Search Results (383)

Smart logistics systems generate massive amounts of data, such as images and videos, requiring real-time processing in edge clusters. However, the edge cluster systems face performance bottlenecks in reception and forwarding high-concurrency data streams from numerous smart terminals, resulting in degraded processing efficiency. To address this issue, a novel high-performance data stream model called CBPS-DPDK is proposed. CBPS-DPDK integrates the DPDK framework from Intel corporations with a content-based publish/subscribe model enhanced by semantic filtering. This model adopts a three-tier optimization architecture. First, the user-space data plane is restructured using DPDK to avoid kernel context switch overhead via zero-copy and polling. Second, semantic enhancement is introduced into the publish/subscribe model to reduce the coupling between data producers and consumers through subscription matching and priority queuing. Finally, a hierarchical load balancing strategy ensures reliable data transmission under high concurrency. Experimental results show that CBPS-DPDK significantly outperforms two baselines—OSKT (kernel-based data forwarding) and DPDK-only (DPDK). Relative to the OSKT baseline, DPDK-only achieves improvements of 37.5% in latency, 11.1% in throughput, and 9.1% in VMAF; CBPS-DPDK further increases these to 51.8%, 18.3%, and 11.2%, respectively. In addition, compared with the traditional publish–subscribe system NATS, CBPS-DPDK maintains lower delay, higher throughput, and more balanced CPU and memory utilization under saturated workloads, demonstrating its effectiveness for real-time, high-concurrency edge scenarios. Full article

The growing demand for electricity and the urgent need to reduce environmental impact have made sustainable energy utilization a global priority. Street lighting, as a significant consumer of urban electricity, requires innovative solutions to enhance efficiency and reliability. This study presents an off-grid smart street lighting system that combines solar photovoltaic generation with battery storage and Internet of Things (IoT)-based control to ensure continuous and efficient operation. The system integrates Long Range Wide Area Network (LoRaWAN) communication technology for remote monitoring and control without internet connectivity and employs the Perturb and Observe (P&O) maximum power point tracking (MPPT) algorithm to maximize energy extraction from solar sources. Data transmission from the LoRaWAN gateway to the cloud is facilitated through the Message Queuing Telemetry Transport (MQTT) protocol, enabling real-time access and management via a graphical user interface. Experimental results demonstrate that the proposed system achieves a maximum MPPT efficiency of 97.96%, supports reliable communication over distances of up to 10 km, and successfully operates four LED streetlights, each spaced 400 m apart, across an open area of approximately 1.2 km—delivering a practical, energy-efficient, and internet-independent solution for smart urban infrastructure. Full article

Traditional Software-Defined Wide Area Network (SD-WAN) solutions lack adaptive load-balancing mechanisms, leading to inefficient traffic distribution, increased latency, and performance degradation. This paper presents an Application-Aware Dynamic Load Balancing (AADLB) framework designed for open-source SD-WAN environments. The proposed solution enables dynamic traffic routing based on real-time network performance indicators, including CPU utilization, memory usage, connection delay, and packet loss, while considering application-specific requirements. Unlike conventional load-balancing methods, such as Weighted Round Robin (WRR), Weighted Fair Queuing (WFQ), Priority Queuing (PQ), and Deficit Round Robin (DRR), AADLB continuously updates traffic weights based on application requirements and network conditions, ensuring optimal resource allocation and improved Quality of Service (QoS). The AADLB framework leverages a heuristic-based dynamic weight assignment algorithm to redistribute traffic in a multi-cloud environment, mitigating congestion and enhancing system responsiveness. Experimental results demonstrate that compared to these traditional algorithms, the proposed AADLB framework improved CPU utilization by an average of 8.40%, enhanced CPU stability by 76.66%, increased RAM utilization stability by 6.97%, slightly reduced average latency by 2.58%, and significantly enhanced latency consistency by 16.74%. These improvements enhance SD-WAN scalability, optimize bandwidth usage, and reduce operational costs. Our findings highlight the potential of application-aware dynamic load balancing in SD-WAN, offering a cost-effective and scalable alternative to proprietary solutions. Full article

