Search Results (3,177)

To address the challenges of improving energy balance and extending the operational lifetime of wireless sensor networks for Automated Railway Track Fastener Maintenance Robots (ARTFMR) along railways, this paper proposes an enhanced LEACH protocol incorporating Particle Swarm Optimization (PSO). Initially, network nodes are deployed, and their energy consumption is calculated to formulate a non-uniform deployment model aimed at improving energy balance, followed by network clustering. Subsequently, a routing protocol is designed, where the cluster head election mechanism integrates two critical factors—dynamic residual energy and distance to the base station—to facilitate dynamic and distributed cluster head rotation. During the communication phase, a Time Division Multiple Access (TDMA) scheduling mechanism is employed in conjunction with an inter-cluster multi-hop routing scheme. Additionally, a joint data-volume and energy optimization strategy is implemented to dynamically adjust the transmission data volume based on the residual energy of each node. Finally, simulations were conducted using MATLAB, and the results indicate that the proposed energy-balanced non-uniform deployment optimization strategy improves network energy utilization, effectively extends network lifetime, and exhibits favorable scalability. Full article

In the digital era, rapid urban growth and the demand for sustainable mobility are placing increasing pressure on transport systems, where congestion, energy consumption, and schedule variability complicate intermodal journey planning. This work proposes an AI-enhanced transfer-graph framework that models each transport mode as an independent subnetwork connected through explicit transfer arcs. This modular structure captures modal interactions while reducing graph complexity, enabling algorithms to operate more efficiently in time-dependent contexts. A Deep Q-Network (DQN) agent is further introduced as an exploratory alternative to exact and meta-heuristic methods for learning adaptive routing strategies. Exact (Dijkstra) and meta-heuristic (ACO, DFS, GA) algorithms were evaluated on synthetic networks reflecting Casablanca’s intermodal structure, achieving coherent routing with favorable computation and memory performance. The results demonstrate the potential of combining transfer-graph decomposition with learning-based components to support scalable intermodal routing. Full article

The proliferation of Internet of Things (IoT) applications in safety-critical domains, such as healthcare, smart transportation, and industrial automation, demands robust solutions for data integrity, traceability, and security that surpass the capabilities of centralized databases. This paper analyzes how blockchain technology can be integrated with core IoT service functions—including data management, security, device management, group coordination, and automated billing—to enhance immutability, trust, and operational efficiency. Our analysis identifies practical use cases such as consensus-driven tamper-proof storage, role-based access control, firmware integrity verification, and automated micropayments. These use cases showcase blockchain’s potential beyond traditional data storage. Building on this, we propose a novel framework that integrates a permissioned distributed ledger with a standardized IoT service layer platform through a Blockchain Interworking Proxy Entity (BlockIPE). This proxy dynamically maps IoT service functions to smart contracts, enabling flexible data routing to conventional databases or blockchains based on the application requirements. We implement a Dockerized prototype that integrates a C-based oneM2M platform with an Ethereum-compatible permissioned ledger (implemented using Hyperledger Besu) via BlockIPE, incorporating security features such as role-based access control. For performance evaluation, we use Ganache to isolate proxy-level overhead and scalability. At the proxy level, the blockchain-integrated path achieves processing latencies (≈86 ms) comparable to, and slightly faster than, the traditional database path. Although the end-to-end latency is inherently governed by on-chain confirmation (≈0.586–1.086 s), the scalability remains high (up to 100,000 TPS). This validates that the architecture secures IoT ecosystems with manageable operational overhead. Full article

Forestry environments—such as logging sites, transport trails, and resource monitoring areas—are characterized by rugged terrain and irregularly distributed obstacles, which pose substantial challenges for AGV route planning. This poses challenges for route planning in automated guided vehicles (AGVs) and forestry machinery. To address these challenges, this study proposes a hybrid path optimization method that integrates an improved A* algorithm with the Dynamic Window Approach (DWA). At the global planning level, the improved A* incorporates a dynamically weighted heuristic function, a steering-penalty term, and Floyd-based path smoothing to enhance path feasibility and continuity. In terms of local planning, the improved DWA algorithm employs adaptive weight adjustment, risk-perception factors, a sub-goal guidance mechanism, and a non-uniform and adaptive sampling strategy, thereby strengthening obstacle avoidance in dynamic environments. Simulation experiments on two-dimensional grid maps demonstrate that this method reduces path lengths by an average of 6.82%, 8.13%, and 21.78% for 20 × 20, 30 × 30, and 100 × 100 maps, respectively; planning time was reduced by an average of 21.02%, 16.65%, and 9.33%; total steering angle was reduced by an average of 100°, 487.5°, and 587.5°. These results indicate that the proposed hybrid algorithm offers practical technical guidance for intelligent forestry operations in complex natural environments, including timber harvesting, biomass transportation, and precision stand management. Full article

