converge. Methods: To address these challenges, this study proposes a quantum-empowered degradation-aware V2G coordination framework for smart-city MMGs considering communication security and user travel demands. At the physical layer, an equivalent RC circuit-based battery degradation model and a traffic flow model are established to quantify capacity loss and travel delays. At the cyber layer, quantum key distribution (QKD) ensures unconditionally secure communication, while a quantum reinforcement learning (QRL) algorithm is developed to achieve fast convergence in high-dimensional multi-objective optimization. Results: Simulation results demonstrate that the proposed framework completely immunizes the system against FDIAs, effectively suppresses frequency fluctuations, and significantly reduces battery degradation costs while preserving user mobility. Conclusions: This framework provides a highly secure and user-friendly pathway for resilient smart-city frequency regulation. Full article

Energy poverty remains a persistent challenge in South Africa, despite relatively high levels of electricity access in urban townships. This study investigates household energy consumption patterns, energy-related challenges, and community perceptions of renewable energy in South Africa. In this study, a quantitative research design was employed, using a structured questionnaire administered to households selected from both electrified and non-electrified areas. The data were analyzed using descriptive statistics and inferential analysis with the Statistical Package for the Social Sciences (SPSS Version 30). The findings demonstrated that although electricity is widely used for basic services such as cooking and lighting, many households continue to rely on multiple fuels to meet their daily energy needs. This practice of energy stacking reflects ongoing concerns about affordability, reliability, and safety, and highlights that access to electricity alone does not guarantee energy security. Households also report significant financial pressure from energy costs, as well as health and fire risks associated with the use of unsafe alternative fuels. While awareness of renewable energy is present within the community, knowledge levels are uneven and not strongly associated with demographic factors, suggesting the need for more inclusive and targeted engagement strategies. Overall, the study demonstrates that energy poverty in urban South African townships extends beyond physical grid connection and is shaped by complex socio-economic and safety considerations. By foregrounding community perspectives, this research contributes to the international literature on multidimensional energy poverty and supports the design of integrated, people-centered interventions that advance affordable, reliable, and clean energy transitions in the Global South. Full article

The increasing frequency of climate change and extreme weather makes the issue of urban flood vulnerability an important one. Flood disaster mitigation efforts have progressed rapidly with the utilization of technology in flood risk assessment and community preparedness. However, there is still a gap between technology and community engagement in preparedness. This study was conducted to analyze the trend of technology-based urban flood vulnerability and community preparedness integration. The study used bibliometric methods to see research trends and a systematic review of Scopus data with Biblioshiny, VOSviewer, and Convidence to identify relevant research articles. Trends in urban flood vulnerability show an increasing use of advanced technologies such as multivariate LSTM (Long Short-Term Memory) artificial neural networks, LiDAR (Light Detection and Ranging), GIS (Geographic Information System), and blockchain for flood risk assessment. There are gaps in community engagement and preparedness, highlighting the need for a comprehensive approach that combines technology with community-based strategies. A balance between technology implementation and community engagement is needed to improve community preparedness and safeguard water security in flood-prone urban areas. The development of an urban flood vulnerability index can bridge the technology gap with standardized community engagement. All the articles in the study contribute greatly to the changes and improvements in flood disaster mitigation, as well as to the role of technology and preparedness. The gap between technology and community engagement in preparedness requires an urban flood vulnerability index with comprehensive strategies. Full article

