Node Selection Method Research Articles

Research on the A* Algorithm for Automatic Guided Vehicles in Large-Scale Maps

The traditional A* algorithm faces the challenges of low search efficiency and large node extension range in the field of path planning. These directly restrict the overall performance of the algorithm. In this study, we aimed to improve the search efficiency and path planning quality of the A* algorithm in complex and large-scale environments through a series of optimisation measures, including the innovation of weight design, flexible adjustment of the search neighbourhood, improvement of the heuristic function, and optimisation of the node selection strategy. Specifically, this study innovatively introduces the local obstacle rate as the core index of weight design, and it dynamically adjusts the weights according to the change of the obstacle rate during the node movement process, which effectively reduces the search space and significantly improves the search speed. At the same time, according to the real-time change of the local obstacle rate, this study dynamically adjusts the range of the search neighbourhood, so that the algorithm can choose the optimal search strategy according to different environmental information. In terms of the improvement of the heuristic function, this study adopted the diagonal distance as the benchmark for cost estimation, and it innovatively introduces the angle coefficient to reflect the complexity of path turning, thus providing the algorithm with a more accurate guidance for the search direction. In addition, this study optimises the node selection method by drawing on the idea of simulated annealing, which eliminates the need to calculate and compare all possible surrogate values during the node selection process, thus significantly reducing the running time of the algorithm. The results of the simulation experiments fully verify the effectiveness and practicality of the improved algorithm. Compared with the traditional A* algorithm, the improved algorithm achieved significant optimisation in terms of the average running time, the number of expansion nodes, and the path length, with the average running time shortened by 84%, the number of expansion nodes reduced by 94%, and the path length also shortened by 2.3%.

Node Localization Method in Wireless Sensor Networks Using Combined Crow Search and the Weighted Centroid Method.

Node localization is critical for accessing diverse nodes that provide services in remote places. Single-anchor localization techniques suffer co-linearity, performing poorly. The reliable multiple anchor node selection method is computationally intensive and requires a lot of processing power and time to identify suitable anchor nodes. Node localization in wireless sensor networks (WSNs) is challenging due to the number and placement of anchors, as well as their communication capabilities. These senor nodes possess limited energy resources, which is a big concern in localization. In addition to convention optimization in WSNs, researchers have employed nature-inspired algorithms to localize unknown nodes in WSN. However, these methods take longer, require lots of processing power, and have higher localization error, with a greater number of beacon nodes and sensitivity to parameter selection affecting localization. This research employed a nature-inspired crow search algorithm (an improvement over other nature-inspired algorithms) for selecting the suitable number of anchor nodes from the population, reducing errors in localizing unknown nodes. Additionally, the weighted centroid method was proposed for identifying the exact location of an unknown node. This made the crow search weighted centroid localization (CS-WCL) algorithm a more trustworthy and efficient method for node localization in WSNs, with reduced average localization error (ALE) and energy consumption. CS-WCL outperformed WCL and distance vector (DV)-Hop, with a reduced ALE of 15% (from 32%) and varying communication radii from 20 m to 45 m. Also, the ALE against scalability was validated for CS-WCL against WCL and DV-Hop for a varying number of beacon nodes (from 3 to 2), reducing ALE to 2.59% (from 28.75%). Lastly, CS-WCL resulted in reduced energy consumption (from 120 mJ to 45 mJ) for varying network nodes from 30 to 300 against WCL and DV-Hop. Thus, CS-WCL outperformed other nature-inspired algorithms in node localization. These have been validated using MATLAB 2022b.

A Path Planning Method Based on Improved A* and Fuzzy Control DWA of Underground Mine Vehicles

In order to solve the problem of low safety and efficiency of underground mine vehicles, a path planning method for underground mine vehicles based on an improved A star (A*) and fuzzy control Dynamic Window Approach (DWA) is proposed. Firstly, the environmental information is analyzed, and the proportion of obstacles is introduced into the A* algorithm to optimize the heuristic function to improve its search efficiency in different environments. Secondly, for the global path, the key node selection strategy is proposed, the node selection method is optimized, and the redundant nodes are deleted. The path is optimized by using a 3 times Clamped-B spline to improve the smoothness of the path. Then, based on the principle of fuzzy control, fuzzy rules are established, and a DWA fuzzy controller is designed to improve the environmental adaptability of the DWA algorithm. Finally, on the basis of the global path, the key node is used as the local target point of the DWA, and the fusion of the improved A* and DWA algorithm is realized. Finally, experiments are conducted to verify the effectiveness and feasibility of the proposed path-planning method. The average deviation of the path is controlled at ±0.109 m, which basically meets the path planning needs of underground mine vehicles.

