Route Selection Strategy Research Articles

Service blockage on the downlink in large-scale satellite optical networks: a multi-downlink scheduling for routing selection

Over years of space laser communication technology advances, satellite optical networks (SONs) have emerged as a pivotal component in 6 G networks. Satellite services are transmitted from the global view, undergoing transmission through SONs, and being downloaded to the targeted areas. However, the transmission capacity of satellites passing through the areas where users are concentrated may be insufficient to download services transmitted worldwide. This problem exists in various kinds of satellite networks and may cause a large amount of service congestion. In this paper, we propose a multi-downlink delivery routing selection (MDD-RS) strategy to study the total utilization of transmission capacity of SONs. We construct an integer linear programming (ILP) model to establish an optimal case study for minimal network capacity occupation. Also, we design an online option, MDD-RS heuristic algorithm, dynamically calculating path routes, considering bandwidth allocation and resource constraints. A comparative analysis against the conventional single-downlink scheme reveals superior performance of the MDD-RS heuristic algorithm, with a reduction in blocking probability of 0.129 and an improvement in bandwidth utilization of 0.032.

Every Second Counts: A Comprehensive Review of Route Optimization and Priority Control for Urban Emergency Vehicles

Emergency vehicles (EMVs) play an important role in saving human lives and mitigating property losses in urban traffic systems. Due to traffic congestion and improper priority control strategies along the rescue route, EMVs may not be able to arrive at rescue spots on time, which also increases traffic risk and has a negative impact on social vehicles (SVs). The greater the negative impact on SVs, such as increased delay times and queue length, the more profound the negative impacts on urban environmental sustainability. Proper rescue route selection and priority control strategies are essential for addressing this problem. Consequently, this paper systematically reviews the studies on EMV routing and priority control. First, a general bibliometric analysis is conducted using VOSviewer. This study also classifies the existing studies into three parts: EMV travel time prediction (EMV-TTP), EMV routing optimization (EMV-RO), and EMV traffic priority control (EMV-TPC). Finally, this study provides future research suggestions on five aspects: 1. uncovering authentic demand characteristics through EMV data mining, 2. incorporating the distinct characteristics of EMV in EMV-RO models, 3. implementing active EMV-TPC strategies, 4. concentrating more on the negative impacts on SVs, and 5. embracing the emerging technologies in the future urban traffic environment.

SoVEC: Social vehicular edge computing-based optimum route selection

This paper proposes a new architecture Social Vehicular Edge Computing (SoVEC) by integrating three domains: social network, vehicular ad-hoc network, and mobile edge computing. The users access various mobile applications and share various types of information on the social network during travel time. Using SoVEC three categories of social networks are generated based on the type of information shared among the users such as traffic information, professional information, and personal interests. To reach the destination in minimal time, this paper proposes an optimum route selection strategy based on TOPSIS method and genetic algorithm. The SoVEC is simulated using the network simulator Qualnet 7, and average delay, jitter, and throughput are determined. A case study of generating social network based on road traffic-related information is also demonstrated. Finally, the effectiveness of the proposed approach for selecting the optimum route is assessed, and the results present that the proposed method outperforms the existing algorithms.

Network lifetime optimization and route selection strategy towards energy enrichment in wireless body area networks

Network lifetime optimization and route selection strategy towards energy enrichment in wireless body area networks

Design and Analysis of a Bandwidth Aware Adaptive Multipath N-Channel Routing Protocol for 5G Internet of Things (IoT)

Large numbers of mobile wireless nodes that can move randomly and join or leave the network at any moment make up mobile ad-hoc networks. A significant number of messages are delivered during information exchange in populated regions because of the Internet of Things' (IoT) exponential increase in connected devices. Congestion can increase transmission latency and packet loss by causing congestion. More network size, increased network traffic, and high mobility that necessitate dynamic topology make this problem worse. An adaptive Multipath Multichannel Energy Efficient (AMMEE) routing strategy is proposed in this study, in which route selection strategies depend on forecasted energy consumption per packet, available bandwidth, queue length, and channel utilization. While multichannel uses a channel-ideal assignment process to lessen network collisions, multipath offers various paths and balances network strain. The link bandwidth is split up into a few sub-channels in the multichannel mechanism. To reduce network collisions, several source nodes simultaneously access the channel bandwidth. The cooperative multipath multichannel technique offers several paths from a single source or from several sources to the destination without colliding or becoming congested. The AMMEE routing approach is the basis for path selection. A load- and bandwidth-aware routing mechanism in the proposed AMMEE chooses the path based on node energy and forecasts their lifetime, which improves network dependability. The outcome demonstrates a comparative analysis of various multichannel medium access control (MMAC) techniques, including Parallel Rendezvous Multi Channel Medium Access Protocol (PRMMAC), Quality of Service Ad hoc On Demand Multipath Distance Vector (QoS-AOMDV), Q-learning-based Multipath Routing (QMR), and Topological Change Adaptive Ad hoc On-demand Multipath Distance Vector (TA-AOMDV) and the proposed AMMEE method. The results show that the AMMEE approach outperforms alternative systems. Doi: 10.28991/ESJ-2024-08-01-018 Full Text: PDF

