Search Results (751)

Smart electric vehicle (EV) charging infrastructures are evolving into large-scale cyber-physical Internet of Things (IoT) systems that depend on distributed communication, real-time sensing, and spatially coordinated charging operations. However, their interconnected communication architecture exposes charging stations, EV communication links, and network gateways to coordinated distributed denial-of-service (DDoS) attacks. Existing intrusion detection approaches primarily rely on localized or static traffic analysis and therefore have limited capability to capture spatially distributed and temporally evolving attack behavior. This study proposes the Spatio-Temporal Attack Graph Guard Network (STAG-GuardNet), an unmanned aerial vehicle (UAV)-assisted spatio-temporal attack graph learning framework for DDoS detection and security monitoring in smart EV charging networks. The framework integrates spatiotemporal signal conditioning, telemetry-adaptive graph aggregation, temporal dependency learning, attack-memory encoding, and adaptive risk-aware attention to model coordinated cyber-physical attack behavior. UAV-assisted telemetry provides complementary spatial and wireless information on communication instability, signal variation, neighboring congestion, and distributed attack-related behavior. A Hybrid Hawk–Manta Adaptive Optimizer (HHMAO) is employed to improve hyperparameter selection and convergence stability under imbalanced, heterogeneous, and nonstationary traffic conditions. The framework is evaluated using a smart-city EV charging cybersecurity dataset and three benchmark IoT intrusion detection datasets, namely TON_IoT, Edge-IIoTset, and X-IIoTID. Experimental results show that STAG-GuardNet achieves 97.7% accuracy, a 97.7% weighted F1-score, and a 98.4% area under the receiver operating characteristic curve (AUC) on the primary dataset. The framework also maintains stable performance under noisy telemetry, missing observations, heterogeneous traffic distributions, and charging-node outages. These findings demonstrate the potential of STAG-GuardNet for resilient and spatially informed security monitoring in UAV-assisted IoT-enabled EV charging infrastructures. Full article

Campaign-level assessments of human lunar exploration architectures are commonly performed over finite horizons, which can conceal systematic buffer drawdown and asset drift beyond the simulated window. This paper presents a reproducible methodology in which sustainability is formalized as a periodic (steady-state) property of the coupled inventory–asset system, defined with respect to the modeled state variables and enforced as a binary feasibility gate on all performance evaluation. The architecture is represented as an event-driven multi-commodity flow over a reduced, SpaceNet-compatible network, equivalent to a time-expanded formulation, with propellant demand coupled to transported mass through the rocket equation. Standard measures of effectiveness are augmented with three dimensionless indicators and a composite index whose alignment with the dominant principal component of the trade space is tested a posteriori. On an Artemis III-derived case study the reference cycle closes ($z=1$, 32.3-day margin), yet 18 of 81 nominally feasible design points lose periodic sustainability under launch-delay perturbations, all 18 classifications being statistically significant at the 95% level. Periodic-state closure thus provides a formal criterion for cycle-to-cycle depletion that requires no arbitrary horizon choice. Applied unchanged to three architectures spanning nearly seven-fold in launch mass, the analysis shows an expendable direct-descent concept leading every mass-normalized indicator at a fixed two-crew objective, an ordering that is stable under $\pm 30\%$ dry-mass and $\pm 10\%$ specific-impulse perturbations, while a Gateway hub becomes preferable once orbital infrastructure and extensibility are valued. This study regenerates deterministically from a single input dataset. Full article

