Path Selection Research Articles

Competitive Online Path-Aware Path Selection

This paper studies an online path selection problem and proposes online mechanisms for a network operator to sequentially update link prices. The aim is to incentivize online-arriving agents to join the network and select paths in a manner that maximizes the social welfare, which comprises both system profit and the quality of service experienced by agents. Competitive analysis is adopted to analyze the performance of the proposed online mechanism, whose best achievable competitive ratio is 4. Sufficient and necessary conditions on a competitive mechanism are established. Moreover, the performance limit of the celebrated multiplethe- index pricing scheme is also analyzed.

Forward stability and model path selection

Most scientific publications follow the familiar recipe of (i) obtain data, (ii) fit a model, and (iii) comment on the scientific relevance of the effects of particular covariates in that model. This approach, however, ignores the fact that there may exist a multitude of similarly-accurate models in which the implied effects of individual covariates may be vastly different. This problem of finding an entire collection of plausible models has also received relatively little attention in the statistics community, with nearly all of the proposed methodologies being narrowly tailored to a particular model class and/or requiring an exhaustive search over all possible models, making them largely infeasible in the current big data era. This work develops the idea of forward stability and proposes a novel, computationally-efficient approach to finding collections of accurate models we refer to as model path selection (MPS). MPS builds up a plausible model collection via a forward selection approach and is entirely agnostic to the model class and loss function employed. The resulting model collection can be displayed in a simple and intuitive graphical fashion, easily allowing practitioners to visualize whether some covariates can be swapped for others with minimal loss.

Reliable data transmission for a VANET-IoIT architecture: A DNN approach

The challenges and resilience of vehicular ad hoc network (VANET) and deep neural network (DNN) hybrid architectures in terms of reliability in smart cities have attracted much global interest stemming from the rollout of the next generation of intelligent networks. In this paper, we propose a novel distributed DNN (D-DNN) scheme with blockchain to support the Internet of Intelligent Things (IoIT) infrastructure in the VANET environment of the future. In particular, because the communication links between edge nodes are very unstable in VANETs, a new neuro-fuzzy server that serves the dual roles of finding reliable links between edge nodes and performing optimal routing path selection is proposed. Next, a blockchain layer is employed at the edge nodes, which are initially scrutinized before establishing communication links to ensure reliability during data transfer. Then, the proposed D-DNN (PD-DNN) scheme is applied to enhance the performance of the VANET-IoIT architecture by improving the data flow and convergence rate and mitigating erratic variations in output. To address reliability concerns, the coverage probability (CP) metric is investigated as a measure of network connectivity. Furthermore, we present an analysis of the PD-DNN scheme in comparison with the traditional DNN (T-DNN) scheme. Finally, simulation results for VANET-IoIT scenarios show that, subject to data protection and privacy constraints, the CP values corresponding to different communication links are improved to a greater extent under our scheme than under the traditional scheme, demonstrating the feasibility of the proposed scheme.

ILBPS: An Integrated Optimization Approach Based on Adaptive Load-Balancing and Heuristic Path Selection in SDN

ILBPS: An Integrated Optimization Approach Based on Adaptive Load-Balancing and Heuristic Path Selection in SDN