The advent of cloud–fog–edge computing has transformed distributed data processing by performing computation closer to end devices. Due to resource constraints at edge nodes and the dynamic nature of fog-assisted communication, secure and efficient group key distribution and batch verification in such decentralized systems remain a major challenge. Many existing protocols based on Chinese remainder theorem (CRT) use a straightforward scalar product to mask the group key and hence fail in multifactor security. Others suffer from architectural overhead since they require distinct and independent sets of moduli equations with multiple mathematical structures for different network layers, which increases computing overhead, limits scalability and delays synchronization during frequent node leave/join. To mitigate these challenges, this paper proposes a unified distributed CRT-based protocol for cloud–fog–edge environments. Our protocol introduces a two-factor modular key masking mechanism by incorporating a unique secret parameter for every edge node to strengthen group key protection and enhance the overall robustness of the key distribution mechanism. Additionally, our protocol uses a single set of moduli equations across cloud–fog–edge networks, which drastically reduces computation and storage costs at the fog layer. Our protocol achieves 𝒪(1) efficiency for rekeying. Formal security analysis using ProVerif and the ROR model demonstrates that our protocol has considerable security advantages. To prove its practicality, an ESP32-based simulation on Wokwi is used to verify the correctness of group key distribution, retrieval, and batch message verification. The performance analysis findings show that our protocol outperforms others in computation cost, communication cost, security and applicability for resource-constrained cloud–fog–edge computing networks. Full article

Recurrent flooding along the Wabi Shabelle River has repeatedly displaced communities in Beledweyne, Somalia, prompting a 2020 government-led relocation policy intended to reduce long-term flood risk exposure. Whether such resettlement constitutes a durable change-detection method disaster risk reduction strategy in semi-arid East Africa remains empirically untested. We integrate ten years of Sentinel-1 SAR (259 scenes, 2015–2025), three global DEMs (Copernicus GLO-30, FABDEM, SRTM), CHIRPS precipitation, and BFAST changepoint analysis to map flood frequency at 10 m resolution. The Z-score showed the strongest coupling with 12-day cumulative precipitation (Pearson r = +0.338; block-bootstrap 95% CI [+0.13, +0.49], excluding zero) and strong agreement with the log-ratio method (r = +0.676), whereas the conventional fixed −17 dB threshold produced a physically implausible negative correlation (r = −0.248). These conclusions were stable across alternative thresholds. HAND from all three DEMs was positively associated with flood frequency (Spearman ρ ≈ +0.30); GLO-30 and FABDEM were near-equivalent in this low-relief setting (median pairwise difference, 0.13 m). BFAST detected 476,955 changepoints (49.9% post-2020 vs. 35.6% pre-2020), concentrated in high-flood-frequency pixels (Kolmogorov–Smirnov D = 0.854, p < 0.001). The mean flooded area fraction rose from 4.68% to 5.61%, a relative increase of +19.8% (95% CI 9.1–32.0); this remained significant after controlling for precipitation (+0.96 pp, p < 0.001) and excluding the extreme 2023 events (+0.81 pp). Because standard optical and multi-year surface water products are unsuitable for pixel-level validation in this turbid seasonal river, we demonstrate that SAR flood frequency is significantly higher within independently mapped JRC water corridors (median, 0.070 vs. 0.042; p < 0.001). These convergent lines of evidence are consistent with re-encroachment into hazardous floodplains, suggesting that structural relocation alone is unlikely to deliver durable flood risk reduction without parallel investment in tenure security, livelihoods, and inclusive governance (SDGs 11.5, 13.1). The reproducible, open-source SAR framework provides a transferable monitoring template for data-sparse Horn of Africa floodplains. Full article

Pesticide use remains important in modern agriculture, vector control, and household pest management. However, exposure to pesticide active ingredients and residues remains a persistent public health concern. In addition to active ingredients, people may also be exposed to other constituents of commercial pesticide formulations, such as adjuvants and solvents, which can influence overall toxicity and health outcomes. These exposures may occur among direct applicators and may also affect other populations through contaminated air, water, soil, food, clothing, and household surfaces. This narrative review examines pesticide exposure from a community health perspective, emphasizing occupational, para-occupational, residential, dietary, drinking-water, airborne, and cumulative exposure pathways. It highlights vulnerable groups, including agricultural workers, children, pregnant women, women in agricultural communities, older adults, people with chronic illness, and marginalized rural populations. Evidence reviewed in this article links pesticide exposure with acute poisoning, respiratory effects, neurobehavioral and neurodevelopmental outcomes, cancer-related risks, reproductive and developmental effects, endocrine and metabolic disruption, cardiovascular outcomes, dermatological reactions, immune dysregulation, and biomarker-based subclinical changes. Beyond disease endpoints, pesticide exposure may also affect household income, education, mental well-being, food security, livelihoods, and intergenerational health. Major challenges include weak exposure assessment, underreporting, limited biomonitoring in low- and middle-income settings, and inconsistent community-level indicators. Strengthening surveillance, risk communication, integrated pest management, safer storage and disposal, protective regulation, and community-centered biomonitoring is essential to reduce pesticide-related health burdens. Full article