Multi-target label backdoor attacks on graph neural networks

Graph neural networks have been shown to have characteristics that make them susceptible to backdoor attacks, and many recent works have proposed feasible graph backdoor attack methods. However, existing graph backdoor attack methods only target one-to-one attack types and lack graph backdoor attack methods that can address one-to-many attack requirements. This paper is the first research work on one-to-many type graph backdoor attacks and proposes the backdoor attack method MLGB, which can achieve multi-target label attacks for GNN node classification tasks. We designed encoding mechanisms to allow MLGB to customize triggers for different target labels and ensure differentiation between triggers for different target labels through loss functions. Additionally, we designed an innovative poisoned node selection method to improve the efficiency of MLGB’s attacks further. Extensive experiments were conducted to validate MLGB’s effectiveness across multiple datasets and model architectures, demonstrating its robustness against graph backdoor attack defense mechanisms. Furthermore, ablation experiments and explainability analyses were conducted to provide deeper insights into MLGB. Our work reveals that graph neural networks are also vulnerable to one-to-many type backdoor attacks, which is important for practitioners to understand model risks comprehensively.

Lc‐Stream: An elastic scheduling strategy with latency constraints in geo‐distributed stream computing environments

SummaryAn effective scheduling strategy is critical for achieving better performance in real‐time stream processing systems. How to quickly and efficiently process real‐time data stream is always challenging, especially when clusters are collaborating in a Geo‐Distributed computing environment. To address these challenges, we propose an elastic scheduling strategy with Latency Constraints in Geo‐Distributed stream computing environments called Lc‐Stream. This article discusses our work from the following aspects: (1) An optimized data stream redirection method that is proposed based on queuing network algorithm, along with a computing resource model, a latency constrained scheduling model and a communication energy consumption model. (2) An updated node selection method based on the inter‐layer task correlation, to reduce the communication latency between groups at the executor granularity. (3) A network cluster distribution for Geo‐Distributed computing environment to ensure energy saving under low transmission latency. Experimental results show that compared to R‐Storm, Lc‐Stream reduces total latency by over 19% and increases throughput by over 37% in typical cross‐domain multi‐task topologies. Compared to Ts‐Stream, Lc‐Stream also reduces total latency by over 15% and increases throughput by over 21%. At the same time, it helps to balance the load among the systems and avoid overuse of compute nodes.

A novel node selection method for wireless distributed edge storage based on SDN and a maldistributed decision model

A novel node selection method for wireless distributed edge storage based on SDN and a maldistributed decision model

Topology uniformity pinning control for multi-agent flocking

The optimal selection of pinning nodes for multi-agent flocking is a challenging NP-hard problem. Current pinning node selection strategies mainly rely on centrality measures of complex networks, which lack rigorous mathematical proof for effective flocking control. This paper proposes a pinning node selection strategy based on matrix eigenvalue theory. First, the effect of the pinning node on the eigenvalue of the Laplacian matrix is analyzed. Then, a synchronization index representing the topology uniformity of the multi-agent system is proposed to exert maximum influence on the system synchronizability. A practical optimal pinning node selection method based on the synchronization index is proposed and analyzed using the eigenvalue perturbation method. Finally, simulations demonstrate that the convergence rate of the system obtained using the optimal synchronizability pinning node selection method is better than that achieved with the maximum degree centrality node selection strategy.

Software/Code for "DIVERSITREE: A New Method to Efficiently Compute Diverse Sets of Near-Optimal Solutions to Mixed-Integer Optimization Problems"

Although most methods for solving mixed-integer optimization problems com-pute a single optimal solution, a diverse set of near-optimal solutions can often lead to improved outcomes. We present a new method for finding a set of diverse solutions by emphasizing diversity within the search for near-optimal solutions. Specifically, within a branch-and-bound framework, we investigated parameterized node selection rules that explicitly consider diversity. Our results indicate that our approach significantly increases the diversity of the final solution set. When compared with two existing methods, our method runs with similar runtime as regular node selection methods and gives a diversity improvement between 12% and 190%. In contrast, popular node selection rules, such as best-first search, in some instances performed worse than state-of-the-art methods by more than 35% and gave an improvement of no more than 130%. Furthermore, we find that our method is most effective when diversity in node selection is continuously emphasized after reaching a minimal depth in the tree and when the solution set has grown sufficiently large. Our method can be easily incorporated into integer programming solvers and has the potential to significantly increase the diversity of solution sets.

