Path Selection Research Articles

Path selection and network equilibrium under non-extreme flood disturbances

Path selection and network equilibrium under non-extreme flood disturbances

PPEIM: A preference path-based early-stage influence accumulation model for influential nodes identification in locally dense multi-core networks

The identification of influential nodes, a critical problem in the field of complex networks, has been extensively studied. However, previous research has primarily focused on maximizing terminal influence across all network structures indiscriminately, making it challenging to accurately identify influential nodes in specific structures. Moreover, overlooking the influence of the temporal dynamics of propagation significantly diminishes the benefits of identifying influential nodes. Therefore, we propose an influential nodes identification model, Preference Path-based Early-stage Influence Accumulation Model (PPEIM), tailored for typical locally dense multi-core networks. The key idea of PPEIM is to identify more influential nodes in early-stage propagation by aggregating dynamic influence propagation volumes superimposed on multiple paths. Specifically, early-stage influence performance is enhanced by sampling paths, mitigating the risk of dense influential nodes resulting from redundant relationships. Moreover, the K-shell, degree, influence distance and link direction are integrated to define connection strength between nodes to guide path selection. And the concept of influence propagation volume is introduced to accurately simulate the influence residuals and losses during the propagation process. To validate the effectiveness and superiority of PPEIM in locally dense multi-core networks, five sets of simulation experiments are conducted on seven real-world datasets. Experimental results demonstrate that PPEIM outperforms six state-of-the-art methods in overall propagation capability, early-stage influence capability, disintegration capability, node dispersion, and discrimination capability.

Path selection algorithms for connecting wireless base stations to power centers in a mine

Relevance. Necessary condition for the systems of safety and process control functioning in a mine is to provide power supply to the relevant facilities. The paper deals with one of the tasks of designing a power supply network in a mine within the framework of a hierarchical approach to organizing the network structure. Within this approach, power controllers are connected to mine lighting breaker. To supply base stations to power controllers, a multi-core cable is used. The number of such cores, as well as the number of such cables emanating from a power controller, are the parameters of the problem. Aim. To consider the problem of choosing routes for connecting base stations of wireless communication in a mine to power centers. It is assumed that base stations and mine lighting breakers are already located in a mine, having the ability to connect a given number of power controllers to them. The connection scheme must be optimal in terms of cost, which is determined by the cost of the cable used. Methods. The authors have proposed several algorithms for solving the mathematical problem, including a greedy algorithm, based on the “go to the nearest point” strategy, and a simulated annealing method. Results. To solve the problem, several approximate methods were proposed and tested. The number of cores is parameter of the problem. The best of the considered algorithms is the annealing simulation algorithm. However, if power centers need to be placed as well, brute force enumeration in the algorithm also gives good results with an appropriate combination of the number of power controllers and the number of possible locations for their placement. Practical relevance. The mathematical problem stated and the mathematical methods make it possible to find minimum cost routes for connecting wireless base stations by multi-core cables to power sources.

Multi-Objective Optimization in Disaster Backup with Reinforcement Learning

Disaster backup, which occurs over long distances and involves large data volumes, often leads to huge energy consumption and the long-term occupation of network resources. However, existing work in this area lacks adequate optimization of the trade-off between energy consumption and latency. We consider the one-to-many characteristic in disaster backup and propose a novel algorithm based on multicast and reinforcement learning to optimize the data transmission process. We aim to jointly reduce network energy consumption and latency while meeting the requirements of network performance and Quality of Service. We leverage hybrid-step Q-Learning, which can more accurately estimate the long-term reward of each path. We enhance the utilization of shared nodes and links by introducing the node sharing degree in the reward value. We perform path selection through three different levels to improve algorithm efficiency and robustness. To simplify weight selection among multiple objectives, we leverage the Chebyshev scalarization function based on roulette to evaluate the action reward. We implement comprehensive performance evaluation with different network settings and demand sets and provide an implementation prototype to verify algorithm applicability in a real-world network structure. The simulation results show that compared with existing representative algorithms, our algorithm can effectively reduce network energy consumption and latency during the data transmission of disaster backup while obtaining good convergence and robustness.