Low Power Wide Area Networks (LPWANs) are essential for enabling Internet-of-Things (IoT) technologies in utility environments. Utilities can leverage these networks to monitor critical remote assets, especially where mobile technologies are unsuitable due to poor power efficiency or insufficient coverage. This paper investigates the use of Long Range (LoRa) Wide Area Network (LoRaWAN) technology as an LPWAN solution for remote grid monitoring within the eThekwini Municipal Area. In addition to evaluating range performance (distance) and the packet reception ratio (PRR) across configurable parameters, such as spreading factor and transmit power, this paper introduces a data-packet security extension for LoRaWAN using NTRU post-quantum cryptography (PQC). The proposed security enhancement provides quantum-resistant encryption for application-layer payloads without violating LoRaWAN duty-cycle constraints or significantly increasing energy consumption. Field tests were performed at 11 geographically dispersed substations using a handheld LoRa device. Test signals were transmitted at four power levels (2 dBm, 8 dBm, 14 dBm, and 20 dBm) and spreading factors (SF7–SF12). Results show that the public LoRaWAN network can achieve communication distances of approximately 30 km in an urban environment, with PRR strongly dependent on SFs and transmit power. The integration of lightweight NTRU-protected payloads was found to be feasible for typical smart grid use cases involving small data packets (1–13 bytes). Full article

Internet of Things (IoT) devices are increasingly targeted by rapidly evolving malware, yet collaborative detection remains challenged by privacy leakage, noisy and imbalanced training data, and weak integrity guarantees when sharing model updates. This paper presents Mal-Fedchain, a secure and privacy-preserving framework for image-based IoT malware detection and prevention that couples federated learning with blockchain and honeypot-assisted behavioral monitoring, targeting Linux-capable IoT gateway devices. Portable Executable (PE) binaries are transformed into grayscale images using a corrected fixed-width byte-mapping pipeline stabilized by an information-maximizing GAN (IMGAN). A bi-level preprocessing pipeline applies two-sided weighted sparse representation (T-WSR) denoising—designed to selectively suppress zero-padding artifacts, high-entropy packed regions, and sparse opcode noise while preserving discriminative section-boundary texture—followed by geometric augmentation to mitigate class imbalance. Malware detection and family attribution are performed using a residual capsule-based network (RBCN) that fuses discriminative visual representations with PE-header features via concatenation, improving robustness against polymorphism and obfuscation. A formal threat model governs three adversary classes: a semi-honest aggregation server, a bounded fraction of malicious clients (up to 30%), and a passive eavesdropper. To enable collaboration without exposing raw data, clients train locally and share only MemCbar-encrypted updates; a permissioned Hyperledger Fabric blockchain ledger records hashed updates and security events to provide integrity, traceability, and tamper resistance. A file-system-integrated honeypot captures evasive behaviors and logs auditable evidence to strengthen prevention. Experiments on the Malimg dataset across five ablation configurations demonstrate that the corrected RBCN pipeline achieves $93.52\%$ accuracy, $92.40\%$ precision, $93.52\%$ recall, $92.52\%$ F-measure, MCC of $0.9245$, and AUC of $0.9976$ in its centralized configuration, and $65.62\%$ accuracy with AUC of $0.9840$ in the full federated configuration with five clients and eight communication rounds, substantially outperforming all baselines across all reported metrics. Full article

