Search Results (1,744)

Named Data Networking (NDN) represents a promising Information-Centric Networking architecture that addresses limitations of traditional host-centric Internet protocols by emphasizing content names rather than host addresses for communication. While NDN offers advantages in content distribution, mobility support, and built-in security features, its stateful forwarding plane introduces significant vulnerabilities, particularly Interest Flooding Attacks (IFAs). These IFA attacks exploit the Pending Interest Table (PIT) by injecting malicious interest packets for non-existent or unsatisfiable content, leading to resource exhaustion and denial-of-service attacks against legitimate users. This survey examines research advances in IFA detection and mitigation from 2013 to 2024, analyzing seven relevant published detection and mitigation strategies to provide current insights into this evolving security challenge. We establish a taxonomy of attack variants, including Fake Interest, Unsatisfiable Interest, Interest Loop, and Collusive models, while examining their operational characteristics and network performance impacts. Our analysis categorizes defense mechanisms into five primary approaches: rate-limiting strategies, PIT management techniques, machine learning and artificial intelligence methods, reputation-based systems, and blockchain-enabled solutions. These approaches are evaluated for their effectiveness, computational requirements, and deployment feasibility. The survey extends to domain-specific implementations in resource-constrained environments, examining adaptations for Internet of Things deployments, wireless sensor networks, and high-mobility vehicular scenarios. Five critical research directions are proposed: adaptive defense mechanisms against sophisticated attackers, privacy-preserving detection techniques, real-time optimization for edge computing environments, standardized evaluation frameworks, and hybrid approaches combining multiple mitigation strategies. Full article

The increase in the number of wildfires in recent years in different parts of the world has caused growing concern among the population, since the consequences of these fires go beyond the destruction of the ecosystem. With the growing relevance of the Internet of Things (IoT) industry, developing solutions for the early detection of fires is of critical importance. This paper proposes a low-cost network based on Long-Range (LoRa) technology to autonomously assess the level of fire risk and the presence of a fire in rural areas. The system consists of several LoRa nodes with sensors to measure environmental variables such as temperature, humidity, carbon monoxide, air quality, and wind speed. The data collected is sent to a central gateway, where it is stored, processed, and later sent to a website for graphical visualization of the results. In this paper, a survey of the requirements of the devices and sensors that compose the system was made. After this survey, a market study of the available sensors was carried out, ending with a comparison between the sensors to determine which ones met the objectives. Using the chosen sensors, a study was made of possible power solutions for this prototype, considering the expected conditions of use. The system was tested in a real environment, and the results demonstrate that it is possible to cover a circular area with a radius of 2 km using a single gateway. Our system is prepared to trigger fire hazard alarms when, for example, the signals for relative humidity, ambient temperature, and wind speed are below or equal to 30%, above or equal to 30 °C, and above or equal to 30 m/s, respectively (commonly known as the 30-30-30 rule). Full article

Background: School-based tobacco control policies are critical for preventing youth tobacco use. While many districts adopt formal policies to create smoke- and vape-free environments, the degree to which these policies are enforced at the school level may vary, influencing their effectiveness. Little is known about how consistently such policies are implemented across schools within urban school districts. Objectives: This study aimed to examine the existence and enforcement of school-level tobacco control policies in an urban public school system, using Baltimore City schools as a case example. Methods: We conducted a survey of school personnel from 20 high schools in Baltimore City in 2024. The survey instrument assessed the presence and enforcement of policies related to tobacco use prevention, communication, signage, disciplinary actions, and institutional support. Descriptive statistics (frequencies and percentages) were used to summarize responses. Spearman correlations were also used for bivariate correlations. Additional school-level and neighborhood-level contextual data were collected from the internet (neighborhood socioeconomic status and school performance). Results: While many policies existed across the 20 participating schools, their enforcement was widely inconsistent. Most schools reported the existence of policies prohibiting tobacco use in school buildings (60%) and vehicles (55%). However, few schools had visible tobacco-free signage (35%) or offered cessation programs (15%). Communication of policies to students (70%) and staff (65%) was the most commonly enforced aspect of tobacco control policies. Conclusions: Findings suggest that while tobacco control policies may be adopted across urban school systems, their enforcement at the school level remains uneven. Greater attention may be needed to support policy implementation and to reduce variability in school-level practices. Baltimore City serves as a useful case study to understand these challenges and identify opportunities for strengthening school-based tobacco prevention efforts. Full article