With the rapid development of the Internet of Things (IoT) applications, the ability to automatically discover and retrieve resource information has become increasingly enhanced. Despite being one of the most commonly used IoT communication protocols, Message Queuing Telemetry Transport (MQTT) does not natively support resource discovery. To address this limitation, MQTT-resource discovery (MQTT-RD), a resource discovery mechanism based on MQTT, has been used for resource management. In this study, we tested and evaluated MQTT-RD using the Sniffer system that manages the resource directory and synchronizes data via MQTT. When too many Sniffers are activated, the MQTT-RD system becomes unsustainable. However, the experimental results in this study revealed that frequent updates to the resource directory (RD) and high-frequency heartbeat messages (pingalive) significantly increase network traffic and system load. In this study, we identified performance and stability issues to propose improvement strategies, including refining the topic design, reducing message transmission frequency, and improving the synchronization mechanism. Additionally, the feasibility of incorporating centralized management was explored to enhance system efficiency. Full article

The paper advances the theory of queuing networks by presenting generalized product-form solutions that explicitly take into account the service intensity depending on the number of customers in the network nodes, including the presence of multiple service channels and multi-threaded nodes. This represents a significant extension of the classical results on the Jackson network by integrating graph-theoretic methods, including basic subgraphs with service rates depending on the number of requests. The originality of the article is in the combination of stationary and non-stationary approaches to modeling service networks within a single approach. In particular, acyclic networks with deterministic service time and non-stationary Poisson input flow are considered. Such systems present a significant difficulty, which is noted in well-known works. A stationary model of an open queuing network with service intensity depending on the number of customers in the network nodes is constructed. The stationary network model is related to the problem of marine linear navigation along a strictly defined route and schedule. A generalization of the product theorem with a new form of stationary distribution is developed for it. It is shown that even a small increase in the service intensity with a large number of requests in a queuing network node can significantly reduce its average value. A non-stationary model of an acyclic queuing network with deterministic service time in network nodes and a non-stationary Poisson input flow is constructed. The non-stationary model is associated with irregular (tramp) sea transportation. The intensities of non-stationary Poisson flows in acyclic networks are represented by product formulas using paths between the initial node and other network nodes. The parameters of Poisson distributions of the number of customers in network nodes are calculated. The simplest formulas for calculating such queuing networks are obtained for networks in the form of trees. Full article

Background: Port congestion contributes significantly to operational inefficiency and environmental impact in maritime logistics. With tightening EU regulations such as the Emissions Trading System (EU ETS) and FuelEU Maritime, understanding and mitigating the economic and environmental effects of vessel delays is increasingly critical. This study focuses on a single bulk cargo pier at Constanta Port (Romania), which has experienced substantial traffic fluctuations since 2021, and examines operational and environmental performance through a queuing-theoretic lens. Methods: The authors have applied an M/G/1/∞/FIFO/∞ queuing model to vessel traffic and service time data from 2021–2023, supplemented by Monte Carlo simulations to capture variability in maneuvering and service durations. Environmental impact was quantified in CO₂ emissions using standard fuel-based emission factors, and a Cold Ironing scenario was modeled to assess potential mitigation benefits. Economic implications were estimated through operational cost modeling and conversion of CO₂ emissions into equivalent EU ETS carbon costs. Results: The analysis revealed high berth utilization rates across all years, with substantial variability in waiting times and queue lengths. Congestion was associated with considerable CO₂ emissions, which, when expressed in monetary terms under prevailing EU ETS prices, represent a significant financial burden. The Cold Ironing scenario demonstrated a substantial reduction in at-berth emissions and corresponding cost savings, underscoring its potential as a viable mitigation strategy. Conclusions: Results confirm that operational congestion at the studied berth imposes substantial environmental and financial burdens. The analysis supports targeted interventions such as Just-In-Time arrivals, optimized berth scheduling, and Cold Ironing adoption. Recommendations are most applicable to single-berth bulk cargo operations; future research should extend the approach to multi-berth configurations and incorporate additional operational constraints for broader generalizability. Full article

