Static Nodes Research Articles

LMH-RPL: a load balancing and mobility aware secure hybrid routing protocol for low power lossy network

Purpose Routing protocol for low-power lossy network (RPL) being the de facto routing protocol used by low power lossy networks needs to provide adequate routing service to mobile nodes (MNs) in the network. As RPL is designed to work under constraint power requirements, its route updating frequency is not sufficient for MNs in the network. The purpose of this study is to ensure that MNs enjoy seamless connection throughout the network with minimal handover delay. Design/methodology/approach This study proposes a load balancing mobility aware secure hybrid – RPL in which static node (SN) identifies route using metrics like expected transmission count, and path delay and parent selection are further refined by working on remaining energy for identifying the primary route and queue availability for secondary route maintenance. MNs identify route with the help of smart timers and by using received signal strength indicator sampling of parent and neighbor nodes. In this work, MNs are also secured against rank attack in RPL. Findings This model produces favorable result in terms of packet delivery ratio, delay, energy consumption and number of living nodes in the network when compared with different RPL protocols with mobility support. The proposed model reduces packet retransmission in the network by a large margin by providing load balancing to SNs and seamless connection to MNs. Originality/value In this work, a novel algorithm was developed to provide seamless handover for MNs in network. Suitable technique was developed to provide load balancing to SNs in network by maintaining appropriate secondary route.

Optimising multi-target multileaf collimator tracking using real-time dose for locally advanced prostate cancer patients

Objective. The accuracy of radiotherapy for patients with locally advanced cancer is compromised by independent motion of multiple targets. To date, MLC tracking approaches have used 2D geometric optimisation where the MLC aperture shape is simply translated to correspond to the target’s motion, which results in sub-optimal delivered dose. To address this limitation, a dose-optimised multi-target MLC tracking method was developed and evaluated through simulated locally advanced prostate cancer treatments. Approach. A dose-optimised multi-target tracking algorithm that adapts the MLC aperture to minimise 3D dosimetric error was developed for moving prostate and static lymph node targets. A fast dose calculation algorithm accumulated the planned dose to the prostate and lymph node volumes during treatment in real time, and the MLC apertures were recalculated to minimise the difference between the delivered and planned dose with the included motion. Dose-optimised tracking was evaluated by simulating five locally advanced prostate plans and three prostate motion traces with a relative interfraction displacement. The same simulations were performed using geometric-optimised tracking and no tracking. The dose-optimised, geometric-optimised, and no tracking results were compared with the planned doses using a 2%/2 mm γ criterion. Main results. The mean dosimetric error was lowest for dose-optimised MLC tracking, with γ-failure rates of 12% ± 8.5% for the prostate and 2.2% ± 3.2% for the nodes. The γ-failure rates for geometric-optimised MLC tracking were 23% ± 12% for the prostate and 3.6% ± 2.5% for the nodes. When no tracking was used, the γ-failure rates were 37% ± 28% for the prostate and 24% ± 3.2% for the nodes. Significance. This study developed a dose-optimised multi-target MLC tracking method that minimises the difference between the planned and delivered doses in the presence of intrafraction motion. When applied to locally advanced prostate cancer, dose-optimised tracking showed smaller errors than geometric-optimised tracking and no tracking for both the prostate and nodes.

Introduction and Evaluation of Attachability for Mobile IoT Routing Protocols With Markov Chain Analysis

Reliability of routing mechanisms in wireless networks is typically measured with Packet Delivery Ratio (PDR). Basically, PDR is reported with an optimistic assumption that the topology is fully constructed, and the nodes have started their packet transmission. This is despite the fact that prior to being able to transmit packets, nodes must first join the network, and then try to keep connected as much as possible. This is a key factor in the overall reliability provided by the routing protocols, especially in mobile IoT applications, where disconnections occur frequently. Nevertheless, there is a lack of appropriate metrics, which could evaluate the routing mechanisms from this perspective. Accordingly, this paper introduces attachability; a new metric for evaluating the capability of routing protocols in assisting the mobile or stationary nodes in joining, and maintaining their connections to the network. Our newly proposed metric is calculated via Markov chain analysis along with the sample frequency-based estimating technique. To evaluate attachability, we have simulated a mobile IoT infrastructure, and conducted a comprehensive set of experiments on different versions of the IPv6 Routing Protocol for Low-power and lossy networks (RPL). Based on our observations, attachability is significantly dependent on the employed metrics and path selection policies in the routing mechanisms. Among the three different versions of RPL, including the original version (ORPL), which is standardized for stationary IoT applications, and two mobility-aware versions, i.e., MARPL, and OMARPL, OMARPL showed up to 42%, and 10% of improvement in terms of attachability against ORPL, and MARPL, respectively.