Internet of Things (IoT)-based solar insecticidal lamps (SIL-IoTs) offer an eco-friendly alternative by merging solar energy harvesting with intelligent sensing, advancing sustainable smart agriculture. However, SIL-IoTs encounter practical challenges, e.g., hardware aging, electromagnetic interference, and abnormal data patterns. Therefore, developing an effective fault detection and diagnosis (FDD) system is essential. In this survey, we systematically identify and address the core challenges of implementing FDD of SIL-IoTs. Firstly, the fuzzy boundaries of sample features lead to complex feature interactions that increase the difficulty of accurate FDD. Secondly, the category imbalance in the fault samples limits the generalizability of the FDD models. Thirdly, models trained on single scenarios struggle to adapt to diverse and dynamic field conditions. To overcome these challenges, we propose a multi-level solution by discussing and merging existing FDD methods: (1) a data augmentation strategy can be adopted to improve model performance on small-sample datasets; (2) federated learning (FL) can be employed to enhance adaptability to heterogeneous environments, while transfer learning (TL) addresses data scarcity; and (3) deep learning techniques can be used to reduce dependence on labeled data; these methods provide a robust framework for intelligent and adaptive FDD of SIL-IoTs, supporting long-term reliability of IoT devices in smart agriculture. Full article

The adoption of digital technologies, such as the Internet of Things (IoT), has emerged as a key strategy to improve efficiency, sustainability, and productivity in the agricultural sector, especially in contexts of modernization and digital transformation in developing regions. This study analyzes the key factors influencing the adoption of IoT technologies by farmers in the province of Guayas, Ecuador, and their impact on agricultural yields. The research is grounded in innovation diffusion theory and technology acceptance models, which emphasize the role of perception, usability, training, and economic viability in digital adoption. A total of 250 surveys were administered, with 232 valid responses (92.8% response rate), reflecting strong interest from the agricultural sector in digital transformation and precision agriculture. Using structural equation modeling (SEM), the results confirm that general perception of IoT (β = 0.514), practical functionality (β = 0.488), and technical training (β = 0.523) positively influence adoption, while high implementation costs negatively affect it (β = −0.651), all of which are statistically significant (p < 0.001). Furthermore, adoption has a strong positive effect on agricultural yield (β = 0.795). The model explained a high percentage of variance in both adoption (R² = 0.771) and performance (R² = 0.706), supporting its predictive capacity. These findings underscore the need for public and private institutions to implement targeted training and financing strategies to overcome economic barriers and foster the sustainable integration of IoT technologies in Ecuadorian agriculture. Full article

This study examines the development of the digital economy and society in the Republic of Kazakhstan by combining international benchmarking with a context-specific national framework. It highlights the limitations of existing global indices such as DESI, NRI, and EGDI in capturing the structural and institutional dimensions of digital transformation in emerging economies. To address this gap, the study introduces a novel composite metric, the Index of Digital Economy Development (IDED), which integrates five sub-indices: infrastructure, usage, human capital, economic digitization, and transformation effectiveness. The methodology involves comparative index analysis, the construction of the IDED, and statistical validation through a public opinion survey and regression modeling. Key findings indicate that cybersecurity is a critical yet under-represented component of digital development, showing strong empirical correlations with DESI scores in benchmark countries. The results also highlight Kazakhstan’s strengths in digital public services and internet access, contrasted with weaknesses in business digitization and innovation. The proposed IDED offers a more comprehensive and policy-relevant tool for assessing digital progress in transitional economies. This study contributes to the literature by proposing a replicable index structure and providing empirical evidence for the inclusion of cybersecurity in national digital economy assessments. The aim of the study is to assess Kazakhstan’s digital economy development by addressing limitations in global measurement frameworks. Methodologically, it combines comparative index analysis, the construction of a national composite index (IDED), and statistical validation using a regional survey and regression analysis. The findings reveal both strengths and gaps in Kazakhstan’s digital landscape, particularly in cybersecurity and SME digitalization. The IDED introduces an innovative, context-sensitive framework that enhances the measurement of digital transformation in transitional economies. Full article

With the rapid proliferation of Internet of Things (IoT) devices and mobile applications and the growing demand for low-latency services, edge computing has emerged as a transformative paradigm that brings computation and storage closer to end users. However, the dynamic nature and limited resources of edge networks bring challenges such as load imbalance and high latency while satisfying user requests. Service migration, the dynamic redeployment of service instances across distributed edge nodes, has become a key enabler for solving these challenges and optimizing edge network characteristics. Moreover, the low-latency nature of edge computing requires that service migration strategies must be in real time in order to ensure latency requirements. Thus, this paper presents a systematic survey of real-time service migration in edge networks. Specifically, we first introduce four network architectures and four basic models for real-time service migration. We then summarize four research motivations for real-time service migration and the real-time guarantee introduced during the implementation of migration strategies. To support these motivations, we present key techniques for solving the task of real-time service migration and how these algorithms and models facilitate the real-time performance of migration. We also explore latency-sensitive application scenarios, such as smart cities, smart homes, and smart manufacturing, where real-time service migration plays a critical role in sustaining performance and adaptability under dynamic conditions. Finally, we summarize the key challenges and outline promising future research directions for real-time service migration. This survey aims to provide a structured and in-depth theoretical foundation to guide future research on real-time service migration in edge networks. Full article

