Optimal Transmission Path Research Articles

Adaptive underwater monitoring system through visible light

Abstract Currently, underwater visible light communication (VLC) in fields such as underwater resource development, scientific research, and monitoring has become a research hotspot. However, challenges persist in underwater optical communication distances and quality monitoring due to factors such as light attenuation and scattering. Therefore, we propose an adaptive underwater visible light communication monitoring system to increase underwater communication distances and facilitate underwater environmental monitoring. First, we design a parallel multi-hop structure for underwater VLC nodes to enhance underwater communication distances, which not only amplifies the received signal power but also dynamically selects the optimal signal transmission path. Second, we implement a low-cost early warning mechanism that utilizes transmission rate variations under different underwater concentrations to indicate bit error rates (BER) macroscopically. By setting appropriate thresholds, monitoring information is reported to the system. Extensive experiments demonstrate that the designed system can achieve a transmission distance and monitor concentration changes within the range of 0.0018g / ml to 0.0073g / ml.

A deep reinforcement learning-based intelligent QoS optimization algorithm for efficient routing in vehicular networks

With the rapid development of Telematics, Vehicle Self-Organizing Networks (VANETs) play an increasingly critical role in Intelligent Transportation Systems (ITS). Especially in the environment without roadside assistance units (RSUs), how to effectively manage inter-vehicle communication and improve the stability and communication efficiency of the network has become a hot topic of current research. In this paper, a Deep Reinforcement Learning-based Intelligent QoS-optimized efficient routing algorithm for vehicular networks (DRLIQ) is proposed for VANETs with/without RSU environments, and routing methods are proposed respectively. Among them, in RSU-free environment, the DRLIQ algorithm utilizes the powerful processing capability of deep reinforcement learning to intelligently select the optimal data transmission path by dynamically learning and adapting to the changes in the vehicular network, thus effectively reducing communication interruptions and delays, and improving the accuracy of data transmission. The performance of the DRLIQ algorithm under different vehicle densities is evaluated in simulation experiments and compared with current popular algorithms. The experimental results show that the DRLIQ algorithm outperforms the comparison algorithms in reducing the number of communication interruptions, BER and network delay, especially in vehicle-dense environments. In addition, the DRLIQ algorithm shows higher adaptability and stability in coping with network topology changes and vehicle dynamics.

IMPROVING THE PROCESSES OF ENSURING THE AIRWORTHINESS OF AIRCRAFT BY CREATING A HIGH-SPEED HETEROGENEOUS INFORMATION SYSTEM

The problem of forming the appearance of a high-speed heterogeneous information system for monitoring airworthiness during the operation of aviation equipment is considered. A mixed-integer model has been formed for forming the optimal data transmission path for verifying the authenticity of airway components, where integer variables determine the set of external systems from where and where data is transmitted, a set of data warehouses and connections between them, and continuous variables – characteristics of data transfer rates by web services and bandwidth of data transmission channels. The criterion for choosing the optimal solution is proposed – the time of data transfer between external data source systems and external data consumer systems, taking into account data warehouses. An algorithm for optimizing the structure and parameters of a heterogeneous information system has been developed, which ensures the organization of information interaction between heterogeneous data sources/consumers in a minimum time. It is shown that the problem under consideration relates to difficult combinatorial optimization problems, that is, there is no polynomial algorithm for solving it in a reasonable time. However, taking into account the specifics of the task, in some cases, allows you to build calculation schemes that provide sufficient accuracy of the solution and an acceptable time. After conducting a brief analysis of possible solutions, including Dijkstra, Johnson, Floyd – Warshall, the authors implemented computational procedures based on the Bellman – Ford method, the main advantages of which are the possibility of obtaining an accurate solution, no restrictions when working with negative edges of the network graph, low value of the algorithm execution time. The results of calculations are presented and the possibility of reducing the data transfer time by 20,2 % is shown by choosing the optimal connections between data sources and consumers, taking into account the data formats of the sources used and the compatibility of web services providing their processing.