Feature Extraction Method of Art Visual Communication Image Based on 5G Intelligent Sensor Network

5G intelligent sensor network technology realizes the perception, processing, and transmission of information. It forms the three pillars of information technology together with computer technology and communication technology and is an important part of the Internet of Things technology. The 5G smart sensor network is a wireless communication module added to the sensor nodes, and a wireless communication network is formed by a large number of stationary or movable sensor nodes in the form of self-organization and multihop transmission. This paper proposes a keypoint feature extraction method based on deep learning, which can extract keypoint local features for matching. This method uses the convolutional network structure, which is pretrained based on the Siamese network structure and then adjusted to the ternary network structure to continue training to improve the accuracy. This paper proposes a high-art visual communication image classification based on multifeature extraction and classification decision fusion. In the data preprocessing stage, the correlation alignment algorithm is performed on the datasets of different domains (source domain and target domain) to reduce the difference in spatial distribution, and then, a multifeature extractor is designed to extract artistic visual communication images and spatial information. In the process, the multitask learning method is introduced to jointly train the networks of multiple data sets to reduce the degree of overfitting of the model, solve the problem of insufficient labeled samples in the target domain data set, and affect the classification accuracy of high-art visual communication images. Finally, the classification results are obtained through the fusion of voting decisions. The experimental results show that the advantage of this framework is that it utilizes the artistic visual communication image and spatial structure information from the source and target scenes, which can significantly reduce the dependence on the number of labeled samples in the target domain and improve the classification performance. In this paper, a dual-channel deep residual convolutional neural network is designed. The multiple convolution layers of the residual module in the network use hard parameters to share, so that the deep feature representation on the joint spatial spectrum dimension can be automatically extracted. The features extracted by the network are transferred to maximize the auxiliary role of the labeled samples in the source domain and avoid the negative transfer problem caused by the forced transfer between irrelevant samples.

Trust and Mobility-Based Protocol for Secure Routing in Internet of Things.

In the Internet of Things (IoT), the de facto Routing Protocol for Low Power and Lossy Networks (RPL) is susceptible to several disruptive attacks based on its functionalities and features. Among various RPL security solutions, a trust-based security is easy to adapt for resource-constrained IoT environments. In the existing trust-based security for RPL routing attacks, nodes’ mobility is not considered or limited to only the sender nodes. Similarly, these trust-based protocols are not evaluated for mobile IoT environments, particularly regarding RPL attacks. Hence, a trust and mobility-based secure routing protocol is proposed, termed as SMTrust, by critically analysing the trust metrics involving the mobility-based metrics in IoT. SMTrust intends to provide security against RPL Rank and Blackhole attacks. The proposed protocol is evaluated in three different scenarios, including static and mobile nodes in an IoT network. SMTrust is compared with the default RPL objective function, Minimum Rank with Hysteresis Objective Function (MRHOF), SecTrust, DCTM, and MRTS. The evaluation results indicate that the proposed protocol outperforms with respect to packet loss rate, throughput, and topology stability. Moreover, SMTrust is validated using routing protocol requirements analysis to ensure that it fulfils the consistency, optimality, and loop-freeness.

Mobile Crowdsourced Guiding and Finding With Precise Target Positioning Based on Internet-of-Things Localization

In this article, we propose a mobile crowdsourced guiding and finding (MCGF) framework using smartphones to guide indoor people and find missing targets through Internet-of-Things (IoT) localization. The MCGF framework can cooperatively find lost/stolen targets equipped with mobile iBeacon nodes through participatory sensing networks formed by mobile users using smartphones in places with static iBeacon nodes. To precisely localize the missing target, fundamental target localization cases in distinct crowdsourced environments are formally classified and efficiently addressed to reduce the positioning errors with different numbers of smartphones detecting the missing target and different numbers of fixed iBeacon nodes nearby these target-detecting smartphones. According to our review of relevant research, this is the first solution that can provide the crowdsourced guiding path to a missing target with high localization accuracy for all densities of participating smartphones and iBeacon nodes. In particular, an Android-based prototype with static and mobile iBeacon nodes is implemented to verify the feasibility and superiority of our framework. Experimental results show that MCGF outperforms the existing methods and can significantly reduce the localization errors of mobile users and missing targets.