Selecting Optimal Path in Multiple-Path Routing for MANETs Using Fuzzy Cost

Background/ Objectives: Mobile Ad Hoc Networks (MANETs) consists of a set of nodes, where the nodes tend to change their positions time to time because of their infrastructure less characteristic. In MANETs, the protocol that is considered to be the appropriate for constructing an efficient route is AODV. Broadcast storm is another approach that can be used for establishing a path between the sender and receiver. Due to the concerns related to contention and collision, broadcast is not considered as an efficient mechanism for path selection. Though the aim of broadcasting is to minimize the flooding other issues like bandwidth consumption, collision and contention cannot be controlled. Since MANETs are associated with inadequate bandwidth, the approach of broadcasting is not efficient. Multipath routing approach permits in constructing multiple paths between the communicating nodes (sender and receiver). This approach is complicated due to the problem of node mobility in MANETs. The other paths available are not reliable because of the ad hoc nature of MANETs. The intention is to design a fuzzy controller that assists in reducing the reconstruction of paths using fuzzy cost assigned to each node in the network. Methods: The concept of this paper is to establish an optimal path from sender node to the receiver node from the available multiple paths in MANETs. The path established in this approach uses fuzzy cost. Fuzzy measure is the criteria used for establishing a path. The concept ImRMR used in this paper has been included with few improvements. The basic functionality of ImRMR is that paths are classified based on rank fitness. The proposal is to classify the paths based on its fuzzy cost. This fuzzy cost classifies the paths fitness. Findings: The proposed fuzzy cost approach discussed in this study is compared with On-Demand Multicast Routing Protocol (ODMRP). The result exhibits that the proposed method works better under differing conditions like traffic load, transmission range between nodes and also considers node mobility. The proposed mechanism with inclusion of fuzzy cost shows 5% energy efficiency, reduces the collisions by about 35% and throughput is enhanced by 30%. There has been a considerable decrease in the number of queries by 23%. The simulation results indicate that ODMRP with fuzzy cost is more useful and significant. Hence, consideration of fuzzy cost as a means for selecting an effective route is assumed to be the best. Novelty: This method contributes in reducing the drawbacks of routing approaches whether it is unipath and multi-path approach. The proposed approach changes the value of the attributes for a given route with fuzzy cost. A threshold value will be considered initially and the route having a cost more than the initialized threshold would be advised as the best route. This route would be considered as the efficient route for transmission between the sender and the receiver. Keywords: Uni­path, multiple paths, Fuzzy cost, Fuzzy fitness, Rank fitness

Trapezoidal cloud based heterogeneous multi-criterion group decision-making for container multimodal transport path selection

Due to the rising globalization of the economy, a single container transportation mode can no longer meet the requirements of global supply chain management. This article proposes a new method combining trapezoidal cloud and MULTIMOORA (Multi-Objective Optimization on the basis of Ratio Analysis plus full multiplicative form) for heterogeneous multi-criterion group decision-making (HMCGDM) to determine the optimal path of container multimodal transport. Firstly, in order to represent the ambiguity and uncertainty of path evaluations, this article uses heterogeneous information to represent the assessments and puts forward some new methods to convert different forms of linguistic evaluation information into trapezoidal clouds. Second, decision makers’ (DMs’) weights are determined through internal weights and external weights. Third, the similarity of trapezoidal clouds is determined by calculating the overlapping area, the criterion weights are objectively obtained by calculating the similarity of trapezoidal clouds under different situations. Fourth, based on MULTIMOORA, a new ranking index is defined to determine the optimal alternative. Thereby, the proposed method for HMCGDM is described step by step. Finally, a real case study of container multimodal transport path selection is analyzed to demonstrate the practicality of the proposed method. The findings of sensitivity and comparative analyses verify the robustness and advantages of the proposed method.

A QoS-Aware IoT Edge Network for Mobile Telemedicine Enabling In-Transit Monitoring of Emergency Patients

Addressing the inadequacy of medical facilities in rural communities and the high number of patients affected by ailments that need to be treated immediately is of prime importance for all countries. The various recent healthcare emergency situations bring out the importance of telemedicine and demand rapid transportation of patients to nearby hospitals with available resources to provide the required medical care. Many current healthcare facilities and ambulances are not equipped to provide real-time risk assessment for each patient and dynamically provide the required medical interventions. This work proposes an IoT-based mobile medical edge (IM2E) node to be integrated with wearable and portable devices for the continuous monitoring of emergency patients transported via ambulances and it delves deeper into the existing challenges, such as (a) a lack of a simplified patient risk scoring system, (b) the need for architecture that enables seamless communication for dynamically varying QoS requirements, and (c)the need for context-aware knowledge regarding the effect of end-to-end delay and the packet loss ratio (PLR) on the real-time monitoring of health risks in emergency patients. The proposed work builds a data path selection model to identify the most effective path through which to route the data packets in an effective manner. The signal-to-noise interference ratio and the fading in the path are chosen to analyze the suitable path for data transmission.

The three dimensions of urbanization and the summary and prospect of China’s urbanization path selection

China has implemented the policy of reform and opening up for 40 years. Under the infl uence of this policy, China’s urbanization has made great development and progress. In the index of population urbanization, the most obvious progress has been made, both the number of cities, the number of urban population, and the urban area have greatly increased, changing the face of ‘rural China’ in the past. However, through in-depth and comprehensive analysis, especially the multidimensional analysis of urbanization, we can fi nd that there is a big gap between the quality and quantity of China’s urbanization in the two dimensions of behavioral urbanization and structural urbanization, that is, ‘the quantity is large but the quality is not high’. This is an issue that China needs to pay attention to in the next stage.

