Search Results (122)

The Social Internet of Things (SIoT) combines social networks and the Internet of Things, enabling closer interaction between devices, users, and services. However, this interaction brings risks of trust attacks. These trust attacks not only affect the stability of SIoT systems but also threaten personal privacy and data security. This paper provides a decade-long review of SIoT trust attack research. First, it outlines the SIoT architecture, social relationship types, concept of trust, and trust management processes. It maps seven attacks—bad mouthing attack (BMA), ballot stuffing attack (BSA), self-promoting attack (SPA), discriminatory attack (DA), whitewashing attack (WWA), on-off attack (OOA), and opportunistic service attack (OSA)—clarifying their mechanisms and traits. Next, we synthesize the literature on SIoT trust models, enumerate which attack types they address, and classify defense strategies. It then conducts simulation-based comparative experiments on trust attacks to reveal their impact on node trust and transaction processing, compares attack capabilities along disruption speed, attack strength, and stealthiness, and summarizes attack surfaces with corresponding defense recommendations to better guide the design of SIoT trust management schemes. Finally, we identify open challenges and future research directions, to support the development of new trust management models better equipped to address evolving trust attacks. Full article

The metaverse represents a transformative integration of virtual and physical worlds, offering unprecedented opportunities for social interaction, commerce, education, healthcare, and entertainment. Establishing trust in these expansive and decentralized environments remains a critical challenge. Blockchain technology, with its decentralized, secure, and immutable nature, is emerging as an essential pillar of trust and digital asset ownership within the metaverse. This paper provides an extensive review of blockchain-enabled trust and reputation frameworks specifically tailored to metaverse ecosystems. We present an in-depth analysis of existing blockchain solutions across diverse metaverse domains, including gaming, virtual real estate, healthcare, and education. Our core contributions include a comprehensive taxonomy that classifies current trust and reputation schemes by their underlying mechanisms, threat models addressed, and their architectural strategies. We provide a comparative benchmark analysis evaluating key performance metrics such as security robustness, scalability, user privacy, and cross-platform interoperability, revealing critical trade-offs inherent in current designs. Our analysis finds that score-based designs trade scalability for nuanced reputation representation, while SSI- and SBT-based approaches improve Sybil-resistance but introduce significant privacy governance challenges. Finally, we outline unresolved research challenges, including cross-platform reputation portability, privacy-preserving computation, real-time trust management, and standardized governance structures. Full article

Accelerating the diffusion of renewable energy requires clear evidence on which determinants enable or hinder deployment across contexts. This study aims to identify the most frequently discussed contemporary determinants of renewable energy deployment. To this end, we conduct a PRISMA-guided systematic review within the SALSA framework, complemented by VOSviewer bibliometric mapping, synthesizing 110 peer-reviewed studies published between 2013 and 2025. We group the most frequently examined determinants into eight domains (economic, environmental, energy, political, regulatory, regional, technological, and social) and summarize the prevalent direction of effect reported in the literature. Economic conditions (e.g., economic growth, financial development, green finance, and trade) and policy/regulation (e.g., institutional quality, instrument stringency, and feed-in and net-billing schemes) emerge as pivotal. Environmental co-benefits (emissions reduction and air quality improvements) and energy system factors (security and energy poverty) are influential, with context-dependent roles for fossil fuel prices and consumption. Regional context (e.g., geopolitical risk) and technological progress (eco-innovation, storage, and grid integration) shape outcomes, while public acceptance, awareness, perceived benefits/costs, and demographics condition uptake. We also document contradictory findings (e.g., foreign direct investment and oil price effects) and gaps (especially social/demographic determinants and causal evaluation of specific policies). Overall, the review offers a coherent synthesis of evidence and an actionable framework of determinants to inform policy design and investment targeting for large-scale diffusion of renewable energy technologies. Full article

