Routing Method Research Articles

A Novel Secure and Energy-efficient Routing Method for the Agricultural Internet of Things Using Whale Optimization Algorithm

The Internet of Things (IoT) is an all-encompassing system that tracks and monitors real-world activities by gathering, handling, and interpreting data from IoT equipment. It has successfully been applied in several fields, particularly smart agriculture since there is a high demand for high-quality foodstuffs worldwide. It is essential to develop new agricultural production schemes to meet these demands. The heterogeneity of IoT devices makes security essential for IoT communication. Also, IoT devices are restricted in terms of processing, memory, and power capacities. Therefore, energy is a key factor in extending the life of an agricultural IoT network. This study presented a novel energy-aware and secure routing scheme using the Whale Optimization Algorithm (WOA) for IoT, referred to as SRWOA. The simulation results indicate that SRWOA uniformly distributes energy consumption in IoT and maximizes the packet delivery ratio.

An Energy-Efficient Logistic Drone Routing Method Considering Dynamic Drone Speed and Payload

Unmanned aerial vehicles (UAVs), or drones, are recognized for their potential to improve efficiency in last-mile delivery. Unlike the vehicle routing problem, drone route design is challenging due to several operational signatures, such as speed optimization, multi-trip operation, and energy consumption estimation. Drone energy consumption is a nonlinear function of both speed and payload. Moreover, the high speed of drones can significantly curtail the drone range, thereby limiting the efficiency of drone delivery systems. This paper addresses the trade-off between speed and flight range in a multi-trip drone routing problem with variable flight speeds (DRP–VFS). We propose a new model to specifically consider energy constraints using a nonlinear energy consumption model and treat drone speeds as decision variables. The DRP–VFS is initially formulated using mixed-integer linear programming (MILP) to minimize energy consumption. To solve large-scale instances, we propose a three-phase adaptive large neighborhood search (ALNS) algorithm and compare its performance with a commercial MIP solver. The experimental results demonstrate that the proposed method is capable of effectively identifying suboptimal solutions in practical scenarios. Furthermore, results indicate that operating drones at variable speeds leads to about 21% energy savings compared to fixed speeds, with advantages in cost savings and range extension.

Impact of COVID-19 pandemic on the dynamic of patients with oral and maxillofacial trauma: interrupted time-series analysis

Oral and maxillofacial trauma is influenced by various factors, including regional characteristics and social background. Due to the coronavirus disease 2019 (COVID-19) pandemic, a state of emergency was declared in Japan in March 2020. In this study, we aimed to examine the dynamics of patients with oral and maxillofacial trauma over a 12-years period using interrupted time-series (ITS) analysis. Patients were examined at the Shimane University Hospital, Maxillofacial Trauma Center from April 2012 to April 2023. In addition to general patient characteristics, data regarding the type of trauma and its treatment were obtained from 1203 patients (770 men and 433 women). Group comparisons showed significant differences in age, trauma status, method of treatment, referral source, route, and injury occasion. ITS analysis indicated significant changes in combined nasal fractures, non-invasive reduction, and sports injuries (P < 0.05), suggesting COVID-19 significantly impacted oral and maxillofacial trauma dynamics. A pandemic of an infectious disease may decrease the number of minor trauma cases but increase the number of injuries from outdoor activities, resulting in no overall change in the dynamics of the number of trauma patients. Medical systems for oral and maxillofacial trauma should be in place at all times, independent of infectious disease pandemics.

Throat packing via nasopharyngeal airway for severe trismus cases requiring surgical intervention under general anesthesia: A simplified and innovative approach

Throat packing is essential in oral and maxillofacial surgeries to prevent blood and tissue debris aspiration, reducing postoperative complications. Traditional oral route methods are often inadequate, especially in severe trismus cases like Oral Submucous Fibrosis (OSMF), TMJ Ankylosis, and post-traumatic conditions due to limited mouth opening. This study introduces a novel technique using a nasopharyngeal airway (NPA) for throat packing. The method involves inserting a hemostatic dressing through an NPA, ensuring minimal invasiveness and effective airway management. Proper positioning is confirmed with a laryngoscope or fiberoptic scope, and the dressing is secured to prevent dislodgement. This technique is easy, reproducible, and less injurious compared to traditional methods. At our center, throat packing via NPA was performed on 35 patients undergoing surgery under general anesthesia, resulting in high satisfaction and no reported complications.

