Route Selection Research Articles

Software-Defined Networking-Enabled Efficient Default Route Configuration in IEEE 802.15.4 Protocol: A Smart Algorithmic Approach

Today’s software-defined networking (SDN) applications have many challenges. Its main applications are focused on networks with nodes with high processing capacity. Applying SDN technology in nodes operating on batteries with limited computing capabilities is challenging. In this context, this paper proposes SDN-enabled algorithms for the remote configuration of the default route to be applied in multi-hop wireless sensor networks (WSNs) with tree-type topology using the IEEE 802.15.4 protocol. The routing algorithm to define the default route of each node is executed in an SDN-enabled WSN controller (SDWSN). The SDWSN controller receives information on the state of the network, executes the Djikstra or Kruskal algorithms, and configures the default route of the nodes remotely. The best route selection is based on the battery level of the nodes and the distance between them. The results show that using network protocols to configure the nodes remotely is unnecessary.

Experience reduces route selection for conspecifics by the collectively migrating white stork

Migration can be an energetically costly behavior with strong fitness consequences in terms of mortality and reproduction.1,2,3,4,5,6,7,8,9,10,11 Migrants should select migratory routes to minimize their costs, but both costs and benefits may change with experience.12,13,14 This raises the question of whether experience changes how individuals select their migratory routes. Here, we investigate the effect of age on route selection criteria in a collectively migrating soaring bird, the white stork (Ciconia ciconia). We perform step-selection analysis on a longitudinal dataset tracking 158 white storks over up to 9 years to quantify how they select their routes based on the social and atmospheric environments and to examine how this selection changes with age. We find clear ontogenetic shifts in route selection criteria. Juveniles choose routes that have good atmospheric conditions and high conspecific densities. Yet, as they gain experience, storks' selection on the availability of social information reduces-after their fifth migration, experienced birds also choose routes with low conspecific densities. Thus, our results suggest that as individuals age, they gradually replace information gleaned from other individuals with information gained from experience, allowing them to shift their migration timing and increasing the timescale at which they select their routes.

A System of IoT Devices to Prevent the Underloading And Overloading of Railway Wagons

Coal transportation poses substantial obstacles for corporations such as Coal India Limited (CIL), including concerns with route selection, rehandling, precise weighing, demurrage costs, and underloading fines. CIL's failure to adopt optimal transportation routes has resulted in needless costs. Coal rehandling at depots has proven expensive, and there are disparities in coal transportation rates. The lack of weighbridges makes accurate weighing difficult, resulting in probable pilferage. Demurrage costs have been a financial hardship, and railway carriages underloaded have resulted in fines. To address these challenges, the proposed IoT solution seeks to reduce financial losses, improve operational efficiency, increase accountability, ensure regulatory compliance, provide real-time monitoring, automate alerts and penalties, improve communication, improve data security, provide multilingual support, and, ultimately, increase revenue. This idea aims to change coal transportation by increasing efficiency, transparency, and cost-effectiveness for CIL and the government. However, it incurs initial expenses and complications associated with IoT deployment, maintenance, and regulatory compliance. Keywords: CIL, IOT, efficiency, pilferage.

Tuning Local Order in Starch Nanoparticles Exploiting Nonsolvency with "Green" Solvents.

Starch is a renewable biopolymer that can be sourced from agricultural waste and used to produce nanoparticles (SNPs). In particular, amorphous SNPs have potential application in numerous fields, including the consolidation of weakened paintings in the cultural heritage preservation. Starch dissolution followed by nanoprecipitation in nonsolvents is an advantageous synthetic route, but new methodologies are needed to feasibly control the physicochemical properties of the SNPs. Here, we explored nanoprecipitation by nonsolvency using a set of "green" solvents to obtain amorphous SNPs, rather than starch nanocrystals already reported in the literature. The effect of the nonsolvent on the ordering of polymer chains in the obtained SNPs was studied. The recovery of local order (e.g., isolated V-type helices) after dissolution was shown to depend on the type of solvents used in the dissolution and precipitation steps, while long-range order (extended arrays of helices) is lost. Aqueous dispersions of the SNPs provided effective consolidation of powdery painted layers, showing that the selection of particle synthetic routes can be dictated by sustainability and scalability criteria. These "green" formulations are candidates as new consolidants in art preservation, and the possibility of tuning local order in amorphous starch assemblies might also impact fields like food chemistry, pharmaceutics, and nanocomposites, where SNPs with tunable amorphousness are more advantageous than nanocrystals.