Perishables Liner Bunkering Strategy and Route Optimization Considering Carbon Tax and Sulfur Emissions

For the issue of perishable goods liner shipping route selection and bunkering strategy, this paper gives full consideration to the characteristics of perishable goods, in the context of the carbon tax and the setting of emission control zones, constructs a mixed-integer nonlinear planning model with the goal of minimizing the total weekly cost of liner service, designs a segmented linear approximation algorithm for model solution, and conducts an arithmetic analysis based on the actual liner voyage as an example, which provides a reference for the actual operation of shipping.

Target number influences strategy use by rats (Rattus norvegicus) in the traveling salesperson problem.

The traveling salesman problem (TSP) is an optimization problem in which the goal is to find the shortest possible route that passes through each of a set of points in space. The TSP is of interest not only in the fields of mathematics, computer science, and engineering, but also in cognitive and behavioral research to study problem-solving and spatial navigation. Humans are able to complete even complex TSPs with a high degree of efficiency, and distance minimization in TSP analogs has been observed in a variety of nonhuman species as well. Tasks based on the TSP also have the potential for translational research on cognitive and neurological disorders such as Alzheimer's disease. The current experiment was designed to examine the effects of target number on TSP performance in rats. After pretraining, rats were tested once on each of several target configurations, and their travel routes were recorded. We examined the routes for general efficiency, as well as evidence for strategy use including the nearest neighbor (NN) strategy and crossing avoidance. Our results indicate that latency and route length increase in proportion to the number of targets. Rats also showed a strong tendency to avoid path crossing, and to select NN targets, which strengthened with increasing target numbers. Taken together, our results indicate that travel efficiency decreases linearly in relation to the target number rather than the number of possible routes, which grows factorially with a target number. Additionally, spatial memory and route selection strategy are also affected by an increasing number of targets. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Research on multi-layer network routing selection strategy based on cooperative evolutionary game in IoT environment

As a new technology and application mode, the Internet of Things has an important impact on social life and economic development. In recent years, low-cost optimization of network transmission to solve the congestion problem of multi-layer communication networks has become one of the research hotspots. In this paper, a multi-dimensional communication data transmission model based on a multi-layer network is proposed. It then uses cooperative evolutionary game theory to calculate revenue, update weights, and adapt neighbors. Finally, the attention mechanism is dynamically introduced to share the weights of the multi-layer network, and the multi-dimensional communication propagation and routing strategies in the Internet of Things are studied and analyzed. The experimental results show that the model proposed in this paper has higher game revenue and application value than traditional single-layer network game theory. In particular, the indicators of cooperation rate, stable state, and maximum cooperation rate are better than the latter. The research results of this paper have important reference value for solving the problems of cooperation dilemma, social stickiness, and synergy in multi-layer networks.

A Graph reinforcement learning based SDN routing path selection for optimizing long-term revenue

Software-Defined Network (SDN) paradigm decouples control plane from data plane and provides a logically-centralized control to whole underlying network, which enables the controller to centrally configure the network, and has demonstrated great potential in fully utilizing network capacity. However, the traditional SDN does not consider the accumulative long-term revenue in routing configuration for network service provider (SP). For example, it is prone to choosing the path passing through important nodes, which may increase the possibility of future congestion, and hamper the SP’s long-term revenue. Deep reinforcement learning (DRL) has brought about the transformative advances in decision-making when facing uncertainty. As a well-known type of DRL, Deep Q-network (DQN) can explore optimal routing schemes from high dimensional network state. However, most of existing DRL based methods fail to provide satisfactory configuration when confronted with a topology unseen during training. Meanwhile, many existing network optimization schemes ignore the impact of nodes and global network attributes on the routing configuration. Consequently, they may perform poorly in real scenarios. In this paper, we propose a graph reinforcement learning (GRL) based SDN routing selection scheme, GN-DQN for optimizing SP’s accumulative long-term revenue. First, besides the link embeddings, GN-DQN explicitly learns the representations of the routers and globe network topology with full graph neural network block. Then, through utilizing the formed network representation as the state, DQN is used to learn to sequentially choose the optimal routing path from the candidate path set to maximize the accumulative long-term revenue. Thorough experiments on multiple real network topologies show that our proposed GN-DQN outperforms other GRL based and traditional routing selection schemes, and can generalize over arbitrary topologies. Moreover, GN-DQN also demonstrates great robustness to the link failure scenario.

