Search Results (7,845)

Energy industrial zones can act as a transformative model for industrial decarbonization by integrating renewable energy infrastructure directly with industrial production. By combining energy industrial zones with the energy community framework and peer-to-peer (P2P) energy trading, this study proposes a pathway toward 100% renewable energy sources. The model was tested using a techno-economic assessment applied to the Bravar-Jasenice case study in Croatia featuring 12 MW of solar PV, 10 MW of wind power, and a 9.3 MW biogas cogeneration plant. This integrated approach can achieve 80–90% energy self-sufficiency and reduce electricity expenditures for participating enterprises by approximately 15%. Furthermore, the system facilitates an annual reduction of roughly 20,000 tonnes of CO₂ emissions, thus directly supporting European Green Deal objectives. The study also highlights the potential for industrial symbiosis, including green hydrogen production, data centre integration, and waste heat recovery. Ultimately, the proposed framework provides a robust strategy for enhancing industrial competitiveness and ensuring energy security through localized, sustainable energy management. Full article

Sustainable agricultural development in natural heritage sites poses a challenge, requiring food security without compromising the conservation of ecosystems and their outstanding universal values (OUV). The Galapagos Islands, recognized as a Natural World Heritage, have problems of scarce water and arable land, compounded by anthropogenic pressures such as high population and tourism growth and dependence on food imports. The objective of this research is to evaluate the environmental impacts of implementing agricultural greenhouses in the Galapagos by applying a traditional environmental matrix alongside a UNESCO World Heritage approach, integrated with a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, to formulate strategies for strengthening local agriculture without compromising ecosystems. This study employed a semi-quantitative methodological approach, integrating three key aspects: (i) a baseline of agricultural information and water availability on the islands; (ii) an integrated Environmental Impact Assessment (EIA) approach to greenhouse implementation; and (iii) sustainable agricultural development and environmental impact mitigation strategies. The results of the traditional EIA and the UNESCO approach through the OUV showed negative impacts classified as insignificant to moderately significant. For the evaluated design, these impacts can be managed through the active participation of academia, the community, and government entities. However, their scalability depends on a more in-depth analysis of the potential long-term risks associated with the availability of natural resources, microplastic pollution, and the use of agrochemicals. Among the proposed strategies, the importance of monitoring water and soil quality and of agricultural and environmental education campaigns in the community was highlighted. This study presents agricultural greenhouses as well-known alternatives for food self-sufficiency, adapted to the realities of the island territory and the objectives of ecosystem conservation. The proposed methodological approach can be applied in protected areas to promote conservation and sustainable agricultural production. Full article

This study examines how climate change interacts with social, ecological, and policy factors to shape food security among Indigenous tribal communities in Jharkhand, focusing on Saraikela Kharsawan district. It combines a scoping review, policy analysis, and a climate–agriculture case study of Saraikela Kharsawan to identify vulnerabilities and pathways for more resilient Indigenous food systems. The research is qualitative, using a scoping review of 28 studies on Indigenous food security and climate impacts in Jharkhand, thematic analysis of nine national and state policies, and a district-level case study using land use, climate trends/projections, and crop statistics for Saraikela Kharsawan. Additionally, findings from participant observation were integrated into how tribal communities in Saraikela Kharsawan experience and respond to climate variability and its implications for local food systems and nutrition. The study identifies a nutrition paradox, where Indigenous communities experience micronutrient deficiencies and anaemia despite rich biodiversity and Indigenous knowledge. This is accompanied by a decrease in the consumption of nutrient-dense Indigenous foods and a predominance of rainfed monoculture rice cultivation. Marked by rising temperatures and erratic rainfall, climate variability is destabilising agroforestry systems, narrowing dietary options and reducing adaptive capacity. Additionally, policy and institutional gaps reveal fragmented support—strong rights laws and calorie-focused welfare schemes but weak integration of Indigenous foods, agroforestry, and traditional ecological knowledge into nutrition and climate programmes. The paper argues that climate change acts as a threat multiplier on already fragile Indigenous food systems and calls for nutrition-sensitive safety nets, community-based agroforestry, gender-inclusive Indigenous knowledge governance, and cross-sectoral policy alignment to support resilient, culturally appropriate food systems in Jharkhand. Full article