Public Vitality-Driven Optimization of Urban Public Space Networks—A Case Study from Nanjing, China

Spontaneous recreational activities in public spaces are a vital source of public vitality. Given the similarity between the walking patterns of recreational crowds in public spaces and the movement of electrons on a two-dimensional circuit surface, this study combines big data from various sources to create an “electrical conductivity surface” that attracts and aggregates recreational crowds. Using current flow simulation, we generate the path selection preferences of people as they move across public spaces. The results reveal an uneven distribution of public spaces in Nanjing’s main urban area, with high-vitality areas mostly concentrated in the urban center. The core demand for enhancing public vitality lies is improving connectivity between multiple spaces. Based on this, the public space plan for Nanjing’s main urban area emphasizes overall connectivity by aligning with the natural landscape, thus linking the city’s green and gray infrastructure. In this study, we have assessed current public space services and their development potential from a number of different angles, developing a digital approach for optimizing the urban layout. We aim to provide a human-centric, bottom-up perspective to complement the top-down city planning and management approach. This will enable urban planners to make informed decisions for creating and managing more vibrant cities.

Reinforcement learning-based vehicle travel path reconstruction from sparse automatic licence plate recognition data

ABSTRACT Automatic licence plate recognition (ALPR) data is a vital source for acquiring large-scale vehicle trajectory data in urban transportation research. However, the sparse distribution of ALPR sensors often results in incomplete vehicle trajectories with unobserved travel paths between adjacent ALPR sensors. To address this challenge, this study proposes a novel travel path reconstruction model based on reinforcement learning. First, the maximum entropy inverse reinforcement learning (MaxEnt IRL) method is employed to learn vehicle path selection strategy, i.e. the reward function, from the real data. Then, a comprehensive reward function is formulated by integrating destination rewards. Finally, the Q-learning algorithm is utilized to reconstruct vehicle travel paths between adjacent ALPR sensors based on the comprehensive reward function. Evaluation on a randomly selected set of 100 pairs of adjacent ALPR sensors demonstrates that the proposed model significantly outperforms two baseline methods, achieving reductions of 24.3% and 22.5% in deviation from the real paths.

Digital Economy, Green Innovation Efficiency, and New Quality Productive Forces: Empirical Evidence from Chinese Provincial Panel Data

As a new economic form driven and empowered by digital technology, the development of the digital economy has become a crucial pathway for boosting the advancement of new quality productive forces. This study employs the entropy method to measure the comprehensive evaluation index system for both the digital economy and new quality productive forces and further utilizes the unexpected output super-efficiency model to calculate the green innovation efficiency index. On this basis, panel data of 31 provinces in China from 2012 to 2022 are selected to conduct a quantitative analysis from a multidimensional perspective innovatively, examining the overall, heterogeneous, and spatial effects of the digital economy on new quality productive forces, as well as the mediating, moderated mediating, and threshold effects of green innovation efficiency. The research findings indicate that (1) the digital economy significantly promotes the development of new quality productive forces, and this conclusion is robust; (2) the impact of the digital economy on new quality productive forces exhibits regional heterogeneity; (3) there is a positive spatial spillover effect of the digital economy on new quality productive forces; (4) the digital economy can facilitate the development of new quality productive forces by enhancing green innovation efficiency; (5) industrial structure upgrading moderates the mediating effect of the digital economy on new quality productive forces, specifically regulating the promoting effect of green innovation efficiency on new quality productive forces; and (6) the impact of the digital economy on new quality productive forces exhibits a nonlinear characteristic of “increasing marginal effect”. Consequently, this study proposes targeted suggestions from four dimensions: accelerating the construction of digital infrastructure, vigorously enhancing green innovation efficiency, promoting the deep integration of digitalization and greenization, and facilitating industrial upgrading and coordinated development. These suggestions serve as valuable empirical insights into China’s transformation path selection and sustainable high-quality development in the new era.