Path selection of liquid fronts in junctions of branching channels

We consider how a liquid front propagating through an initially dry channel network selects its path when encountering branch junctions. We employ both experimental observation and theoretical analysis to investigate the path selection dynamics depending on liquid properties, pressure-driven flow rate, and channel geometry. We identify three distinct front propagation types at the junction, namely, straight, diverging, and diverting flows, and construct their regime map with theoretical regime boundaries. These flow types at the junction determine the subsequent channel-filling patterns, which we also identify and categorize. Our results allow us to design small-scale channel networks to precisely deliver or distribute functional liquids, which can be applied to lab-on-a-chip systems, liquid sculpture, and porous flow control.

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

An adaptive dynamic window approach for UUV obstacle avoidance planning in 3D environments

Unmanned Underwater Vehicles (UUVs) are essential equipment for Marine development, widely used in Marine scientific research, Marine resource survey, and Marine security. The autonomous navigation planning ability in unknown environments is a critical indicator for UUV intelligence. This paper focuses on the particularity of UUV motion and the complexity of the underwater environment and proposes an adaptive Dynamic Window Approach (DWA) for UUV obstacle avoidance planning. The adaptive DWA introduces novel heading angle evaluation and adaptive dynamic strategies to solve the problems of poor adaptability of traditional DWA to complex environments and unreasonable path selection in encountering dynamic obstacles and approaching targets. Simulations verify the effectiveness and superiority of the proposed method.

Device-to-device based path selection for post disaster communication using hybrid intelligence

Public safety network communication methods are concurrence with emerging networks to provide enhanced strategies and services for catastrophe management. If the cellular network is damaged after a calamity, a new-generation network like the internet of things (IoT) is ready to assure network access. In this paper, we suggested a framework of hybrid intelligence to find and re-connect the isolated nodes to the functional area to save life. We look at a situation in which the devices in the hazard region can constantly monitor the radio environment to self-detect the occurrence of a disaster, switch to the device-to-device (D2D) communication mode, and establish a vital connection. The oscillating spider monkey optimization (OSMO) approach forms clusters of the devices in the disaster area to improve network efficiency. The devices in the secluded area use the cluster heads as relay nodes to the operational site. An oscillating particle swarm optimization (OPSO) with a priority-based path encoding technique is used for path discovery. The suggested approach improves the energy efficiency of the network by selecting a routing path based on the remaining energy of the device, channel quality, and hop count, thus increasing network stability and packet delivery.

Analysis of carbon neutrality technology path selection in the steel industry under policy incentives

As an energy-intensive industry, the steel industry incentivizes carbon neutrality technology innovation through carbon neutrality policies, thereby promoting the industry's low-carbon transformation. However, how policy promotes technological innovation in the steel industry to achieve net zero emissions is an urgent practical and theoretical problem that needs to be solved. This study constructs a comprehensive model that combines steel demand forecasting, technology evaluation, and bottom-up technology selection models. Described the cost-effectiveness of emission reduction technologies from a technical and economic perspective, quantitatively analyzed the emission reduction potential and evolution path of carbon neutrality technology under policy incentives in the steel industry, and provided policy support and specific deployment time for different technologies. The results indicate that under the scenario of strong subsidies and carbon prices, by 2060, the share of traditional long-process steelmaking was only 25 %, and the CO2 emissions per unit of crude steel were 0.88t CO2/t. At the same time, the implementation of policies has increased the cumulative emission reduction of the steel industry by 1.56 times from 2022 to 2060.

The dynamic organicity theory of consciousness: how consciousness arises from the functionality of multiscale complexity in the material brain