We investigate secure data transmission in a unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) sensing network, focusing on maximizing multi-sensor uplink secrecy capacity under practical power constraints and severe co-channel interference. Due to the coupled signal-to-interference-plus-noise ratio (SINR) expressions and the non-smooth secrecy-rate function, the formulated power allocation problem is highly nonconvex and mathematically challenging. To efficiently solve this problem, we exploit a novel mathematical reformulation by introducing a smooth approximation of the secrecy metric and developing a computationally efficient optimization framework based on sequential quadratic programming (SQP) with analytically derived gradients. The main strength of this framework lies in its low-complexity, deterministic nature, which eliminates the need for computationally exhaustive search heuristics while guaranteeing fast, stable convergence to a Karush–Kuhn–Tucker (KKT) point. Furthermore, we incorporate a robust worst-case eavesdropper modeling approach to guarantee secure communication under severe adversarial conditions. Numerical results demonstrate that the proposed method significantly improves sum secrecy performance compared to conventional equal-power and baseline allocation schemes, proving highly scalable for real-time data collection in environmental monitoring, smart cities, and surveillance applications. Full article

Smart Hospitals integrated within 6G edge networks aim to enhance hospital connectivity and operational efficiency by enabling intelligent and personalized e-health services and applications while optimizing resource utilization and maintaining a high degree of autonomy. Nevertheless, the interconnectivity and 6G integration, which comprise core components of Smart Hospitals, are susceptible to a wide range of security threats, posing significant risks to the confidentiality, integrity, and availability of hospital data and operations. Given that security is a critical concern for Smart Hospitals, there is an urgent need to develop novel security mechanisms to safeguard these environments within 6G edge networks. In particular, this work highlights how defining 6G characteristics, such as Ultra-Reliable Low-Latency Communications, massive IoMT connectivity, distributed edge intelligence, and AI-native network operation, not only enable next-generation hospital services but also reshape the security and privacy threat landscape and the requirements of mitigation mechanisms. In this context, the first essential step is to comprehensively understand both existing and emerging threats targeting Smart Hospitals in the 6G edge network ecosystem. Therefore, this article provides a categorization of security and privacy attacks based on their primary targets. Moreover, it presents a survey of mitigation techniques derived from recent literature, specifically designed to counter threats facing Smart Hospitals in 6G edge networks. The intent is to establish a foundation that supports ongoing research towards the development of effective, 6G-aware security countermeasures capable of protecting Smart Hospitals under the stringent latency, scalability, and reliability requirements of future healthcare environments. Full article

In this paper, we propose a lightweight authentication and identification protocol based on different phosphorescent Strontium aluminate color labels. The excitation sources are pulsed UV LEDs emitting at 365 nm and 385 nm, causing different RGB-dependent time responses of the label that are measured using a smartphone recording at 30 FPS. The rise and decay time responses as measured by the red (R), green (G) and blue (B) pixels were separately analyzed and were found to follow a power law with individual parameters depending on the excitation wavelength, pulse duration and duty cycle, which serve as security features and are suitable for authentication purposes in IoT applications. Our solution uses simple cryptographic functions such as HMAC and XOR. We performed a security analysis of our protocol to prove its resistance to known attack vectors. The proposed scheme has minimal computation and communication costs and can be deployed on resource-constrained Internet of Things devices. Full article