Lymph node metastases rate of locoregional and non-locoregional lymph node stations in gastric cancer.

BackgroundThe incidence of lymph node metastases is closely related to the T-stage, and therefore Eastern guidelines advice a D1 lymphadenectomy for early gastric cancer and a D2 lymphadenectomy for advanced gastric cancer. The aim of this study was to compare the lymph node metastases rate in the stations dissected with a D2-lymphadenectomy (stations 8–12) yet spared with a D1-lymphadenectomy, between different T-stages in a Western patient cohort.MethodsFor this retrospective study, patients who underwent a gastrectomy in the Amsterdam University Medical Center (UMC), location Academic Medical Center (AMC), between 2011 and 2016 were identified from a prospectively maintained database. The primary outcome was to compare the rate of lymph node metastases in station 8–12 between different cT-stages.ResultsOne hundred twelve patients met our inclusion criteria. There were no positive lymph nodes in the lymph nodes stations 8–12 in cT1 and (y)pT1-stage tumors. The more advanced cT2-4 and (y)pT2-4 stage tumors show a high metastases rate (11.1% to 40.0%) in the lymph node stations 8–12.ConclusionsThe results from this study endorse the Japanese Gastric Cancer Guideline; in early gastric cancer, a D1 lymphadenectomy is sufficient, while in advanced gastric cancer a D2 lymphadenectomy should be performed.

Mitigating Traffic Remapping Attacks in Autonomous Multihop Wireless Networks

Multihop wireless networks with autonomous nodes are susceptible to selfish traffic remapping attacks (TRAs). Nodes launching TRAs leverage the underlying channel access function to receive an unduly high Quality of Service (QoS) for packet flows traversing source-to-destination routes. TRAs are easy to execute, impossible to prevent, difficult to detect, and harmful to the QoS of honest nodes. Recognizing the need for providing QoS security, we use a novel network-oriented QoS metric to propose a self-enforcing game-theoretic mitigation approach. By switching between TRA and honest behavior, selfish nodes engage in a noncooperative multistage game in pursuit of high QoS. We analyze feasible node strategies and design a distributed signaling mechanism called DISTRESS, under which, given certain conditions, the game produces a desirable outcome: after an upper bounded play time, honesty tends to become a selfish node’s best reply behavior, while yielding acceptable QoS to most or all nodes. We verify these findings by Monte Carlo and ns-3 simulations of static and mobile nodes.

Dynamic Scheduling Strategy for Multiple AGVs based on 0-1 Planning Model

The coordination of handling robots and AGVs can greatly improve the efficiency of unmanned warehouse picking operations and realize the automated management of warehouse logistics. The unreasonable scheduling scheme can lead to local congestion and collision of AGVs and reduce the picking efficiency of the unmanned warehouse. Therefore, this paper conducts an in-depth study on the scheduling mode of AGVs. When assigning orders, this paper designs an order-oriented double matching method, with the shortest total path length of AGV as the objective function, and establishes a 0-1 planning model to complete the matching of order-storage position nodes-AGV. The AGV searches and selects the nearest picking station node whose docking position is not full when it leaves the warehouse. In addition to the main order list, a workstation task list is created, and the workstation tasks are released by the picking stations, including return and recovery. In this paper, the default order and workstation tasks have the same priority, and the task selection order is the same as the release order. The 0-1 planning model is solved using a genetic algorithm to match the AGV with the order or station task that has the shortest path to completion.

SocialRoute: A low-cost opportunistic routing strategy based on social contacts

The study of social characteristics in human interactions is a fundamental topic in mobile networks. By taking advantage of this type of information, we can leverage the knowledge about the behavior of the nodes in a network, leading to better routing strategies, especially in opportunistic dissemination scenarios. The state of the art of social-based opportunistic routing algorithms applies simple social characterization metrics, such as node’s properties and communities, which cannot capture individual social links that last longer and represent stronger bonds when compared to communities. This work proposes SocialRoute, a social-based opportunistic routing algorithm that considers individual social links instead of communities to route messages efficiently. Additionally, we evaluate how deploying static relay nodes at popular locations can aid in the transmission by using dissemination profiles that can be selected based on the message content and priority. We show that SocialRoute can transmit data with a low computational cost, both in terms of obtaining the social characteristics and overhead generated during dissemination. Additionally, this approach provides a more equally distributed dissemination between the nodes, in opposition to strategies that rely heavily on individual nodes due to their popularity. Finally, we evaluate our proposed algorithm using two real mobility traces and compare it to four state-of-the-art solutions, showing that SocialRoute obtains higher delivery ratios while maintaining a fraction of the overhead.