Promoting demand-responsive transit (DRT) is crucial for developing sustainable and green transportation systems in urban areas, especially in light of decreasing transit ridership and increasingly varying demand. However, the effectiveness of such services hinges on their ability to efficiently match varying travel demand. This paper presents a data-driven framework for the joint optimization of customized bus routes and timetables, to enhance both service quality and operational sustainability. Our approach leverages large-scale taxi trip data to identify latent travel demand, applying a spatial–temporal clustering method to group trip requests and identify DRT stops by trip origin, destination, and direction. An adaptive large neighborhood search (ALNS) algorithm is improved to co-optimize passenger waiting times and bus operation costs, where an unbalanced penalty for early or late schedule deviations is developed to better reflect passengers’ discomfort. The framework’s performance is validated through a real-world case study, demonstrating its ability to generate efficient routes and schedules. The model manages to improve passenger experience and reduce operation costs. By creating a more appealing and efficient service, this model contributes directly to the goals of green transport in terms of reducing the total vehicle kilometers that are traveled, and demonstrating a viable, high-quality alternative to private car usage. This study offers a practical and robust tool for transit planners to design a next-generation DRT system that is both economically viable and environmentally sustainable. Full article

Hydrogen production from biomass gasification has emerged as a strategic pathway for achieving carbon-neutral energy systems, circular resource utilization, and sustainable fuel generation. As global energy systems transition toward renewable sources, biomass-derived hydrogen represents a cornerstone of waste valorization, negative-emission bioenergy, and green hydrogen economies. Among all technologies, hydrogen production through gasification is one of the most consolidated routes with plenty of operative industrial-scale plants. The field of gasification is quite complex, and this comprehensive review describes the current scientific and technological achievements of biomass gasification for hydrogen production, describing the effect of feedstock, reactivity phenomena, reactor design, and catalyst systems. Furthermore, we report on a quantitative analysis regarding the operative cost of gasification of biomass compared with green hydrogen production and methane reforming. We provide a complete and synthetic picture for one of the most critical fields in the hydrogen economy that can actively promote a transition towards a more sustainable society. Full article

On-demand public transport systems are increasingly adopted to improve service flexibility, reduce operating costs, and meet emerging mobility needs. Evaluating their performance under realistic demand and operational conditions, however, remains a complex challenge. This study presents a hybrid simulation framework that integrates deep learning-based demand forecasting, behavioural survey data, and agent-based simulation to assess system performance. A BiLSTM neural network trained on real-world smartcard data forecasts short-term passenger demand, which is embedded into an agent-based model simulating vehicle dispatch, routing, and passenger interactions. The framework is applied to a case study in Melbourne, Australia, comparing a baseline fixed-route service with two on-demand scenarios. Results show that the most flexible scenario reduces the average passenger trip time by 32%, decreases the average wait time by 34%, increases vehicle occupancy from 12.1 to 18.6 passengers per vehicle, lowers emissions per passenger trip by 72%, and cuts the service cost per trip from AUD 6.82 to AUD 4.73. These findings demonstrate the potential of hybrid on-demand services to improve operational efficiency, passenger experience, and environmental outcomes. The study presents a novel, integrated methodology for scenario-based evaluation of on-demand public transportation using real-world transportation data. Full article

Flight delay serves as a pivotal metric for assessing service quality in the aviation industry. Accurately estimating flight delay duration is increasingly acknowledged as a cornerstone of aviation management, with significant implications for operational efficiency, passenger satisfaction, and economic outcomes. Most existing approaches often focus on single airports or airlines and overlook the complex interdependencies within the broader aviation network, limiting their applicability for system-wide planning. To address this gap, this study proposes a novel integrated framework that combines deep learning and complex network theory to predict flight arrival delay duration from a multi-airport and multi-airline perspective. Leveraging Bayesian optimization, we fine tune hyperparameters in the XGBoost algorithm to extract critical aviation network features at both node (airports) and edge (flight routes) levels. These features, which capture structural properties such as airport congestion and route criticality, are then used as inputs for a deep kernel extreme learning machine to estimate delay duration. Numerical experiment using a high-dimensional flight dataset from the U.S. Bureau of Transportation Statistics reveals that the proposed framework achieves superior accuracy, with an average delay error of 3.36 min and a 7.8% improvement over established benchmark methods. This approach fills gaps in network-level delay prediction, and the findings of this research could provide valuable insights for the aviation administration, aiding in making informed decisions on proactive measures that contribute to the sustainable development of the aviation industry. Full article