Federated learning (FL) is a promising paradigm for privacy-preserving IoT intrusion detection, but its effectiveness can be substantially degraded by the combination of heterogeneous non-IID client distributions and severe multi-class imbalance. Under such conditions, conventional size-based aggregation may overemphasize large yet highly skewed clients, limiting the representation of minority attack classes in the global model. To address this issue, we propose Mean/Std, a lightweight distribution-aware aggregation strategy that combines a client-size proxy with two complementary statistics of local label distributions, namely the standard deviation and the dominance gap of class proportions, while preserving a communication footprint comparable to FedAvg. Experiments on the N-BaIoT benchmark, comprising seven heterogeneous IoT clients and eleven traffic classes, are conducted under a privacy-oriented update-perturbation setting inspired by secure aggregation workflows. The results show that Mean/Std consistently provides the strongest imbalance-aware performance among the evaluated FL baselines, achieving a Macro-F1 score of 0.8418 and a Balanced Accuracy of 0.8722 while improving the representation of minority attack classes. Additional experiments across five independent random seeds and a comprehensive hyperparameter sensitivity analysis further confirm the robustness and stability of the proposed aggregation mechanism. Overall, the results demonstrate that lightweight distribution-aware aggregation offers an effective, robust, and practically deployable solution for mitigating aggregation bias under simultaneous non-IID heterogeneity and severe multi-class imbalance in FL-based IoT botnet detection. Full article

Winter cereals, which are vital for global food security in temperate regions, face severe challenges during overwintering due to the development of snow mold—a complex disease caused by different microorganisms that combine phytopathogenicity with cold tolerance. Even within a single field plot, individual plants exhibit significant variation in snow mold severity. This natural variation was exploited to achieve the aim of the present study—the comparison of microbiomes of healthy and diseased plants of winter cereal crops (rye, triticale, and wheat) at the peak of snow mold manifestation to interpret differential disease severity through differences in plant-associated microbial communities and to obtain information necessary for the biological control of snow mold. Fungi of the genus Herpotrichia were implicated as novel candidate causal agents of snow mold in winter cereals. Variations in snow mold severity defy simple explanations tied solely to pathogen abundance or broad changes in overall microbial community composition. Instead, the most striking contrast between healthy and diseased plants was observed in the inferred candidate hub taxa, accompanied by marked changes in exploratory co-occurrence networks involving the candidate snow mold pathogens. These network alterations were crop-specific. Several key taxa were implicated as probable influencers of snow mold dynamics. Full article

The widespread use of antimicrobial biocides and metals has led to the continuous accumulation of biocide and metal resistance genes (BMRGs) in the environment. The issue is of growing concern, as it reduces the efficacy of these agents and poses a potential threat to coastal ecological security. However, the extent of coastal BMRG pollution, its transmission mechanisms, and the influence of seasonal variations on its assembly remain poorly understood. In this study, metagenomic sequencing was employed to investigate BMRGs, microbiomes, and mobile genetic elements (MGEs) within the subtropical nearshore ecosystem of the Beibu Gulf during the autumn and winter seasons. A total of 33 BMRG types and 457 subtypes were detected, with higher subtype diversity in winter than in autumn (440 vs. 326 subtypes). Notably, genes resistant to multi-biocides exhibited the highest diversity, whereas those resistant to both biocides and metals were the most abundant. Co-occurrence network analysis showed that 22 of the 23 detected BMRGs in the winter network were associated with MGEs, especially transposase-related elements such as tnpA. Path modeling indicated that BMRG abundance was more strongly associated with bacterial community composition in autumn, whereas MGE-related variables showed stronger associations in winter. These findings suggest a pronounced seasonal shift in the underlying mechanisms shaping BMRG dynamics, with bacterial communities playing a dominant role in autumn and MGEs playing a more critical role in winter. This seasonal shift highlights the need for season-specific monitoring of BMRGs, coastal pollution control, and resistance-risk management in subtropical coastal ecosystems. Full article