Background and Objectives: Rumors, disinformation, and fake news are problems of contemporary society. We live in a world where the truth no longer holds much importance, and the line that divides the truth from lies, between real news and disinformation, becomes increasingly blurred and difficult to identify. The purpose of this study is to describe this concept, to draw attention to one of the “pandemics” of the 21st-century world, and to find methods by which we can defend ourselves against them. Materials and methods. A cross-sectional study was conducted based on a sample of 442 respondents. Results. For 77.8% of the people surveyed, the concept of “fake news” is important in Romania. Regarding trust in the mass media, a clear dominance (72.4%) was observed among participants who have little trust in the mass media. Although 98.2% of participants detect false information found on the internet, 78.5% are occasionally deceived by the information provided. Of the participants, 47.3% acknowledged their vulnerability to disinformation. The main source of disinformation is the internet, as 59% of the interviewed subjects believed. As the best measure against disinformation, the study group was divided almost equally according to the three possible answers, all of which were considered to be equally important: imposing legal restrictions and blocking the posting of certain news (35.4%), imposing stricter measures for authors (33.9%), and increasing vigilance among people (30.5%). Conclusions. According to the statistics based on the participants’ responses, the main purposes of disinformation are propaganda, manipulation, distracting attention from the truth, making money, and misleading the population. It can be observed that the main intention of disinformation, in the perception of the study participants, is manipulation. Full article

Electronic voting allows people to participate more easily in their country’s electoral events. Nevertheless, its adoption is still far from widespread. In this paper, we provide a detailed survey of the state of adoption worldwide and investigate which socio-economic factors may influence such an adoption. Its usage is wider in North and South America, while remaining considerably lower in Europe and Asia and practically absent in Africa. We distinguish between e-voting, which maintains the traditional polling station structure while adding technological components, and i-voting, which enables remote participation from any location using personal devices. Five factors (country’s surface and population, Gross Domestic Product, Internet Usage, and Democracy Index) are investigated to predict adoption, and an accuracy of over 79% is achieved through a machine learning random forest model. Larger, wealthier, and more democratic countries are typically associated with a larger adoption of internet voting. Full article

Background/Objectives: The rapid ageing of the European population presents growing challenges for mental health, highlighting the need to identify factors that can prevent or delay psychological decline and promote a higher quality of life in later life. This study aims to provide an updated and comprehensive overview of mental health among older adults in Europe by examining the prevalence of depressive symptoms and identifying key associated factors. Methods: We analysed data from individuals (n = 45,601) aged 65 years and older across 27 European countries and Israel who participated in Wave 9 of the Survey of Health, Ageing and Retirement in Europe (SHARE). This study assessed the prevalence of depressive symptoms, which were evaluated using the EURO-D scale (score range: 0–12), with a cut-off of ≥4 indicating clinically relevant symptoms. It also explored associations with sociodemographic characteristics, physical health, behavioural factors, social participation, internet skills and living conditions. Results: Our findings confirm that depressive symptoms remain highly prevalent among older adults in Europe, with 35.1% of women and 21.5% of men affected, reflecting persistent gender disparities in mental health. Depression in later life was significantly associated with poor physical health, loneliness and lower quality of life. Conversely, moderate involvement in grandchild care and in social participation emerged as potential protective factors. Conclusions: Late-life depression has substantial implications for both mental and physical well-being. Our findings suggest that social integration, gender related factors and physical health are closely associated with depressive symptoms in older adults. These associations highlight the importance of considering these domains when designing interventions and policies aimed at promoting mental health in ageing populations. Full article

The emergence of the Internet of Vehicles (IoV) has revolutionized intelligent transportation and communication systems. However, IoV presents many complex and ever-changing security challenges and thus requires robust cybersecurity protocols. This paper comprehensively describes and evaluates ensemble learning approaches for multi-class intrusion detection systems in the IoV environment. The study evaluates several approaches, such as stacking, voting, boosting, and bagging. A comprehensive review of the literature spanning 2020 to 2025 reveals important trends and topics that require further investigation and the relative merits of different ensemble approaches. The NSL-KDD, CICIDS2017, and UNSW-NB15 datasets are widely used to evaluate the performance of Ensemble Learning-Based Intrusion Detection Systems (ELIDS). ELIDS evaluation is usually carried out using some popular performance metrics, including Precision, Accuracy, Recall, F1-score, and Area Under Receiver Operating Characteristic Curve (AUC-ROC), which were used to evaluate and measure the effectiveness of different ensemble learning methods. Given the increasing complexity and frequency of cyber threats in IoV environments, ensemble learning methods such as bagging, boosting, and stacking enhance adaptability and robustness. These methods aggregate multiple learners to improve detection rates, reduce false positives, and ensure more resilient intrusion detection models that can evolve alongside emerging attack patterns. Full article

