Search Results (101)

The optimal deployment of Low-Power Wide-Area Networks (LPWANs) such as LoRaWAN in complex urban environments remains an NP-Hard Set Covering Problem. Traditional network planning often relies on 2D mathematical grids that ignore physical RF barriers, leading to topographic shadowing and single points of failure. This research proposes the Native 3D Memetic Spatially Aware Genetic Algorithm (3D-M-SAGA), an optimization framework that operates over a Morphological Digital Twin. By fusing OpenStreetMap (OSM) vector topologies with NASA SRTM elevation data and autonomous urban clutter classification, the framework evaluates physical constraints—including ITU-R knife-edge diffraction and dielectric absorption—directly within the evolutionary loop. To counteract the epistatic variance inherent to standard genetic algorithms, the 3D-M-SAGA integrates a vectorized memetic “Smart Repair” operator driven by heuristic attraction and repulsion forces. Formulated as a multi-objective optimization problem balancing Capital Expenditure (CAPEX) and topological Quality of Service (QoS) through K-coverage, the framework is evaluated using a 36-scenario parametric grid search and a 50-iteration Monte Carlo benchmark. Results show that the 3D-M-SAGA tightly bounds stochastic CAPEX variance ($\sigma =\pm 0.51$ gateways) while reducing single-point-of-failure network fragility ($K=1$) by up to 20%, guaranteeing fault tolerance ($K\ge 2$) without over-provisioning civic infrastructure. Full article

Maritime heterogeneous wireless networks are characterized by dynamic topology and significant heterogeneity in bandwidth, latency, and coverage across communication paradigms, rendering traditional terrestrial routing protocols inadequate. To address these challenges, this paper proposes a unified multi-radio access fusion infrastructure featuring a gateway that enables protocol conversion and collaborative resource management across heterogeneous systems. Building upon this infrastructure, we introduce CMPGNN-DQN, an intelligent routing algorithm that integrates Contrastive Message Passing Graph Neural Networks with Deep Reinforcement Learning. Specifically, the algorithm employs k-hop neighbor aggregation to expand the receptive field for routing decisions, and utilizes a dual-view contrastive learning mechanism—encompassing both homogeneous and heterogeneous perspectives—to enhance representation robustness against dynamic topology perturbations. By deeply fusing network topology features with real-time state information, including bandwidth, delay, and queue length, the agent makes hop-by-hop routing decisions via an ε-greedy policy within the DQN framework. Extensive simulations conducted across various scales of dynamic maritime communication scenarios demonstrate that CMPGNN-DQN outperforms state-of-the-art benchmark algorithms, including AODV, DQN, and GCN, across key metrics such as packet delivery ratio, transmission latency, and bandwidth utilization. Quantitatively, compared to the best-performing alternative (MPNN-DQN), our algorithm achieves throughput improvements of 2.06–5.04% under standard traffic loads and 6.6–27.1% under partial link failure conditions, while converging within merely 25 training episodes. Notably, under heavy network loads (40% load rate) or partial link failures, the algorithm maintains stable communication performance, demonstrating strong adaptability to complex dynamic environments. Full article

Multi-hop networks’ performance strongly depends on relay node placement, which affects delay, throughput, and coverage. This work introduces a dual-layer protocol combining Slotted ALOHA for node-to-relay communication and TDMA for relay-to-gateway transmission. Using a Java-based simulator, we evaluate three relay placement strategies—random, square grid, and hexagonal grid—considering metrics such as delay, throughput, packet collisions, and coverage. Results show that the hexagonal grid offers superior performance, reducing collisions, minimizing delay, and expanding coverage. A fallback mechanism for out-of-range nodes and sensitivity analysis of different backoff values are also included. The study quantifies the benefits of structured relay placement for LoRaWAN and wireless sensor networks, while also identifying challenges for realistic deployments. These findings provide guidelines for designing scalable and reliable IoT networks and highlight directions for future work involving irregular placements and dynamic routing. The simulation results are intended to provide comparative, trend-based insights under conservative modeling assumptions, rather than absolute performance predictions. Full article

