Path Selection Algorithm Research Articles

SkipResNet: Crop and Weed Recognition Based on the Improved ResNet

Weeds have a detrimental effect on crop yield. However, the prevailing chemical weed control methods cause pollution of the ecosystem and land. Therefore, it has become a trend to reduce dependence on herbicides; realize a sustainable, intelligent weed control method; and protect the land. In order to realize intelligent weeding, efficient and accurate crop and weed recognition is necessary. Convolutional neural networks (CNNs) are widely applied for weed and crop recognition due to their high speed and efficiency. In this paper, a multi-path input skip-residual network (SkipResNet) was put forward to upgrade the classification function of weeds and crops. It improved the residual block in the ResNet model and combined three different path selection algorithms. Experiments showed that on the plant seedling dataset, our proposed network achieved an accuracy of 95.07%, which is 0.73%, 0.37%, and 4.75% better than that of ResNet18, VGG19, and MobileNetV2, respectively. The validation results on the weed–corn dataset also showed that the algorithm can provide more accurate identification of weeds and crops, thereby reducing land contamination during the weeding process. In addition, the algorithm is generalizable and can be used in image classification in agriculture and other fields.

Energy-Efficient Algorithm for Data Path Selection in High-Density Wireless Sensor Networks

Relevance. Increasing the number of sensor devices per unit area consequently reduces the physical distance between the devices in the sensor network. Such networks are usually deployed over a large area and the sensor device that wants to transmit a data packet is located far away from the base station. In such a case, the source device is challenged to choose a transmission path that consumes the least amount of energy resources and satisfies the delivery time requirements. The objective of this study is to develop and validate the effectiveness of an empirical algorithm for selecting a transmission path that reduces the energy consumption of high-density wireless sensor networks. Methods of system analysis, analytical modeling, geometry and probability theories are used. Solution. It is assumed that the sensor network is deployed in a limited area and is a set of devices that are connected to each other informationally and energetically. When building data transmission routes, any sensor devices can be used as repeaters. At the same time, the increase in the number of repeaters leads to an increase in the time of data delivery. Novelty. It is assumed that the sensor network is deployed in a limited area and is a set of devices that are connected to each other informationally and energetically. Any sensor devices may be used as repeaters when constructing data transmission routes.Significance (theoretical). Dependences of power consumption level on various system parameters affecting the processes of functioning of high-density wireless sensor networks have been obtained. Significance (practical). The proposed empirical algorithm for selecting a rational data transmission route in a wireless sensor network allows us to determine, among all alternatives, the route to the coordinator that requires the least power. The effectiveness of the proposed empirical power-saving algorithm is confirmed by simulation modeling.

QoS based meta-heuristic algorithm for path selection in the peer-to-peer network video streaming

QoS based meta-heuristic algorithm for path selection in the peer-to-peer network video streaming

Factor graph-based deep reinforcement learning for path selection scheme in full-duplex wireless multihop networks

A wireless multihop network (WMN) is set of wirelessly connected nodes without an aid of centralized infrastructure that can forward any packets via intermediate nodes by a multihop fashion. In the WMN, there are still some issues that need to be resolved, like due to any source node may choose an uncertainty path to send their packets through the multihop fashion and this leads to the performance of network capacity can degrade drastically. To solve this problem, in this research, we propose two novel path selection algorithms called SNR-based learning path selection (NLPS) algorithm and SINR-based learning path selection (INLPS) algorithm, which are incorporated with the deep reinforcement learning (DRL) to select the best multihop path from any source node to a destination node with highest end-to-end (E2E) throughput. Besides that, a factor graph (FG) approach and a nested lattice code (NLC) representation are used to reduce the computation time. According to the numerical studies with the NLC is applied, our simulation results reveal that the proposed NLPS and INLPS algorithms can improve the overall average network capacity up to 3.1 times and 10.5 times compared to FG, respectively. However, the overall average computation time are highly increased for NLPS and INLPS, i.e., about 0.627 s and 1.221 s, respectively compared to FG, which is about 0.006 s. In other words, both NLPS and INLPS algorithms can achieve high network capacity and moderate computation time.

Multi-objective Sand Cat Swarm Optimization Algorithm for Cluster Head and Routing Path Selection in WSN

Multi-objective Sand Cat Swarm Optimization Algorithm for Cluster Head and Routing Path Selection in WSN

Research on the control strategies of data flow transmission paths for MPTCP-based communication networks.