Partitioned uneven cluster routing algorithm based on gray wolf optimization in WSNs

WSNs have various uses across numerous industries and are one of the essential technologies of modern life. Energy consumption is the issue that has drawn the greatest attention and still has to be resolved because the nodes that comprise WSNs have a limited amount of energy. Numerous factors influence energy consumption, and our algorithm design considerations are centered on extending the network lifetime and energy efficiency through the resolution of imbalance and hotspot issues related to WSNs clustering. Because of this, we suggest a partitioned uneven cluster routing algorithm based on gray wolf optimization. To find the ideal cluster head, we first divide the network into areas with distinct important influence factors, then we improve the final cluster head election function and the candidate cluster head competition radius. Subsequently, to reduce the energy consumption resulting from multiple rounds of clustering, similarity determination is introduced. Finally, the optimal transmission path in the multi-hop process is obtained by combining the Gray Wolf optimization algorithm with the relay node selection function. Simulation results show that the network lifetime of the proposed algorithm is extended by 54.6 %, 46.2 %, 58.6 %, and 18.5 % compared to LEACH, DEBUC, LEACH-EDP, and LEACH-IM, respectively. The energy efficiency of the proposed algorithm is extended by 40.8 %, 7.1 %, 22.7 %, and 34.0 %, respectively. The proposed algorithm significantly extends the network lifetime and improves the energy efficiency of the network.

PDASTSGAT: An STSGAT-Based Multipath Data Scheduling Algorithm

How to select the transmitting path in MPTCP scheduling is an important but open problem. This paper proposes an intelligent data scheduling algorithm using spatiotemporal synchronous graph attention neural networks to improve MPTCP scheduling. By exploiting the spatiotemporal correlations in the data transmission process and incorporating graph self-attention mechanisms, the algorithm can quickly select the optimal transmission path and ensure fairness among similar links. Through simulations in NS3, the algorithm achieves a throughput gain of 7.9% compared to the PDAA3C algorithm and demonstrates improved packet transmission performance.

A Spatial Clustering and Matching Game–Based Multihop Routing Algorithm for Heterogeneous UAVs

Unmanned aerial vehicle (UAV) clusters are increasingly deployed in military and civilian applications, necessitating efficient and reliable communication networks for cooperative operations. However, heterogeneous UAVs pose significant challenges for data transmission within and outside the cluster due to their high mobility, rapid topology changes, and limited node energy. For the routing planning problem of transmitting data from autonomous, heterogeneous UAVs to the cloud, existing methods struggle to accommodate the heterogeneity of UAVs and the communication bandwidth requirements, given the NP‐hard nature of the problem. To address this issue, this paper first constructs an exact model based on mixed‐integer linear programming to describe the heterogeneous UAVs, comprehensively searches the solution space, and generates a reasonable routing scheme for the heterogeneous UAVs, guiding the cluster communication network system design. Secondly, a heuristic algorithm is proposed that leverages density–based spatial clustering and matching game theory. The algorithm calculates relay nodes based on local density and relative distance. It generates an approximate optimal data transmission path for each UAV through a multilevel matching process, effectively reducing communication delay within the cluster. Finally, the paper conducts simulation experiments in randomly generated regional scenarios to validate the efficiency and effectiveness of the proposed method.

Heuristic Path Search and Multi-Attribute Decision-Making-Based Routing Method for Vehicular Safety Messages

Efficient routing in urban vehicular networks is essential for timely and reliable safety message transmission, and the selection of paths and relays greatly affects the quality of routing. However, existing routing methods usually face difficulty in finding the globally optimal transmission path due to their greedy search strategies or the lack of effective ways to accurately evaluate relay performance in intricate traffic scenarios. Therefore, we present a vehicular safety message routing method based on heuristic path search and multi-attribute decision-making (HMDR). Initially, HMDR utilizes a heuristic path search, focusing on road section connectivity, to pinpoint the most favorable routing path. Subsequently, it employs a multi-attribute decision-making (MADM) technique to evaluate candidate relay performance. The subjective and objective weights of the candidate relays are determined using ordinal relationship analysis and the Criteria Importance Through Intercriteria Correlation (CRITIC) weighting methods, respectively. Finally, the comprehensive utility values of the candidate relays are calculated in combination with the link time and the optimal relay is selected. In summary, the proposed HMDR method is capable of selecting the globally optimal transmission path, and it comprehensively considers multiple metrics and their relationships when evaluating relays, which is conducive to finding the optimal relay. The experimental results show that even if the path length is long, the proposed HMDR method gives preference to the path with better connectivity, resulting in a shorter total transmission delay for safety messages; in addition, HMDR demonstrates faster propagation speed than the other evaluated methods while ensuring better one-hop distance and one-hop delay. Therefore, it helps to improve the performance of vehicular safety message transmission in intricate traffic scenarios, thus providing timely data support for secure driving.