Global container shipping faces increasing pressure to reduce fuel consumption and greenhouse gas (GHG) emissions while still meeting strict port schedules under highly uncertain terminal operations and met-ocean conditions. However, most existing voyage-planning approaches either ignore real port operation variability or treat fuel optimization and just-in-time (JIT) arrival as separate problems, limiting their applicability in actual operations. This study presents a data-driven just-in-time voyage optimization framework that integrates port-side uncertainty and marine environmental dynamics into the routing process. A dwell-time prediction model based on Gradient Boosting was developed using port throughput and meteorological–oceanographic variables, achieving a validation accuracy of R² = 0.84 and providing a data-driven required time of arrival (RTA) estimate. A Transformer encoder model was constructed to forecast fuel consumption from multivariate navigation and environmental data, and the model achieved a segment-level predictive performance with an R² value of approximately 0.99. These predictive modules were embedded into a Deep Q-Network (DQN) routing model capable of optimizing headings and speed profiles under spatially varying ocean conditions. Experiments were conducted on three container-carrier routes in which the historical AIS trajectories served as operational benchmark routes. Compared with these AIS-based baselines, the optimized routes reduced fuel consumption and CO₂ emissions by approximately 26% to 69%, while driving the JIT arrival deviation close to zero. The proposed framework provides a unified approach that links port operations, fuel dynamics, and ocean-aware route planning, offering practical benefits for smart and autonomous ship navigation. Full article

This article examines the spatial and temporal unevenness of the transport complex operation in a large city with a population of about 0.9 million people and without off-street transport. The patterns of changes in the number of passengers transported in the city are described by a harmonic model, and seasonal unevenness with different numbers of peak values is noted. All routes can be divided into three groups based on the trend in passenger volume. The largest number of routes exhibited a downward trend in passenger volume. A downward trend in passenger volume is observed in the total number of passengers on all routes, despite an increase in the city’s population. Parking occupancy rates also show seasonal fluctuations. A downward trend in paid parking occupancy rates is emerging in the city’s central administrative and business district. The results of the study are relevant for choosing methods for managing the transport behavior model. Analysis of uneven passenger numbers on individual routes is necessary for improving the route network and determining the optimal number and passenger capacity of buses. Analyzing uneven occupancy rates in paid parking lots allows for the development of differentiated rates. The methods used in this article can be integrated into a city’s digital twin to improve forecasting accuracy. Full article

Vehicular ad hoc networks (VANETs) must simultaneously satisfy stringent reliability, latency, and sustainability targets under highly dynamic urban and highway mobility. Existing solutions typically optimise one or two dimensions (link stability, clustering, or energy) but lack an integrated, adaptive mechanism that fuses heterogeneous metrics while remaining lightweight and deployable. This paper introduces a VANET routing protocol named SYMPHONY (Synergistic Hierarchical Metric-Fusion and Predictive Hybrid Optimization for Network Yield) that operates in three coordinated layers: (i) a compact neighbourhood filtering stage that reduces forwarding scope and eliminates transient relays, (ii) a cluster layer that elects resilient cluster heads using fuzzy energy-aware metrics and backup leadership, and (iii) a global inter-cluster optimizer that blends a GA-reseeded swarm metaheuristic with a stability-aware pheromone scheme to produce multi-objective routes. Crucially, SYMPHONY employs an ultra-lightweight online weight-adaptation module (contextual linear bandit) to tune metric fusion weights in response to observed rewards (packet delivery ratio, end-to-end delay, and Green Performance Index). We evaluated the proposed routing protocol SYMPHONY versus strong modern baselines across urban and highway scenarios with varying density and resource constraints. The results demonstrate that SYMPHONY improves packet delivery ratio by up to 12–18%, reduces latency by 20–35%, and increases the Green Performance Index by 22–45% relative to the best baseline, while keeping control overhead and per-node computation within practical bounds. Full article