DiversiTree: A New Method to Efficiently Compute Diverse Sets of Near-Optimal Solutions to Mixed-Integer Optimization Problems

Although most methods for solving mixed-integer optimization problems compute a single optimal solution, a diverse set of near-optimal solutions can often lead to improved outcomes. We present a new method for finding a set of diverse solutions by emphasizing diversity within the search for near-optimal solutions. Specifically, within a branch-and-bound framework, we investigated parameterized node selection rules that explicitly consider diversity. Our results indicate that our approach significantly increases the diversity of the final solution set. When compared with two existing methods, our method runs with similar runtime as regular node selection methods and gives a diversity improvement between 12% and 190%. In contrast, popular node selection rules, such as best-first search, in some instances performed worse than state-of-the-art methods by more than 35% and gave an improvement of no more than 130%. Furthermore, we find that our method is most effective when diversity in node selection is continuously emphasized after reaching a minimal depth in the tree and when the solution set has grown sufficiently large. Our method can be easily incorporated into integer programming solvers and has the potential to significantly increase the diversity of solution sets. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: This work was supported by the Army Research Office [Grant W911NF-21-1-0079]. The views expressed in this study do not represent those of the U.S. Government, the U.S. Department of Defense, or the U.S. Army. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0164 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0164 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

A drug molecular classification model based on graph structure generation

Molecular property prediction based on artificial intelligence technology has significant prospects in speeding up drug discovery and reducing drug discovery costs. Among them, molecular property prediction based on graph neural networks (GNNs) has received extensive attention in recent years. However, the existing graph neural networks still face the following challenges in node representation learning. First, the number of nodes increases exponentially with the expansion of the perception field, which limits the exploration ability of the model in the depth direction. Secondly, the large number of nodes in the perception field brings noise, which is not conducive to the model’s representation learning of the key structures. Therefore, a graph neural network model based on structure generation is proposed in this paper. The model adopts the depth-first strategy to generate the key structures of the graph, to solve the problem of insufficient exploration ability of the graph neural network in the depth direction. A tendentious node selection method is designed to gradually select nodes and edges to generate the key structures of the graph, to solve the noise problem caused by the excessive number of nodes. In addition, the model skillfully realizes forward propagation and iterative optimization of structure generation by using an attention mechanism and random bias. Experimental results on public data sets show that the proposed model achieves better classification results than the existing best models.

Target set selection in social networks with tiered influence and activation thresholds.

Thanks to the mass adoption of internet and mobile devices, users of the social media can seamlessly and spontaneously connect with their friends, followers and followees. Consequently, social media networks have gradually become the major venue for broadcasting and relaying information, and is casting great influences on the people in many aspects of their daily lives. Thus locating those influential users in social media has become crucially important for the successes of many viral marketing, cyber security, politics, and safety-related applications. In this study, we address the problem through solving the tiered influence and activation thresholds target set selection problem, which is to find the seed nodes that can influence the most users within a limited time frame. Both the minimum influential seeds and maximum influence within budget problems are considered in this study. Besides, this study proposes several models exploiting different requirements on seed nodes selection, such as maximum activation, early activation and dynamic threshold. These time-indexed integer program models suffer from the computational difficulties due to the large numbers of binary variables to model influence actions at each time epoch. To address this challenge, this paper designs and leverages several efficient algorithms, i.e., Graph Partition, Nodes Selection, Greedy algorithm, recursive threshold back algorithm and two-stage approach in time, especially for large-scale networks. Computational results show that it is beneficial to apply either the breadth first search or depth first search greedy algorithms for the large instances. In addition, algorithms based on node selection methods perform better in the long-tailed networks.

A Reverse Shortest Path Tree-Based Multicast Joining Node Selection Method

Network layer multicast is a powerful method for transmitting data from sources to multiple group members. When joining a multicast group, a group member first sends a request to a designated router (DR). Then, the DR selects a node in the existing multicast tree (known as a multicast joining node, or MJN) to establish a multicast distribution path from the MJN to itself. The MJN selection method runs on the DR and has a significant impact on the distribution of the multicast tree, that directly affects the load distribution in the network. However, the current MJN selection method cannot effectively detect the load status of the downlink multicast path in the case of asymmetric routing, leading to network congestion and limiting the number of multicast groups that the network can accommodate (multicast capacity). To solve this problem, we propose an MJN selection method based on the reverse shortest path tree (RSPT). RSPT can effectively detect the load status of downlink multicast paths in case of routing asymmetry. Based on the detection results of RSPT, DR can select the MJN with the lowest path load to join the multicast tree. Our experimental results indicate that compared to existing multicast methods, our method has a lower cost and delay, and can effectively balance the network load in the case of asymmetric routing, increasing multicast capacity by more than two times.