The performance of multipath transmission control protocol (MPTCP) subflow through the enhancement mechanism of the MPTCP communication is improved. When dealing with multiple MPTCP subflows occupying the same transmission path, critical issues such as selection and optimization of multipath, and efficient scheduling of available multiple tracks are effectively addressed by incorporating the technology called software defined network (SDN) that is constructed based on four key parameters, namely, network transmission bandwidth, transmission paths, path capacity, and network latency. Besides, critical equipment such as the network physical device layer and SDN controller are integrated with the four parameters. So, the network model defines the transmission control process and data information. Considering the predetermined total network bandwidth capacity to select multiple paths, the adequate bandwidth capacity is determined by defining the data transfer rate between MPTCP terminals and MPTCP servers. However, the processing latency of the OpenFlow switch and the SDN controller is excluded. The effective network transmission paths are calculated through two rounds of path selection algorithms. Moreover, according to the demand capacity of the data transmission and the supply capacity of the required occupied network resource, a supply and demand strategy is formulated by considering the bandwidth capacity of the total network and invalid network latency factors. Then, the available network transmission path from the valid network transmission path is calculated. The shortest path calculation problem, which is the calculation and sorting of the shortest path, is transformed into a clustering, Inter-Cluster Average Classification (ICA), problem. The instruction of the OpenFlow communication flow is designed to schedule MPTCP subflows. Thus, various validation objectives, including the network model, effective network latency, effective transmission paths, supply-demand strategies, ineffective transmission paths, shortest feasible paths, and communication rules are addressed by the proposed method whose reliability, stability, and data transmission performance are validated through comparative analysis with other conventional algorithms. Found that the network latency is around 20 s, the network transmission rate is approximately 10Mbps, the network bandwidth capacity reaches around 25Mbps, the network resource utilization rate is about 75%, and the network swallowing volume is approximately 3M/s.

Swapping-Based Entanglement Routing Design for Congestion Mitigation in Quantum Networks

The quantum network is designed to connect numerous quantum nodes and support various ground-breaking quantum applications. Most of these applications require communicating parties to share entangled pairs. Therefore, entanglement routing, a technology distributing entangled pairs between distant quantum nodes, plays a vital role in realizing quantum networks’ capability. However, due to the limitation of quantum memory size and quantum decoherence, the entangled pairs shared by adjacent quantum nodes can hardly satisfy concurrent entanglement routing requests, thus leading to severe network congestion. In this paper, we propose a novel congestion mitigation (CM) scheme to tackle such bottleneck problems. The basic idea of CM is to “recycle” idle link-level entanglement resources from well-resourced links to bottleneck links utilizing a unique enabling technology of quantum networks, called entanglement swapping. CM can increase the capacity of each bottleneck link, thus overcoming resource limitations to improve resource utilization and network throughput. To complete our work, we also propose a swapping-based entanglement routing design, including path selection and resource allocation algorithms. Extensive simulations show that our design can significantly alleviate network congestion and improve the request service rate of quantum networks compared to the traditional entanglement routing designs.

A path selection scheme for detecting malicious behavior based on deep reinforcement learning in SDN/NFV-Enabled network

The SDN/NFV network is prone to different types of attacks. The Distributed Denial of Service (DDoS) attack has the most severe impact as it can overwhelm the critical components of SDN/NFV to degrade its performance. We propose a closed-loop security architecture (SFCSA) and virtualize detection methods as network service functions in this article. Combining the detection methods forms detection paths, in which different detection paths affect security performance differently. Further, we model the path selection problem as a Markov Decision Process, where the reward balances the malicious traffic detection capability and end-to-end latency. Then, an integrated deep reinforcement learning and convolution neural network path selection algorithm (CNNQ) is proposed. Furthermore, we define a total path malicious traffic detection capability metric. The defined metrics and common metrics are applied to evaluate the building prototype, with the corresponding experimental results demonstrating that the detection performance when combining multiple detection modules outperforms a single detection-based module. Besides, we verify the effectiveness of the CNNQ method under various DDoS attacks scenarios and present the fine-grained classification results of the selected detection modules.