JPEG images are widely used in multimedia transmission, such as on social media platforms, owing to their efficiency for reducing storage and transmission requirements. However, because such images may contain sensitive information, encryption is essential to ensure data privacy. Traditional image encryption schemes face challenges when applied to JPEG images, as maintaining compatibility with the JPEG structure and managing the effects of lossy compression can distort encrypted data. Existing JPEG-compatible encryption methods, such as Encryption-then-Compression (EtC) and Compression-then-Encryption (CtE), typically employ a single encryption stage, either before or after compression, and often involve trade-offs between security, storage efficiency, and visual quality. In this work, an Encryption–Compression–Encryption algorithm is presented that preserves full JPEG compatibility while combining the advantages of both EtC and CtE schemes. In the proposed method, pixel-block encryption is first applied prior to JPEG compression, followed by selective coefficient encryption after compression, in which the quantized DC coefficient differences are permuted. Experimental results indicate that the second encryption stage enhances the entropy achieved in the first stage, with both stages complementing each other in terms of resistance to attacks. The addition of this second layer does not significantly impact storage efficiency or the visual quality of the decompressed image; however, it introduces a moderate increase in computational time due to the two-stage encryption process. Full article

Armed conflicts continue to severely impact human populations and essential infrastructure, particularly water supply systems. This study examines the Yechilla area, a high-intensity battle corridor during the Tigray (between 12°15′26″ 14°57′49″ N latitude; and 36°20′57″–39°58′54″ E longitude) war (2020–2022). Using Cochran’s formula, a representative sample of 89 water schemes was selected for onsite assessment. Additional data on damages to water offices, personnel, equipment, and related infrastructure were gathered through face-to-face interviews with local officials and water professionals, onsite visits, and reviews of governmental and non-governmental archives, and previous studies. The findings reveal that 48.3% of water schemes in the study area are non-functional (does not deliver water), which is a significant increase from pre-war non-functionality rates of approximately 7.1% regionally and 21.1% nationally. Despite the Pretoria peace agreement, non-functionality levels remain critically high two years after conflict. Damage includes partial impairments, lack of technical and spare part support, complete destruction, and looting of water scheme components. The widespread destruction of civilian water infrastructure during the Tigray conflict underscores the insufficiency of existing international legal frameworks, such as the International Humanitarian Law and International Water Law, which are inadequately protecting civilians and their property. Understanding the broader consequences of armed conflicts requires examining the indirect effects and the complex interactions within and between social, economic, and environmental systems. These interconnected impacts are essential to fully grasp how conflict affects livelihoods and human security on a wider scale. Full article

This study examines the portrayal of Ukrainian refugee women in Polish media, specifically in Gazeta Wyborcza and Rzeczpospolita, over three years of the full-scale Russian–Ukrainian war (24 February 2022–24 February 2025). Using an interdisciplinary approach that combines gender studies, critical discourse analysis, and migration studies, the research aims to identify dominant narratives, stereotypes, and framing strategies in media representations of Ukrainian refugee women. A mixed-methods analysis was conducted, incorporating both qualitative and quantitative techniques. A total of 235 articles were manually examined and categorized using a coding scheme that analyzed attitude of voice (positive, neutral, negative), dominant narratives (e.g., victimhood, resilience, economic impact), descriptors (e.g., hard-working, dependent, caring), and framing strategies (humanitarian, security, economic, cultural). Additionally, the study examined how a range of contextual factors—including political discourse, economic conditions, and social dynamics—influenced the framing and specificity of media coverage related to Ukrainian women refugees in Poland. The findings reveal key trends in the representation of Ukrainian refugee women, including their portrayal as victims, economic contributors, or burdens to society. By analyzing the construction of these narratives, this research contributes to a broader understanding of media-driven stereotypes and their impact on social attitudes and policymaking in Poland. Full article