As artificial intelligence systems increasingly serve as a gateway for access to information, economic opportunity, and civic life, higher education programs training AI developers must evolve to prepare practitioners who are not only technically proficient in building AI solutions but also socially accountable in their work. This paper proposes a conceptual framework for integrating explainable AI (XAI) and social accountability into data science, computer science, and information science curricula. Based on accountability theory, information science, and recent XAI research, the proposed framework is organized around four interrelated pillars: answerability, responsibility, enforcement, and reflexivity. These pillars are further situated within technical, social, organizational, and political dimensions of XAI implementation, with particular focus on how XAI techniques such as LIME, SHAP, model cards, and counterfactual explanations can be operationalized as instruments of meaningful accountability. This paper then proposes a multi-level governance framework that links interpretability methods to institutional oversight, regulatory literacy, and participatory design, illustrated through a concrete scenario grounded in graduate data science education. Together, these elements represent a new pedagogical approach that can equip future AI developers to design and deploy AI systems that are accurate as well as transparent, justifiable, and responsive to the communities they serve. Full article

Controller Area Network (CAN) is widely used in in-vehicle communication systems because of its reliability and robustness. However, conventional CAN networks are primarily designed for wired communication and do not directly support wireless access for local visualization, remote monitoring, or connected-vehicle services. This paper presents an embedded CAN–Wi-Fi gateway architecture for connected-vehicle monitoring applications. The proposed system integrates a CAN interface, an embedded gateway controller, a wireless communication module, a mobile human–machine interface (HMI), and an edge-server-based remote monitoring service. A modular hardware and software framework is developed to support CAN message acquisition, identifier-based filtering, priority classification, queue-based buffering, structured packet generation, wireless transmission, local visualization, and remote data access. To reduce unnecessary wireless traffic and improve the responsiveness of urgent monitoring traffic, the gateway selectively forwards monitoring-related CAN identifiers and applies strict-priority dispatch using separate High-, Medium-, and Low-priority queues. The system is verified using representative Saab P-bus-based vehicle data packets derived from publicly available reverse-engineering information. Controlled wireless experiments achieved 100% packet delivery under offered traffic rates of 20, 50, and 100 frames/s, with application-level throughput increasing from approximately 33.8 to 170.0 kbps and round-trip time (RTT)-based estimated one-way wireless delay ranging from approximately 4.1 to 10.8 ms. Under transient burst contention, priority-based dispatch reduced the mean High-priority warning-traffic queue waiting time from 31.116 ms with single-FIFO forwarding to 2.268 ms, reduced the P95 value from 53.560 to 4.808 ms, and reduced the 25 ms threshold-exceedance ratio from approximately 61.7% to 0%. The results demonstrate the feasibility of the proposed CAN–Wi-Fi gateway for integrated local and remote vehicle monitoring and show that strict-priority dispatch can substantially improve the responsiveness of urgent monitoring traffic under temporary gateway contention. Full article

Environmental-monitoring systems often bundle sensing, communication, storage, visualization, and control into one application, making later changes difficult. We designed a service-oriented platform that separates these functions through defined interfaces. It combines a Raspberry Pi gateway, a dedicated motor-control microcontroller, five environmental sensor modules, Node-RED middleware, a database, and a web interface. Deterministic code alone evaluates threshold and composite rules and controls safety-relevant alerts; an optional large language model (LLM) turns pre-computed statistics and rule outcomes into narrative reports. We examined data acquisition and rule processing during two short indoor campaigns. In the residential campaign, the SCD41 yielded 78 valid three-minute bins (234 min of recorded data) across four sessions between 09:18 and 17:12 local time; binned CO₂ concentrations ranged from 679 to 1471 parts per million (ppm). Using the initial campaign for development and the residential campaign as a temporal holdout, the persistence model produced a 15 min forecast mean absolute error of 58.3 ppm and a root mean square error of 78.8 ppm. A separate controlled experiment generated 270 reports from nine deterministic synthetic scenarios. Every reporter preserved all deterministic alert identifiers, while the fixed template and seven of the nine locally hosted LLMs achieved 100% numerical fidelity. Qwen 3.5 9B was the only LLM that returned all required measured content without automated claim-review flags and produced identical outputs across repetitions for every scenario. These results confirm integration and functional separation under the tested conditions, but they do not demonstrate week-scale reliability, longer-horizon forecasting accuracy, robotic mobility performance, or load scalability. Full article