To support NASA’s mission to use nuclear thermal rockets for future Mars missions, an instrumentation and control test bed has been built at Oak Ridge National Laboratory. The system is designed as a hardware-in-the-loop test bed for testing control elements and autonomous control algorithms for nuclear thermal propulsion rockets. The mock reactor system consists of a modular and scalable framework, using inexpensive components and open-source software. The hardware system consists of a two-phase flow loop and a mock reactor with six control drums. A single-board computer (NVIDIA Jetson) handles reactor core emulation and hosts a message queuing telemetry transport broker that allows user-deployed control algorithms to interact with the system hardware. The reactor emulator receives sensor data from the hardware and provides the simulated performance of the reactor under steady-state, transient, and fault conditions. The emulator uses a reactivity lookup table and the point kinetics equations to solve for the reactor dynamics in real time. Emulated reactor dynamics and sensor input inform the autonomous control algorithm’s decision-making in a closed-loop manner. The current system is capable of operating at 10 Hz, but faster cycle rates are an area of ongoing research. This test bed will enable NASA and other space vendors to rigorously test their autonomous control systems for NTP rockets under transient (reactor startup and shutdown), steady-state, and fault conditions to reduce development time and risk for autonomous control systems in future missions. Full article

Background/Objectives: Elderly patients who require aftercare in an intramural care (IMC) facility may contribute to “bed blocking,” which occurs when patients who are ready for discharge remain hospitalized longer than medically necessary. While most bed-blocking studies focus on capacity issues, this study also investigates the coordination process. In a regional hospital in the Netherlands, we examine the extent to which bed blocking occurs due to patients awaiting IMC, and how this issue can be characterized in terms of capacity and coordination challenges. Methods: The case study employs a mixed-methods approach, analyzing system data, documents, and interviews from the hospital, IMC organizations, and a health insurance provider. The location of each patient (organization and department) was collected and reconstructed to a patient path. All patient paths together formed a network enabling data analysis both on the level of patient paths as well as on the level of the networks as they developed through time. This gave insight into the complexity of the total network that has to be coordinated. Results: In 2023, 6% of the hospital capacity was occupied by patients awaiting IMC. Delays were observed at various coordination stages. Due to a lack of data on IMC bed capacity, we were unable to establish whether capacity limitations also contributed to bed blocking. Conclusions: The coordination system is complex and includes waiting times at each coordination stage, resulting in bed blocking. The absence of a centralized capacity overview, coupled with limited data, prevents decision-makers from identifying bed blocking arising from capacity shortages. Greater insight is needed to coordinate patient flow and determine the required slack capacity. Full article

Water quality is a key factor in environmental and agronomic sustainability. Due to the influence of human activity and industrial development, the composition of rivers or lakes can experience significant variations both immediately and over time. In order to obtain a more accurate and documented assessment of these data, distributed monitoring with multiple sampling points is necessary. This paper presents the design and implementation of a scalable monitoring network based on long range (LoRa) and Message Queuing Telemetry Transport (MQTT), integrating a submersible sensor module (SSM) that works as a static measuring station or as a complement to sediment collectors, capable of measuring key water quality parameters such as TDS, turbidity, pH, temperature, and river kinematics with a gyroscope. The system includes a LoRa repeater (LRR) and a gateway, in addition to the SSM, which manages information transmission to a monitoring server (MS) using a tree topology. This configuration allows for dynamic antenna power adjustment based on the Received Signal Strength Indicator (RSSI) between the LRR and the gateway. Evaluations were performed on the Chil River in Arequipa, Peru, a rapid river that demonstrated ideal characteristics for validating the system’s efficacy. The results confirm the design’s efficacy and its capacity for real-time remote water quality monitoring. Full article