Globalization has reorganized industrial spatial patterns, embedding regional economies into complex global production systems. However, the existing literature primarily focuses on the national level, leaving the “global-national-local” multi-scale linkages of sub-national regions underexplored. Focusing on Guangdong, which is China’s most open economic gateway, this study constructs a nested Multi-Regional input–output (MRIO) model to systematically reveal its industrial linkage paths across multiple scales. The results demonstrate that Guangdong features “strong local services and extensive global connections.” Specifically, the network is led by the high-R&D-intensity category and supported by energy and low-R&D categories, highlighted by two core supply paths, which are non-metallic mineral supply for construction and metal product support for optical–electrical manufacturing. Four heterogeneous modes are identified: resource security, innovation-driven dual circulation, cost-competitive regional division, and export-oriented service support. Crucially, the provincial “domestic intermediate chains plus international core chains” logic underscores Guangdong’s role as a bridge connecting Global and Domestic Value Chains. Theoretically, this work enriches the local dimension of Global Production Network theory. Methodologically, it provides an operational tool for nested analysis. Practically, it offers policy evidence for open economies to optimize industrial layouts and enhance supply chain resilience. Full article

Botnet attacks in Internet of Things (IoT) environments often occur as multi-stage campaigns, making early and reliable detection difficult across distributed and privacy-sensitive networks. Centralized detection approaches are often limited by heterogeneous traffic characteristics, severe data imbalance, and the need to aggregate large volumes of raw network data, raising scalability and privacy concerns. To address these challenges, this paper proposes FDA, a federated learning-based and data mining-driven framework for stage-aware botnet attack detection in IoT networks. FDA operates at network gateways, where anomalous traffic is first detected and then abstracted into compact and interpretable patterns using Frequent Itemset Mining (FIM). This pattern-based representation reduces noise and local traffic bias, enabling more robust learning across different IoT networks. Lightweight neural network models are trained locally at gateways, and a global model is learned through federated aggregation of model parameters, avoiding direct sharing of raw network data while enabling gateways to collaboratively learn evolving attack patterns across different IoT networks. Experimental results show that FDA achieves anomaly detection F1-scores above 99% across all gateways and multi-stage botnet attack classification F1-scores in the range of 48–49%, which are comparable to centralized machine-learning baselines while operating under decentralized and privacy-preserving constraints. Overall, FDA provides a practical, privacy-preserving, and effective solution for distributed botnet attack stage detection in real-world IoT deployments. Full article

Advancement of precision agriculture increasingly relies on cost-effective and scalable technologies for real-time environmental management, particularly in greenhouse environments where vertical and spatial microclimate heterogeneity influences crop performance. This study presents the design, implementation, and experimental validation of an Android-based smartphone application edge supervisory monitoring system integrated with multi-layer wireless sensing and control nodes for real-time monitoring in a smart greenhouse. The system combined multi-layer wireless sensor nodes, wireless control nodes, a Long-Range Wide Area Network (LoRaWAN) gateway, Message Queuing Telemetry Transport (MQTT) communication, and a cloud-synchronized smartphone-based supervisory interface for visualizing environmental data, detecting defined abnormal events, and controlling actuators remotely. For feasibility tests, 54 sensing nodes and 12 actuator nodes were deployed across three vertical layers in two sections, measuring temperature, humidity, CO₂ concentration, and light intensity. Abnormality was defined as environmental threshold violations, statistical signal deviations, actuator power inconsistencies, and communication timeout events. Experimental results revealed vertical and spatial environmental variability across greenhouse sections, while real-time time-series and 3D spatial maps enabled the rapid detection of abnormal conditions. The rule-based abnormality detection engine identified out-of-range environmental values and sensor-related inconsistencies and generated immediate notifications. Smartphone profiling revealed that display and system-level processes accounted for energy consumption, with battery power reaching a peak of 3.5 W and application CPU utilization ranging from 40% to 70% during active monitoring. The results demonstrate system-level feasibility, responsiveness, and scalability under commercial greenhouse workloads, supporting future integration of predictive control and energy-efficient operation. Full article

This article presents a scalable IoT-based architecture for continuous and passive monitoring of human behavior in home environments, designed as a technical foundation for future dementia risk assessment systems. The architecture addresses three fundamental challenges: achieving room-level spatial localization without privacy-invasive methods, balancing temporal resolution with bandwidth efficiency in continuous data streams, and enabling multi-institutional model development under GDPR constraints. The system integrates (1) wearable BLE sensors with infrared room-level localization; (2) edge computing gateways with local preprocessing and machine learning; (3) a three-channel data architecture that simultaneously achieves full 1 s temporal resolution for machine learning training, low-latency real-time visualization, and 41.2% network bandwidth reduction; and (4) a federated learning framework enabling collaborative model development without data sharing between institutions. Technical validation in two apartments (three participants, 7 days) demonstrated: 97.6% room-level localization accuracy using infrared beacons; less than 7 s end-to-end latency for 99.5% of critical events; and 98.5% deduplication accuracy in multi-gateway configurations. Federated learning simulation demonstrates algorithmic convergence (84.3% IID, 79.8% non-IID) and workflow feasibility, establishing a foundation for future production deployment. Cost analysis shows approximately €490 for initial implementation and approximately €55 monthly operation, representing substantially lower costs than existing research systems. The work establishes architectural and technical feasibility, as well as system-level economic viability, of continuous home monitoring for behavioral analysis within the evaluated residential scenarios. Clinical validation of diagnostic capabilities through longitudinal studies with validated cognitive assessments and patients with mild cognitive impairment remains to be studied in future work. Full article