Optimal path selection using reinforcement learning based ant colony optimization algorithm in IoT-Based wireless sensor networks with 5G technology

The Internet of Things (IoTs) expanded quickly, giving rise to numerous services, apps, electronic devices with integrated sensors, and associated protocols, which are still being developed today. By enabling physical objects to communicate with each other and share important information while making decisions and carrying out their essential jobs, the IoTs enable them to see, hear, think, and execute crucial tasks. Wireless sensor networks (WSN), which act as the IoT's permanent layer, are essential for fifth-generation (5G) communications, which need the IoT to be considerably helped. A WSN comprises many sensor nodes that track and transmit data to the sink. Every round's data transmission ends at the sink (or base station). This work presents a Proximal Policy Optimization based Ant Colony Optimization (PPO-ACO) algorithm for optimal path selection in WSN. The proposed algorithm combines the strengths of both PPO with a reinforcement learning (RL) method, and ACO, a swarm intelligence method, to address the stochastic nature of the network and the complex trade-off between energy efficiency and security. The PPO component learns the policy for path selection based on the sampled rewards, while the ACO component updates the pheromone levels to guide the search toward the optimal path. Compared to the state-of-the-art, our simulation findings show that the suggested PPO-ACO algorithm performs better in terms of the number of active nodes. The average residual energy of the suggested algorithm decreases later than existing algorithms, indicating its higher efficiency.

Automatic Trajectory Determination in Automated Robotic Welding Considering Weld Joint Symmetry

The field of inspection for welded structures is currently in a state of rapid transformation driven by a convergence of global technological, regulatory, and economic factors. This evolution is propelled by several key drivers, including the introduction of novel materials and welding processes, continuous advancements in inspection technologies, innovative approaches to weld acceptance code philosophy and certification procedures, growing demands for cost-effectiveness and production quality, and the imperative to extend the lifespan of aging structures. Foremost among the challenges faced by producers today is the imperative to meet customer demands, which entails addressing both their explicit and implicit needs. Furthermore, the integration of emerging materials and technologies necessitates the exploration of fresh solutions. These solutions aim to enhance inspection process efficiency while providing precise quantitative insights into defect identification and location. To this end, our project proposes cutting-edge technologies, some of which have yet to gain approval within the sector. Noteworthy among these innovations is the integration of vision systems into welding robots, among other solutions. This paper introduces a groundbreaking algorithm for tool path selection, leveraging profile scanning and the concept of joint symmetry. The application of symmetry principles for trajectory determination represents a pioneering approach within this expansive field.

Green Energy Consumption Path Selection and Optimization Algorithms in the Era of Low Carbon and Environmental Protection Digital Trade

In order to better study the chosen path of the consumption model of public green energy and more accurately predict consumers’ green energy consumer behavior, we take new energy vehicles as an example to explore the driving mechanism and internal mechanism of the public green energy consumption model from the perspective of motivation. We propose an ensemble learning model based on a stacking strategy. The model uses XGBoost, random forest and gradient lifting decision trees as primary learners to transform features, and uses logistic regression as a meta-learner to predict users’ consumer behavior. The experimental results show that this feature engineering method can significantly improve the accuracy rate in multiple model algorithms, and the prediction effect of the ensemble learning model is better than that of a single model, with the accuracy rate of 82.81%. In conclusion, the ensemble learning model based on a stacking strategy can effectively predict the public’s consumer behavior. This provides a theoretical basis and policy recommendations for promoting green energy products represented by new energy vehicles, thereby improving the practical path for proposing green energy consumption.

Research on path selection and flow scheduling of time-sensitive networks for power substations

In view of the problem that the flow of intelligent substation traffic scheduling increases, resulting in the transmission delay also increases, this paper proposed a joint optimization method of Time Sensitive Network (TSN) path selection and scheduling, which combines the resource sensing K-Shortest Path Algorithm (KPA) path selection algorithm and the constraint based gating method. This method can select an optimal transmission path for TT flows and plan the transmission path for non TT flows. Based on the scheduling mechanism of TSN, constraints and optimization objective functions are established, and a gating scheduling method based on constraints is proposed. Finally, the test and verification platform of the scheme is designed and built. Through the test and verification platform, the data stream transmission delay and delay jitter of the scheme are analyzed, and the algorithms are compared and analyzed from the aspects of running time and schedulability. With the continuous evolution of the follow-up TSN technology and the in-depth promotion of the power industry, more and more TSN key technologies will be applied to the smart grid.

