Energy Consumption Of Nodes Research Articles

A Decentralized Dynamic Self-Triggered Control Approach to Consensus of Multiagent Systems

This article studies the self-triggered consensus control problem for linear multiagent systems (MASs) with the aim of improving resources utilization efficiency, e.g., saving communication bandwidth and reducing node energy consumption. First, a distributed control protocol is designed and a novel decentralized dynamic self-triggering mechanism (DDSTM) is proposed, with which, each agent can not only compute its own next triggering instant but also is able to compute the neighbors’ next triggering instant. Then, a hybrid system model is constructed to completely describe the close-loop system with jump dynamics. Based on the hybrid model, Lyapunov conditions are derived for MAS consensus analysis. The proposed DDSTM can overcome some limitations of the existing distributed STMs in the sense of synchronously acquiring consensus error or uniquely determining triggering instants, moreover, it allows transceivers to be turned on only at triggering instants and enter into sleep mode at the rest time, such that node energy is efficiently saved. Preadjustable minimum triggering interval is imposed between any consecutive triggering events; thus, a minimum sleep period for transceivers is always guaranteed. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed approach.

Collaborative communication and computational design for energy-efficient edge based learning network

In this paper, the potential for conserving energy has been used inside the collaborative network. The purpose of this work is to examine a model of a collaborative mobile cloud with the objective of lowering the total energy consumption of network nodes. This model is made up of many different network nodes, all of which collaborate with one another to build the network. We performed a theoretical examination into the quantity of energy that is used by the nodes when they are functioning as part of the network. In addition, a user grouping and scheduling-based system was established in order to establish when and how users should participate in the creation of the network. This was done so that the system could identify when and how users should contribute to the construction of the network. The proposed methodology took into consideration both the user and the base station components, and it made an effort to strike a balance between reducing the amount of energy that was consumed and ensuring that everyone was treated in an equitable manner. Even with higher delay values, the collaborative method may minimise energy use. The suggested approach uses slower but more energy-efficient nodes by increasing the allowable latency, which reduces energy usage and benefits from the diversity of nodes. The results of the simulation are presented by our proposed method from the perspective of user fairness as well as presenting the advantages of conserving energy that are associated with employing the collaborative strategy.

Energy efficient congestion control scheme based on Modified Harris Hawks Optimization for heavy traffic Wireless Sensor Networks

The performance of Wireless Sensor Networks (WSNs), a subset of Wireless Ad-hoc Networks, is significantly influenced by the application, lifetime, storage capacity, processing power, changes in topology, communication medium, and bandwidth. These restrictions call for a strong data transport control in WSNs that takes into account quality of service, energy efficiency, and congestion management. Wireless networks face a significant difficulty with congestion which impacts on the loss rate, channel quality, link utilization, the number of retransmissions, traffic flow, network lifetime, latency, energy, and throughput are all negatively impacted by congestion in WSNs. Since the routing problem has been shown to be NP-hard and it has been realized that a heuristic based method delivers better performance than their traditional counterparts, routing is one of the most popular methods for reducing the energy consumption of nodes and increasing throughput in WSNs. This research provides a Rate Aware Congestion Control (RACC), an effective congestion avoidance method that enhances network performance by applying Modified Harris Hawks Optimization (MHHO). Nodes are initially clustered using the DBSCAN clustering algorithm. When compared to existing approaches, the simulation outcomes of the developed technique indicate superior service, low delay, high energy, packet delivery ratio and increased living nodes.

APAP: An adaptive packet-reproduction and active packet-loss data collection protocol for WSNs