Path Selection via Mutual Coherence Optimization in Network Monitoring

Path Selection via Mutual Coherence Optimization in Network Monitoring

Cross-level interaction mechanism for high growth among digital start–ups: an fsQCA analysis

Purpose In the field of digital entrepreneurship, achieving high growth in digital start-ups is crucial. This paper aims to explore how causal conditions at the individual, organizational and environmental levels interact in the growth process of digital start-ups. Furthermore, it uses a configurational approach to conduct a holistic analysis of the cross-level interaction mechanisms that enable digital start-ups to achieve high growth. Design/methodology/approach Based on a holistic perspective, the data collection included 57 representative cases of digital start-ups in China in the past decade (2014–2023). A fuzzy set qualitative comparative analysis was conducted to explore the cross-level interaction mechanism of the three-dimensional causal conditions of the environment, organization and individual entrepreneurs on high growth among digital start-ups. Findings This study found three models for digital start-ups to achieve high growth. These models − the resource network orchestration model, the innovation resource development model and the entrepreneurial spirit coherence model, respectively, reflect the multiple paths of high growth achieved by various digital start-ups based on their entrepreneurial environment, organizational capability and the personality of the founder. This reflects that digital start-ups face different conditions and entrepreneurial situations, with differing cross-level interaction mechanisms for achieving high growth. Practical implications First, digital start-ups should focus on strengthening their digital resource integration capabilities to lay the foundation for their subsequent high-growth path selection. Second, entrepreneurs need to adopt a holistic perspective to change or strengthen the causal conditions required to achieve high growth. Third, emphasis should be placed on the coherence between the entrepreneur’s need for achievement and the internal entrepreneurial culture. Originality/value First, this study contributes to the literature on high growth among digital start-ups by introducing a cross-level interaction mechanism comprising environmental, organizational and entrepreneurial causal conditions. Second, it reveals that individual factors at the environmental, organizational and entrepreneurial levels are not necessary conditions for high entrepreneurial growth and that digital resource integration capabilities play a more universal role in achieving high digital start-up growth. Finally, the study provides a cross-level holistic explanation of how digital start-ups can achieve high growth and reveals various configuration models conducive to high growth, thus offering practical insights for businesses.

Path Selection for Intelligent Robot Mobile Obstacle Avoidance based on Variable Step Size Ant Colony Algorithm

Path Selection for Intelligent Robot Mobile Obstacle Avoidance based on Variable Step Size Ant Colony Algorithm

Theoretical Connotations and Path Selection for Building China’s Global Commodity Partnership

Theoretical Connotations and Path Selection for Building China’s Global Commodity Partnership

An innovative recommendation-driven friendship path selection strategy utilizing multi-agent collaborative edge caching for social IoT networks

An innovative recommendation-driven friendship path selection strategy utilizing multi-agent collaborative edge caching for social IoT networks

PENENTUAN LINTASAN OPTIMAL DISTRIBUSI BARANG MENGGUNAKAN HYPERGRAPH - PARTITIONING DAN ALGORITMA GENETIKA

Efficient distribution of goods is critical in logistics management, which requires the selection of optimal distribution paths to achieve delivery targets with minimal total distance. This research combines Hypergraph-Partitioning and genetic algorithm to determine the optimal distribution path of goods to several customers. The Hypergraph-Partitioning divides the goods to be distributed equally to several vehicles, while the genetic algorithm is applied to determine the best distribution path in each partition. The results showed that the Hypergraph-Partitioning method successfully divided 62 customers into two partitions. The first partition serves 31 customers with a total demand of 865 loaves of bread, while the second partition also serves 31 customers with a total demand of 1,035 loaves of bread. The genetic algorithm was then used to find the shortest path for each partition, resulting in an efficient distribution solution.

Enhancing IoT‐Enabled Healthcare Applications by Efficient Cluster Head and Path Selection Using Fuzzy Logic and Enhanced Particle Swarm Optimization

ABSTRACTLeveraging Internet of Things (IoT) technologies in healthcare, including wireless sensor networks (WSN) and new generation networks, facilitates the integration of diverse medical equipment and enables smart interactions among them. This innovation contributes significantly in meeting the needs of healthcare professionals and improving the quality of life of patients. However, ensuring efficient communication within IoT systems is crucial to meet the critical demands of healthcare, such as real‐time monitoring and emergency situations. This paper proposes a novel approach for determining cluster heads and selecting efficient paths in IoT‐enabled healthcare applications. The cluster head selection process utilizes a fuzzy logic mechanism, considering factors like energy, distance, and latency. Subsequently, the particle swarm optimization (PSO) technique is employed to identify optimal pathways for routing. MATLAB‐based simulations have been conducted to evaluate the proposed approach in terms of several key metrics, including average delay, packet delivery ratio, energy efficiency, and throughput. The results of the evaluation demonstrate significant improvements over comparable works. Specifically, our solution achieves a packet delivery ratio of 91.3%, an average delay of 0.12 s, a throughput of 60.1 bps, and an energy efficiency of 8.9 J/bit. These findings underscore the effectiveness of our proposed approach in meeting the stringent requirements of IoT‐enabled healthcare systems, particularly by achieving lower delays and higher throughput.