A New Region-Based Minimal Path Selection Algorithm for Crack Detection and Ground Truth Labeling Exploiting Gabor Filters

Cracks are fractures or breaks that occur in materials such as concrete, metals, rocks, and other solids. Various methods are used to detect and monitor cracks; among many of them, image-based methodologies allow fast identification of the distress and easy quantification of the percentage of cracks in the scene. Two main categories can be identified: classical and deep learning approaches. In the last decade, the tendency has moved towards the use of the latter. Even though they have proven their outstanding predicting performance, they suffer some drawbacks: a “black-box” nature leaves the user blind and without the possibility of modifying any parameters, a huge amount of labeled data is generally needed, a process that requires expert judgment is always required, and, finally, they tend to be time-consuming. Accordingly, the present study details the methodology for a new algorithm for crack segmentation based on the theory of minimal path selection combined with a region-based approach obtained through the segmentation of texture features extracted using Gabor filters. A pre-processing step is described, enabling the equalization of brightness and shadows, which results in better detection of local minima. These local minimal are constrained by a minimum distance between adjacent points, enabling a better coverage of the cracks. Afterward, a region-based segmentation technique is introduced to determine two areas that are used to determine threshold values used for rejection. This step is critical to generalize the algorithm to images presenting close-up scenes or wide cracks. Finally, a geometrical thresholding step is presented, allowing the exclusion of rounded areas and small isolated cracks. The results showed a very competitive F1-score (0.839), close to state-of-the-art values achieved with deep learning techniques. The main advantage of this approach is the transparency of the workflow, contrary to what happens with deep learning frameworks. In the proposed approach, no prior information is required; however, the statistical parameters may have to be adjusted to the particular case and requirements of the situation. The proposed algorithm results in a useful tool for researchers and practitioners needing to validate their results against some reference or needing labeled data for their models. Moreover, the current study could establish the grounds to standardize the procedure for crack segmentation with a lower human bias and faster results. The direct application of the methodology to images obtained with any low-cost sensor makes the proposed algorithm an operational support tool for authorities needing crack detection systems in order to monitor and evaluate the current state of the infrastructures, such as roads, tunnels, or bridges.

A hybrid Square Shaped Adaptive Neuro-Fuzzy Interference with M-PeSOA model for optimal path selection

In Vehicular cloud computing (VCC), at the time of congestion, to deal with traffic, the vehicles’ underutilized resources are shared; subsequently, these resources aren’t constrained to computing power, storage, along with internet connectivity. Nevertheless, owing to the vehicular network characteristics, attaining the QoS requirements search together with the allocation of resources in the Vehicular Cloud (VC) has turned into a complicated task. An intelligent Square Shaped (SS)-Adaptive Neuro-Fuzzy Interference (SS-ANFIS) methodology for Resource Scheduling (RS) in addition to Mean-centered Penguins Search Optimization Algorithm (M-PeSOA) for Optimal Path Selection (OPS) in the VC is proposed here for efficient resource allocation. (a) Feature extraction, (b) Vehicles clustering, (c) OPS, (d) Resource information extraction, and (e) RS included in the proposed methodology. First, the vehicular network is initialized following that the vehicle features are extracted. Next, Cluster Heads (CHs) are generated regarding which vehicles are clustered; subsequently, the multi-paths are generated. After that, by employing the M-PeSOA, the OPS procedure is conducted; thus, the VC’s resource information is extracted aimed at scheduling the resources efficiently. Lastly, by employing the SS-ANFIS, vehicles are scheduled in the optimal paths. The proposed resource allocation system’s performance is assessed, and the experiential outcomes are analogized using the sumo tool and java platform.

Review of Path Selection Algorithms with Link Quality and Critical Switch Aware for Heterogeneous Traffic in SDN

Software Defined Networking (SDN) introduced network management flexibility that eludes traditional network architecture. Nevertheless, the pervasive demand for various cloud computing services with different levels of Quality of Service requirements in our contemporary world made network service provisioning challenging. One of these challenges is path selection (PS) for routing heterogeneous traffic with end-to-end quality of service support specific to each traffic class. The challenge had gotten the research community's attention to the extent that many PSAs were proposed. However, a gap still exists that calls for further study. This paper reviews the existing PSA and the Baseline Shortest Path Algorithms (BSPA) upon which many relevant PSA(s) are built to help identify these gaps. The paper categorizes the PSAs into four, based on their path selection criteria, (1) PSAs that use static or dynamic link quality to guide PSD, (2) PSAs that consider the criticality of switch in terms of an update operation, FlowTable limitation or port capacity to guide PSD, (3) PSAs that consider flow variabilities to guide PSD and (4) The PSAs that use ML optimization in their PSD. We then reviewed and compared the techniques' design in each category against the identified SDN PSA design objectives, solution approach, BSPA, and validation approaches. Finally, the paper recommends directions for further research.