The China–Russia northeast–far east transboundary region is ecologically complex and economically promising, but fragmented cross-border management poses challenges to ecological security and regional sustainable development. To scientifically reveal functional differentiation and support bilateral cooperation, this study established a comprehensive evaluation system comprising 21 indicators across five categories: natural, ecological, economic, social, and resource. Using the Partitioning Around Medoids (PAM) clustering algorithm at the grid scale, eight initial clusters with distinct eco-economic characteristics across administrative boundaries were identified. Based on these results, spatial patterns were refined using expert knowledge from both China and Russia, ultimately delineating ten core eco-economic functional zones. The study finds that (1) the results of the eco-economic zoning scheme reveal clear spatial functional differentiation, with the northern part of the region focusing on ecological conservation and resource development, and the southern part on agricultural and forestry production as well as port trade; and (2) China and Russia show significant differences in natural resource endowments, infrastructure levels, and population distribution, indicating strong potential for functional complementarity and coordinated development. Further, this study breaks through traditional administrative-unit-based zoning approaches and proposes a grid-scale eco-economic zoning scheme across administrative boundaries, providing spatial support for ecological protection, resource development, and regional governance in the border areas between China and Russia. The findings may also serve as a methodological reference and practical demonstration for eco-economic zoning scheme and coordinated management in other complex transboundary regions around the world. Full article

Medical insurance fraud, especially organized drug resale schemes, has become increasingly sophisticated, challenging traditional supervision methods. This paper presents an AI-powered legal supervision model that automatically detects fraudulent drug resale activities in medical insurance claims. Unlike rule-based approaches, our solution employs multi-dimensional behavioral analysis and adaptive clustering techniques to identify both individual anomalies and organized fraud networks. The proposed model follows a three-stage detection pipeline: (1) automated clue generation through feature aggregation across frequency, cost, and behavioral dimensions; (2) group behavior analysis using spatiotemporal patterns and medication similarity metrics; (3) risk stratification via FLASC clustering to dynamically determine suspicion thresholds. Key innovations include a data-driven threshold generation mechanism that eliminates expert bias and a cross-dimensional fraud pattern recognition system that connects individual outliers with group behaviors. Validated on real-world medical insurance data (8917 insurance cards, 1.1 million records), the model achieved 89% precision, 42% recall, and 87% accuracy in detecting high-risk fraud cases while uncovering previously unnoticed organized fraud rings. This research provides a scalable framework for intelligent healthcare fund supervision, with potential applications in other social security domains. Full article

The Social Opportunistic Internet of Things (SO-IoT) is a rapidly emerging paradigm that enables mobile, ad-hoc device communication based on both physical and social interactions. In such networks, routing decisions heavily depend on the selection of intermediate nodes to ensure secure and efficient data dissemination. Traditional approaches relying solely on reliability or social interest fail to capture the multifaceted trustworthiness of nodes in dynamic SO-IoT environments. This paper proposes a trust-based route optimization framework that integrates social interest and behavioral reliability using Bayesian inference and Jeffrey’s conditioning. A composite trust level is computed for each intermediate node to determine its suitability for data forwarding. To validate the framework, we conduct a two-phase simulation-based analysis: a scenario-driven evaluation that demonstrates the model’s behavior in controlled settings, and a large-scale NS-3-based simulation comparing our method with benchmark routing schemes, including random, greedy, and AI-based protocols. Results confirm that our proposed model achieves up to an $88.9\%$ delivery ratio with minimal energy consumption and the highest trust accuracy ($86.5\%$), demonstrating its robustness and scalability in real-world-inspired IoT environments. Full article

Rapid socio-economic development has intensified the conflict between supply and demand for regional water resources, necessitating optimized water resource allocation to enhance water security. This study establishes a multi-objective water resource optimization model by comprehensively considering economic, social, and ecological benefits. Based on the Non-dominated Sorting Genetic Algorithm-III (NSGA-III), we propose the I-NSGA-III algorithm by integrating reference point improvement strategies, dynamic retention of high-quality solutions, and optimized selection strategies to solve the multi-objective optimization model. A multi-system coupling coordination evaluation model is constructed to assess the final allocation schemes. Compared with some commonly used multi-objective algorithms and tested using the DTLZ series functions, the proposed algorithm demonstrates improved overall performance. Specifically, the IGD indicator decreases by 5.17–50.22%, and the HV indicator increases by 2.71–25.51% compared to NSGA-III. The proposed model is applied to Jinzhong City, China, with four scenarios set for the years 2030 and 2035 at P = 50% and P = 75% to derive reasonable water resource allocation schemes. The results show that the economic benefits range from 161.94 × 10⁸ to 212.74 × 10⁸ CNY, the water shortage rate is controlled between 1.38% and 10.86%, and COD emissions are maintained between 6.03 × 10⁴ and 6.91 × 10⁴ tons. Except for the 2030 drought scenario (P = 75%) with a coordination degree of 0.7847, classified as a medium coordination level, all other scenarios have coordination degrees greater than 0.8, indicating a good coordination level. The optimized allocation scheme can serve as a reference for the rational allocation of water resources in Jinzhong City. Moreover, the method proposed in this paper is a general approach that can be extended to other similar water-scarce cities with appropriate parameter adjustments, contributing to the sustainable development of urban water resources. Full article