Fire Station Management System Based on Digital Twin

To streamline travel, alleviate traffic congestion, and enhance overall traffic safety, the Internet of Vehicles (IoV) technology has emerged. This not only simplifies the lives of modern individuals but also integrates with other technologies, fostering advancements in various fields. Notably, the integration of digital twin technology with IoV is widespread, enabling the visualization of road conditions for informed route decisions and optimization. This paper focuses on applying this capability to a fire truck management system, empowering swift route selection, optimal vehicle deployment, and efficient execution of rescue activities in the shortest time and at the lowest cost.

Application of Fuzzy Computing for VANET Routing

In the realm of vehicular ad hoc networks (VANETs), routing plays a pivotal role in ensuring efficient communication among vehicles and infrastructure. Traditional routing protocols often struggle to adapt to the dynamic and uncertain environment of VANETs, characterized by rapidly changing network topologies and unpredictable traffic conditions. In any case, the without a doubt solid and stunning nature of vehicular conditions presents superb difficulties for controlling shows, including unusual straightforwardness, changing association geologies, and dynamic traffic conditions [1, 2]. By incorporating fuzzy logic into routing algorithms, vehicles can make more informed decisions regarding route selection, considering factors such as traffic congestion, road conditions, and vehicle mobility patterns. This paper provides an overview of the key concepts of fuzzy computing and discusses its potential applications in VANET routing. Furthermore, it explores existing research efforts in this area, highlighting the benefits and challenges associated with fuzzy based routing approaches. Through a comprehensive review of the literature, this paper identifies current trends and future directions for leveraging fuzzy computing in VANET routing, aiming to improve the reliability, scalability, and efficiency of communication in vehicular networks.

Multi-Objective ED2MT Strategy-based Clustering Algorithm for Large Scale WSN

Wireless Sensor Networks play a crucial role in everyday life, providing a wide range of networking structures to develop real-time applications. Energy consumption is limited by WSN devices, so reducing the energy consumption of malicious nodes is crucial to improving network performance. A main concern of WSNs from an application perspective is the prolonged lifetime of the network and reduced energy usage. To overcome these challenges a novel multi-objective ED2MT (Energy, Distance, Degree, Mobility, Time) driven strategy has been proposed which consists of three phases namely dual Cluster Head Selection, data compression, and routing. In the first phase, the proposed ED2MT uses a Fuzzy C-Means (FCM) clustering algorithm to select dual Cluster Head (CH) which has been performed in two stages, namely Inter-Cluster and Intra-Cluster. In the second phase, a Neighborhood Indexing Sequence (NIS) technique is utilized to compress the number of bits in the data before it is transmitted. In the third phase, a multi-objective shortest-path routing algorithm is utilized for route selection. The performance of the ED2MT method has been evaluated using network lifetime, delay, throughput, alive nodes, energy efficiency, and packet delivery ratio to provide a better result. The proposed ED2MT framework achieves 20.55%, 18.4%, and 15.2% more residual energy than C3HA, CAFL, and DHSCA algorithms.

Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm for Secure Cluster Head and Routing Selection in WSN

Wireless Sensor Networks (WSN) is a group of Sensor Nodes (SNs) which are performed to intellect a normal singularity from an environment. The minimum resources and computational power of WSN creates vulnerable to number of security attacks. To addresses this problem, this paper proposes a Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm (M-TDWPOA)-based secure and energy aware multi-hop routing in WSN with the Base Station (BS). The proposed method comprises of two significant steps: Initially, dynamic energy-efficient Cluster Head (CH) selection by utilizing the multi-objective functions such as Distance between neighbor nodes, Distance between BS to CH, Energy, Centrality and Trust Threshold. Then, dynamic secure multi-hop routing is selected by the multi-objective functions such as Distance between BS to CH, Energy, Trust Threshold which is dynamically reducing the network overhead. Furthermore, a security aware multi-hop routing is employed through the different trust classes like direct, indirect, recent and authentication. The proposed M-TDWPOA approach is estimated with various performance metrics like alive nodes, dead nodes, energy consumption, Throughput and so on. The proposed M-TDWPOA achieves the minimum energy consumption of 0.4927J, 0.4851J, 0.4771J, 0.4693J and 0.4617J when compared to the existing methods like Fractional Artificial Lion (FAL).

Nutrition in Pediatric Burns.

Nutrition and modulation of the hypermetabolic response to acute burns are reviewed in this article. Methods to determine caloric requirements are evaluated, including indirect calorimetry and predictive equations. Individual nutritional components of carbohydrates, fat, protein, vitamins, and trace elements are discussed specifically in relation to acute burn care. Selection of formula and route of administration are outlined, with an enteral high-carbohydrate, low-fat diet being preferable. Awareness and recognition of the signs and symptoms of malnutrition is critical in the management of variable caloric needs throughout hospitalization. Lastly, the catabolic state of acute burns is addressed through early excision and grafting and implementation of various pharmacologic agents, including growth hormone, insulin-like growth factor-1, insulin-like growth factor-binding protein-3, insulin, propranolol, and oxandrolone. Through a multipronged approach to nutrition, pediatric burn patients are provided the substrates for successful recovery and rehabilitation.

Route selection of periodic multimodal transport for logistics company: An optimisation approach

For transporting cargo from an origin to a destination, there are several routes with various transport modes, but in practice, only one route should be selected. In this regard, one of the main activities of Logistics Companies (LCs) is to select an optimal route for transporting freight. This complexity intensifies in periodic transport across multiple origins and destinations. This paper aims to develop a dual-phase mathematical model for LCs, considering cost, time, reliability, and air and noise pollution. Indeed, in periodic transport, there is a possibility of merging the cargo with the next shipment, which can lead to lower total costs. Multimodality is also another important consideration for LCs that can reduce cost and environmental impacts. The model leverages Data Envelopment Analysis in the tactical planning phase to filter efficient routes and a multi-objective model in the operational phase for optimal route selection. The proposed approach is validated by the real case of Thailand and Vietnam, which presents practical and managing insights for LCs and shippers. The study underscores the significance of route adjustments in periodic transport and provides valuable insights for LCs and shippers, emphasising the benefits of multimodal solutions in optimising freight logistics.

Reverse Logistics Network Design for Medical Waste Disposal under the Scenario of Uncertain Proposal Demand

With the development of the healthcare industry, the demand for medical services and protective equipment is boosted, causing the generation rate of infectious medical waste to increase rapidly. Therefore, it is of utmost importance for decision makers to effectively predict the potential risks and propose corresponding solutions. This paper investigates the reverse logistics network optimization for medical waste under the conditions of an uncertain proposal demand. Firstly, a prediction model of medical waste based on the SEIR epidemiological dynamics is constructed, in which both routine and public health emergency scenarios are simultaneously considered. Secondly, a bi-objective location-routing optimization model for a medical waste reverse logistics network is proposed, by simultaneously optimizing the total economic cost and potential risk throughout the entire logistics process. Subsequently, an NSGA-II algorithm is designed for a model solution in response to the model’s characteristics. The epidemiological dynamics-based prediction model is validated by the real case to be scientifically effective in predicting the amount of generated medical waste with a mean absolute percentage error (MAPE) of 18.08%. The constructed reverse logistics network model and the NSGA II algorithm provide a medical waste process center location, transportation routing, and vehicle selection solutions for both routine and emergency public health cases of Xi’an city with large, medium, and small scales. The above results indicate that the research scheme proposed in this paper could significantly reduce the medical waste logistics-related risks and costs and provide decision makers with more safe and reliable logistical solutions.

