Routing Management Research Articles

Wine Routes and Sustainable Social Organization within Local Tourist Supply: Case Studies of Two Italian Regions

This paper is aimed at investigating wine companies’ perceptions and attitudes towards the role of wine routes as an actual tool to improve their tourist attractiveness and sustainable tourism on a territorial level. Through a comparative approach, some wineries from the Italian regions of Abruzzo and Tuscany have been surveyed and in-depth-interviews to key local stakeholders have been conducted. The aim of the survey is to investigate the companies’ perception about wine tourists’ characteristics and wine route management. Through a multivariate analysis, the reasons behind wineries’ satisfaction/discontent have been analyzed. The findings identify a close relation between the complexity of the services offered, the companies’ involvement in the wine routes management and their satisfaction about the results obtained. The more complex the services are, the more satisfied the companies. The comparative analysis of the strengths and weaknesses pointed out by the wineries’ keepers has allowed some general considerations about the tools to use for the improvement of wine routes management. In a broader sense, the wineries’ direct involvement both in investment and in governance appears to be key in the success of the routes as a model for local tourism development.

Ensuring safety and security in employing drones at airports

Currently, the use of civil drones at airports is statutorily prohibited in most countries. This is due to threats to safety associated with the flying of drones over such facilities. The research reported in this paper deals with the development of a method for enhancing the safety of the movement of drones and, in that context, a special system for managing the route of drones. The proposed system makes it possible to employ drones safely at airports via confining the zone of their flight to a strictly designated corridor. This should help stave off the threat of drones colliding with other, land and air, transportation vehicles employed at an airport, particular elements thereof, and people. The development of this system may create the conditions for the removal of existing restrictions on the use of drones at airports. In addition to providing boosts in safety, the system helps manage the route of a drone. To enable the practical use of the proposed route management system, the authors have developed a special methodology for deploying it at airports.

An Efficient Multipath Routing Protocol for Decentralized Wireless Sensor Networks for Mission and Safety-Critical Systems

Background: Wireless Sensor Network (WSN) is a useful integral part of mission and safety-critical systems, whose failure may result in injury, loss of life, serious environmental damage and/or may result in the failure of some goal-directed activities. Its major challenges are reliability, and security related issues during data delivery. To mitigate these, the development of secure routing protocols, which are capable of optimum utilization of WSNs’ nodes resources for effective data delivery, has been one of the major research interests. However, most of the existing single path routing protocols for WSNs are not able to meet with up with the performance requirements of the mission and safety-critical systems. Meanwhile, a few that are multipath require high computational power, energy and their multiple paths are not ranked, this makes them unsuitable as routing protocol for WSNs in mission and safety-critical systems. Objective: In this work, we propose a secure multipath routing protocol based on sectorisation and best neighbouring nodes selection models to meet up with the performance requirements of WSNs in the mission and safety-critical systems. The protocol is capable of providing optimal multiple ranked route paths for reliable data delivery. Methods: A route management technique, using direct approach, is developed for selecting different optimal data paths for reliable data routing. Also, simple but efficient lightweight privacy preserving authentication scheme is proposed for the protocol to ensure security and privacy during data routing. Computational and security analysis were performed to ascertain its efficiency in terms of computational cost, energy consumption and security. Results: The results showed that SMRP achieved better performance compared to the two stateof- the-art solutions in terms of end-to-end delay, energy consumption and data routing reliability. Conclusion: The proposed protocol is suitable for wireless sensor network due to its low delay, low energy consumption and routing reliability metrics.

Travelling Salesman Problem in The Case of Refined Sugar Shipment From Distribution Centers For the beverage Industries

Travelling Salesman Problem (TSP) is one of the route management models with a higher level of complexity than the shortest path problem (SPP). In the logistic context, the distribution of products can be in the form of resources such as trucks, ships, aircraft, and other transportation fleets that can be completed with different settlement models. In TSP, researchers look for the shortest route that crosses all cities the product will be sent to and will end up in the city that first sent the product. The distribution of refined sugar that occurs is a problem that is not following its designation, causing the proposed creation of a distribution center to streamline the distribution of refined sugar to a beverage company that is closest to the processing plant and its delivery with the same truck trailer. with more structured through the formulation of a linear program for TSP with asymmetrical distance matrix. So that resulting in minimizes the distance distribution of refined sugar by trucks in distributing its products by 74.28%.