Galor: Global view assisted localized fine-grained routing for LEO satellite networks

Low Earth Orbit (LEO) satellite networks have been expected to provide global coverage for Internet services with immediacy requirements. The dynamics of an LEO satellite network topology induce the challenges of achieving efficient content retrieval. This article takes an initial step toward achieving efficient content retrieval in LEO satellite networks from the routing perspective. We start with investigating the topology characteristics of LEO satellite networks in terms of the deterministic neighbor relation and the intermittent inter-satellite links. We then propose a Global view Assisted Localized fine-grained Routing (GALOR), which is an information-centric routing mechanism customized to LEO satellite networks. Specifically, GALOR disseminates the link state within a predefined range instead of the entire constellation, incurring less convergence time and control overhead. Therefore, GALOR can calculate the routing table (to guide interest forwarding) based on the local link state and the global neighbor relations. Moreover, GALOR improves the forwarding method of the information-centric routing by reconstructing a failed Pending Interest Table (PIT) entries in response to occasional link failures. Our packet-level experiments show that GALOR outperforms state-of-the-art mechanisms (up to 103.4%) in terms of average packet delivery ratio in content-sharing.

Development of a Distributed Physics‐Informed Deep Learning Hydrological Model for Data‐Scarce Regions

AbstractClimate change has exacerbated water stress and water‐related disasters, necessitating more precise streamflow simulations. However, in the majority of global regions, a deficiency of streamflow data constitutes a significant constraint on modeling endeavors. Traditional distributed hydrological models and regionalization approaches have shown suboptimal performance. While current deep learning (DL)‐related models trained on large data sets excel in spatial generalization, the direct applicability of these models in certain regions with unique hydrological processes can be challenging due to the limited representativeness within the training data set. Furthermore, transfer learning DL models pre‐trained on large data sets still necessitate local data for retraining, thereby constraining their applicability. To address these challenges, we present a physics‐informed DL model based on a distributed framework. It involves spatial discretization and the establishment of differentiable hydrological models for discrete sub‐basins, coupled with a differentiable Muskingum method for channel routing. By introducing upstream‐downstream relationships, model errors in sub‐basins propagate through the river network to the watershed outlet, enabling the optimization using limited downstream streamflow data, thereby achieving spatial simulation of ungauged internal sub‐basins. The model, when trained solely on the downstream‐most station, outperforms the distributed hydrological model in streamflow simulation at both the training station and upstream held‐out stations. Additionally, in comparison to transfer learning models, our model requires fewer gauge stations for training, but achieves higher precision in simulating streamflow on spatially held‐out stations, indicating better spatial generalization ability. Consequently, this model offers a novel approach to hydrological simulation in data‐scarce regions, especially those with poor hydrological representativeness.

Ground Fault Routing Method for Flexible Grounding System Based on Transient Zero Sequence Current Comparison

Abstract As a new development direction of the domestic medium voltage distribution network, the flexible grounding method has a rapid expansion trend, and accordingly, the ground fault protection scheme needs to follow up quickly. This paper calculates and analyzes the distribution characteristics of transient electric quantities in the system under different fault conditions by establishing the transient equivalent model of shunt small resistor casting and cutting in the flexible grounding system. It is found that if transition resistance is small, the application of parallel small resistors will cause the transient zero sequence current amplitude of the faulty line to be maximum and have opposite polarity to the healthy line. When transition resistance is high, distribution characteristics of the transient zero sequence current projection component are also the same. Based on the above features, a flexible grounding system ground fault routing method is proposed, which can cope with a variety of ground fault situations in engineering practice with high reliability, and the correctness of the method is verified through simulation.