Reliable state estimation is essential for the secure and efficient operation of sustainable energy systems, especially under the increasing integration of renewable energy, distributed resources, and heterogeneous sensing devices. However, in practical power systems, SCADA, PMU, and AMI measurements often have different sampling rates, accuracies, communication delays, and availability levels, which makes reliable data completion and multi-source fusion difficult. This paper focuses on the state estimation problem of renewable-integrated distribution networks under multi-source heterogeneous measurement conditions. In such distribution networks, the increasing penetration of distributed renewable energy resources and the joint deployment of multiple measurement devices, including SCADA, PMU, and AMI, may lead to incomplete measurements, asynchronous sampling, differences in measurement accuracy, and reduced system observability. To address these issues, this paper proposes a model-based digital twin reference-guided physics-constrained DDPM framework to improve the quality of missing-measurement completion and the reliability of state estimation in distribution-network scenarios. A four-layer simulation-oriented cyber–physical framework is first constructed to integrate physical sensing, model-based digital twin reference mapping, AI-based measurement completion, and state estimation feedback. Within this framework, a physics-constrained self-supervised denoising diffusion probabilistic model is developed to recover missing measurements by combining observed data, digital twin reference measurements, real-time topology information, and power system operational constraints. The completed pseudo-measurements and physical measurements are then fused through a credibility-aware weighting strategy that considers timeliness, data integrity, measurement accuracy, and virtual–real consistency verification under simulation settings. Simulation results on the IEEE 14-bus system show that the proposed method improves pseudo-measurement completion and supports more reliable voltage magnitude and phase angle estimation under different measurement configurations. Under the tested simulation settings and multi-source measurement configurations, the results indicate that the proposed method can improve pseudo-measurement completion and support more reliable voltage magnitude and phase angle estimation. However, its performance under frequent topology switching, high missing-data ratios, and complex abnormal data conditions remains to be further evaluated. Full article

Tiered Mobile Wireless Sensor Networks (TMWSNs), consisting of mobile sensor nodes and storage nodes, are widely used in various fields due to their scalability, energy efficiency, and flexibility. Most existing secure query algorithms assume that data packets generated by sensor nodes can always be delivered to storage nodes. This assumption does not hold in practice, where packets may be lost due to attacks or adverse communication conditions. This paper proposes a loss-tolerant wireless network model for TMWSNs and a novel threat model tailored to this scenario, in which packet-dropping attacks compromise the integrity of query results. To counter these attacks, we present a baseline integrity verification algorithm, the Neighbor Node-Forwarding Digest Algorithm (NNFDA). Each sensor generates a digest of its data and forwards it to neighboring nodes. These digests are then transmitted to storage nodes together with the neighbors’ data, thereby establishing a chained relationship among sensor data. The base station verifies query results using this relationship. The baseline algorithm, however, causes high communication overhead. To reduce this cost, we propose an improved version, NNFDA-BM (NNFDA with Bitmap), which optimizes digest generation and transmission. Experimental results show that NNFDA-BM verifies query result integrity effectively while achieving a significant reduction in communication overhead compared with the baseline algorithm. Full article

Cyber-physical power systems face multi-stage attacks that exploit both communication-network topology and power-grid interdependencies to reach critical substations from low-security entry points. Attack graphs systematically enumerate multi-step attack paths. However, existing approaches either ignore physical network topology or separate attack-graph construction from defense placement, limiting operational usefulness. This paper presents an enhanced topology-aware greedy (TAG) framework that couples source-to-critical attack-path search with dual-mode cyber defense and explicit cyber-physical interdependency modeling. A hierarchical attack graph is constructed directly on the physical network graph, encoding compromise probabilities conditioned on both cyber vulnerability profiles and power-grid criticality. TAG employs topology-aware candidate screening, deterministic probabilistic propagation, beam search, and one-swap local refinement, followed by a dual-mode defense package combining node hardening, micro-segmentation, and monitored-neighbor shielding. Monte Carlo experiments on the 179-bus medium-voltage feeder, IEEE 39-bus New England, IEEE 118-bus, and RTS-96 benchmarks demonstrate that TAG reduces critical-reach probability by $54.3$–$82.0\%$ versus no-defense baselines (mean $70.5\%$), and by $30.2$–$51.5\%$ versus vanilla greedy placement. A cyber-physical impact-weighted risk analysis further shows that TAG’s structural defense placement yields proportional reductions in power-flow-consequence-weighted risk. Parameter sensitivity across the segmentation/containment factor $\eta \in [0.30,0.80]$ and the monitored-neighbor shielding factor $\rho \in [0.05,0.30]$ confirms robust method superiority (68–$72\%$ risk reduction across all tested values). Full article