The fine structure of consciousness is temporally experienced. This makes possible a dynamic organicity theory of consciousness through disunified order in the pre-, sub- and noncognitive levels of causal processes and dynamics. This multilevel approach is based on functional systems where space is implicitly grounded as changeable boundary conditions (due to organicity), and energy capture and storage under energy flow entail structuring intrinsic information (as both hidden thermodynamic energy and hidden thermodynamic information). The use of anaesthesia indicates consciousness disappears when there is an obliteration of intrinsic information within membrane protein amino acids, suggesting that quantum-thermal fluctuations of the electromagnetic field can be functional and non-vacuous. The origin of thermo-qubit syn-tax is derived from the principle of self-reference as the syntax of consciousness. In addition to the classical Brownian motion of macroscopically observable mean values, the negentropically derived quantum potential is an additional degree of freedom where the structuring of intrinsic information takes place by negentropic action from the microscopically random quantum-thermal fluctuations. Such functional fluctuations provide a way for intentionality to come in from spontaneous ordering, leading to path selection as an instruction to act. The resultant intentionality becomes part of information-based action when functional interactions are selected and boundary conditions change, causing interference pattern matching through temporal re-organization of informational redundancy structures (not used in functional interactions). The self-referential dynamic structures (of evolving informational holons) transform syntac-tic structures into experienceable forms. In comparing the interference patterns of functional interactions during restructuring informational redundancy structures, the functionality of multiscale complexity as functional systems patterns gives experienceable forms the potential to understand “meaning” by reducing uncertainty in the process of structuring intrinsic information. The self-reference principle establishes dynamical pathways from the microscale to the macroscale (this includes nonlocal pathways), in which diachronic causation and how the disunity of causal order in the redundancy creates a weak unity of consciousness through its temporal structure, which has an inferred purpose that gives rise to a sense of self.

ANN-Based Intelligent Secure Routing Protocol in Vehicular Ad Hoc Networks (VANETs) Using Enhanced AODV

A vehicular ad hoc network (VANET) is a sophisticated wireless communication infrastructure incorporating centralized and decentralized control mechanisms, orchestrating seamless data exchange among vehicles. This intricate communication system relies on the advanced capabilities of 5G connectivity, employing specialized topological arrangements to enhance data packet transmission. These vehicles communicate amongst themselves and establish connections with roadside units (RSUs). In the dynamic landscape of vehicular communication, disruptions, especially in scenarios involving high-speed vehicles, pose challenges. A notable concern is the emergence of black hole attacks, where a vehicle acts maliciously, obstructing the forwarding of data packets to subsequent vehicles, thereby compromising the secure dissemination of content within the VANET. We present an intelligent cluster-based routing protocol to mitigate these challenges in VANET routing. The system operates through two pivotal phases: first, utilizing an artificial neural network (ANN) model to detect malicious nodes, and second, establishing clusters via enhanced clustering algorithms with appointed cluster heads (CH) for each cluster. Subsequently, an optimal path for data transmission is predicted, aiming to minimize packet transmission delays. Our approach integrates a modified ad hoc on-demand distance vector (AODV) protocol for on-demand route discovery and optimal path selection, enhancing request and reply (RREQ and RREP) protocols. Evaluation of routing performance involves the BHT dataset, leveraging the ANN classifier to compute accuracy, precision, recall, F1 score, and loss. The NS-2.33 simulator facilitates the assessment of end-to-end delay, network throughput, and hop count during the path prediction phase. Remarkably, our methodology achieves 98.97% accuracy in detecting black hole attacks through the ANN classification model, outperforming existing techniques across various network routing parameters.

PathMap: a path-based mapping tool for long noisy reads with high sensitivity.

With the rapid development of single-molecule sequencing (SMS) technologies, the output read length is continuously increasing. Mapping such reads onto a reference genome is one of the most fundamental tasks in sequence analysis. Mapping sensitivity is becoming a major concern since high sensitivity can detect more aligned regions on the reference and obtain more aligned bases, which are useful for downstream analysis. In this study, we present pathMap, a novel k-mer graph-based mapper that is specifically designed for mapping SMS reads with high sensitivity. By viewing the alignment chain as a path containing as many anchors as possible in the matched k-mer graph, pathMap treats chaining as a path selection problem in the directed graph. pathMap iteratively searches the longest path in the remaining nodes; more candidate chains with high quality can be effectively detected and aligned. Compared to other state-of-the-art mapping methods such as minimap2 and Winnowmap2, experiment results on simulated and real-life datasets demonstrate that pathMap obtains the number of mapped chains at least 11.50% more than its closest competitor and increases the mapping sensitivity by 17.28% and 13.84% of bases over the next-best mapper for Pacific Biosciences and Oxford Nanopore sequencing data, respectively. In addition, pathMap is more robust to sequence errors and more sensitive to species- and strain-specific identification of pathogens using MinION reads.