TEE-AODV Trust-based Route Selection and Improving Energy Efficiency in MANET

Mobile ad hoc networks (MANETs) are the future communication infrastructure for all devices that operate without the elaborate design found in wired infrastructure networks. Constructing a routing protocol that helps MANETs to achieve (QoS). It is quite challenging to achieve energy-efficient routing, due to its limited resources and node mobility concerns, which would impact node resource stability and lead to congestion and lower user QoS. A novel Trustworthy Energy Efficient AODV protocol has been proposed for improving path selection and locating trusted stations to maximize QoS. This approach employs location data, transmission speed, and directions to estimate the energy usage and trustworthiness of distinct nodes. The proposed TEE-AODV protocol includes two new control information namely Trust Request packet and Trust Reply. (T-RREP) AODV's packet includes a neighbor list and channel trustworthiness. The number of working neighbors in every path is counted, and this information is used to efficiently choose paths. The TEE-AODV calculates a QoS supporting metric for each detected route to identify a traffic-free path at any moment and determines whether it is a trusted route. The NS-2 simulator has been used to assess the suggested approach in terms of specific characteristics, including energy consumption (EC), (PDR), end-to-end delay (), and number of dead nodes. According to the experimental results, the suggested framework enhances reliable route selection while lowering e nd-to-end latency and improving energy efficiency. The experimental findings indicate that the suggested TEE-AODV protocol achieves high energy efficiency rate in the selection of trustworthy route and minimizing the E D .

A Novel Computational Mathematical Model for Team and Route Selection of the Emergency Response Operations

During the daily operations of emergency response, the decision maker is faced with the complex challenge of selecting a team and route in a short time period to respond and attend to the emergency. This study presents the combined use of the Analytic Hierarchy Process (AHP), the Élimination Et Choix Traduisant la Realité II (ELECTRE II), and the Dijkstra algorithm to deal with such situations. First, the AHP method is implemented to rank the aspects that are most relevant to a given emergency. Subsequently, this ranking is employed in ELECTRE-II to determine which emergency response team is best prepared to provide support. In the last stage of the proposed model, regarding the geographic coordinates of the team and the emergency, the Geographic Information System (GIS) utilizes the Dijkstra algorithm to regulate the most suitable route for assistance.

A secure framework for effective workload resource management

An efficient and dynamic role-based access-control (RBAC) model is presented in this work which utilizes access-control for internet of things (IoT) nodes while minimizing storage and computational overhead. Also, for the identification of the malicious packets at the gateway server, a machine learning method has been presented. In addition, a framework for data management techniques in the IoT environment is designed to ensure efficient and secure storage, management, and processing of IoT data. The results have been evaluated by using the Montage and Cybershake workload in terms of energy consumption, processing time, detection accuracy and misclassification rate. The results show that the proposed secure framework for effective workload resource management (SFE-WRM) attains better performance in comparison to the reliable and energy‐efficient route selection (REERS) and FTA-WRM method. Also, by using the security method, the proposed method provides better security to the IoT nodes during the data aggregation and processing of the workload. The ultimate aim of this work is to provide a solution for the development of a secure and efficient IoT environment that can address critical security challenges and enable the widespread adoption of IoT devices and services.

Intranasal administration of unadjuvanted SARS-CoV-2 spike antigen boosts antigen-specific immune responses induced by parenteral protein subunit vaccine prime in mice and hamsters.