Layout Evaluation of New Energy Vehicle Charging Stations: A Perspective Using the Complex Network Robustness Theory

At present, the new energy vehicle industry is developing rapidly, but the relative lag in the development of its supporting infrastructure, especially charging stations, has become a bottleneck that restricts the development of the electric vehicle industry. In this paper, we propose a model for constructing a network of new energy vehicle charging facilities based on complex network theory and analyze the operation and the rationality of the layout of the new energy vehicle (NEV) charging stations in Wuhan and Hangzhou, respectively. The results show that the current layout of new energy vehicle charging stations in the city is relatively reasonable, but the allocation of charging pile resources is unreasonable. Our results of the virtual charging station network constructed by adding new charging station nodes show that the change in network structure helps to enhance the performance of the charging station system.

Wearable Sensor-Based Motion Data Analysis and Dance Performance Using Images and Cloud Computing

China’s cultural performance career has been developed in recent times, resulting in a large number of students embarking on the professional path of cultural performance. To reduce the teaching pressure on school teachers, dance training gradually introduces the idea of “digital dance”, using sensors to monitor posture. The nonwearable monitoring method does not require contact with the subject and typically relies on images or signal waves for positioning, but it has stringent requirements for the measurement scene. On the other hand, the wearable monitoring method requires the sensor to be worn on the subject’s body all the time, so it needs to be light and energy-efficient. Therefore, this paper designs and implements a set of application platforms, which parametrically analyze the dance movements, consume low energy, are lightweight, and are simple to use. For this purpose, a wearable digital dance training system based on microelectro-mechanical system (MEMS) sensors has been developed, and a solution to the challenges of teaching movement precision, scientific teaching methodologies, and early warning analysis of joint is also proposed. Firstly, the basic knowledge of human posture measurement and human model is merged to build a new modifiable human model. Secondly, the overall design of the posture data acquisition device is presented, which includes a base station and several nodes. Finally, the overall framework of the software platform is designed, which consists of client and server. The experimental results show that the system can achieve the purpose of providing teaching programs for dance students.

Hierarchical Blockchain Design for Distributed Control and Energy Trading Within Microgrids

Blockchain technology is recognized as a suitable tool to secure the energy trading because it could perfectly match the distributed structure of peer-to-peer (P2P) energy market. But its usage is stuck on the transaction level. Control systems are significant to the microgrid as they ensure a stable power delivery system and regulate the performance of parameters such as active power and frequency. This paper proves that the blockchain technology is also effective in securing the distributed control systems against the false data injection attack. A six-prosumer microgrid is tested with the implementation of the hierarchical blockchain system. The security of both the control system and energy trading system of the microgrid is ensured. Smart contracts are created to calculate the feedback measurements for the control system and execute the energy transactions. According to the hierarchical structure, the private blockchain with static nodes is implemented for the distributed control to match the sampling rate. A Proof-of-Authority based blockchain is utilized to support the energy trading. In addition, a double auction based simple iteration (DA-SI) pricing scheme is designed to improve the social welfare of the microgrid. Finally, case studies are presented to verify the proposed hierarchical blockchain system as an effective method to safeguard the control system and maximize the benefits of prosumers. Numerical results show the effectiveness and feasibility of the proposed approach.

Development methodology of metrological support of maintenance by the technical state of communication means