The widespread use of smart devices, such as phones and live-streaming cameras, has ushered in an era where digital images can be captured and shared on social networks anytime and anywhere. Sharing images demands more bandwidth and stricter security than text. This prevalence poses challenges for secure image forwarding, as it is susceptible to privacy leaks when sharing data. While standard encryption algorithms can safeguard the privacy of textual data, image data entail larger volumes and significant redundancy. The limited computing power of smart devices complicates the encrypted transmission of images, creating substantial obstacles to implementing security policies on low-computing devices. To address privacy concerns regarding image sharing on social networks, we propose a lightweight data forwarding mechanism for resource-constrained environments. By integrating large-scale data forwarding with visual cryptography, we enhance data security and resource utilization while minimizing overhead. We introduce a downsampling-based non-expansive scheme to reduce pixel expansion and decrease encrypted image size without compromising decryption quality. Experimental results demonstrate that our method achieves a peak signal-to-noise ratio of up to 20.54 dB, and a structural similarity index of 0.72, outperforming existing methods such as random-grid. Our approach prevents size expansion while maintaining high decryption quality, addressing access control gaps, and enabling secure and efficient data exchange between interconnected systems. Full article

In this digital era, social images are the most vital information carrier on multimedia social platforms. More and more users are interested in sharing social images with mobile terminals on multimedia social platforms. Social image sharing also faces potential risks from malicious use, such as illegal sharing, piracy, and misappropriation. This paper mainly concentrates on secure social image sharing. To address how to share social images in a safe way, a social image security scheme is proposed. The technology addresses the social image security problem and the active tracing problem. First, discrete wavelet transform (DWT) is performed directly from the JPEG image. Then, the high-bit planes of the LL, LH, and HL are permuted with cellular automation (CA), bit-XOR, and singular value decomposition (SVD) computing, and their low-bit planes are chosen to embed a watermark. In the end, the encrypted and watermarked image is again permuted with cellular automation in the discrete cosine transform (DCT) domain. Experimental results and security analysis show that the social image security method not only has good performance in robustness, security, and time complexity but can also actively trace the illegal distribution of social images. The proposed social image security method can provide double-level security for multimedia social platforms. Full article

Exploring the spatiotemporal evolution pattern of agricultural functions, analyzing their tradeoff and synergies, carrying out multifunctional zoning, and determining the combination and expansion direction of agricultural functions in combination with regional development strategies are conducive to guiding the adjustment of agricultural structure and promoting the sustainable development of regional agriculture. In this context, based on the county scale and statistical data, this paper uses the agricultural function evaluation index system to measure and analyze the agricultural function index of Sichuan Province and its mutual relations. Spatial overlay analysis is used to analyze the agricultural function index for agricultural leading function zoning. Cluster analysis is used to evaluate the agricultural function results to explore the agricultural multifunctional zoning scheme of Sichuan Province. The results show that the spatial and temporal distribution of agricultural product supply, agricultural leisure, ecological services, employment and social security services are heterogeneous, and the agricultural multifunction index of Sichuan Province shows a spatial distribution pattern of high in the east and low in the west. The synergistic effect between the supply function of agricultural products and the function of employment and life security is the strongest. From 2010 to 2020, the relationships between the ecological service function and the supply function of agricultural products, the agricultural leisure function, employment, and the social security function change from irrelevant or there being a tradeoff effect to there being a significant synergistic effect. The leading areas of the ecological service function are mainly distributed in western and northern Sichuan. The leading areas of the agricultural product supply function are mainly distributed in eastern and southern Sichuan. Agricultural multifunctional zoning in Sichuan Province is divided into the agricultural leisure function, the agricultural product supply cooperative functional area, the weak cooperative functional area, the strong cooperative functional area, and the agricultural leisure priority functional area. The spatiotemporal heterogeneity of agricultural functions and the changes in tradeoffs and synergies in Sichuan Province have a significant impact on the development of agricultural functions. The research results can provide s theoretical reference for agricultural multifunctional zoning in the study area and provide guidance and suggestions for the sustainable development of agricultural economy and society in Sichuan Province. Full article