Hybrid optimization routing management for autonomous underwater vehicle in the internet of underwater things

Internet of Underwater Things (IoUTs) is completely embraced by acoustic sensor nodes that are commonly battery consumption. The sensor’s battery life is restricted and it is problematic while it needs to recharge. Moreover, these kinds of underwater sensor nodes may form the cluster to store the huge amount of energy. The cluster head formation is the main problem that the cluster head needs to use added energy for aggregation and data collection to broadcast the data packets to the destination. The autonomous underwater vehicles (AUVs) have to be used to connect the sensor nodes with the internet or particular devices, the path discovery within the AUV is the primary issue for producing the efficient routing in IoUTs. In this paper, the proposed routing strategy is used to provide energy proficiency while performing the cluster-based routing. AUV is responsible for selecting the cluster head and maintaining cluster-based scheduling. The AUV path is constructed to increase the residual energy for the sensor nodes in the network. It coordinates the AUV’s effective way of arranging calculations into the steering convention. This incorporated specialized strategy depends on two stages: A-ANTD (AUV-Assisted Network Transmit Devising) and TARD (Transmit Assisted Route Devising). A-ANTD uses the collaboration of performance of multiple ventures to decrease energy utilization for the system and stays away from the problem area and sector issue with a vital GN (Gateway Nodules) plans. The experimental results show that the proposed methodology has improved performance compared with the other methods.

Desarrollo de una aplicación móvil utilizando el framework MEAN Stack e IONIC: Un estudio de caso en una compañía de transporte

La utilización de aplicaciones móviles se ha convertido en un componente relevante transaccional dentro de una organización. Por tal motivo, se desarrolló un sistema móvil informativo para la gestión de rutas dentro de la compañía de transporte pesado Torres Revelo S.A, basado en la metodología ágil de desarrollo SCRUM y el framework MEAN Stack e Ionic basados en JavaScript. En este proyecto, se sometió a una prueba de usabilidad en función a la norma ISO/IEC 25010, tomando como referencias la inteligibilidad, aprendizaje, operabilidad, protección frente a errores de usuario, estética y accesibilidad. Se utilizó un cuestionario de 26 preguntas con el uso del método de Nielsen, aplicando a un total de 73 usuarios en todo el proceso del desarrollo de la aplicación, basado en prototipos. Al tabular los resultados obtenidos, se aplicó la prueba de chi cuadrado para determinar que la aplicación móvil y web es fácil de usar, aprender y permite al usuario realizar la trazabilidad de la ruta más corta. Estos procesos permiten tener una mejor gestión de la información dentro de la compañía.

Traffic modeling and optimization in datacenters with graph neural network

Traffic Optimization (TO) is a well-known and established topic in datacenters with the fundamental goal of operating networks efficiently. Traditional TO heuristics may suffer from performance penalty as it mismatches actual traffic, while Artificial Intelligence (AI) which has undergone a renaissance recently is gradually being applied to the network optimization and has shown excellent advantages. However, the current AI technologies (e.g., DGN, DRL, DBA, etc.) have difficulty in adapting to the dynamic and variable characteristics of the network due to their lack of generalization ability, which limits the development of intelligent networks. As Graph Neural Network (GNN) can support relational reasoning and combinatorial generalization, we research how to model and optimize traffic in datacenters with GNN in this paper. First, we proposed a GNN model for reasoning Flow Completion Time (FCT), which is able to provide accurate estimation of never-seen network states. Then we designed a GNN-based optimizer for TO, which can be used to in flow routing, flow scheduling and topology management. Finally, the experimental results verify that the GNN model has a high inference accuracy, and the GNN-based optimizer can significantly reduce the average / p10 (the 10th percentile) FCT. Therefore, GNN has a great potential in network modeling and optimization, and has a wide range of applications.