A clock synchronization and trigger distribution scheme for China Spallation Neutron Source

China Spallation Neutron Source (CSNS) is a complex system engineering composed of particle accelerators, a target station, and neutron spectrometers with various equipment types and distribution. The collaborative operation of the equipment requires accurate clock synchronization and trigger distribution. Currently, to meet the requirements of different system devices, clock synchronization and trigger distribution in CSNS are implemented based on different methods, resulting in complex clock and trigger networks, demanding upgrades, and a lack of unified timestamps. This paper proposes a clock synchronization and trigger distribution scheme based on the field-programmable gate array (FPGA) and single-mode dual-core fiber link. The decoupling of clock phase synchronization and time base synchronization improves the frequency of phase compensation. Benefiting from the phase interpolator (PI) in the FPGA transceivers, high-precision phase compensation and phase deterministic control are realized, achieving picoseconds synchronization precision and stability in the four-level network. Based on the custom lightweight link layer protocol, by optimizing the routing method of data frames and adopting priority control, the transmission of multiple data is realized, and the hardware delay of the critical data, such as interlock, is guaranteed to be less than 1.5 μs in the four-level system. The scheme provides a general precise clock synchronization and distributed trigger method for CSNS devices, simplifies the system complexity, and is conducive to the system upgrade in the second stage.

Comparative analysis of HEC-HMS and SWAT hydrological models for simulating the streamflow in sub-humid tropical region in India.

Assessment of water availability in sub-humid regions is important due to distinct climatic and environmental conditions. In this study, Soil and Water Assessment Tool (SWAT) and Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) models have been assessed in simulating streamflows in the sub-humid tropical Kabini basin in Kerala, India, spanning 1260 km2. Calibration and validation utilized daily weather data from 1997 to 2015 from the Muthankera gauging station. The study investigated the impact of routing methods on runoff simulation in the ArcSWAT, exploring Muskingum and Variable Storage methods. Evaluation metrics encompassed Nash-Sutcliffe Efïciency (NSE), Coefficient of Determination (R2), Percent bias (PBIAS), RMSE-observations standard deviation ratio (RSR), and Peak Percent Threshold Statistics (PPTS) approach for high-flow values. The result indicates that HEC-HMS outperforms SWAT concerning R2 and NSE values during daily calibration and validation. Monthly simulations showed HEC-HMS closely aligning with SWAT (Variable storage), outperforming SWAT (Muskingum). The PPTS approach proved effective in simulating high-flow values. Both models exhibited proficiency in streamflow analysis within the study area, promising predictive potential for future hydrological studies in sub-humid regions.

Enhancing routing efficiency in social internet of things: R-OPTICS and vEBT based congestion free model

The emergence of the social internet of things (SIoT) network has brought forth distinctive challenges, including node mobility and varying densities, leading to congestion and hampered network efficiency. To overcome these issues, a congestion-free routing model for SIoT is proposed. This model combines the relationship-ordering points to identify the clustering structure (R-OPTICS) algorithm for intelligent node clustering based on relationships and ordering,along with a van emde boas tree (vEBT) for efficient path selection. R-OPTICS enables effective network management by clustering nodes appropriately. The model’s performance is evaluated using metrics such as Rand-Index (1.5765),Davies-Bouldin (-0.4305), and Silhouette Coefficient (1.71685) to assess average goodness values. vEBT identifies optimal paths between clusters, facilitating smart routing decisions. The primary objective of the model is to enhance network efficiency and alleviate congestion by intelligently routing data between clusters. Through extensive simulations, the proposed model outperforms existing routing methods, resulting in improved efficiency and congestion reduction. This congestion-free routing model presents a promising solution to address the unique challenges of SIoT networks, ensuring optimal performance and effective resource management.