This paper develops a reliability-based Decision Support System (DSS) for logistics networks, grounded in the Eurocode EN 1990 and Recommendations for Maritime Works ROM 0.0 framework. The DSS defines logistics-specific limit states (i.e., operational failure thresholds for the overall network) and computes annual exceedance probabilities through a multi-hazard fault-tree model. Its contribution is conceptual and regulatory: it transfers structural reliability principles to system-level assessment, generating auditable, norm-referenced indicators aligned with the EU Critical Entities Resilience Directive (CER) and the Network and Information Security Directive (NIS2). A central result is the Criticality Flip: Systemic vulnerability does not decline monotonically with hub density. Instead, risk shifts non-linearly between gateways and inland integrators, yielding a narrow operating range where the reliability margin (β) is maximized and annual limit-state exceedance is minimized. Beyond this range, additional hubs may provide limited—or even adverse—reliability improvement. The system operates as a compliance audit tool rather than a simulation engine: it evaluates whether a given network configuration meets declared reliability thresholds under multi-hazard scenarios, using standardized input formats and static topology. To support strategic decision-making, the DSS provides normalized and reproducible compliance indicators—such as annual limit-state exceedance probabilities and the associated reliability margin ($\mathsf{\beta }$) referenced to declared thresholds—supporting cross-network benchmarking under CER and NIS2 constraints within an engineering reliability framework. Full article

In Internet of Things (IoT) systems, data collected by geographically distributed sensors is often incomplete due to device failures, harsh deployment conditions, energy constraints, and unreliable communication. Such data gaps can significantly degrade downstream data processing and decision-making, particularly when failures result in the loss of all locally redundant sensors. Conventional imputation approaches typically rely on historical trends or multi-sensor fusion within the same target environment; however, historical methods struggle to capture emerging patterns, while same-location fusion remains vulnerable to single-point failures when local redundancy is unavailable. This article proposes a correlation-aware, cross-location data fusion framework for data imputation in IoT networks that explicitly addresses single-point failure scenarios. Instead of relying on co-located sensors, the framework selectively fuses semantically similar features from independent and geographically distributed gateways using summary statistics-based and correlation screening to minimize communication overhead. The resulting fused dataset is then processed using a lightweight KNN with an Iterative PCA imputation method, which combines local neighborhood similarity with global covariance structure to generate synthetic data for missing values. The proposed framework is evaluated using real-world weather station data collected from eight geographically diverse locations across the United States. The experimental results show that the proposed approach achieves improved or comparable imputation accuracy relative to conventional same-location fusion methods when sufficient cross-location feature correlation exists and degrades gracefully when correlation is weak. By enabling data recovery without requiring redundant local sensors, the proposed approach provides a resource-efficient and failure-resilient solution for handling missing data in IoT systems. Full article

Industrial gateways serve as critical data aggregation points within the Industrial Internet of Things (IIoT), enabling seamless data interoperability that empowers enterprises to extract value from equipment data more efficiently. However, their role exposes a fundamental trade-off between computational efficiency and prediction accuracy—a contradiction yet to be fully resolved by existing approaches. The rapid proliferation of IoT devices has led to a corresponding surge in network traffic, posing significant challenges for traffic forecasting methods, while deep learning models like Transformers and GNNs demonstrate high accuracy in traffic prediction, their substantial computational and memory demands hinder effective deployment on resource-constrained industrial gateways, while simple linear models offer relative simplicity, they struggle to effectively capture the complex characteristics of IIoT traffic—which often exhibits high nonlinearity, significant burstiness, and a wide distribution of time scales. The inherent time-varying nature of traffic data further complicates achieving high prediction accuracy. To address these interrelated challenges, we propose the lightweight and theoretically grounded DOA-MSDI-CrossLinear framework, redefining traffic forecasting as a hierarchical decomposition–interaction problem. Unlike existing approaches that simply combine components, we recognize that industrial traffic inherently exhibits scale-dependent temporal correlations requiring explicit decomposition prior to interaction modeling. The Multi-Scale Decomposable Mixing (MDM) module implements this concept through adaptive sequence decomposition, while the Dual Dependency Interaction (DDI) module simultaneously captures dependencies across time and channels. Ultimately, decomposed patterns are fed into an enhanced CrossLinear model to predict flow values for specific future time periods. The Dream Optimization Algorithm (DOA) provides bio-inspired hyperparameter tuning that balances exploration and exploitation—particularly suited for the non-convex optimization scenarios typical in industrial forecasting tasks. Extensive experiments on real industrial IoT datasets thoroughly validate the effectiveness of this approach. Full article