A Parallel Consensus Mechanism Using PBFT Based on DAG-Lattice Structure in the Internet of Vehicles

The blockchain technology in the Internet of Vehicles (IoV) can be applied to solve security problems. Due to the high dynamics and resource constraints of the IoV, the traditional single-chain blockchain system is not suitable for the practical scenarios. In our previous work, we proposed a lightweight blockchain architecture based on the directed acyclic graph lattice (DAG-lattice) structure for the IoV. However, the current consensus algorithms are not suitable for the previous architecture mentioned above. Therefore, this article proposes a parallel consensus mechanism based on the DAG-lattice structure in the IoV using practical Byzantine fault tolerance (PBFT) as consensus algorithm. This mechanism solves the problem of low efficiency of PBFT caused by too many consensus nodes and the mobility of nodes by adopting the method of network sharding, and meanwhile to ensure that transactions can be reached on consensus parallelly. To avoid the selection of primary nodes from being predicted in advance, this article proposes a primary node selection method based on verifiable random function (VRF). Moreover, a method is proposed of selecting transactions based on weighted round robin (WRR) scheduling to motivate nodes for the purpose of maintaining a high reputation score. Finally, the performance of the proposed scheme is verified by experiments whose results show that the proposed consensus mechanism shows better performance than other comparable schemes in terms of the time added to the blockchain, transaction per second (TPS), consensus success rate, and the time of obtaining transaction. And the proposed mechanism is less dependent on roadside unit (RSU).

An improved stochastic configuration network for concentration prediction in wastewater treatment process

A learner model with fast learning and compact architecture is expected for industrial data modeling. To achieve these goals during stochastic configuration networks (SCNs) construction, we propose an improved version of SCNs in this paper. Unlike the original SCNs, the improved one employs a new inequality constraint in the construction process. In addition, to speed up the construction efficiency of SCNs, a node selection method is proposed to adaptively select nodes from a candidate pool. Moreover, to reduce the redundant nodes of the built SCNs model, we further compress the model based on the singular value decomposition algorithm. The improved SCNs are compared with other methods over four datasets and then applied to the ammonia–nitrogen concentration prediction task in the wastewater treatment process. Experimental results indicate that the proposed method has good potential for industrial data analytics.

PERFORMANCE ANALYSIS ON GPSR PROTOCOL IN VANETS ENVIRONMENT OF OVERLAY NETWORK AND FORWARDING NODE SELECTION METHODS

VANETs are technology in Intelligent Transport Systems (ITS) in driving safety and convenient network services for vehicle users. However, the high mobility of VANETs makes the topology change frequently, resulting in the unavailability of routes and causing communication failures between nodes. Greedy Perimeter Stateless Routing (GPSR) is a routing protocol that solves communication problems in the VANETs environment. GPSR is a geographic routing protocol that uses information on the location of neighbouring nodes and the transmission range to the nearest destination node. But, GPSR does not always find the optimal route because not all nearby nodes can forward packets to their destination during heavy traffic and many intersections. In this research, the overlay network and forwarding node selection methods will be used to solve problems in the GPSR routing protocol related to communication instability because of changing node positions. Based on the results of testing and analysis that has been carried out on a grid scenario with 50 nodes, the Packet Delivery Ratio results are 32.60. In contrast, the results for the real scenario with the same nodes produce a value of 64. The analysis of End to End Delay in the 50-node grid scenario produces 5.64, while the real scenario results with 50 nodes yield the value of 24.11. The analysis Routing Overhead in the grid scenario with 50 nodes produces a value of 39,978, while the results of the real scenario with 50 nodes get a value of 10,239. With the result of these two analysis methods, it is expected to increase the average value of package delivery.