Intelligent recommendation method of intelligent tourism scenic spot route based on collaborative filtering

Intelligent recommendation method of intelligent tourism scenic spot route based on collaborative filtering

A Performance Efficient Quadrant-Based Scheme for Multiple Assets Location Preservation in Wireless Sensor Networks

The usefulness of wireless sensor networks has grown throughout numerous areas, many of which demand constant monitoring of the event of interest by the sensor nodes. The wireless transfer of information and the connection of the sensors, however, present the network with a number of security risks. A real-world concern is a danger to source location privacy for networks when several events of interest are monitored. In this research, we present the quadrant-based boundary routing (QBR) method for multiple source location privacy protection. In the first phase, the packet is routed arbitrarily away from the source nodes by random routing, and then quadrant routing begins. Before being sent to the base station using a forward random walk, the packet is transmitted to either side of the network border. We present a theoretical analysis of the maximum delay for the proposed technique. Extensive simulations are carried out and results demonstrate that in contrast to Shortest Path Routing, QBR elevates security by 880.95%, entropy by 623.28%, and capture rate by 71.42% for multiple assets, whereas contemporary PRLPRW increases the corresponding metrics by 692%, 390%, and 49.28%, respectively.

A two-phase nonlinear optimization method for routing and sizing district heating systems

This paper presents a method to find the optimal topology, pipe sizing, and operational parameters of a district heating system under consideration of one design point. The current high costs of district heating systems set limits regarding the minimum heat demand density required for economic network expansions. Optimized routing with ideal pipe sizing and optimal operating parameters offers a potential for cost reduction. Therefore, this paper introduces a new two-phase method for district heating network expansion planning. This method consists of consecutive optimizations, starting with a mixed-integer linear programming followed by a nonlinear optimization. During the mixed-integer linear programming, the district heating system is optimized with continuous diameters, and the nonlinear pressure and temperature dependencies must be linearized. The resulting topology and the continuous diameters are afterward handed over to a nonlinear sparse sequential quadratic programming. The continuous diameters are discretized using a numerical continuation strategy that gradually forces the continuous diameter variables into discrete diameter choices. As a proof of concept, the district heating system for a small town with 400 consumers is optimized and analyzed. The two-phase optimization is performed in 251.68sec, and in most cases, discrete or near discrete diameters are achieved in a nonlinear continuous optimization.

Synthesis and characterization of C@CdS core-shell structures for high-performance capacitors

The present study investigates the synthesis and characterization of carbon@cadium sulphide (C@CdS) core-shell nanostructures for their potential application in electrochemical double-layer (ECDL) capacitors. Two distinct synthesis methods, hydrothermal route and co-precipitation method, were employed to fabricate C@CdS materials, each resulting in unique morphology and growth mechanisms. Detailed microstructural analyses, including high-resolution transmission electron microscopy (HR-TEM) and X-ray diffraction (XRD), revealed nanosheets in co-precipitated sample (CCSP) and core-shell structures in hydrothermally synthesized sample (CCSA). Optical investigations using UV-Vis spectroscopy provided insights into the absorbance properties of the synthesized materials. Electrical characterization, including frequency response testing and impedance analysis, demonstrated distinct capacitive behaviours for CCSP and CCSA, highlighting their potential for use in energy storage applications. Electrical characterization, including frequency response testing, impedance analysis, and galvanostatic charge-discharge (GCD) curve analysis, demonstrated distinct capacitive behaviours for CCSP and CCSA, highlighting their potential for use in energy storage applications. The integration of optical, structural, and electrical analyses provides a comprehensive understanding of the synthesized C@CdS core-shell nanostructures, offering insights into their potential application for ECDL capacitor technology and other emerging energy storage applications. Further optimization of synthesis parameters and exploration of scalable production methods are suggested for future research endeavors.