This paper presents a two-stage stochastic programming model for the joint optimization of fleet deployment and sailing speed in liner shipping under fuel price volatility and carbon tax uncertainty. The integrated framework addresses strategic fleet planning by determining optimal fleet composition in the first stage, while the second stage optimizes operational decisions, including vessel assignment to routes and sailing speeds on individual voyage legs, after observing stochastic parameter realizations. The model incorporates nonlinear fuel consumption functions that are approximated using piecewise linearization techniques, with the resulting formulation being solved using the Sample Average Approximation (SAA) method. To enhance computational tractability, we employ big-M methods to linearize mixed-integer terms and introduce auxiliary variables to handle nonlinear relationships in both the objective function and constraints. The proposed model provides shipping companies with a comprehensive decision-support tool that effectively captures the complex interdependencies between long-term strategic fleet planning and short-term operational speed optimization. Numerical experiments demonstrate the model’s effectiveness in generating optimal solutions that balance economic objectives with environmental considerations under uncertain market conditions, highlighting its practical value for resilient shipping operations in volatile fuel and carbon pricing environments. Full article

Advanced alkaline water electrolysis (AWE) in “zero-gap” configuration is a promising approach for low-temperature hydrogen production, but its efficiency strongly depends on the design and surface chemistry of nickel-based electrodes. Here, we present a simple dip-and-drying method (DDM) to modify commercial nickel foam with a Ni–FeOOH/PTFE microporous catalytic layer and evaluate its electrochemical performance in 1 M KOH and in a laboratory zero-gap cell with a Zirfon^® Perl 500 UTP diaphragm, through circulating 25 wt.% KOH. The FeSO₄-assisted DDM treatment generates mixed Ni–Fe oxyhydroxide surface species, while PTFE imparts control hydrophobicity, enhancing both catalytic activity and gas-release behavior. Annealing the electrode (DDM-NF-CAT-A) results in a cell voltage of 2.45 V at 1 A·cm⁻² and 80 °C, demonstrating moderate performance comparable to other Ni-based electrodes prepared via low-complexity methods, though below that of optimized state-of-the-art zero-gap systems. Short-term durability tests (80 h at 0.5 A·cm⁻²) indicate stable operation, but long-term industrial performance was not assessed. These findings illustrate the potential of the DDM approach as a simple, low-cost route to structured nickel foam electrodes and provide a foundation for further optimization of catalyst loading, microstructure, and long-term stability for practical AWE applications. Full article

Ship positional data are widely used for route inference, yet most existing studies rely on automatic identification system data, which contain irregular transmission intervals and limit the ability to capture vessel-specific operational habits and subtle route choices. This study addresses these limitations by proposing a methodology to infer customary routes using periodic 3 s ship position data collected through the Korean e-Navigation system based on long-term evolution maritime communication. The method comprises three main steps: constructing a sea-area grid with an associated weight map, determining data-driven importance and updating weights, and performing pathfinding. Domestic waters are divided into 100 m grids, and navigable and non-navigable areas are binarized to establish a framework for route exploration. Ship positional data are processed to extract inter-port trajectories, which are then classified by ship size and tidal time zone to account for navigational differences arising from vessel characteristics and tide-dependent accessibility. These trajectories are combined with spatial grids and transformed into a document–word structure, enabling the calculation of movement importance between grid cells using a modified term frequency–inverse document frequency measure. The resulting weights are applied to a pathfinding graph to derive routes that reflect vessel size and tidal conditions. The effectiveness of the proposed method is evaluated by computing cosine similarity between the inferred routes and actual trajectories. Full article

Drones have emerged as critical infrastructure for enhancing logistics efficiency in the emerging low-altitude economy, particularly in collaborative vehicle–drone research. However, existing research often neglects the impact of fair task allocation on workload balance among formations in large-scale routing scenarios, which compromises service quality. To address this gap, we introduce the Multi-vehicle with drones Collaborative Routing Problem with Large-scale Packages (MCRPLP), formulated as a bi-objective model aiming to minimize both operational cost and workload imbalance. A Hybrid Strategy-assisted Multi-objective Optimization Algorithm (HSMOA) is developed to overcome the limitations of existing methods, which struggle with balancing solution quality and computational efficiency in solving large-scale routing. Based on a Non-dominated Sorting Genetic Algorithm (NSGA-II), the HSMOA integrates a heuristic task assignment strategy that greedily reassigns packages between adjacent clusters. Then, by integrating a Pareto-front superiority evaluation model, an elite individual supplement strategy is designed to dynamically prune sub-optimal solution subspaces while enhancing the search within high-quality Pareto-front subspaces in HSMOA. Extensive experiments demonstrate the effectiveness of HSMOA in terms of solution quality and computational efficiency compared to multiple state-of-the-art methods. Further sensitivity analysis and managerial insights derived from a real-world case are also provided to support practical logistics implementation. Full article