With the continued transmission of SARS-CoV-2 across widely vaccinated populations, it remains important to develop new vaccines and vaccination strategies capable of providing protective immunity and limiting the spread of disease. Heterologous prime-boost vaccination based on the selection of different vaccine formulations and administration routes for priming and booster doses presents a promising strategy for inducing broader immune responses in key systemic and respiratory mucosal compartments. Intranasal vaccination can induce mucosal immune responses at the site of SARS-CoV-2 infection; however, the lack of clinically approved mucosal adjuvants makes it difficult to induce robust immune responses with protein subunit vaccines. Herein, we evaluated the immunogenicity of heterologous prime-boost regimens in mice and hamsters based on a parenteral vaccination of the antigen in combination with sulfated lactosylarchaeol (SLA) archaeosomes, a liposome adjuvant comprised of a single semisynthetic archaeal lipid, followed by an intranasally administered unadjuvanted SARS-CoV-2 spike antigen. Intranasal administration of unadjuvanted spike to mice and hamsters increased serum spike-specific IgG titers and spike-neutralizing activity compared with nonboosted animals. Spike-specific IgA responses were also detected in the bronchoalveolar lavage fluid in the lungs of mice that received an intranasal boost. In hamsters, the intranasal boost showed high efficacy against SARS-CoV-2 infection by protecting from body weight loss and reducing viral titers in the lungs and nasal turbinate. Overall, our heterologous intramuscular prime-intranasal boost with SLA-adjuvanted and unadjuvanted spike, respectively, demonstrated the potential of protein subunit formulations to promote antigen-specific systemic and mucosal immune responses.

Comparative analysis of process selection and carbon emissions assessment of innovative steelmaking routes

Innovative emission reduction technologies combined with optimized processes can effectively reduce the environmental pressure in the iron and steel industry (ISI). Identifying and comparatively analyzing the carbon emissions (CE) from steel productions is conducive to achieving China's ambitious carbon reduction targets. In this paper, we investigated two innovative routes: the shaft furnace-induction furnace-basic oxygen furnace route (SF-IF-BOF) and shaft furnace-electric smelting furnace-basic oxygen furnace route (SF-ESF-BOF), alongside two traditional routes: the blast furnace-BOF route (BF-BOF) and SF-electric arc furnace route (SF-ESF). The analysis was based on a functional unit of 1000 kg of crude steel (CS). By establishing the mass and energy balance models and decision criteria, 12 typical smelting scenarios for these routes were analyzed and compared in terms of materials consumption and CE. The results showed that increasing the proportion of scrap was beneficial for reducing CE. Under the condition of a 50% scrap ratio, the CE of the SF-IF-BOF, SF-ESF-BOF, BF-BOF, and SF-EAF routes were 768, 823, 946, and 870 kg, respectively. The SF-IF-BOF route displayed the most significant advantage of CE reduction. In addition, by using 100% zero-carbon electricity substitution, the SF-EAF route exhibited the greatest CE reduction effect of 41% (from 870 to 510 kg/t). While deploying 100% biomass fuels to replace coal, the CE of the BF-BOF route reduced by 80%. Finally, after introducing biomass fuels and zero-carbon electricity, the CE of the SF-IF-BOF route was minimized to a significantly low 13 kg/t (from 768), nearing zero CE. This research is expected to provide a novel solution for further CE reduction in the future of ISI, contributing to environmental sustainability and carbon reduction goals.

Fuzzy logic Based Seagull Optimization Algorithm for Efficiency and Security in Wireless Sensor Networks

Wireless sensor networks (WSN) find applications in diverse fields such as environmental monitoring, healthcare, and industrial control systems. The pivotal components of these networks are the sensor nodes, which, unfortunately, consume a substantial amount of energy when transmitting information directly to the base station (BS). To mitigate energy consumption associated with direct transmission, this paper proposes a two-phase approach utilizing hybrid clustering and routing algorithms. The proposed approach incorporates fuzzy and seagull techniques for clustering and adopts optimal CH (cluster head) selection, CBRP (Cluster-Based Routing Protocol), and AES (Advanced Encryption Standard) for secure routing. The system employs rule-based fuzzy logic to correlate input values in both clustering and routing algorithms. Decision-making is based on factors such as the residual energy of sensor nodes, distance from the BS, and the number of nodes within the communication range. Input variables' crisp values are transformed into diverse fuzzy values, and the fuzzy output values are converted back to crisp values using the centroid defuzzification method. Selection of cluster heads and routers is determined by the output values, with sensor nodes being allocated to respective cluster heads based on their load-handling capacity. The routing path is then generated considering the capacity of routers. Simulations are conducted to evaluate energy consumption, active sensor nodes per round, and the sustainability period of the network. This proposed hybrid clustering and routing system aim to enhance the overall efficiency of wireless sensor networks by optimizing energy consumption and ensuring secure data transmission. The optimization model identifies the most suitable nodes in the routing cycle, starting with chosen cluster heads. The overarching goal is to enhance network indicators, including network lifespan, power consumption per node, and packet delivery percentage. The proposed solution achieved a network lifetime of 100 hours and a data delivery rate of 98%. additionally, it consumed the least amount of energy, measuring at 95,000 joules.