Reliable multi-path route selection strategy based on evidence theory for internet of things enabled networks

Internet of Things enabled Network (IOTN) is one of the active research areas these days. Though IOTN has founds its place in several real-time applications, the major constraint of IOTN is the energy restriction. The energy restriction is managed by introducing several energy efficient protocols. Among all the activities performed by IOTN, routing is the most energy consuming activity. Taking this into account, this article proposes a route selection scheme that considers the reliability score and sensitiveness of the data. The reliability score of the nodes is computed by means of evidence theory and the user is allowed to set the expected reliability score for forwarding the data. Based on the requirement, the route is selected by giving the first preference to the reliability score, while the second preference is given to the length of the route. The performance of the proposed approach is analysed in terms of standard performance metrics and the proposed approach proves better results.

Trust-based clustering and best route selection strategy for energy efficient wireless sensor networks

Energy efficiency is one of the most researchable topics of the energy constrained Wireless Sensor Networks (WSN). Due to the widespread usage of WSN in real-time applications, network lifetime is the serious concern and numerous research solutions are presented in the literature for handling this issue. Several techniques are employed to handle energy consumption and the most promising techniques are clustering and routing other than data compression. Hence, this paper presents a clustering and routing-based solution for energy efficient WSN with trust notion as its base. The nodes are clustered with the help of Dempster-Shafer Theory (DST), while the best route is selected by employing Lion Optimization Algorithm (LOA). The work efficiency of the proposed work is proven with reasonable outcomes with respect to different performance measures. This work proves an average packet delivery rate of 94.24%, average latency of 13.43 s and 191 alive nodes at the end of 500th second better network lifetime when compared to the existing works.

Balanced Off-Chain Payment Channel Network Routing Strategy Based On Weight Calculation

AbstractOff-chain payment channel network is an effective solution to deal with low throughput and high load on the blockchain. However, the quality of the routing scheme will directly affect the performance of the off-chain network system. Existing routing schemes cannot select an appropriate transaction path according to the actual needs of user nodes. And they may also lead to network congestion, channel imbalance and node centralization. This paper proposes a new balanced routing selection scheme based on weight calculation called BRBW, which builds a weight model by Analytic Hierarchy Process. BRBW comprehensively considers the channel capacity, handling fee and path length to improve transaction success rate, reduce channel congestion and maintain the long-term sustainability of the payment channel. It uses a modified maximum flow algorithm to find paths with sufficient capacity and selects the transaction path for a large payment through linear programming. Finally, we do some experiments and compare with state-of-the-art approaches in the test environment. Simulation results show that under the same network environment, the payment success rate of the BRBW is above 75%, and the channel utilization rate is about 10% higher than other schemes.

A survivable multipath resource allocation strategy based on fragmentation-sensitive fragmentation-aware in space division multiplexing elastic optical networks

The space division multiplexing elastic optical network (SDM-EONs) can effectively supply the large-capacity for the current optical network. If a link failure occurs, it will bring huge data losses or disruptions for the operator. We propose a survivable multipath fragmentation-sensitive fragmentation-aware routing, core and spectrum assignment (SM-FSFA-RSCA) scheme to solving the problem of survivability of the request in SDM-EONs and spectrum fragmentation that caused by the dynamic allocation and release of spectrum resources. We design a fragmentation-sensitive fragmentation metric (FSFM) to estimate the spectrum fragmentation in the established lightpaths, and design a dynamic routing and core selection strategy for the request to control spectrum fragmentation in the core according to FSFM. We consider survivable multipath routing strategy for saving spectrum resources, each path of the multipath can be used as a working path or a protection path. In addition, for spectrum allocation, a survivable multipath with shared spectrum allocation strategy is adopted to maximize the utilization of frequency resources. The simulation results show that the algorithm proposed in this paper significantly reduces the bandwidth blocking probability,fragmentation ratio and also improves the spectrum utilization.

EnPlace: Energy-Aware Network Partitioning for Controller Placement in SDN

This paper addresses the energy-aware controller placement problem (CPP) in Software-Defined Networking (SDN), considering the Internet of Things (IoT) flows. CPP involves partitioning the network into multiple subsets of switches with a single controller assigned to each subset. On the other hand, an energy-aware CPP reduces the energy consumption by link deactivation and ensures that each controller is reachable from the associated switches with a minimal set of active links. Existing literature considers static data traffic and out-of-band control plane having dedicated control links. However, the out-of-band control plane increases the infrastructure cost. Moreover, with IoT devices, SDN experiences uneven data traffic volume due to diverse activation models of the IoT devices. Hence, an energy-aware CPP should consider the effects of dynamic data traffic as improper controller placement and unplanned link deactivation cause link congestion and controller overload. In this work, we present an energy-aware controller placement scheme, named EnPlace, considering in-band control plane and IoT traffic. Additionally, we propose an energy-aware route selection scheme for existing flows. EnPlace increases energy savings significantly as compared to the existing works. In particular, for 200 IoT devices, the proposed scheme increases energy savings by 22.74% as compared to GreCo, an existing scheme.