Migration, Refugees and Illegal Routes in Times of Health Crisis: The Case of the Lebanon-Syria Border

Lebanon’s border management in times of conflict, and particularly throughout the Syrian crisis has been subject to multiple criticisms across policymakers, academics and the international community alike. Lebanon’s post-hoc approach to tackling its border management and ‘sensitive’ matters of national security, coupled with the fact that it has not signed the 1951 Refugee Convention have proven to be fundamental obstacles when it comes to the country’s maintenance of human rights standards in the areas of its treatment of various refugee populations within its borders. They have also proven to be fundamental obstacles to its management of illegal routes and illegal movement amid an escalating pandemic. In times of COVID-19, Lebanon’s lack of preparedness, its lack of coordination between its governmental institutions could mean that their border policy will once again serve as a means to hurt both sides.

A Comprehensive Analysis of ACO for Wireless Sensor Network Routing

The proliferations of IoT technologies and applications have led to an increased interest in Wireless Sensor Networks (and in particular, multi-hop networks). Wireless sensor networks are composed of small mobile terminals which have limited system resources. Due to this, these networks are vulnerable to changes in network status arising from changes in the network parameters such as, position / layout of sensors, signal strength, environmental conditions, etc. In addition, the network nodes are also constrained in terms of energy provided by the battery. It is an significant consideration to be accounted so as to prolong their operational time, since this adds to the network lifetime. Lot of research has gone into routing and transmission technologies for wireless sensor networks. Conventional routing mechanisms for WSNs still suffer from energy-hole problem caused by difficulties in adaptive route management. Thus, it is imperative that efficient routing mechanisms be developed in order to conserve energy and improve network lifetime. One popular approach is to use meta-heuristic algorithms for optimal path selection in a WSN route management system. A very popular meta-heuristic algorithm used for this objective is the Ant Colony Optimization (ACO) algorithms. ACO has been used as a base for many routing management systems. In this paper an extensive analysis of the performance of ACO based route selection mechanism is reported and also reporting a comparative analysis of efficacy of the ACO routing algorithm over the standard Greedy algorithm in finding routes with different count of sensor nodes and different count of ants. Then find that the ACO routing algorithm outdoes the Greedy algorithm with respect to the number of routes identified.

Dispatch optimisation in O2O on-demand service with crowd-sourced and in-house drivers

O2O (Online to Offline) services enable customers to place orders online and receive products/services offline. In addition to traditional in-house drivers, the emergence of crowd-sourced drivers provides an opportunity to re-organise offline delivery services. In practice, three types of workforce, namely, in-house, full-time, and part-time crowd-sourced drivers, coexist in the system while exhibiting different characteristics. This situation creates challenges for the management of order assignment and routing. In particular, we study three settings in response to different driver preferences: the guaranteed minimum daily number of orders for full-time drivers; the maximally allowed number of orders per trip; and the detour proportion for part-time drivers. This paper aims to provide a method for O2O platforms to optimise order assignment and routing, considering these designs about driver preferences. We further validate our model and study managerial insights using real datasets. Specifically, the results show that among all designed parameters for the O2O on-demand delivery system, two parameters – the maximally allowed number of orders per trip and the detour proportion – are critical for the design. Moreover, we find that incentive mechanisms for inexperienced and experienced drivers are different because of their service capacities. The managerial insights are expected to guide practitioners.

Virtualised Environment for Learning SDN-based Networking

ABSTRACT Computer networking is an important subject in the computer science curriculum. However, owing to its constant evolutionary nature and inherent complexity, learning and teaching new paradigms in computer networking can be difficult. The design and implementation of a fully operational SDN (software defined networking) network is practically illustrated using a virtualisation environment, which is aimed at learning the fundamentals of routing management of SDN networks. We describe the virtualisation environment, software tools, SDN controllers, and programming details of networking equipment for the implementation of an SDN network. For educational purposes, a detailed description of the network configuration process in also presented. The level of details presented in this paper can be useful for both instructors and students to grasp the fundamental concepts of SDN routing management at an educational level in a friendly manner. A virtualised environment, as the one described in this paper, can also be useful for understanding other advanced computer networking concepts.