Smart systems, the Industrial Internet of Things, and cyber-physical networks increasingly make decisions on the devices where data is generated, on nodes short of memory, compute, energy, and bandwidth, and exposed to real adversaries. Two research currents have grown to meet this: one makes artificial intelligence small and distributed (quantization, pruning, distillation, TinyML, federated and split learning), the other makes it safe (defenses against poisoning, backdoors, inversion, and evasion). This review argues that the two are entangled rather than parallel. Operators that shrink a model or scatter it across nodes also redraw its attack surface, each carrying a security dividend and a security liability, and because a node’s resources are finite and shared, model capacity and defense strength compete for one multi-dimensional budget. We formalize this as an efficiency-decentralization-security (EDS) design tension, explicitly a tension and not an impossibility, and show with published measurements that the coupling is non-monotonic. Around this thesis we build three artifacts, following an explicit design-science research process: an evidence-graded scoring matrix that separates each operator’s security dividend from its liability across seven axes and reports the direction of every effect separately from the confidence in the evidence behind it; a resource-aware threat model that judges attack and defense feasibility against a tiered device, gateway, network, and server budget with stated units; and a co-design framework whose decision workflow terminates in a defense-selection program and a verification step under adaptive attack. We work the framework through an industrial predictive-maintenance scenario with the resource arithmetic computed line by line, and evaluate it retrospectively against six published edge-AI systems. The result is a decision-support guide for building edge AI that is efficient, decentralized, and secure at once. Full article

By combining digital sensing, network connectivity, data-driven analyses, cloud services, and automated controls, smart farming has been increasingly adopted in agricultural production. Although these technologies have improved the precision and efficiency of farm management, they also increase cybersecurity exposure as agricultural facilities are connected to external networks, platforms, and remote-control environments. This review seeks to clarify why cybersecurity should be considered a fundamental requirement in smart farming and details the major system components, cybersecurity risks, and network design considerations required for secure operation. This review first explains the concept and application scope of smart farming, and then examines how sensors, communication networks, gateways, control systems, data platforms, user interfaces, cloud infrastructure, and physical support systems contribute to farm management and cybersecurity exposure. The review also emphasizes that smart farming differs from ordinary information systems because digital data and control commands can directly affect physical processes, such as irrigation, ventilation, heating, nutrient supply, and livestock management. Based on these cyber-physical characteristics, the review summarizes the key architectural considerations for reducing cybersecurity risks, including network segmentation, data and command flow mapping, gateway and wireless security, remote access management, cloud access control, device inventory, logging, monitoring, resilience, and local fail-safe operation. Overall, ensuring cybersecurity in smart farming requires an integrated approach that protects not only data and accounts but also the reliability and continuity of agricultural production. Full article

Next-generation Intelligent Transportation Systems (ITSs) require ultra-reliable, low-latency Vehicle-to-Everything (V2X) communication frameworks that support safety-critical vehicular services. Conventional centralized, monolithic architectures suffer from excessive transmission latency, limited scalability, and authentication overheads that are ill-suited to the highly dynamic and dense vehicular environment. This paper presents a secure and low-latency edge–cloud architecture for intelligent V2X communications based on a lightweight microservice-driven design paradigm. A formal latency-constrained model is presented to ensure that the end-to-end delay satisfies tight real-time constraints. The proposed framework is lightweight and includes HMAC-based authentication, nonce-based replay protection, timestamp validation, and short-lived encrypted session tokens in a stateless architecture using the Laravel framework deployed at the edge layer. Security validation is performed at edge gateways, and asynchronous SQLite-backed job queues support non-blocking telemetry processing and scalable service orchestration. Experimental evaluation shows that the edge-based deployment achieves a mean response time of 2.58 ms with small variance under repeated request conditions, while centralized processing exhibits significantly higher latency. The results demonstrate that secure authentication and telemetry exchange can be achieved without breaching strict latency requirements. The proposed solution creates a deployable, scalable, and security-aware foundation for next-generation V2X ecosystems and Intelligent Transportation Systems (ITSs) in real time. Full article