A reinforcement learning approach for widest path routing in software-defined networks

In this paper, a routing method based on reinforcement learning (RL) under software-defined networks (SDN), namely the Q-learning widest-path routing algorithm (Q-WPRA), is proposed. This algorithm processes the reward function according to the link bandwidth in the execution environment to find the optimal (i.e., widest) transmission path with the maximum bandwidth between the source and the destination through RL. The experimental results reveal that the Q-WPRA is outperformance than Dijkstra’s algorithm and Dijkstra’s widest-path algorithm to find the widest transmission path in SDN environment under different bandwidths, loss rates, and background traffic.

Rationally Designed ZnTe@C Nanowires with Superior Zinc Storage Performance for Aqueous Zn Batteries.

Te-based materials with excellent electrical conductivity and ultra-high volume specific capacity have attracted much attention for the cost-efficient aqueous Zn batteries. However, the construction of functional structures with mild volume expansion and suppressed shuttle effects, enabling an expanded lifespan, is still a challenge for conversion-type materials. Herein, the carbon-coated zinc telluride nanowires (ZnTe@C NWs) are rationally designed as a high-performance cathode material for aqueous Zn batteries. The carbon-coated1Dnanostructure could not only provide optimized transmission path for electrons and ions, but also help to maintain structure integrity upon volume variation and suppress intermediates dissolution, endowing the ZnTe@C NWs with improved cycling stability and reaction kinetics. Consequently, a reversible six-electron reaction mechanism of ZnTe@C NWs based on Te2- /Te4+ conversion with excellent output capacity (586 mAh g-1 at 0.1 A g-1 ) and lifespan (>250 mAh g-1 retained for 400 cycles at 1 A g-1 ) is eventually achieved.

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.

Research on elevated orbit noise propagation based on AI algorithm and propagation path analysis

With the rapid development of high-speed railway, noise reduction is becoming more and more important. At the same time, the study of elevated rail noise is also very important for the engineering field. The noise reduction of elevated rail noise is a part of the engineering construction, which is conducive to the economic development and the improvement of people’s life, and the reduction of noise has an impact on residents’ life. This article aims to investigate the analysis of the vibration and noise transmission path of the elevated railway. An optimized transmission path analysis method, which is based on the artificial intelligence algorithms, is used to optimize the path analysis, so as to trace the source of the noise and analyze the noise reduction. This method performs well in the analysis of vibration and noise of elevated railways. After this optimization, the contribution degree and frequency response function in the path analysis have better analysis performance. Through the actual measurement and analysis of the noise of the elevated railway, it can be proved that the traditional path method can better analyze the noise source. The environmental noise caused by high-speed trains passing bridges easily exceeds the acoustic environmental standards. After noise reduction, it can be reduced by about 5–10 dB.

Distributed Data Collection Algorithm With Unknown Objective Function in Adaptive Duty-Cycle WSNs

With the rapid development of wireless sensor networks (WSNs), higher requirements are raised for the flexibility and applicability of data collection algorithms. Therefore, a novel distributed algorithm for data collection in adaptive duty-cycle WSNs is proposed in this article. On the one hand, the priority in data transmission is considered, in which the queues are used to realize the data storage and data transmission with different priorities and then a scheme for selecting the optimal transmission path for the sensor nodes is developed to ensure that high-priority data can be transmitted more efficiently. Next, a method that can adaptively adjust the duty cycle of the sensor nodes is designed to reduce the transmission delay, where each sensor can update its own duty cycle according to the amount of data with different priorities. On the other hand, the relation between the objective function and the variables in the data collection problem may be difficult to establish accurately or even unknown due to the complexity of WSNs, while the most existing data collection algorithms require the objective function to be explicitly expressed, making them inapplicable to the above scenarios. Hence, the data collection problem with unknown objective function in adaptive duty-cycle WSNs is also investigated in this article. Since the objective function cannot be established, a distributed optimization algorithm with randomized gradient-free is developed to solve that problem. In particular, the proposed algorithm solely relies on the row-stochastic weight matrix, where each sensor needs to communicate only with its neighbors to make decision and locally allocates the information weight of its neighbors, making the algorithm easier to implement. Under some reasonable assumptions, the explicit analysis for the convergence of the proposed algorithm is provided. Finally, the convergence and effectiveness of the proposed algorithm are substantiated through numerical simulations.