A Mininet emulation study for SDN fat tree data center sleep mode routing algorithms

Big-data networks are raising energy use and associated ecological effects and have become a significant cause for concern in the last decade. With the expected significant increase in the traffic demand in data centres escalates the need to be energy efficient. In this scenario, the implementation of sleep mode in software defined network (SDN) data centres could provide an energy aware solution. Energy consumed of the network can be reduced by putting underutilized network devices or components to sleep. Employing sleep mode aligns with sustainability goals and can reduce the environmental impact of data centers by lowering carbon emissions. In this work meta heuristic algorithm is incorporated at the SDN central controller in a fat tree-based data centre for bandwidth usage monitoring, sleep decisions and path selection using Mininet emulation. The Mininet emulation performance study was conducted based on comparison between metaheuristic and Dijkstra method. The proposed sleep mode method obtained enhancement in the performance parameter with uses less energy in the network.

Trajectory Planning for Unmanned Vehicles on Airport Apron Under Aircraft-Vehicle-Airfield Collaboration.

To address the issue of safe, orderly, and efficient operation for unmanned vehicles within the apron area in the future, a hardware framework of aircraft-vehicle-airfield collaboration and a trajectory planning method for unmanned vehicles on the apron were proposed. As for the vehicle-airfield perspective, a collaboration mechanism between flight support tasks and unmanned vehicle departure movement was constructed. As for the latter, a control mechanism was established for the right-of-way control of the apron. With the goal of reducing waiting time downstream of the pre-selected path, a multi-agent reinforcement learning model with a collaborative graph was created to accomplish path selection among various origin-destination pairs. Then, we took Apron NO.2 in Ezhou Huahu Airport as an example for simulation verification. The results show that, compared with traditional methods, the proposed method improves the average vehicle speed and reduces average vehicle queue time by 11.60% and 32.34%, respectively. The right-of-way signal-switching actions are associated with the path selection behavior of the corresponding agent, fitting the created aircraft-vehicle collaboration. After 10 episodes of training, the Q-values can steadily converge, with the deviation rate decreasing from 40% to below 0.22%, making the balance between sociality and competitiveness. A single trajectory can be planned in just 0.78 s, and for each second of training, 7.54 s of future movement of vehicles can be planned in the simulation world. Future research could focus on online rolling trajectory planning for UGSVs in the apron area, and realistic verification under multi-sensor networks can further advance the application of unmanned vehicles in apron operations.

Adjacent-Ligand Tuning of Atomically Precise Cu-Pd Sites Enables Efficient Methanol Electrooxidation with a CO-Free Pathway.

Whether the catalyst can realize the non-CO pathway is the key to greatly improve the catalytic activity and stability of methanol oxidation reaction (MOR). It is feasible to optimize the reaction path selectivity by modifying organic ligands and constructing single-atom systems. At the same time, heterogeneous metal nanosheets with atomic thickness have been shown to significantly enhance the catalytic activity of materials due to their ultra-high exposure of active sites and synergistic effects. Herein, we synthesize an ultra-thin heterogeneous alloy metallene with organic ligand-modified surface Cu single atom by one-pot wet chemical method, and further construct an efficient Cu-Pd active sites. The prepared octanoic acid ligand modified PdCu single-atom alloys metallene (SAA OA-Cu-Pdene) shows excellent catalytic activity and stability, with mass activity up to 5.64 A mgPd -1 and electrochemical active surface area (ECSA) up to 160.39 m2 gPd -1. Structural characterization and in situ experiment jointly indicate that ligand modulation brings about charge transfer, and the accompanying rapid migration of OH- greatly improves the selectivity of non-CO pathways while improving the catalytic activity. The results highlight the importance of adjacent-ligand regulation and provide a new strategy for the design of MOR catalysts with high selectivity of non-CO pathway.