RMS: A Delay Sensitive Road Monitoring System Using Edge Intelligence

Smart vehicles equipped with onboard sensors can detect the surrounding cars, cycles, pedestrians, and other objects in real time and can make control decisions to achieve road safety. The high dynamism of vehicular mobility switches base stations in their trajectory, and at an instant, one edge server services one base station only. So, making control decisions by processing and transmitting vehicular data in real time is challenging. In this article, we propose a road monitoring system (RMS) in which moving vehicles collect road data and offload collected data to the paired edge server. The edge devices and connected vehicular networks receive road-hindering alerts. It comes up with Road Ambient Intelligence that supports ubiquitous computing and generates context-aware alerts with low latency. It uses a deep learning model for the decision-making about road-hindering alerts and a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -nearest neighbor algorithm to form clusters for the distribution of alert messages. For intense occurrences, the data are sent to the cloud server for further notifications to remote-control offices. The proposed study is compared with social vehicle route selection (SVRS) and delay tolerant processing (DTP) schemes. The performance analysis shows that rms presents 12%–44% better performance compared to the SVRS scheme and 15%–53% better compared to the DTP scheme in terms of delay. The energy requirement analysis shows that compared to SVRS, RMS involves 50% less energy when processed at edge clouds, and DTP involves 51%–65% more energy requirements compared to the RMS scheme.

Route selection strategy with minimised mobile charger in wireless rechargeable sensor networks

Route selection strategy with minimised mobile charger in wireless rechargeable sensor networks

Route selection strategy with minimised mobile charger in wireless rechargeable sensor networks

Route selection strategy with minimised mobile charger in wireless rechargeable sensor networks

Pilot Study Using Decision Trees to Diagnose the Efficacy of Virtual Offshore Egress Training

For the offshore energy industry, virtual environment technology can enhance conventional training by teaching basic offshore safety protocols such as onboard familiarization and emergency evacuation. Virtual environments have the added benefit of being used to investigate the impact of different training approaches on competence. This pilot study uses decision tree modeling to examine the efficacy of two pedagogical approaches, simulation-based mastery learning (SBML) and lecture-based training (LBT), in a virtual environment. Decision trees are an inductive reasoning approach that can be used to identify learners’ egress strategies in offshore emergencies after training. The efficacy of the virtual training is evaluated in three ways: 1) analyzing participants’ performance scores in test scenarios; 2) comparing the decision tree depiction of participant's understanding of emergency egress to the intended learning objectives; and 3) comparing the decision strategies developed under a different pedagogical approach. A comparison of the resulting decision trees from the SBML training with trees generated from the LBT showed that the different training methods influenced the participants’ egress strategies. The SBML approach resulted in concise decision trees and better route selection strategies when compared to the LBT training. This pilot study demonstrates the diagnostic capabilities of decision trees as training assessment tools and recommends integrating decision trees into virtual training to better support the learning needs of individuals and deliver adaptive training scenarios.

Construction of Mathematical Model of Logistics Delivering Based on Intelligent Mobilization

Distribution process is the core of logistics enterprise system. Efficient distribution process is the key for enterprises to improve logistics service level, gain competitive advantage, and win customers. In order to reduce the design error of the system and ensure the effective operation of the system, this paper establishes the system model and analyzes the properties of the model through the evaluation and analysis of various resources of the distribution system. The future development of intelligent logistics system is not only the key for logistics enterprises to win competition, but also a new measure to promote China’s economic development. This is not only a typical NP hard problem, but also a major challenge for the rational and scientific development of the intelligent logistics industry. Based on the previous theoretical research results, this paper intends to explore the intelligent logistics distribution route selection scheme by using mathematical modeling methods such as particle swarm optimization, so as to provide new technologies and methods for logistics distribution operation and management decision-making. The results show that compared with the traditional genetic algorithm, the accuracy of the improved particle swarm optimization algorithm is improved by 10.36%. This method effectively improves the operation cycle and link efficiency and achieves the purpose of optimization. The improved particle swarm optimization algorithm proposed in this paper is not only more suitable and effective for enterprise decision makers to deal with subjective judgments in an imprecise environment. It is also based on the evaluation sequence of each indicator of the alternative obtained from the evaluation and the comprehensive evaluation of all indicators. By weighting and considering the results, the best solution for enterprise logistics and distribution is selected.

Retraction Note to: Energy aware reliable route selection scheme with clustered RP model for wireless sensor networks to promote interaction between human and sensors

Retraction Note to: Energy aware reliable route selection scheme with clustered RP model for wireless sensor networks to promote interaction between human and sensors