Energy-efficient cluster head using modified fuzzy logic with WOA and path selection using enhanced CSO in IoT-enabled smart agriculture systems

Energy-efficient cluster head using modified fuzzy logic with WOA and path selection using enhanced CSO in IoT-enabled smart agriculture systems

Quantitative analysis of three‐dimensional fatigue crack path selection in Mg alloy WE43 using high‐energy X‐ray diffraction microscopy

AbstractFatigue short‐cracks in Mg alloys display complex growth behavior due to high plastic anisotropy and crack path dependence on local microstructural features. In this study, the three‐dimensional crystallography of short‐crack paths in Mg alloy WE43 was characterized by mapping near‐field high‐energy X‐ray diffraction microscopy (HEDM) reconstructed grain maps to high‐resolution X‐ray CT reconstructions of the fracture surfaces in the crack initiation and short‐crack growth regions of six ultrasonic fatigue specimens. Crack–grain–boundary intersections were analyzed at 81 locations across the six crack paths. The basal intragranular, non‐basal intragranular, or intergranular character of short‐crack growth following each boundary intersection was correlated to crystallographic and geometric parameters of the trailing and leading grains, three‐dimensional grain boundary plane, and advancing crack front. The results indicate that crack paths are dependent on the combined crystallographic and geometric character of the local microstructure, and crack path prediction can be improved by use of dimensionality reduction on subsets of high‐linear‐correlation microstructural parameters.

VAWGAN-NPOA: Energy-Aware Routing for Innocuous Data Transmission in WSN

The shielding from and prediction of harmful activity in the wireless sensor network (WSN) are vital research problems. To solve these problems, an Energy-Aware Routing using a Variational auto-encoder Wasserstein generative adversarial network with a Nomadic People Optimizer (VAWGAN-NPOA-EAR-WSN) is proposed in this paper for secure data transmission in WSN. The proposed Variational auto-encoder Wasserstein generative adversarial network (VAWGAN) selects the Cluster Head (CH) based on multiple objective fitness functions such as delay, distance, energy, cluster density, and traffic rate. After that, energy-aware trust path selection is selected based on parameters such as trust, connectivity, and degree of amenity using the proposed Nomadic People Optimization Algorithm (NPOA). The data are transmitted from the cluster head to the base station and vice versa via a perfect trust path. The proposed VAWGAN-NPOA-EAR-WSN method is done in MATLAB and its efficacy is assessed with performance metrics, such as energy consumption, network lifetime, throughput, etc. compared with existing such as DNN-WSN, EESNN-WSN, and ANN-WSN. Finally, the proposed method provides a lower energy consumption of 2 mJ compared with existing methods.

Routing and dynamic core allocation with fragmentation optimization in EON-SDM

The increasing need for rapid data transmission in optical networks has made the effective allocation of spectrum resources a crucial necessity. This study presents a new spectrum allocation approach that incorporates path selection and careful consideration of cross-talk (XT) limitations. We propose a novel fragmentation coefficient and dynamic core-changing-based routing, spectrum, and core allocation (FC-DCC-RSCA) technique. The algorithm begins by searching for the shortest paths using the Dijkstra algorithm. It then proceeds to evaluate fragmentation coefficients (FC) using the Continuous Aligned Slot Ratio (CASR) model. Subsequently, the FC undergoes a filtration process wherein only those falling inside predetermined fragmentation ranges (min and maxFC) are retained. Further spectrum allocation has been done based on XT constraints. The algorithm has better proficiency in dynamic flexibility, enabling it to effectively handle varying traffic loads. By conducting a series of rigorous simulations, our proposed approach exhibits a notable decrease in bandwidth blocking probability (BBP) while also opening the door for considerable improvements in spectrum utilization. The algorithm’s proficiency in adapting to changing network circumstances signifies its importance as a helpful thing in enhancing the allocation of resources within optical networks. It is well-positioned to address the developing requirements of contemporary communication systems. The spectrum utilization of the FC-DCC-RSCA increases by 12.5% and 6.25%, exhibits a reduction in its FR by 13.5% and 2.7%, and has a BBP of 0.051 for the traffic load of 450 Erlang, compared to SBP-PMC and FMDE-RSCA. The algorithm that has been provided provides opportunities for more investigation and enhancement. Potential areas for future research include the examination of strategies to improve energy efficiency, the development of scalable solutions for bigger network infrastructures, and the incorporation of machine learning methodologies to facilitate dynamic decision-making processes. Furthermore, it is worth investigating the potential of the method to be applied in developing optical communication technologies, such as space-division multiplexing. These endeavors hold the potential to enhance the current level of knowledge and practices in the field of optical network management and optimization.