Existing rice harvesting models often lack depth or extensibility and are limited in their scope across the agriculture value chain, from crop planting to postharvest handling. A multi-agent system (MAS) offers flexibility and scalability and supports the simulation and modeling of complex real-world scenarios. This paper introduces a novel approach utilizing an MAS to simulate rice harvesting operations (including additional pre- and post-harvesting operations). Initially, a generic MAS was created, and it was then subsequently adapted to the agricultural context of rice farming in Central Japan. The localized MAS consists of agents such as weather, farm, rice centers, fields, crops and multiple agriculture machinery. Additionally, the introduced MAS environment is based on a discrete event simulation that enables communication across various independent agents. The system includes different harvesting schedule policies which determine the harvesting order for multiple paddy fields on specific days. The system was evaluated through two distinct experiments: (i) ‘Model Verification Simulation’, which successfully demonstrated the replication of actual historical farming practices, and (ii) ‘Operational Efficiency Simulation’, which compared the overall farm efficiency under different scheduling policies as well as different environmental conditions (e.g., rainfall). The simulation successfully generated a dataset containing traits and performance indicators that replicate the patterns observed in real-world data, while also approximating the operational behaviors and workflows of actual rice harvesting systems. Future studies could further evaluate the model’s robustness to confirm its practical applicability. Full article

With the deep integration of Internet of Vehicles (IoV) and edge computing technologies, the spatiotemporal dynamics, burstiness, and load fluctuations of user requests pose severe challenges to microservices auto-scaling. Existing static or periodic resource adjustment strategies struggle to adapt to IoV edge environments and often neglect service dependencies and multi-objective optimization synergy, failing to fully utilize implicit regularities like the symmetry in spatiotemporal patterns. This paper proposes a dual-phase dynamic scaling mechanism: for long-term scaling, the Spatio-Temporal Graph Transformer (STGT) is employed to predict traffic flow by capturing correlations in spatial–temporal distributions of vehicle movements, and the improved Multi-objective Graph-based Proximal Policy Optimization (MGPPO) algorithm is applied for proactive resource optimization, balancing trade-offs among conflicting objectives. For short-term bursts, the Fast Load-Aware Auto-Scaling algorithm (FLA) enables rapid instance adjustment based on the M/M/S queuing model, maintaining balanced load distribution across edge nodes—a feature that aligns with the principle of symmetry in system design. The model comprehensively considers request latency, resource consumption, and load balancing, using a multi-objective reward function to guide optimal strategies. Experiments show that STGT significantly improves prediction accuracy, while the combination of MGPPO and FLA reduces request latency and enhances resource utilization stability, validating its effectiveness in dynamic IoV environments. Full article

This study presents a comprehensive analysis of the M/Hypo_K/1/m queuing model to evaluate the performance of marshaling yards in freight rail classification systems. The model effectively captures the complex, multi-phase nature of service and repair processes by incorporating hypo-exponential probability distributions. The marshaling yard is modeled as a finite-capacity, single-server queue subject to potential server failures, reflecting real-world disruptions. Two complementary methodological frameworks are employed: a mathematical model based on continuous-time Markov chains (CTMCs) and a simulation model constructed using Colored Petri Nets (CPNs). In the analytical approach, both service time and repair time follow hypo-exponential distributions, which are used to approximate the gamma distribution. The simulation model built in CPN Tools allows for dynamic visualization and performance evaluation. In the CPN model, we applied a gamma distribution, which allowed us to evaluate the accuracy of the approximation implemented in the analytical model. The result indicated that utilization of the marshaling yard in primary shunting was approximately 23.81%, and with secondary shunting, 22.53%. The study output proves that the hypo-exponential distribution is able to approximate the gamma distribution. This dual-framework approach, combining analytics with simulation, provides a deeper understanding of system behavior, supporting data-driven decisions for capacity planning, failure mitigation, and operational optimization in freight rail networks. Full article