This study contributes to the design of a bidirectional communication architecture based on the ISA-95 standard, which is integrated with an IoT gateway network to digitize plant-floor data, a manufacturing execution system (MES) with local backup and automatic inventory updates using YOLOv5-based computer vision, and a cloud instance for product order management. According to interoperability metrics, the results demonstrate the feasibility of developing digital scalability in an architecture composed of heterogeneous devices, maintaining interconnectivity throughout the entire value chain. Full article

Floods degrade road networks at the same time as demand for emergency medical services (EMSs) rises, yet national EMS command systems rarely receive any information on flood-induced road barriers. This paper presents Ambulance STARS (SaTellite-assisted Ambulance Routing System), a service-oriented framework that links satellite observation with ambulance dispatch. A cloud-based flood detection service derives flood extent from Sentinel-1 SAR amplitude change detection executed in a cloud-based Earth observation data and compute backend and translates it into road passability layers. A routing engine then maintains an in-memory road graph whose travel times are calibrated with empirical ambulance speed models built from four years (2020–2023) of GPS records of an EMS fleet in southern Poland, with separate speeds for driving with and without emergency signals (61.8 and 37.2 km/h, respectively). An API gateway with single-file tile delivery, a replicated relational data tier, and an observability stack complete the architecture, and a web client offers dispatchers live routing and multi-unit incident simulation. The framework was tested on the September 2024 flood in the Municipality of Nysa, Poland. The SAR module delineated 665 ha of inundation and marked 8.5 km of the 656.7 km routing network as impassable (508 barrier points), and the same procedure applied to a reference optical mask of 18 September yielded 17.3 km and 1006 points. Because the SAR and optical acquisitions captured different phases of the flood wave, agreement on the rare impassable-road class was low, and the two products were, therefore, used to bracket operational uncertainty rather than to define a single ground truth. Applied without local retuning to Lewin Brzeski, the same flood detection workflow showed consistent performance against the CEMS reference product. The routing module produced statutory 8/15/20 min accessibility maps in 12–34 s under warm-cache benchmark conditions. With SAR-derived barriers, the share of the network reachable within 15 min fell from 88% to 80%, and 2 villages with 938 inhabitants lost road access to EMS entirely. With barriers derived from the optical mask, the 15 min share fell to 39.8% and seventeen settlements lost road access entirely, underlining how strongly the barrier source shapes the operational picture. Post-acquisition processing completes in under one minute under warm-cache conditions with road data preloaded, and satellite-derived road passability is fast enough to support near-real-time decision-making, subject to the constellation revisit time and to integration with EMS command systems. Full article

The increasing interconnection of smart-grid operational technology, industrial-edge services, and utility information systems creates a critical need for resilient and continuously monitored industrial cyber-physical communication. Although quantum key distribution (QKD) can strengthen session-key establishment for advanced metering infrastructure, distributed energy resources, substation automation, supervisory control, and utility-core services, practical QKD deployments remain vulnerable to implementation-level side-channel attacks that can compromise the cryptographic protection layer without directly targeting conventional network packets. This paper presents a QKD-secured industrial smart-grid cyber-physical system framework for simulating and detecting adaptive side-channel attacks. The proposed 36-node industrial communication architecture integrates AMI devices, DER controllers, PMU and substation automation components, industrial-edge gateways, QKD modules, key-management services, SCADA and utility-core servers, security-operation-center components, and adversarial access points. A 100,000-record cyber-quantum dataset is generated across 12 operating conditions comprising normal communication and 11 adaptive QKD side-channel attacks: detector blinding, time shift, wavelength switching, Trojan-horse probing, photon-number splitting, decoy-state spoofing, RNG bias, calibration manipulation, local-oscillator manipulation, synchronization spoofing, and combined adaptive quantum hacking. Each scenario introduces coupled primary and secondary perturbations across optical, detector, timing, synchronization, randomness, calibration, photon-statistical, leakage, key-generation, encryption, and industrial-network-performance features. To support intelligent industrial security monitoring, the proposed Quantum-aware Mamba–Kolmogorov–Arnold Network (Q-MambaKAN) organizes device, network, QKD, side-channel, encryption, and risk evidence into an ordered cyber-quantum representation processed through selective state-space learning, side-channel attention, nonlinear KAN mapping, adaptive fusion, and multi-task prediction heads. Results show that the QBER increases from 0.071 during normal operation to 0.426 under combined adaptive quantum hacking, while encryption success decreases from 98.1% to 0%. Q-MambaKAN achieves a 99.48% binary detection accuracy, a 99.70% binary F1-score, a 97.60% multiclass macro-F1, and a risk RMSE of 0.021. Full article