Pediatric Seizure Team-based Learning.

The target audiences for this team-based learning are emergency medicine and emergency medicine-pediatric resident physicians. Pediatric seizure is a common presenting complaint in the emergency department. It is said that over 470,000 children have a diagnosed seizure disorder1 and 2%-5% of children aged 6 months to five years will have a febrile seizure at some point during childhood.2 While there are many published educational materials related to pediatric seizure, they are simulation-based, and/or isolated to management of one underlying diagnosis.3,4,5,6 Therefore, this team-based learning uses four cases to provide an understanding of the possible causes of seizure in children, as well as the management, workup, and disposition for emergency medicine residents in training. By the end of this TBL session, learners should be able to:Define features of simple versus complex febrile seizureDiscuss which patients with seizure may require further diagnostic workupSummarize a discharge discussion for a patient with simple febrile seizuresIdentify a differential diagnosis for pediatric patients presenting with seizureDefine features of status epilepticusReview an algorithm for the pharmacologic management of status epilepticusIndicate medication dosing and routes of various benzodiazepine treatmentsObtain a thorough history in an infant patient with seizures to recognize hyponatremia due to improperly prepared formulaChoose the appropriate treatment for a patient with a hyponatremic seizureDescribe the anatomy of a ventriculoperitoneal (VP) shuntRelate a differential diagnosis of VP shunt malfunctionCompare and contrast the neuroimaging options for a patient with a VP shunt. This team-based learning is a classic TBL because it contains learner responsible content (LRC), an individual readiness assessment test (iRAT), a multiple-choice group RAT (gRAT) with immediate feedback assessment technique (IF/AT), and a group application exercise (GAE). We received formative feedback through conversations with learners afterwards, who stated they enjoyed the activity and felt it was highly useful for their learning; in addition, instructors discussed after the session and made changes accordingly. We collected verbal feedback from instructors and learners after the session. Learners and instructors felt that it was very successful with limited modifications, in particular, the need for more time to complete the activity. Therefore, we suggest a 90 minute rather than 60-minute timeframe to adequately cover all material. Pediatric seizure is a common complaint in the emergency department. It can be a difficult subject for the emergency medicine resident to master based on the variety of presentations. Indeed, the cause, management, and disposition may vary greatly; the etiology may range from benign to life-threatening, sometimes requiring minimal and at other times an extensive workup, with an ultimate disposition of either discharge home or admission to a pediatric intensive care unit. Therefore, team-based learning is well-suited to work through some of the complexities of such cases, and we found this educational session to be highly effective. Pediatric seizure, simple febrile seizure, complex febrile seizure, status epilepticus, hyponatremic seizure, ventriculoperitoneal (VP) shunt, team-based learning.

A shortest-path tree approach for routing in space networks

A new space era is bringing deployments of thousands of networked assets in near-Earth and deep-space. Delay-Tolerant Networking (DTN) protocols and algorithms such as Contact Graph Routing (CGR) are state-of-the-art technologies certainly capable of achieving this ambitious objective. Nevertheless, in this paper, we highlight and analyze the roots of current scalability limits of CGR, to then propose more efficient alternatives. In particular, we leverage a different graph modeling approach that enables optimizations such as priority queues and spanning-tree searches in the core route determination process. While such abstract data types are difficult to adapt to CGR, we incorporate them into a novel Shortest-Path Tree Approach for Routing in Space Networks (SPSN), which is presented as a compute-efficient alternative. This paper thoroughly describes the SPSN approach including route object modeling, capacity-oriented search, and potential route management techniques. Prototype evaluations analyzed in this paper present significant evidence that SPSN outperforms legacy CGR in more than an order of magnitude in contact plans comprising thousands of contacts.