Maximizing wireless power transmission for electric vehicles with high-intensity laser power beaming and optical orthogonal frequency division multiplexing

This paper presents a method for determining the optimal power transmission path for wirelessly charging electric vehicles using high-intensity laser power beaming in environments with multiple power transmitters and receivers. The proposed method uses an optical orthogonal frequency division multiplexing system. The power receiver selects the optimal power transmitter and wireless power channel based on the maximum power requirement. The paper also validates the proposed method through simulation and experimentation.

An Energy Efficient and Reliable Multipath Transmission Strategy for Mobile Wireless Sensor Networks.

Multipath data transmission is a key problem that needs to be solved urgently in wireless sensor networks. In this paper, sensor node failure, link failure, energy exhaustion, and external interference affect the stability and reliability of network data transmission. A multipath transmission strategy for wireless sensor networks based on improved shuffled frog leaping algorithm is proposed. A mathematical model of multipath transmission in wireless sensor networks is established. In the shuffled frog leaping algorithm, combined with the transition probability in the particle swarm optimization algorithm, random individuals in the subgroup are introduced to assist the search when updating the frog individual position, which improves the algorithm's ability to jump out of the local optimum and improves the quality of the optimization algorithm solution. The model is applied to multipath transmission in wireless sensor networks. Then, the shuffled frog leaping algorithm is used to update, divide, and reorganize the sensor nodes to select the optimal node to establish the optimal transmission path and improve the stability and reliability of the network. Simulation experiments show that the algorithm in this paper can ensure the reliability of data transmission, reduce the network packet loss rate and network energy consumption, and reduce the average delay of data transmission.

Efficient addressing schemes for internet of things

The internet of things (IoT) defines the connectivity of physical devices to provide the machine to machine communication. This communication is achieved through various wireless standards for sensor node connectivity. The IoT calls from the formation of various wireless sensor nodes (WSNs) in a network. The existing neighborhood discovery method had the disadvantage of time complexity to calculate the cluster distance. Our proposed method rectifies this issue and gives accurate execution time. This paper proposed mobility management system based on proxy mobile IPv6 as distributed PMIPv6 with constrained application protocol (CoAP-DPMIP) and PMIPv6 with constrained application protocol (CoAP-PMIP). It also provides the optimized transmission path to reduce the delay handover in IoT network. The PMIPv6 described the IPv6 address of mobile sensor device for efficient mobility management. The network architecture explains three protocol layers of open systems interconnection model (OSI model). The OSI layers are data link layer, network layer and transport layer. We have proposed the distance estimation algorithm for efficient data frames transmission. This paper mainly focuses the secure data transmission with minimum loss of error. The evaluation result proved that proposed technique performance with delay, energy, throughput and packet delivery ratio (PDR). Also, it measures the computational time very effectively.

Anti-Jamming Path Selection Method in a Wireless Communication Network Based on Dyna-Q

Aiming at efficiently establishing the optimal transmission path in a wireless communication network in a malicious jamming environment, this paper proposes an anti-jamming algorithm based on Dyna-Q. Based on previous observations of the environment, the algorithm selects the optimal sequential node by searching the Q table to reduce the packet loss rate. The algorithm can accelerate the updating of the Q table based on previous experience. The Q table converges to the optimal value quickly. This is beneficial for the optimal selection of subsequent nodes. Simulation results show that the proposed algorithm has the advantage of faster convergence speed compared with the model-free reinforcement learning algorithm.

An Adaptive Information Security System for 5G-Enabled Smart Grid Based on Artificial Neural Network and Case-Based Learning Algorithms.