Influencing Pedestrian Route Choice Through Environmental Stimuli: A Long-Term Ecological Experiment

Urban centers are getting crowded, public transportation is becoming congested, and mass events are attracting an increasing number of people. Crowd disasters are not rare, and to prevent them the careful planning of pedestrian facilities and collaboration among stakeholders in the organization of events are crucial. When communication and coordination among stakeholders are sufficient, safety can usually be achieved; however, even in such cases, unexpected situations may occur. Automated crowd-control methods are required to address such situations. However, little is known about how crowd behavior can be influenced without direct human intervention. In this study, we investigated the use of environmental stimuli to modify pedestrian behavior (more specifically, route choice) in an educational facility. Colors, lights, signs, and sounds were used to influence route selection. The results show that light and, in part, LED information displays are somehow effective and could be valid candidates to pave the way for automated crowd control systems (especially for night events). The experiment presented here considers low crowd density. However, we believe that this could help encourage the balanced use of space by pedestrians under normal conditions and establish good practices. In turn, this can delay the creation of high densities, which are often the cause of fatalities in crowd disasters, and provide staff with time for intervention.

Short-term forecasting method for prevailing orientation of leads in the Laptev Sea ice cover during winter-spring season

Sea ice leads are potential routes of easier navigation in the Arctic seas during winter-spring season. Forecasts of the prevailing orientation of ice leads are required for the early selection of the optimal route of a ship in ice. An automated method for short-term forecasting of the prevailing orientation of leads in the Laptev Sea is developed in AARI based on the analog method. Data sources for making a forecast include predictive fields of atmospheric pressure up to 3 days in advance and historical data on surface atmospheric pressure, ice drift and sea ice leads. The data on ice leads are obtained by interpreting SuomiNPP satellite images in a visible and infrared range at 375 m spatial resolution for the cold seasons 2016–2022. The ice drift data are freely available OSI SAF data. In order to make a forecast of the prevailing orientation of leads by a predictive atmospheric pressure field, it is necessary to select from an electronic data archive a historical field of atmospheric pressure that is the most similar to the predictive field. The lead characteristics on the selected date are taken as the predictive ones. Automated selection of an analog consists in searching for an atmospheric pressure field from the electronic data archive which has minimal metrics for the forecast date together with 2 previous days. The metric is calculated as the sum of the squared differences between the change in atmospheric pressure per 1 km in the latitudinal and meridional directions between the predictive atmospheric pressure field and the potential analog field. Actual lead data for a date of the selected analog may not be available due to thick clouds. In this case, the direction of the minor axis of the strain ellipse calculated from ice drift data is taken as the prevailing orientation of leads. The success ratio of diagnostic forecasts by the method developed is 72 %. Comparison of the success ratio of the diagnostic forecasts with the results of inertial and climatic forecasts demonstrates a high effectiveness of the method for cases of abrupt change in the direction of air flows over the Laptev Sea within the first 1–2 days.

Multi-criteria analysis for the best route selection of resilience hinterland connection: a case study of South Sumatra province, Indonesia

Multi-criteria analysis for the best route selection of resilience hinterland connection: a case study of South Sumatra province, Indonesia