As the indispensable basic resource of agricultural production, cultivated land has always carried the important mission of maintaining food stability, promoting rural economic development, and maintaining ecological balance. However, in application, there is often a conflict between the multiple functions of cultivated land and the limited ability of cultivated land to perform multiple functions. Therefore, this paper uses hot spot analysis, the IUEMS model, the InVEST model, Pearson correlation coefficients and self-organizing feature maps (SOFMs) to explore the multifunctional trade-offs and synergistic relationships of cultivated land in the Hexi Corridor at the grid scale and the zoning optimization scheme. The results revealed that from 2000 to 2020, the cultivated land production functions and social security functions in the Hexi Corridor maintained a high level and continued to rise, and the hot spots exhibited a stable pattern of “central and southeast concentration”. The ecological function performance is relatively weak, and the hot spots are concentrated mainly in the southeast, whereas the landscape view recreational functions as a whole show a trend of gradual recovery after weakening. In terms of mutual relationships, there are significant synergies between cultivated land production and social security functions, whereas the trade-offs and synergies between other functions are complex and changeable. Production and social security show a coordinated spatial distribution pattern. Production, social security, and ecological functions are dominated by spatial trade-offs. The production and landscape recreation functions, social security and ecological functions, social security and landscape recreation functions, and ecological and landscape recreation functions are mainly synergistic in space. Through self-organizing feature map analysis, the cultivated land in the Hexi Corridor is divided into four functional areas: agricultural production-dominant areas, agricultural social security areas, ecological agriculture areas, and balanced development areas, and management objectives are proposed. This study can provide useful lessons and references for land use planning and management in other similar areas. Full article

Cloud-to-Vehicle (C2V) integration serves as a fundamental infrastructure to provide robust computing and storage support for Vehicular Social Networks (VSNs). However, the proliferation of sensitive personal data within VSNs poses significant challenges in achieving secure and efficient data sharing while maintaining data usability and precise retrieval capabilities. Although existing searchable attribute-based encryption schemes offer the secure retrieval of encrypted data and fine-grained access control mechanisms, these schemes still exhibit limitations in terms of bilateral access control, dynamic index updates, and search result verification. This study presents a Dual-Policy Attribute-based Searchable Encryption (DP-ABSE) scheme with dynamic keyword update functionality for VSNs. The scheme implements a fine-grained decoupling mechanism that decomposes data attributes into two distinct components: immutable attribute names and mutable attribute values. This decomposition transfers the attribute verification process from data owners to the encrypted files themselves, enabling data attribute-level granularity in access control. Through the integration of an identity-based authentication mechanism derived from the data owner’s unique identifier and bilinear pairing verification, it achieves secure updates of the specified keywords index while preserving both the anonymity of the non-updated data and the confidentiality of the message content. The encryption process employs an offline/online two-phase design, allowing data owners to pre-compute ciphertext pools for efficient real-time encryption. Subsequently, the decryption process introduces an outsourcing local-phase mechanism, leveraging key encapsulation technology for secure attribute computation outsourcing, thereby reducing the terminal computational load. To enhance security at the terminal decryption stage, the scheme incorporates a security verification module based on retrieval keyword and ciphertext correlation validation, preventing replacement attacks and ensuring data integrity. Security analysis under standard assumptions confirms the theoretical soundness of the proposed solution, and extensive performance evaluations showcase its effectiveness. Full article