Explicit Scheme for a Hydrological Channel Routing: Mathematical Model and Practical Application

The computation of hydrographs in large watersheds necessitates utilizing channel routing, which calculates the movement of hydrographs along channel branches. Routing methods rely on an implicit scheme to facilitate numerical resolution, which requires more computational time than the explicit scheme. This study presents an explicit scheme channel routing model that offers a versatile approach to open channel flow analysis. The model is based on mass conservation principles and Manning equations, and it can accommodate varying bed slopes, making it highly adaptable to diverse hydraulic scenarios. In addition, the proposed model considers backwater effects, which enhances its applicability in practical scenarios. The model was tested in a practical application on a rectangular channel with a width of 7 m, and the results showed that it can accurately predict outflow hydrographs and handle different flow conditions. Comparative analyses with existing models revealed that the proposed model’s performance in generating water flow oscillations was competitive. Moreover, sensitivity analyses were performed, which showed that the model is highly responsive to parameter variations, such as Manning’s coefficient, bed slope, and channel width. The comparison of peak flows and peak times between the proposed model and existing methods further emphasized the model’s reliability and efficiency in simulating channel routing processes. This research introduces a valuable addition to the field of hydrology by proposing a practical and effective channel routing model that integrates essential hydraulic principles and parameters. The results of the proposed model (lumped routing) are comparable with the solution provided by the Muskingum–Cunge method (distributed routing). It is of utmost importance to note that the proposed model applies to channel branches with bed slopes below 6°.

Collaborative last mile delivery: A two-echelon vehicle routing model with collaboration points

Collaboration is key to addressing operational efficiencies, and in the context of last mile delivery, operational inefficiencies arising from empty trips, low load factor, and long dwell times require collaboration amongst multiple logistics service providers (LSPs). However, existing studies in the last mile delivery adopting collaboration as a means of sharing strategic infrastructure assets, such as distribution centers (DCs), satellites, and driving vehicles, might not be seen as favorable for logistics service providers due to ownership disputes and loss of control over assets. To address this limitation, a new routing method for addressing operational efficiencies in last mile delivery has been proposed in this study, allowing for collaboration across multiple logistics service providers without sharing strategic assets. We have formulated a two-echelon vehicle routing problem with collaboration Points (2E-VRP-CP) where the exchange of goods happens between second-echelon vehicles belonging to different logistics service providers. The method uses a mixed-integer linear programming model (MILP) that minimizes the total distribution cost and has been tested on randomly generated instances. Results suggest that the proposed collaborative approach of exchanging goods between second-echelon vehicles belonging to different logistics service providers can reduce costs by up to 10% relative to the non-collaborative approach and up to 9% compared to the existing collaborative approach that shares strategic assets. A four-phase heuristic algorithm has also been developed to tackle computationally expensive larger instances, which can obtain cost savings of up to 15% compared to a non-collaborative approach. Future work will involve developing a profit-allocation mechanism and will consider additional constraints to make the model more realistic in addressing a real-world problem. Overall, this model can help fleet managers achieve efficient fulfillment in last mile delivery, while the proposed heuristics can enable stakeholders to scale their solutions to real-world scenarios.

A novel solid waste instance creation for an optimized capacitated vehicle routing model using discrete smell agent optimization algorithm

This paper presents an optimal vehicle routing model for an efficient waste collection process using the Ogun State Waste Management Agency (OGWAMA) as a case study. Just like in many cases, the current manual predetermined routing method used by OGWAMA is inefficient and contributes to excessive fuel usage. These challenges, in addition to the small instances reported in most literature, inspire this research to propose an improved routing scheme that takes into account real-time costs and eventually develops a novel instance based on OGWAMA's operation mode. The developed model was optimized using a new discrete smell agent optimization (SAO) algorithm and compared to firefly algorithm (FA) and particle swarm optimization (PSO). The SAO outperformed FA and PSO, achieving 3.92 % and 19.38 % improvements in service cost (SC) and 2.65 % and 14.96 % improvements in total travel distance (TTD), respectively. The convergence rates of the algorithms were also compared; using the Optimized Depot (OD) techniques and results shows the acceptability of the proposed approaches.