To address the stringent demands of intelligent connected vehicles for high bandwidth, low latency, and highly reliable communication, this paper systematically summarizes the semantic communication technology of the Internet of Vehicles (IoV) based on information “meaning” transmission, covering basic theory, key technologies, application practice and challenge and trends. First, the paper expounds the knowledge driven and task oriented paradigm characteristics of semantic communication and its efficiency advantages in the IoV. Second, in terms of key technologies, semantic extraction achieves efficient feature compression through multimodal fusion and Generative Artificial Intelligence (GAI); semantic coding employs hierarchical codebooks and adaptive strategies to optimize transmission efficiency; semantic transmission leverages deep reinforcement learning for the joint scheduling of resources such as spectrum and power; and semantic decoding utilizes reconstruction networks and GAI to enhance resilience against impairments. Application practices demonstrate that semantic communication can significantly compress image data transmission volume for autonomous driving collaborative perception while maintaining high-fidelity reconstruction under adverse channel conditions. It significantly reduces the communication load and improves the system utility in vehicle-to-infrastructure coordination and in-vehicle service. Despite facing technical challenges such as semantic consistency, dynamic adaptability, and security trustworthiness, future semantic communication will evolve towards deep integration with distributed collaborative knowledge networks, lightweight real-time decision-making agents, and integrated “communication, sensing, and computing” architectures, positioning itself as a key enabling technology for empowering Sixth Generation mobile communication (6G) of intelligent vehicular networks. Full article

The article discusses the development of a communication protocol that provides consistent security and reliability during data exchange in sensor networks. Different communication technologies are supported. The motivation for this work is presented against the background of contemporary communication technologies and capabilities. The article summarizes relevant application constraints. The capabilities of popular communication technologies are briefly analyzed. Typical sensor networks serve as examples. Work on the protocol design begins with identifying important network features that serve as requirements. The design and implementation work continues with establishing a suitable packet structure, packet processing strategies, and an overall communication flow between the nodes in the network. A concept for packet routing in large networks with different communication technologies is developed and presented. The strengths and weaknesses are summarized and discussed after testing and assessment. Future work will include enhancing protocol features to improve practical applicability in different scenarios. Full article

Semantic communication has emerged as a promising paradigm for next-generation wireless networks, offering substantial efficiency gains by prioritizing the transmission of task-relevant meaning over bit-level accuracy. However, while its benefits in bandwidth reduction and intelligent data representation are well established, its potential to provide intrinsic reconstruction resistance without relying on conventional cryptographic mechanisms remains largely unexplored. This paper investigates whether semantic communication system architectures themselves can contribute to intrinsic reconstruction resistance for image transmission. We propose an autoencoder-based semantic communication framework in which images are encoded into latent representations and transmitted over a wireless channel, with decoding performed using architecture-specific neural networks. Unlike traditional secure communication approaches that depend on encryption, the proposed method leverages architectural uniqueness and representation-level abstraction to limit unauthorized reconstruction. To systematically analyze this, we evaluate eight adversarial scenarios encompassing variations in encoder–decoder architecture and initialization, including both matched (worst-case) and maximum mismatched (best-case) conditions. The system is modeled using a standard Alice–Bob–Mallory framework, where an adversary attempts to reconstruct intercepted semantic representations without full architectural knowledge. Performance is evaluated using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) for reconstruction quality, alongside semantic accuracy measured via a convolutional neural network (CNN)-based classifier and embedding cosine similarity to assess information leakage. Experimental results demonstrate that architectural mismatches substantially degrade both visual reconstruction and semantic interpretability for unauthorized receivers, while matched configurations enable substantial recovery. It is important to emphasise that the proposed approach does not provide cryptographic confidentiality; rather, it offers architecture-dependent resistance to unauthorised semantic reconstruction under restricted adversarial assumptions. Overall, the results show that semantic communication systems can exhibit intrinsic reconstruction resistance through architecture-dependent latent-space organisation, reducing reliance on additional cryptographic overhead under restricted adversarial assumptions, while also highlighting limitations when adversaries possess full architectural and initialisation knowledge. Full article