Unmanned Aerial Vehicles (UAVs) exhibit significant potential in enhancing the wireless communication coverage and service quality of Mobile Edge Computing (MEC) systems due to their superior flexibility and ease of deployment. However, the rapid growth of tasks leads to transmission queuing in edge networks, while the uneven distribution of user nodes and services causes network load imbalance, resulting in increased user waiting delays. To address these issues, we propose a multi-UAV collaborative MEC network model based on Non-Orthogonal Multiple Access (NOMA). In this model, UAVs are endowed with the capability to dynamically offload tasks among one another, thereby fostering a more equitable load distribution across the UAV swarm. Furthermore, the integration of NOMA is strategically employed to alleviating the inherent queuing delays in the communication infrastructure. Considering delay and energy consumption constraints, we formulate a task offloading strategy optimization problem with the objective of minimizing the overall system delay. To solve this problem, we design a delay-optimized offloading strategy based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. By jointly optimizing task offloading decisions and UAV flight trajectories, the system delay is significantly reduced. Simulation results show that, compared to traditional approaches, the proposed algorithm achieves a delay reduction of 20.2%, 9.8%, 17.0%, 12.7%, 15.0%, and 11.6% under different scenarios, including varying task volumes, the number of IoT devices, UAV flight speed, flight time, IoT device computing capacity, and UAV computing capability. These results demonstrate the effectiveness of the proposed solution and offloading decisions in reducing the overall system delay. Full article

Digital Twins (DTs) are transforming manufacturing by bridging the physical and digital worlds, enabling real-time insights, predictive analytics, and enhanced decision making. In Industry 4.0, DTs facilitate automation and data integration, while Industry 5.0 emphasizes human-centric, resilient, and sustainable production. However, implementing DTs in robotic metal additive manufacturing (AM) remains challenging because of the complexity of the wire-based laser metal deposition (LMD) process, the need for real-time monitoring, and the demand for advanced defect detection to ensure high-quality prints. This work proposes a structured DT architecture for a robotic wire-based LMD cell, following a standard framework. Three DT implementations were developed. First, a real-time 3D simulation in RoboDK, integrated with a 2D Node-RED dashboard, enabled motion validation and live process monitoring via MQTT (message queuing telemetry transport) telemetry, minimizing toolpath errors and collisions. Second, an Industrial IoT-based system using KUKA iiQoT (Industrial Internet of Things Quality of Things) facilitated predictive maintenance by analyzing motor loads, joint temperatures, and energy consumption, allowing early anomaly detection and reducing unplanned downtime. Third, the Meltio dashboard provided real-time insights into the laser temperature, wire tension, and deposition accuracy, ensuring adaptive control based on live telemetry. Additionally, a prescriptive analytics layer leveraging historical data in FireStore was integrated to optimize the process performance, enabling data-driven decision making. Full article

This study applies stochastic discrete event modeling to demonstrate that reducing wait times for specialized outpatient clinics in the Costa Rican public healthcare system is possible. The classification process identified four medical specialties with the longest wait times. It includes the creation of a separate queuing theory model for each specialty. The birth and death model allowed for estimating the number of arrivals and consultations in the simulation. Validation was performed by comparing the model’s input and output data with real-world statistical reports. An analysis of medical specialists revealed that approximately 22% of patients referred to secondary care did not require specialized medical consultation. Through simulation and the use of stochastic input data, patient waiting times decreased. In an optimistic scenario, waiting times decreased steadily across all specialties over 24 months. Ophthalmology and orthopedics reduced their waiting times to less than 300 days. Otorhinolaryngology decreased from 370 to 250 days, and urology showed the most significant improvement, decreasing from 350 to 100 days in the first year and remaining stable. This evidence transforms the traditional paradigm of increasing capacity as the only solution to the waiting list problem and positions improving the referral process as an alternative. To achieve these results, the study highlights the importance of implementing improved triage protocols in primary care, integrating decision-support tools for general practitioners using machine learning, for example, to reduce unnecessary referrals. Training programs and feedback mechanisms could also align referral practices with specialty criteria. While these strategies were not implemented in this study, the simulation results provide a solid basis for their design and future evaluation. Full article