A chance constrained fuzzy goal programming approach for perishable pharmaceutical supply chain network design

In this paper, a bi-objective mixed-integer linear programming model is formulated for designing a perishable pharmaceutical supply chain network under demand uncertainty. The objectives of the proposed model are to simultaneously minimize the total cost of the network and lost demand amount. The proposed model is multi-product and multi-period and includes simultaneous facilities location, vehicle routing, and inventory management; hence, it is considered an operational-strategic model. Procurement discounts, the lifetime of products, storing products for future periods, lost demand, and soft and hard time windows are the main assumptions of the proposed model. A novel hybrid approach, based on fuzzy theory, chance constrained programming, and goal programming approach, is developed for solving the proposed bi-objective model. The validity of the proposed model and developed solution approach is evaluated using data from Avonex, a prefilled syringe distribution chain serving 11 health centers in Tehran. The proposed model indicates that some lost sales exist, and to overcome the lost sales, the case company needs to invest a little more in addition to the initial investment of around 2 billion tomans. The results obtained from implementing the model and the sensitivity analysis, using real-world data, confirm the efficiency and validity of the proposed mathematical model and solution approach.

The two-echelon inventory-routing problem with fleet management

This paper introduces the Two-Echelon Inventory-Routing Problem with Fleet Management. This problem arises under a two-echelon vendor-managed inventory system when a company must make vehicle routing and inventory management decisions, while renting a fleet subject to short- and mid-term agreements. Different chemical products are transported contaminating the vehicles that may require cleaning activities. Pickups of input take place in the first echelon, and the final product deliveries are performed in the second echelon. Based on a real-life case in the petrochemical industry, we introduce a formulation that takes into account vehicle rentals, cleanings, transportation, inventory management, and vehicle returns decisions. We design a branch-and-cut algorithm to solve it and also propose a matheuristic, in which vehicle routes are handled by an adaptive large neighborhood mechanism, while input pickups, product deliveries, and fleet planning are performed by solving several subproblems to optimality. Moreover, we introduce a hybrid parallel framework, combining our matheuristic and the branch-and-cut algorithm in order to solve very large instances exactly. We validate our methods by solving a set of instances of the two-echelon multi-depot inventory-routing problem from the literature, obtaining new best solutions for all instances. We have introduced a set of instances for this rich and new problem, and performed an extensive assessment of our methods. The results provide interesting data about the supply chain structure.

Nebengin: Android-based Collaborative Transportation Application

Online transportation services are transportation services that take advantage of advances in information technology. In Indonesia, several online transportation service providers have grown, such as Gojek, Grab and several other startup startups. In addition to the many benefits and conveniences that have been provided, the service price factor is still felt expensive for the community, especially the lower classes. One solution is to build a Collaborative Transportation Application that utilizes the Collaborative Transportation Management (CTM) interaction method. The main features contained in the application, namely the feature of finding driver routes in the direction of the consumer route, online chat, and route management by utilizing the Android mobile application based on Google Map API and Firebase Cloud Messaging. Performance testing using JMeter from a total of 300 virtual users performs 6 HTTP requests resulting in an average response time of 0.852 alias tolerated according to Apdex standards. Based on the results of testing of 15 respondents obtained the results that this application is easy to use, supports traveling activities, fast response time when used, features provided are quite complete, an accurate position tracking system, unidirectional route search is very compatible with consumer routes, and the process of coordination and driver collaboration is very good so that the travel costs borne by consumers become more economical..

Crowds in front of bottlenecks at entrances from the perspective of physics and social psychology.

This article presents an interdisciplinary study of physical and social psychological effects on crowd dynamics based on a series of bottleneck experiments. Bottlenecks are of particular interest for applications such as crowd management and design of emergency routes because they limit the performance of a facility. In addition to previous work on the dynamics within the bottleneck, this study focuses on the dynamics in front of the bottleneck, more specifically, at entrances. The experimental set-up simulates an entrance scenario to a concert consisting of an entrance gate (serving as bottleneck) and a corridor formed by barriers. The parameters examined are the corridor width, degree of motivation and priming of the social norm of queuing. The analysis is based on head trajectories and questionnaires. We show that the density of persons per square metre depends on motivation and also increases continuously with increasing corridor width, meaning that a density reduction can be achieved by a reduction of space. In comparison to other corridor widths observed, the narrowest corridor is rated as being fairer, more comfortable and as showing less unfair behaviour. Pushing behaviour is seen as ambivalent: it is rated as unfair and listed as a strategy for faster access.