Electroencephalography (EEG) has attracted significant attention as an effective modality for interaction between the physical and virtual worlds, with EEG-based person identification serving as a key gateway to such applications. Despite substantial progress in EEG-based person identification, several challenges remain: (1) how to design an end-to-end EEG-based identification pipeline; (2) how to perform automatic electrode selection for each user to reduce redundancy and improve discriminative capacity; (3) how to enhance the backbone network’s feature extraction capability by suppressing irrelevant information and better leveraging informative patterns; and (4) how to leverage higher-level information in EEG signals to achieve intent recognition (i.e., EEG-based task/activity recognition under controlled paradigms) on top of person identification. To address these issues, this article proposes, for the first time, a unified deep learning framework that integrates automatic electrode selection, person identification, and intent recognition. We introduce a novel backbone network, AES-MBE, which integrates automatic electrode selection (AES) and intent recognition. The network combines a channel-attention mechanism with a multi-scale bidirectional encoder (MBE), enabling adaptive capture of fine-grained local features while modeling global temporal dependencies in both forward and backward directions. We validate our approach using the PhysioNet EEG Motor Movement/Imagery Dataset (EEGMMIDB), which contains EEG recordings from 109 subjects performing 4 tasks. Compared with state-of-the-art methods, our framework achieves superior performance. Specifically, our method attains a person identification accuracy of 98.82% using only 4 electrodes and an average intent recognition accuracy of 91.58%. In addition, our approach demonstrates strong stability and robustness as the number of users varies, offering insights for future research and practical applications. Full article

In this study, using global liner shipping schedules, UNCTAD’s Port Liner Shipping Connectivity Index and Liner Shipping Bilateral Connectivity Index, together with bilateral trade-value data for 2022–2024, we construct a multilayer weighted port-to-port network that explicitly embeds port-level cargo-handling and service organisation capabilities, as well as demand-side routing pressure, into node and edge weights. Building on this network, we apply CONCOR-based structural-equivalence analysis to delineate functionally homogeneous port clusters, and adopt a structural role identification framework that combines multi-indicator connectivity metrics with Rank-Sum Ratio–entropy weighting and Probit-based binning to classify ports into high-efficiency core, bridge-control, and free-form bridge roles, thereby tracing the reconfiguration of cluster-level functional structures before and after the Red Sea crisis. Empirically, the clustering identifies four persistent communities—the Intertropical Maritime Hub Corridor (IMHC), Pacific Rim Mega-Port Agglomeration (PRMPA), Southern Commodity Export Gateway (SCEG), and Euro-Asian Intermodal Chokepoints (EAIC)—and reveals a marked spatial and functional reorganisation between 2022 and 2024. IMHC expands from 96 to 113 ports and SCEG from 33 to 56, whereas EAIC contracts from 27 to 10 nodes as gateway functions are reallocated across clusters, and the combined share of bridge-control and free-form bridge ports increases from 9.6% to 15.5% of all nodes, demonstrating a thicker functional backbone under rerouting pressures. Spatially, IMHC extends from a Mediterranean-centred configuration into tropical, trans-equatorial routes; PRMPA consolidates its role as the densest trans-Pacific belt; SCEG evolves from a commodity-based export gateway into a cross-regional Southern Hemisphere hub; and EAIC reorients from an Atlantic-dominated structure towards Eurasian corridors and emerging bypass routes. Functionally, Singapore, Rotterdam, and Shanghai remain dominant high-efficiency cores, while several Mediterranean and Red Sea ports (e.g., Jeddah, Alexandria) lose centrality as East and Southeast Asian nodes gain prominence; bridge-control functions are increasingly taken up by European and East Asian hubs (e.g., Antwerp, Hamburg, Busan, Kobe), acting as secondary transshipment buffers; and free-form bridge ports such as Manila, Haiphong, and Genoa strengthen their roles as elastic connectors that enhance intra-cluster cohesion and provide redundancy for inter-cluster rerouting. Overall, these patterns show that resilience under the Red Sea crisis is expressed through the cluster-level rebalancing of core–control–bridge roles, suggesting that port managers should prioritise parallel gateways, short-sea and coastal buffers, and sea–land intermodality within clusters when designing capacity expansion, hinterland access, and rerouting strategies. Full article