Secure Cluster Based Routing Scheme for Neuro‐Fuzzy Assisted MANET in 5G

ABSTRACTThe Fifth Generation (5G) of wireless networks has increasingly focused significantly on energy‐saving methods for mobile communication systems. The objective of combining MANET with 5G communication is to achieve a high data throughout while simultaneously reducing latency and cost. The main issues with MANET are elevated energy use, security, and reduced longevity. The self‐configuring characteristics and dynamic architecture of MANET may lead to frequent changes in topology and resource allocation. This mobility results in frequent connection failures, which increase routing overheads and decrease data transmission dependability. A secured clustering technique is essential to enhance the routing performance and establish reliable routing. In MANET, the cluster mechanism is the prominent way to improve the network lifetime and routing mechanism. The proposed secure cluster routing‐based neuro‐fuzzy logic (SCR‐NFL) establishes a secure cluster mechanism via the use of the Trust value method. It guarantees cluster stability by taking into account node mobility and energy while selecting cluster heads. The important components in the proposed system are Mobility (M), Residual Energy Level (REL), and Trust Value (TV). The neural fuzzy logic system is implemented to handle the data uncertainty and make effective decisions during routing path selections. The proposed SCR‐NFL performance is measured in an NS‐2 simulation environment. The result is compared with the existing Improved Particle Swarm Optimization Algorithm, Trust‐Based Secure Neuro Fuzzy Clustering mechanism and Mobility Aware Energy Efficient Clustering system using Particle Swarm Optimization mechanism. The performance parameters for comparison are packet delivery ratio, average number of clusters, end‐to‐end delay, average number of cluster instances, mobility speed and mobility distance.

INT-MC: Low-Overhead In-Band Network-Wide Telemetry Based on Matrix Completion

In-band Network Telemetry (INT) enhances real-time, high-resolution network monitoring capabilities by incorporating fine-grained internal state information into packets. Utilizing INT for network-wide visualization can significantly bolster network management and operation. Although existing studies have made significant contributions, they have not been able to simultaneously meet the following two objectives: 1) Comprehensive visibility of the network's internal state, which refers to obtaining information on all switch ports and their corresponding link states; 2) Low measurement overhead, which involves reducing measurement bandwidth overhead and minimizing the impact of the measurement process on the network. We designed INT-MC to meet both of these objectives. INT-MC is an efficient and cost-effective network-wide telemetry solution based on matrix completion. By modeling link metadata as a matrix and leveraging its low-rank property, INT-MC selectively measures certain links. It then employs matrix completion algorithms to infer information about unmeasured links, thereby achieving low-overhead network-wide telemetry. Designing paths to cover the target links involves an NP-complete problem, and the high computational complexity may delay the measurement tasks. To circumvent this, we propose an improved scheme based on Eulerian digraph decomposition, transforming the path calculation problem into a high-information path selection problem, significantly reducing computational costs. We have implemented an INT-MC prototype within the NSFNet topology, consisting of 14 Tofino switches and 10 end-hosts, and conducted extensive experiments and evaluations. The results indicate that INT-MC incurs only 16% of the measurement overhead compared to existing network-wide telemetry solutions, while achieving nearly identical accuracy. Even under high-frequency measurements of 20 times per second, the bandwidth overhead of INT-MC is approximately 0.075% of the total bandwidth, exerting minimal impact on the network.

An optimisation model of hierarchical facility location problem for urban last-mile delivery with drones

Drones have, in recent years, been considered as a promising way for the last-mile delivery of urban logistics. In view of limited or short battery life of drones in every operation, the drone recharging stations and their locations are crucial to ensure delivery accessibility and to accomplish the required mission. Therefore, in addition to considering the selection of service centres and demand nodes, the location of recharging stations should also be incorporated into the proposed model of hierarchical facility location problem (HFLP). Many existing researches on drone recharging station locations assume that the delivery demand is known and only a single path is designed to meet the demand by using queuing theory models to describe node-based service waiting time. However, the reality is that we are still in the infrastructure planning stage for drone delivery, in which the demand is unknown in most cases. Our research proposes a novel mix-integer programming model to solve the HFLP for the last-mile drone delivery in urban areas, aiming to minimise the deployment, operation, and maintenance (DOM) costs of recharging stations, service centres, and paths. We have also incorporated a demand satisfaction constraint into the selection of effective alternative paths that meet the delivery accessibility. On the other hand, a sensitivity analysis in our study reveals varying degrees of impact on DOM costs and effective alternative path quantities, whose results would be useful to practical drone logistics delivery in urban areas.