In lossy, large-scale wireless sensor networks (WSNs), data collection protocols face the challenge of balancing expected data transmission reliability and limited resources of sensors. Packet redundancy strategies have been widely employed to guarantee transmission reliability. However, these schemes often result in energy waste, especially in sink-neared hotspot districts. We propose the APAP to balance the two competing interests in energy-constrained WSNs. APAP mainly consists of two parts: adaptive packet-reproduction routing and active packet-loss mechanism. In the first part, we design a compensation function to compensate for packet losses and employ it in the packet-reproduction routing. Through available network parameters, such a function offers an optimized number of redundant packets. In the second part, we devise a novel distributed packet-loss mechanism to detect and intercept redundant packets in the hotspot area. Hotspot nodes record the simple traffic information in their vicinity on a local micro-table, these nodes transform their mode based on the records to reach a basic consensus with one-hop neighbors, they then collaboratively intercept excess packets from outside, thus the injection traffic can be mitigated. Such an interception area has the potential to be expanded and prolonged as the redundancy becomes more severe. Our mechanism imposes low-complexity requirements on general devices. Moreover, thorough mathematical analyses are offered for the performance of APAP. Simulation results indicate that with high reliability, the maximum node energy consumption is reduced by 44.7% to 66.3% compared to conventional protocols in one round. Besides, the network lifetime is prolonged by 91.1% to 200%.

Software Defined Networking Controller (SDNC): a robust security architecture for SDN-based 5G networks

The efficacy of Wireless Sensor Networks (WSNs), a subset of Wireless Ad-hoc Networks, is significantly impacted by application, lifetime, storage capacity, processing power, topology changes, communication medium, and bandwidth. These constraints necessitate a robust data transport control in WSNs that considers service quality, energy efficiency, and congestion management. Congestion is a significant issue for wireless networks. Congestion in WSNs has deleterious effects on loss rate, channel quality, link utilization, number of retransmissions, traffic flow, network lifetime, latency, energy, and throughput. The predominance of WSNs necessitates the development of more efficient congestion control algorithms. Since it has been demonstrated that the routing problem is NP-hard and that heuristic-based methods outperform their traditional counterparts, routing is one of the most prevalent techniques for reducing the energy consumption of nodes and increasing throughput in WSNs. This study presents Rate Aware Congestion Control (RACC), an efficient method for avoiding congestion that improves network performance by employing Modified Harris Hawks Optimisation (MHHO). Initially, nodes are clustered utilizing the DBSCAN clustering algorithm. The simulation results of the developed technique indicate superior service, low latency, high energy, a high packet delivery ratio and an increasing number of living nodes when compared to existing approaches.

BACSSOC: A novel clustering method for mobile forest protection using wireless sensor network with lower energy consumption and lower latency

The utilization of Mobile Forest Protection Using Wireless Sensor Network (Mobile-FPWSN) has exhibited notable advantages in mitigating economic losses in forestry and enhancing the efficiency of fire prevention, thereby leading to its widespread adoption in the forestry protection industry. However, in the current developmental stage of Mobile-FPWSN, the impact of sensor nodes on the system is often disregarded in forest fire prevention and control. Latest studies have indicated that clustering techniques exhibit noteworthy benefits, including a decrease in energy consumption and an extension of system life. However, current clustering methodologies do not comprehensively address the concerns of latency, energy consumption, and delay while operating under Mobile-FPWSN conditions. In order to tackle these challenges, a novel clustering method called Boltzmann adaptive chaotic salp swarm optimization clustering (BACSSOC) has been proposed for Mobile-FPWSN. This method aims to significantly prolong the system’s lifetime, efficiently reduce energy consumption, and minimize system latency to the greatest extent possible. BACSSOC dramatically reduces the energy consumption of cluster heads during the adaptive clustering phase of Mobile-FPWSN, thereby decelerating the depletion of node energy and improving the forest fire monitoring time in comparison with other state-of-the-art algorithms. To evaluate the efficacy of BACSSOC in comparison to ML-AEFA, PCFMO, and SOA-EACR algorithms in mobile FPWSNs, we have conducted a unique forest fire simulation experiment based on a realistic forest fire scenario. The results have unequivocally demonstrated that BACSSOC surpasses ML-AEFA, PCFMO, and SOA-EACR algorithms, exhibiting advantages of at least 15% in reducing node energy consumption, 7% in decreasing system latency, and 9% in increasing system lifespan. These findings highlight the remarkable potential of BACSSOC as an effective network clustering solution in mobile FPWSNs.