The proliferation of Internet of Things (IoT) deployments demands Authentication and Key Agreement (AKA) protocols that scale from one initiator to many devices while preserving strong security guarantees on constrained hardware. Prior lightweight one-to-many designs often rely on a network-wide secret, reuse a single group session key across devices, or omit Perfect Forward Secrecy (PFS), leaving systems vulnerable to compromise and traffic exposure. To this end, we present in this paper a lightweight protocol, named Lightweight One-To-many User-to-Sensors Authentication and Key Agreement (LOTUS-AKA), that achieves mutual authentication, PFS, and per-sensor key isolation while keeping devices free of public-key costs. The user and gateway perform an ephemeral elliptic-curve Diffie–Hellman exchange to derive a short-lived group key, from which independent per-sensor session keys are expanded via Hashed Message Authentication Code HMAC-based Key Derivation Function (HKDF). Each sensor receives its key through a compact Authenticated Encryption with associated data (AEAD) wrap under its long-term secret; sensors perform only hashing and AEAD, with no elliptic-curve operations. The login path uses an augmented Password-Authenticated Key Exchange (PAKE) to eliminate offline password guessing in the smart-card theft setting, and a stateless cookie gates expensive work to mitigate denial-of-service. We provide a game-based security argument and a symbolic verification model, and we report microbenchmarks on Cortex-M–class platforms showing reduced device computation and linear low-constant communication overhead with the number of sensors. The design offers a practical path to secure, scalable multi-sensor sessions in resource-constrained IoT. Full article

The exponential proliferation of the Internet of Things (IoT) has catalyzed a paradigm shift in industrial automation and smart city infrastructure. However, this rapid expansion has engendered significant heterogeneity in communication protocols, creating critical barriers to seamless data integration and interoperability. Conventional gateway solutions frequently exhibit limited flexibility in supporting diverse protocol stacks simultaneously and often lack granular user controllability. To mitigate these deficiencies, this paper proposes a novel, modular IoT gateway architecture, designated as MIGS (Modular IoT Gateway System). The proposed architecture comprises four distinct components: a Management Component, a Southbound Component, a Northbound Component, and a Cache Component. Specifically, the Southbound Component employs instance-based isolation and independent task threading to manage heterogeneous field devices utilizing protocols such as Modbus, MQTT, and OPC UA. The Northbound Component facilitates reliable bidirectional data transmission with cloud platforms. A dedicated Cache Component is integrated to decouple data acquisition from transmission, ensuring data integrity during network latency. Furthermore, a web-based Control Service Module affords comprehensive runtime management. We explicate the data transmission methodology and formulate a theoretical latency model to quantify the impact of the Python Global Interpreter Lock (GIL) and serialization overhead. Functional validation and theoretical analysis confirm the system’s efficacy in concurrent multi-protocol communication, robust data forwarding, and operational flexibility. The MIGS framework significantly enhances interoperability within heterogeneous IoT environments, offering a scalable solution for next-generation industrial applications. Full article

Monitoring the curing process of concrete remains a challenging and critical aspect of modern construction, often hindered by labour-intensive, invasive, and inflexible methods. The primary aim of this study is to develop an integrated IoT-enabled platform for automated, real-time monitoring of concrete curing, using a combination of LoRa-based sensor networks and an LTE-M backhaul. The resulting ConMonity system employs embedded multi-sensor nodes—capable of measuring strain, temperature, and humidity–connected via an energy-efficient, TDMA-based LoRa wireless protocol to an LTE-M gateway with cloud-based management and analytics. By employing a robust architecture with battery-powered embedded nodes and adaptive firmware, ConMonity enables multi-modal, multi-site assessments and demonstrates stable, autonomous operation over multi-modal, multi-site assessment and demonstrates stable, autonomous operation over multi-month field deployments. Measured data are transmitted in a compact binary MQTT format, optimising cellular bandwidth and allowing secure, remote access via a dedicated mobile application. Operation in laboratory construction environments indicates that ConMonity outperforms conventional and earlier wireless monitoring systems in scalability and automation, delivering actionable real-time data and proactive alerts. The platform establishes a foundation for intelligent, scalable, and cost-effective monitoring of concrete curing, with future work focused on extending sensor modalities and enhancing resilience under diverse site conditions. Full article