This paper presents the design and experimental characterization of a distributed ESP32/MicroPython edge–cloud sensing system with packet-level latency decomposition. Sensor nodes transmit periodic telemetry to an ESP32 gateway over ESP-NOW; the gateway appends reception and MQTT-publication timestamps and forwards records through a local Mosquitto bridge v2.1.2, EMQX Cloud v5, Telegraf v1.36.0, and InfluxDB Cloud Serverless (Storage Engine Version 3). A three-probe two-way gateway-referenced synchronization procedure provides corrected sender timestamps while exposing an interval-based synchronization-uncertainty diagnostic. The bridge-assisted campaign comprised three independent 30 min repetitions with one, three, and five active nodes. Across runs, mean gateway-referenced node-to-gateway latency was 23.17 ± 0.13 ms, 24.13 ± 0.12 ms, and 24.84 ± 0.47 ms, respectively; the corresponding p95 values were 28 ms, 33 ms, and 37–38 ms. Mean gateway-processing latency remained nearly unchanged at 13.31–13.46 ms. Exact full-run database-visible PDR was 100% in all one-node runs, 99.28–99.88% in the three-node runs, and 96.75–97.05% in the five-node runs. Independent GPIO/oscilloscope validation showed a reproducible positive software-to-hardware difference of 12.132 ± 1.819 ms across run means, so the local metric is interpreted as a gateway-referenced application-level delivery metric rather than unbiased physical one-way radio latency. Relative to aggregate end-to-end reporting, the instrumentation separates local, gateway, and downstream ingestion contributions rather than claiming a universally faster transport method. Quantitative performance and scaling claims are confined to the evaluated bridge-assisted configuration and controlled indoor periodic workload. Full article

Conventional forest inventories provide standardized but temporally discrete DBH observations, whereas some research and management applications require continuous observations of diameter dynamics between remeasurement campaigns. This study designed and implemented a Smart Tree Monitoring Network based on Internet of Things (IoT) and cloud storage technologies as a complementary intensive-monitoring approach for selected forest plots. The system enables automatic, continuous, networked observation of standing-tree diameter growth and consists of Tree Sensor Nodes (TSNs), Stand Gateways (SGs), and a cloud management platform. The independently designed tree diameter growth monitoring instrument senses micro-variations in DBH and conducts scheduled data acquisition. Low-power wireless transmission from TSNs to gateways is achieved through LoRa/LoRaWAN, while stand gateways aggregate multi-node data and environmental parameters and upload them to the cloud platform via a 4G network. Field deployment involved 426 devices in 10 sample plots with different terrain and climatic conditions in Hubei Province. The results showed that (1) with a 3.6 V, 19,000 mAh lithium battery and a 5 min sampling interval, daily power consumption was 5.37 mAh, corresponding to a theoretical battery-life estimate of 9.69 years under the tested duty-cycle assumptions; (2) at initial deployment, device-measured DBH showed strong agreement with manual measurements, with R² = 0.9996, RMSE = 0.215 cm, MAE = 0.170 cm, and Bias = −0.089 cm, while subgroup analyses indicated larger underestimation for large-diameter trees; and (3) monthly mean RSSI and SNR remained above the adopted reference thresholds throughout 2025, while rainfall and temperature were associated with limited variation in signal quality. These results support the technical feasibility of the system for high-frequency DBH monitoring in selected plots, while long-term measurement drift, end-to-end data completeness, battery life under field aging, and physical durability require further validation. Full article