With the deployment of 5G Internet of Things (IoT) in the power system, the efficiency of smart grid is improved by increasing two-way interactions in different layers in smart grid. However, it introduces more attack interfaces that the traditional information security system in smart grid cannot response in time. The neuroscience-inspired models have shown their effectiveness in solving security and optimization problems in smart grid. How to improve the security mechanism in smart grid while taking into account the optimization of data transmission efficiency using neuroscience-inspired algorithms is the problem to be solved in this study. Therefore, an information security system based on artificial neural network (ANN) and improved multiple protection model is proposed. Based on the ANN algorithm, the link state sample space is used to train the model to obtain the optimal transmission path in 5G power communication network. Integrating the intelligent link state module, the zero-trust security protection platform using case-based learning algorithm is designed and taken as the first protection, the network security logical isolation facility is taken as the second protection, and the forward and backward isolation facilities are set as the third protection to achieve the strengthened security of 5G IoT in smart grid. The experimental results show the efficiency and effectiveness of the proposed algorithms. In addition, the experimental results also show that the proposed system can resist malicious terminal access, terminal hijacking, data tampering and eavesdropping, protocol fuzzy, and denial-of-service attacks, so as to reduce the security risks of 5G IoT in smart grid. Since the proposed system can be easily integrated into the existing smart grid structure in China, the proposed system can provide a reference for the design and implementation of 5G IoT in smart grid.

An Energy-Balance Unequal Clustering Routing Protocol for Wireless Sensor Networks

On the basis of traditional unequal clustering protocol for wireless sensor networks, an energy-balance unequal clustering routing protocol is proposed. In the clustering stage, the entire network is divided into clusters of different sizes based on the remaining energy and the distance from the sink node to the node. In the cluster establishment phase, each node calculates the cluster head selection time based on the relative remaining energy, the position, the average distance between the nodes in the cluster. The cluster head is selected by time series, and non-cluster head nodes are selected to join the cluster based on the distance between cluster heads and the remaining energy of the relay cluster head. In the route establishment phase, an optimal transmission path based on minimum spanning tree is built, based on the distance between cluster heads, the residual energy and the distance from node to the sink node. During data transmission, a single hop within a cluster is adopted, and a minimum spanning tree multi-hop method is used between clusters, and the data is finally transmitted to the sink node. The simulation shows that the routing protocol can effectively balance energy consumption and save energy, and the life cycle of the network is prolonged.

A mean field game-theoretic cross-layer optimization for multi-hop swarm UAV communications

Unmanned aerial vehicle (UAV) multi-hop communication networks are foreseen to be widely employed in both military and civilian scenarios. However, in ultra-dense scenarios with swarm UAVs, nodes are highly dynamic mobile, ultra-dense deployment and non-centralized distribution. These characteristics make the centralized resource management policy not apply. Meanwhile, existing routing protocols can't meet the performance challenges of high dynamic, topology and link frequency changes of ultra-dense scenarios with swarm UAVs. To solve the above challenges of resource management and routing protocol, a cross-layer optimization method is presented with a novel mean field game (MFG) in this paper. It is based on the cross-layer design method of the MFG theory and jointly considers the power resources in the physical layer, frequency resources in the medium access control (MAC) layer, and routing resources in the network layer. By dividing into subproblems, the original problem is solved. Meanwhile, the optimal data transmission path can be selected through the management and allocation of frequency resources and power resources. A cross-layer resource management dynamic source routing (CLRM-DSR) protocol is designed based on that which adds link quality measurement. The simulation results show that the presented CLRM-DSR with the proposed resource management scheme can improve the data packet transmission rate, reduce end-to-end delay, and lower routing overhead for the multi-hop swarm UAV communication network.

The RapidIO Routing Strategy Based on the Double-Antibody Group Multi-Objective Artificial Immunity Algorithm.

The RapidIO standard is a packet-switching interconnection technology similar to the Internet Protocol (IP) conceptually. It realizes the high-speed transmission of RapidIO packets at the transport layer, but this greatly increases the probability of network blocking. Therefore, it is of great significance to optimize the RapidIO routing strategy. For this problem, this paper proposes a Double-Antibody Group Multi-Objective Artificial Immune Algorithm (DAG-MOAIA), which improves the local search and global search ability of the population by adaptive crossover and adaptive mutation of the double-antibody groups, and uses co-competition of multi-antibody groups to increase the diversity of population. Through DAG-MOAIA, an optimal transmission path from the source node to multiple destination nodes can be selected to solve the Quality Of Service (QoS) problem during data transmission and ensure the QoS of the RapidIO network. Simulation results show that DAG-MOAIA could obtain high-quality solutions to select better routing transmission paths, and exhibit better comprehensive performance in all simulated test networks, which plays a certain role in solving the problem of the RapidIO routing strategy.