Adaptive Path Selection Algorithm with Flow Classification for Software-Defined Networks

Software-Defined Networking (SDN) is a trending architecture that separates controller and forwarding planes. This improves network agility and efficiency. The proliferation of the Internet of Things devices has increased traffic flow volume and its heterogeneity in contemporary networks. Since SDN is a flow-driven network, it requires the corresponding rule for each flow in the flowtable. However, the traffic heterogeneity complicates the rules update operation due to varied quality of service requirements and en-route behavior. Some flows are delay-sensitive while others are long-lived with a propensity to consume network buffers, thereby inflicting congestion and delays on the network. The delay-sensitive flows must be routed through a path with minimal delay, while congestion-susceptible flows are guided along a route with adequate capacity. Although several efforts were introduced over the years to efficiently route flows based on different QoS parameters, the current path selection techniques consider either link or switch operation during decisions. Incorporating composite path metrics with flow classification during path selection decisions has not been adequately considered. This paper proposes a technique based on composite metrics with flow classification to differentiate congestion-prone flows and reroute them along appropriate paths to avoid congestion and loss. The technique is integrated into the SDN controller to guide the selection of paths suitable to each traffic class. Compared to other works, the proposed approach improved the path load ratio by 25%, throughput by 35.6%, and packet delivery ratio by 31.7%.

Optimal selection algorithm of logistics distribution route based on machine vision

In order to improve the centralized planning ability of logistics distribution path data, improve the efficiency of logistics distribution and reduce the cost of logistics distribution, this paper proposes an optimal path selection algorithm based on machine vision. Using machine vision technology to calibrate the coordinates of logistics distribution path, combined with EMD decomposition method and wavelet denoising method to remove redundant data in logistics distribution data, particle swarm optimization algorithm to complete logistics distribution path planning, and ant colony algorithm to realize the optimal path selection of logistics distribution. The experimental results show that the average distribution cost of this method is only 766.7 yuan, the distribution time is less than 0.3 h, and the customer satisfaction is as high as 98%, which shows that this method can effectively optimize the distribution path.

Critical Relation Path Aggregation-Based Industrial Control Component Exploitable Vulnerability Reasoning

With the growing discovery of exposed vulnerabilities in the Industrial Control Components (ICCs), identification of the exploitable ones is urgent for Industrial Control System (ICS) administrators to proactively forecast potential threats. However, it is not a trivial task due to the complexity of the multi-source heterogeneous data and the lack of automatic analysis methods. To address these challenges, we propose an exploitability reasoning method based on the ICC-Vulnerability Knowledge Graph (KG) in which relation paths contain abundant potential evidence to support the reasoning. The reasoning task in this work refers to determining whether a specific relation is valid between an attacker entity and a possible exploitable vulnerability entity with the help of a collective of the critical paths. The proposed method consists of three primary building blocks: KG construction, relation path representation, and query relation reasoning. A security-oriented ontology combines exploit modeling, which provides a guideline for the integration of the scattered knowledge while constructing the KG. We emphasize the role of the aggregation of the attention mechanism in representation learning and ultimate reasoning. In order to acquire a high-quality representation, the entity and relation embeddings take advantage of their local structure and related semantics. Some critical paths are assigned corresponding attentive weights and then they are aggregated for the determination of the query relation validity. In particular, similarity calculation is introduced into a critical path selection algorithm, which improves search and reasoning performance. Meanwhile, the proposed algorithm avoids redundant paths between the given pairs of entities. Experimental results show that the proposed method outperforms the state-of-the-art ones in the aspects of embedding quality and query relation reasoning accuracy.

Balancing module in evolutionary optimization and Deep Reinforcement Learning for multi-path selection in Software Defined Networks

Software Defined Network (SDN) has been used in many organizations due to its efficiency in transmission. Machine learning techniques have been applied in SDN to improve its efficiency in resource scheduling. The existing models in SDN have limitations of overfitting, local optima trap and lower efficiency in path selection. This study applied Balancing Module (BM)-Spider Monkey Optimization (SMO)-Crow Search Algorithm (CSA) for multi path selection in SDN to improve its efficiency. The balancing module applies Gaussian distribution to balance between exploration and exploitation in the multi-path selection process. The Balancing module helps to escape local optima trap and increases the convergence rate. Deep Reinforcement learning is applied for resource scheduling in SDN. The Deep reinforcement learning technique uses the reward function to improve the learning performance, and the BM-SMO-CSA technique has 30 J energy consumption, where the existing models: DRL has 40 J energy consumption, and Graph-ACO has 62 J energy consumption.

QoS correlation-based service composition algorithm for multi-constraint optimal path selection

QoS correlation-based service composition algorithm for multi-constraint optimal path selection