An adaptive read/write optimized algorithm for Ceph heterogeneous systems via performance prediction and multi-attribute decision making

The advent of the Big Data era has brought considerable challenges to storing and managing massive data. Moreover, distributed storage systems are critical to the pressure and storage capacity costs. The Ceph cloud storage system only selects data storage nodes based on node storage capacity. This node selection method results in load imbalance and limited storage scenarios in heterogeneous storage systems. Therefore, we add node heterogeneity, network state, and node load as performance weights to the CRUSH algorithm and optimize the performance of the Ceph system by improving load balancing. We designed a cloud storage system model based on Software Defined Network (SDN) technology. This system model can avoid the tedious configuration and significant measurement overhead required to obtain network status in traditional network architecture. Then we propose adaptive read and write optimization algorithms based on SDN technology. The Object Storage Device (OSD) is initially classified based on the Node Heterogeneous Resource Classification Strategy. Then the SDN technology is used to obtain network and load conditions in real-time and an OSD performance prediction model is built to obtain weights for performance impact factors. Finally, a mathematical model is proposed for multi-attribute decision making in conjunction with the OSD state and its prediction model. Furthermore, this model is addressed to optimize read and write performance adaptively. Compared with the original Ceph system, TOPSIS_PA improves the performance of reading operations by 36%; TOPSIS_CW and TOPSIS_PACW algorithms improve the elastic read performance by 23 to 60% and 36 to 85%, and the elastic write performance by 180 to 468% and 188 to 611%, respectively.

A Novel Path Planning and Node Selection Method Using Reinforcement Learning in NTN IoT Networks

With the rapid deployment of 5G networks in recent years, the characteristics of high bandwidth, low latency, and low energy consumption of 5G networks have enabled the rapid development of IoT (Internet of things) technology. However, 5G networks cannot provide high-quality wireless coverage for many IoT devices in border areas and hotspots with a high signal density that lack fixed infrastructure. Therefore, this paper uses the UAV (unmanned aerial vehicle) to carry the communication platform to build the NTN (nonterrestrial network) to provide wireless coverage for terrestrial fixed and mobile IoT devices. Meanwhile, since the NTN needs to provide wireless coverage for many IoT devices, we use deep reinforcement learning to provide path planning for the UAV communication platform to improve the efficiency of wireless coverage. We build a simulation environment to evaluate the performance of the NTN network for wireless coverage of IoT devices in urban hotspot areas. Experimental results show that the method proposed in this paper can provide higher downlink rates for more IoT devices than NB-IoT (narrowband Internet of things).

A node selection method for cooperative location estimation in high density wireless networks

A node selection method for cooperative location estimation in high density wireless networks

Honesty aware congestion concerned secured edge disjoint multi path routing with fuzzy rule descriptors

Congestion and security plays a most important key role in the wireless sensor network. In our previous work, energy balances routing is ensured by introducing the Energy Balancing and Optimal Routing Based Secured Data Transmission (EBORDT). But in this research work, congestion and security is not focused. This will lead to increased data loss rate along with entire network collision. These issues are focused and resolved in the proposed research work by introducing the method namely Honest aware Congestion concerned Secured Edge Disjoint Multipath Routing (HC-SEDMR) Scheme. In this work cluster head selection is done using hybridized simulated annealing with fuzzy rule descriptors. Base on these selected cluster head, clustering is done using Normalized optimal clustering algorithm. After clustering Energy and Edge disjoint aware optimal routing is done using Congestion and Collision aware Edge Disjoint multipath routing. Here secured routing is ensured by choosing honest nodes for the forwarding process. This is done by using honest forwarding node selection method. The performance analysis of the research work is done in the NS2 simulation environment from which it is proved that the proposed method attains better performance than the existing methodology.

Power-Efficient Data Collection Scheme for AUV-Assisted Magnetic Induction and Acoustic Hybrid Internet of Underwater Things

Power efficiency is a big concern in the Internet of Underwater Things (IoUT). The power consumption of underwater acoustic communications is typically in the scale of watts, which may drain the battery of underwater devices quickly. Whereas, the power consumption of underwater magnetic induction (MI) wireless communications is in the scale of milliwatt. Therefore, this article devotes to combine the underwater MI and acoustic communications to form a power-efficient underwater hybrid wireless network. Specifically, we investigate the power-efficient autonomous underwater vehicle (AUV) data collection schemes in an underwater MI and acoustic hybrid sensor network. We propose an alternating anchor nodes selection and flow routing (AANSFR) AUV data collection method, which alternately optimizes the AUV path planning and network data flow routing. The simulation results show that the proposed hybrid data collection scheme can significantly prolong the lifespan of underwater sensor networks.