Energy efficient data dissemination in wireless sensor network enabled IoT using domain‐adaptive message passing graph neural network

SummaryIn the past few years, restricted wireless sensor networks (WSNs) enabled the Internet of Things (IoT) have attracted significant attention and expansion to enhance service delivery and resource efficiency. Dissemination is a service offered by WSN that uses radio transmission and over‐the‐air programming for updating the deployed sensor nodes through online. The centralized data dissemination methods are replaced by the distributed approaches because they affect the drawbacks of a single point of failure, no scalability, and insecurity. Therefore, an Energy Efficient Protocol for Data Dissemination in Wireless Sensor network‐enabled IoT using Domain‐Adaptive Message Passing Graph Neural Network (EEP‐WSN‐IoT‐DMPGNN) is proposed in this paper. The nodes are formed as clusters utilizing the Deep Fuzzy Curriculum Clustering (DFCC) technique that rewards nodes belonging to a given cluster. By using the Crayfish Optimization Algorithm (COA), the Cluster Head (CH) selection optimally chose the ideal CH and satisfies the multiple objective functions, such as energy, delay, traffic density, and distance. Afterward, domain‐adaptive Message Passing Graph Neural Network (DMPGNN) based routing protocol is developed, the input given to the routing protocol includes a sink, action history, future node, and maximum‐distance node, which attains enhanced data transfer in the chosen path. The proposed technique attains a lower no. of dead nodes, lower energy consumption, and higher Network Lifetime while analyzed with existing techniques, such as routing technique depending on deep learning for effectual data transmission in 5G WSN communication (DL‐RPDT‐WSN), Reinforcement‐Learning base energy effectual optimized routing protocol in WSN (RL‐EERP‐WSN), and Energy‐efficient intellectual routing method for IoT‐enabled WSN (EIR‐IoT‐WSN), respectively.

The single picker routing problem with scattered storage: modeling and evaluation of routing and storage policies

Despite ongoing automation efforts, most warehouses are still manually operated using a person-to-parts collection strategy. This process of collecting items of customer orders from different storage locations accounts for the majority of the operating costs of the warehouse. Hence, optimizing picker routes is an important instrument to reduce labor costs. We examine the scattered-storage variant of the single picker routing problem in a one-block parallel-aisle warehouse. With scattered storage, an article can be stored at several storage locations within the warehouse, whereas with classic storage, each article has a unique storage location. We use our recently published network-flow model with covering constraints that is based on an extension of the state space of the dynamic-programming formulation by Ratliff and Rosenthal. With modifications in the state graph, this model serves for both exact and all established heuristic routing methods for picker routing. The latter include traversal, return, largest gap, midpoint, and composite. We show that these routing policies can also be implemented through adaptations in the state space. Extensive computational studies highlight a comparison of the different routing and storage policies (in particular class-based storage policies) in the scattered storage context. Analyses demonstrate which combinations of policies are advantageous for the given warehouse layout. For class-based storage policies, we emphasize how the scattering of articles of different classes should be performed: scattering of C-articles is advantageous with reductions of up to 25%. In contrast, when articles are uniformly distributed, A-articles should be scattered.

An Automated Method for Pipe Routing in Ship Unit Modules

This study addresses challenges in ship arrangement design by proposing an automated pipe routing method in ship unit modules. Currently, designers rely on experience, lacking quantitative assessments and causing difficulties for non-experts. The proposed method incorporates expert knowledge and design rules into an expert system, evaluating expertise and adherence to rules. The system’s evaluation result was used as the objective function of an optimization problem formulated for pipe routing in conjunction with metrics such as total pipe length, the number of bends, and space availability. Through validation by comparing actual unit module designs, it is demonstrated that the proposed method suggested improved pipe routing design while adhering to expert knowledge.