The increasing complexity and scale of Internet of Vehicles (IoV) networks pose significant security challenges, necessitating the development of advanced intrusion detection systems (IDS). Traditional IDS approaches, such as rule-based and signature-based methods, are often inadequate in detecting novel and sophisticated attacks due to their limited adaptability and dependency on predefined patterns. To overcome these limitations, machine learning (ML) and deep learning (DL)-based IDS have been introduced, offering better generalization and the ability to learn from data. However, these models can still struggle with zero-day attacks, require large volumes of labeled data, and may be vulnerable to adversarial examples. In response to these challenges, Generative AI-based IDS—leveraging models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers—have emerged as promising solutions that offer enhanced adaptability, synthetic data generation for training, and improved detection capabilities for evolving threats. This survey provides an overview of IoV architecture, vulnerabilities, and classical IDS techniques while focusing on the growing role of Generative AI in strengthening IoV security. It discusses the current landscape, highlights the key challenges, and outlines future research directions aimed at building more resilient and intelligent IDS for the IoV ecosystem. Full article

This paper provides a comprehensive survey of 58 lightweight block ciphers (LWBCs) introduced between 2018 and 2025, designed specifically for securing resource-constrained environments such as the Internet of Things (IoTs). The ciphers are systematically categorized into five structural classes: substitution-permutation network (SPN), Feistel network (FN), generalized Feistel network (GFN), addition-rotation-XOR (ARX), and hybrid architectures. For each cipher, key characteristics—block size, key length, structural design, number of rounds, implementation cost in gate equivalents (GEs), and known limitations—are analyzed in detail. The study offers an in-depth comparative assessment of performance, security, and implementation efficiency, providing a clear understanding of design trade-offs and cryptographic innovations. By consolidating and evaluating recent advancements in lightweight cryptography, this survey fills a crucial gap in the literature. It equips researchers, engineers, and system designers with the insights needed to make informed decisions when selecting or developing efficient cryptographic solutions tailored for modern IoTs systems. Its comprehensive scope and practical relevance make it an essential reference for advancing secure, lightweight cryptographic implementations in an increasingly connected world. Full article

The convergence of next-generation wireless communication technologies and modern energy infrastructure presents a promising path toward sustainable and intelligent systems. This survey explores how beyond-5G and 6G communication technologies can support the greening of Industrial Internet of Things (IIoT) systems and smart grids. It highlights the critical challenges in achieving energy efficiency, interoperability, and real-time responsiveness across different domains. The paper reviews key enablers such as LPWAN, wake-up radios, mobile edge computing, and energy harvesting techniques for green IoT, as well as optimization strategies for 5G/6G networks and data center operations. Furthermore, it examines the role of 5G in enabling reliable, ultra-low-latency data communication for advanced smart grid applications, such as distributed generation, precise load control, and intelligent feeder automation. Through a structured analysis of recent advances and open research problems, the paper aims to identify essential directions for future research and development in building energy-efficient, resilient, and scalable smart infrastructures powered by intelligent wireless networks. Full article

This study examines whether China’s rapid spread of internet and mobile information technologies has translated into greater household support for government air-quality programs. Using nationally representative data from the Chinese General Social Survey (2018), this study estimates the causal impact of digital media use on residents’ willing to pay (WTP) each month for one additional “good-air” day. Ordinary least squares shows that individuals who rely primarily on the internet or mobile push services are willing to contribute CNY 1.9–2.7 more—about 43 percent above the sample mean of CNY 4.41. To address potential endogeneity, we instrumented digital media adoption using provincial computer penetration; two-stage least squares yielded roughly CNY 10.5, confirming a causal effect. Mechanism tests showed that digital access lowers complacency about local air quality, strengthens anthropogenic attribution of pollution, and heightens the moral norm that economic sacrifice is legitimate, jointly mediating the rise in WTP. Heterogeneity analyses revealed stronger effects among high-income households and renters, while extended tests showed that (i) the impact intensifies when the promised environmental gain rises from one to three or five clean-air days, (ii) attention to international news can crowd out local WTP, and (iii) digital media raise not only the likelihood of paying but also the amount paid among existing contributors. The findings suggest that targeted digital outreach—especially messages with concrete, locally salient goals—can substantially enlarge the fiscal base for air-quality initiatives, helping China advance its ecological-civilization and dual-carbon objectives. Full article