The growing deployment of Internet of Things (IoT) monitoring systems has resulted in demands for low-latency, secure, and energy-efficient intelligence on embedded chips. But most cloud-based and edge-assisted solutions are prone to high communication latency, lack adaptability, consume more energy, and lack decision security under resource-limited conditions. This paper introduces an Edge-Intelligent IoT Framework for Real-Time Adaptive Monitoring and Trust-Aware Secure Decision Validation with Resource-Aware Artificial Intelligence and Machine Learning (AI/ML) on embedded chips. Unlike conventional TinyML or Edge AI deployments that use a fixed inference model, the proposed framework introduces a validation-calibrated adaptive inference mechanism that jointly considers chip resources, input complexity, and sensor trust before accepting an embedded decision. The main scientific contribution is the unified coupling of resource-aware model selection with trust-aware decision validation for low-power embedded IoT inference. The framework dynamically selects the inference path and validates sensor trust before decision acceptance. Through experimentation, the proposed framework is demonstrated with 97.2% accuracy, 96.4% F1-score, and 98.1% AUROC, and 40.4% lower inference latency (31.2 ms to 18.6 ms) and 39.6% lower energy (9.6 mJ to 5.8 mJ) compared with traditional TinyML deployment. These results were obtained using the MHEALTH wearable IoT dataset with a leakage-safe 70:15:15 split and were statistically validated across five independent runs. The findings demonstrate a promising resource-aware TinyML-style embedded inference pipeline for wearable IoT monitoring, with improved latency-energy efficiency and trust-aware decision validation under the evaluated settings. Full article

One of the major challenges in securing medical image communication systems is the secure and efficient management of cryptographic key material. In this paper, we propose a multi-layer image encryption algorithm that addresses image security while reducing per-image key-storage and transmission overhead under a pre-shared protected-generator model. The proposed algorithm integrates a Generative Adversarial Network, a Piecewise Linear Chaotic Map, DNA complement operations, and bit-level zigzag permutation. A distinguishing feature of the proposed algorithm is that the key image is generated from an image-specific 100-dimensional noise vector, which serves exclusively as the input to the trained generator, while the chaotic parameters and diffusion materials are derived from the generated key image. In this approach, under the assumption of a pre-shared protected generator, transmitting only the image-specific 100-dimensional noise vector that bears no structural relationship to the key image reduces per-image key storage and transmission overhead. Comprehensive numerical evaluations were performed on eleven images, comprising both standard test images and medical images, to assess the security and robustness of the proposed algorithm. The experimental results demonstrate entropy values exceeding 7.996 bits, along with NPCR and UACI values of 99.60% and 33.46%, respectively. Adjacent pixel correlations are reduced to near-zero levels across all tested images. The proposed algorithm exhibits strong robustness against common attacks, including up to 75% cropping and 50% salt-and-pepper noise. The proposed algorithm achieves competitive performance compared with several existing encryption methods. Successful decryption requires the correct image-specific noise vector and the original trained generator. Full article

In the modern world, hybrid and fully remote work formats are becoming increasingly widespread. The volume of digital communication continues to grow, and the need to exchange documents and information through email, corporate messengers (such as Microsoft Teams), and collaborative workspaces is rising. Personal data is often involved in these exchanges, which increases the risk of unintentional disclosure. To comply with GDPR requirements and ensure information security, it is necessary to implement methods for the automatic detection of personal data. This task is particularly relevant for low-resource languages, such as Latvian, for which existing tools often operate with limited accuracy and efficiency. This work proposes a hybrid approach to the automatic detection of personal data in Latvian texts from Microsoft Teams messages, emails, and documents, combining a transformer-based NER model with rule-based detection of structured identifiers. The approach builds on a multilingual NER model, supplements it with Latvian-specific rules and structured-identifier detectors, and demonstrates the potential of adapted solutions to improve the accuracy and robustness of personal-data detection in real-world workflows. Full article