Automated vehicular traffic management in Latin American cities requires solutions capable of capturing, processing, and visualizing events through measurable operational update intervals. Conventional automatic number plate recognition (ANPR) pipelines can return plate text while leaving the visual adequacy of the associated crop unassessed and disconnected from downstream cloud persistence, alerts, and monitoring. Because this separation can propagate visually unreliable evidence into operational records, a unified workflow is needed to evaluate plate-crop legibility before the event is exposed to operators. This work presents a three-phase methodology, applied to the SAVH system (Sistema de Aforo Vehicular, Vehicle Counting System), which integrates a Dahua ANPR camera (Zhejiang Dahua Vision Technology Co., Ltd., Hangzhou, China) with a cloud architecture on Amazon Web Services (AWS). The camera captures the vehicle and performs textual reading of the license plate, sending vehicle notifications directly to the FastAPI service deployed on Amazon EC2. This service extracts the visual evidence and runs the YOLOv8n classification variant (YOLOv8n–CLS), which classifies the legibility of the plate crop into two classes: legible plate and non-legible plate. Structured events and visual evidence are persisted in managed storage services, and a serverless function serves the web dashboard queries through an application programming interface (API) managed by Amazon API Gateway. The model was trained on a relabeled dataset derived from the public LPLCv2 collection, split into training, validation, and test subsets. Evaluation on 720 independent images from the test set achieved 97.78% overall accuracy, with 97.75% macro precision, 97.82% macro recall, 97.78% macro F1-score, and an AUC of 0.9981. External validation on 2000 real Guayaquil images achieved 86.10% accuracy, 85.84% macro F1-score, and an AUC of 0.9742. The external results show a performance gap consistent with domain shift and motivate cautious interpretation of deployment results. The contribution is the integration and evaluation methodology rather than a new neural architecture: YOLOv8n–CLS, FastAPI, and the AWS services are existing components assembled into a documented operational workflow. Full article

Continuous monitoring of water quality is essential for supporting environmental management and enabling timely decision-making in wastewater treatment facilities. Although numerous Internet of Things (IoT) solutions have been proposed for environmental monitoring, many rely on proprietary cloud platforms or commercial gateways that limit flexibility, scalability, and integration with customized applications. This paper presents the design, implementation, and field validation of a modular IoT architecture for real-time water quality monitoring based on distributed sensor nodes, long-range LoRa communication, and a self-hosted web platform. The proposed architecture integrates sensor nodes equipped with calibrated pH, dissolved oxygen, and turbidity sensors, a hybrid LoRa/Wi-Fi Main Controller implementing a custom master–slave communication protocol, and a Python-based back-end with a PostgreSQL database for data acquisition, storage, visualization, and historical analysis. The complete system was deployed and experimentally validated in a real coupled constructed wetland located at the Universidad del Atlántico, Colombia, where three monitoring stations continuously acquired and transmitted water quality measurements over a one-month evaluation period. During the experimental deployment, the system generated more than 4.5 million measurement records (297 MB) while recording average RSSI values between −55.7 and −58.1 dBm (standard deviation: 1.6–2.2 dB). The developed web platform successfully supported real-time visualization and historical analysis of all acquired measurements. These results demonstrate the feasibility of the proposed architecture as a practical, scalable, and modular solution for continuous environmental monitoring that can be readily adapted to other distributed water quality monitoring applications. Full article