Peculiarities of metrological support for technical maintenance based on the condition of large-scale communication devices (tens and hundreds of thousands of elements) consisting of separate subsystems, whose functionality can be checked, maintenance and restoration of functionality can be performed autonomously, are considered. It is suggested to increase the efficiency of technical maintenance according to the condition to take into account the peculiarities of metrological support, to establish a rational sequence of operations, and to reasonably choose measuring equipment depending on the requirements. For this, the methodology uses a complex indicator that combines individual parameters of checks and the probability of their preferred choice. This makes it possible to assess the technical condition of the means of communication in a minimum time with a given probability. In addition, it is possible to quantitatively estimate the time of maintenance, taking into account metrological reliability and the probability of correct assessment of the result of checking parameters by means of measuring equipment. The procedure for using the obtained results is formalized and an example of implementation is given. For the first time, the results of the latest research in the field of technical and metrological support of means of communication during their technical operation are taken into account, namely, the possibility of erroneous assessment of the results of parameter checks, the possibility of failure of measuring equipment, which allows to significantly increase the probability of correct assessment of the technical condition of means of communication during their maintenance according to condition. In addition, taking into account modern achievements in technical diagnostics made it possible to quantitatively estimate the maximum value of the maintenance time based on the state of the means of communication and compare it with the acceptable value specified in the technical documentation. The functional dependencies of the proposed complex coefficient of the feasibility of each inspection, on the metrological reliability of the measuring equipment and the probability of erroneous decisions in the evaluation of the inspection values after measuring the relevant parameters, were obtained and investigated. The area of existence of solutions for calculating the complex coefficient from the quality of metrological support is defined. It is advisable to use the obtained results during the development of the technological process of maintenance of promising and existing means of communication, as well as when planning the activities of specialists during scheduled maintenance work on equipment at field and stationary communication nodes.

Evaluation of a UAV-Aided WSN for Military Operations

A Wireless Sensor Network (WSN) assisted with Unmanned Aerial Vehicles (UAVs) is flexible and its lifetime becomes longer due to reduced energy consumption in the static sensor nodes. This paper analyses the capabilities of using UAVs in WSNs for enhancing various military and surveillance capabilities. It presents three architectures of UAV-aided WSNs and it uses a realistic simulation environment to evaluate the energy consumption and network lifetime of a UAV-assisted WSN under two different use cases: (1) the UAV is used as relay node; and (2) the UAV is used as mobile sink. In Use Case 1, the network lifetime is more than five times longer than in Use Case 2. Using a UAV as a relay node preserves the network energy for a longer period, regardless of the flying distance of the UAV. In our experiment, three different percentages of distance from the remote Base-Station to the Cluster-Head were involved. If a UAV is flying on 50% distance, the average energy per node expires very quickly. If the distance percentage is at 10%, the average energy per node drops at a much slower rate.

Node localization algorithm for wireless sensor networks based on static anchor node location selection strategy

To better solve the contradiction between the localization accuracy, localization coverage, and the location of anchor nodes in wireless sensor networks, a node localization algorithm for wireless sensor networks based on static anchor node location selection strategy is proposed in this paper. Firstly, collect the signal strength between wireless sensor network nodes, judge whether there is a connection between nodes according to the set signal strength threshold, and convert the node distribution diagram into the node connection relationship diagram, that is, the topology diagram. Then, the closeness centrality value of each node is calculated by using the closeness centrality algorithm, and the obtained closeness centrality values are sorted in descending order, the node with the largest closeness centrality value is the first anchor node, the closeness centrality values are traversed at equal intervals, and the optimal equal interval is selected by using the quick sort algorithm, and the selected nodes are used as the other anchor nodes. Finally, other unknown nodes in the network are located according to the location of anchor nodes. Simulation results show that the proposed algorithm is superior to the existing typical algorithms in localization accuracy and localization coverage.

Quantum Internet protocol stack: A comprehensive survey

Classical Internet evolved exceptionally during the last five decades, from a network comprising a few static nodes in the early days to a leviathan interconnecting billions of devices. This has been possible by the separation of concern principle, for which the network functionalities are organized as a stack of layers, each providing some communication functionalities through specific network protocols. In this survey, we aim at highlighting the impossibility of adapting the classical Internet protocol stack to the Quantum Internet, due to the marvels of quantum mechanics. Indeed, the design of the Quantum Internet requires a major paradigm shift of the whole protocol stack for harnessing the peculiarities of quantum entanglement and quantum information. In this context, we first overview the relevant literature about Quantum Internet protocol stack. Then, stemming from this, we sheds the light on the open problems and required efforts toward the design of an effective and complete Quantum Internet protocol stack. To the best of authors’ knowledge, a survey of this type is the first of its own. What emerges from this analysis is that the Quantum Internet, though still in its infancy, is a disruptive technology whose design requires an inter-disciplinary effort at the border between quantum physics, computer and telecommunications engineering.