Cross-layer QoS enabled SDN-like publish/subscribe communication infrastructure for IoT

Publish/subscribe paradigm is often adopted to create the communication infrastructure of the Internet of Things (IoT) for many clients to access enormous real-time sensor data. However, most current publish/subscribe middlewares are based on traditional ossified IP networks, which are difficult to enable Quality of Service (QoS). How to design the next generation publish/subscribe middleware has become an urgent problem. The emerging Software Defined Networking (SDN) provides new opportunities to improve the QoS of publish/subscribe facilities for delivering events in IoT owing to its customized programmability and centralized control. We can encode event topics, priorities and security policies into flow entries of SDN-enabled switches to satisfy personalized QoS needs. In this paper, we propose a cross-layer QoS enabled SDN-like publish/subscribe communication infrastructure, aiming at building an IoT platform to seamlessly connect IoT services with SDN networks and improving the QoS of delivering events. We first present an SDN-like topic-oriented publish/subscribe middleware architecture with a cross-layer QoS control framework. Then we discuss prototype implementation, including topic management, topology maintenance, event routing and policy management. In the end, we use differentiated services and cross-layer access control as cross-layer QoS scenarios to verify the prototype. Experimental results show that our middleware is effective.

A secure multipath reactive protocol for routing in IoT and HANETs

In numerous Internet of Things (IoT) scenarios (such as Intelligent Transportation System (ITS) or environment sensing), the ad hoc mode is widely used. Ad-hoc networks have several benefits including infrastructureless and spontaneous deployment, and the opportunity to set up temporary and dynamic networks cost-effectively. However, some of their characteristics such as the unreliability of wireless links, dynamic topology and resource constraints in energy and processing power of objects, make new protocols’ development ignore security requirements of applications in order to satisfy some quality of service. Consequently, there is a lack of standardization works in the field of secure routing among IoT devices. Furthermore, recently proposed secure routing approaches (mostly from the academic community) do not meet the current needs and requirements of secure routing for IoT applications. In this context, we propose a lightweight and secure routing scheme that combines multipath routing and trust management, that can adapt to different scenarios in the IoT environment. To analyze and evaluate our scheme, we propose a probabilistic model that considers two types of events that affect routing performance for a given node: mobility and uncooperative behavior. Based on this mathematical model, we compared the performance of our proposed routing scheme with two other multipath routing protocols namely, SMORT and DMRP. Our performance results show that our scheme is highly scalable, more efficient, and resilient compared with previous related works and is particularly suitable for insecure large and dense IoT networks.

Mobility and energy prediction models: Approach toward effective route management in mobile wireless sensor networks

AbstractMobile wireless sensor network (MWSN), as a backbone of most communication and embedded systems, is characterized by low data delivery as a result of the energy constraint and in‐deterministic movement of the nodes. To select optimal route path for a reliable data routing, protocols need to select neighbouring nodes with low mobility and residual energy parameters along the route. However, obtaining these parameters from the targeted nodes may encourage mischievous nodes to manipulate their energy and mobility parameters in their favor, leading to the selection of non‐optimal route path and/or exposing the network to other attacks. Rather than depending on the targeted nodes for these parameters, they can be accurately and securely obtained by sink node or base station using effective predictive models. However, most of the existing predictive models engaged by MWSN routing protocols either provide only the energy level or mobility factor of the nodes for optimal route path selection. This, therefore, reduces the reliability of the obtained optimal route paths in terms of data delivery. In this article, we propose predictive models for an accurate prediction of mobility and residual energy parameters of nodes in MWSN for efficient route management. The models are simulated on an existing MWSN protocol to determine their performance. The simulation results show that the adoption of these models in the development of routing protocol will not only lead to optimal route paths' selection but also enhance data delivery rate in MWSN.