Selfish Node Detection by Modularized Deep NMF Autoencoder Based Incentivized Reputation Scheme

Delay tolerant network is a boon in emergency fields like flood and war zones. The data gathered by the sensor nodes is transmitted whenever any aggregator node comes in contact with those stationary sensor nodes. However, few nodes can behave differently and don't transmit the information. These nodes which don't take part in communication to preserve it's battery or are compromised are selfish nodes and have to be identified to avoid communication disruption. This article discusses the selfish node's detection schemes and proposes a novel hybrid scheme. Most of the recent schemes work with on node's reputation or incentives if it takes part in communication. This paper has proposed an incentivized reputation scheme that first clusters the nodes using their social features, calculates the weighted social tie as their social connection strength and updates the weighted social tie by applying reward or penalty. The incentive is offered if residual energy and packet delay has a tradeoff or are penalized. A new modularized deep nonnegative matrix deep autoencoder is developed to calculate the reputation of nodes using social features and named IRU-mDANMF (incentivized reputation update by modularized DANMF). The scheme has been experimented with in several scenarios and is performing significantly better than state-of-the-art schemes.

Mobility Models as a Key-Performance Factor for Wireless Networks

Nodes in wireless networks can be static or dynamic. Static nodes are stationary and their positions are fixed and their distribution may follow a particular topology. On the other hand, dynamic nodes are considered mobile and their positions changed over time due to following a specific pattern of movement, which also lead to the concept of the topology being irrelated. Simulating such networks needs a lot of attention and many requirements should be held. For instance, nodes in dynamic wireless networks are distributed according to a distribution that reflects the nature of the environment (e.g., Gaussian, Power-Law, Uniform distribution, etc.). Moreover, describing the movement patterns of nodes in dynamic wireless networks need to follow a particular mobility model that describes the movements of nodes in terms of speed and direction (e.g., Individual mobility model, Levy Flight Model, Cauchy Model). In addition to the aforementioned requirements, it is needed to incorporate a routing protocol that governs data spread within the simulation environment. This work designed a variety of experiments that combine the mentioned requirements and measure the performance of dynamic wireless networks under colorful settings and configurations. The metrics used in this approach are the amount of data exchanged, coverage area, and power and memory consumption. Mobility models, node deployment strategy (distributions), and routing protocols are the three factors that will be involved in the experiments. The experiments deal with four mobility models Random Way Point Mobility Model (RWPM), Street Random Way Point Mobility Model (SRWPM), Manhattan Mobility Model (MM), and Levy Flight Mobility Model (LFM). Three distribution models will be used in the experiments Power-Law Distribution, Chi-Squared Distribution, and Normal Distribution. The third factor that will be involved in the routing protocol, is Probabilistic Flooding Routing Protocol. Different experiments are benchmarked using these metrics. The results of this work will reveal facts about dynamic network simulations and provide some recommendations to network developers and architects.

Meta-learning based spatial-temporal graph attention network for traffic signal control

Traffic signal control is of great importance to the urban transportation systems and public travel, yet it becomes challenging because of two essential factors. First, spatial–temporal correlations are crucial to an intersection scenario. However, existing works have either considered only one of these features or a simple fusion of spatial and temporal information without adequately exploiting the potential correlations. Second, some works using graph neural network treats static graph nodes among adjacent intersections, ignoring the fact that intersection traffic is changing dynamically. These dynamically changing characteristics of an intersection are likewise significant for traffic signal prediction. If these problems are not resolved, the traffic pressure will increase and people’s time will be wasted.To resolve these challenges, we put forward a novel meta-learning spatial–temporal graph attention network (MetaSTGAT) for adaptive traffic signal control. Specifically, we design a graph neural framework with a graph attention network (GAT) and long short-term memory (LSTM) network to obtain spatial and temporal information. The spatial–temporal features are elaborately merged to improve its performance. Besides, to adapt the graph network to the dynamic traffic flow, i.e., the dynamics of the nodes, we propose a meta-learning method for graph neural network’s weights generation. This dynamic weight generation process captures the dynamic changes of graph nodes, and thus the dynamic changes of intersections. In this way, the neighboring nodes in the graph network get new weights in advance by additional features when they influence each other. Comprehensive experiments performed in the multi-intersection scenario on synthetic and real-world datasets demonstrate the effectiveness of MetaSTGAT against other state-of-the-art methods. Our method reduces travel time by 12.23%, 19.30%, 13.84%, 10.91%, 8.24%, and 8.74% over the graph-level method CoLight on four synthetic datasets and two real-world datasets, respectively.