Energy balanced routing protocol based on improved particle swarm optimisation and ant colony algorithm for museum environmental monitoring of cultural relics

AbstractThe environmental monitoring of cultural relics based on wireless sensor networks in museums demands for the transmission and processing of massive data, which in turn leads to problems, such as heavy network traffic, high time delay, and unbalanced node energy consumption. To solve these problems, an energy balanced routing protocol which can minimise the network energy consumption is proposed. The improved swarm intelligence optimisation algorithm which combined particle swarm optimisation with ant colony optimisation is adopted to construct an environmental monitoring system for cultural relics preservation. Simulation results show that the decrease of the path length for data transmission under the proposed algorithm is 2.5% higher than that of other classical algorithms, indicating that the proposed algorithm can effectively reduce the length of network path and improve the transmission performance of network system. In view of optimisation rounds and simulation time, the energy consumption of the network system under our algorithm is 57.5% less compared with other classical algorithms, and the average residual energy variance is 57.1% less. The cultural relics environmental monitoring system based on the proposed routing protocol has better network load balancing performance.

An optimal fog-cloud offloading framework for big data optimization in heterogeneous IoT networks

Executing complex and time-sensitive operations has become difficult due to the increased acceptance of Internet of Things (IoT) devices and IoT-generated big data, which can result in problems with power consumption and lag time. The fog computing layer is a distributed computing option that can handle some of these issues. However, fog computing devices’ limited computing and power capabilities make it difficult to complete operations under time constraints while minimizing service latency and fog resource energy consumption in delay-sensitive IoT-Fog applications. This paper suggests a dynamic integer linear programming technique for facilitating optimal task offloading that distributes resources from the fog computing layer to IoT devices while considering the constraints on timely task execution and resource availability. The offloading challenge is modeled as an integer linear programming (ILP) problem to ease the burden on finite fog resources and speed up the completion of time-sensitive operations. Given the large dimensionality of tasks in a dynamic environment, a task prioritization method is adopted to minimize tasks’ latency and the energy consumption of fog nodes. The findings demonstrate that the suggested approach performs better than benchmark approaches regarding energy utilization and service latency. Overall, the proposed technique offers an efficient and practical response to the issues raised by IoT devices and fog computing while enhancing system efficiency regarding power consumption and latency.

Optimal Selection of the Cluster Head in Wireless Sensor Networks by Combining Particle Swarm Optimization and Efficient Genetic Algorithm

Wireless Sensor Networks (WSNs) have become a crucial component of numerous applications, including the military, healthcare, and environmental monitoring. A promising approach to increasing the lifespan of the sensor network is cluster-based WSNs. In WSNs, choosing the best cluster head is a crucial task that has an impact on the network's performance and energy efficiency. There are various issues with current methods for choosing the cluster head, including nodes dying too soon, uneven energy usage, and shorter network lifetimes. Moreover, traditional methods such as Randomized Clustering and Fixed Cluster Head are not effective in prolonging the network lifetime as they do not consider the energy consumption and residual energy of nodes. In this paper, an optimal selection of cluster head is presented where we combine the Particle Swarm Optimization (PSO) and Efficient Genetic Algorithm (EGA). Firstly, PSO is used to randomly select the cluster head and update the position of each cluster. Thereafter, EGA invokes its fitness values to select the best cluster head that transmits information to the base station. The simulation result shows that the performance improvement of the proposed method PSO_EGA in terms of network lifetime is 0.10% against Improve Cuckoo Search Algorithm (ICSA) and 0.20% against Hybrid Crow Search Algorithm (HCSA), packet to cluster head is 7% against ICSA and 16% against HCSA, packet to sink is 11% against ICSA and 22% against HCSA and number of alive node is 28% against ICSA and 48% against HCSA. Therefore, our proposed method outperforms ICSA and HCSA in terms of the aforementioned parameters.

An Advanced Energy-Efficient Environmental Monitoring in Precision Agriculture Using LoRa-Based Wireless Sensor Networks.

Sensor networks, as a special subtype of wireless networks, consist of sets of wirelessly connected sensor nodes often placed in hard-to-reach environments. Therefore, it is expected that sensor nodes will not be powered from the power grid. Instead, sensor nodes have their own power sources, the replacement of which is often impractical and requires additional costs, so it is necessary to ensure minimum energy consumption. For that reason, the energy efficiency of wireless sensor networks used for monitoring environmental parameters is essential, especially in remote networking scenarios. In this paper, an overview of the latest research progress on wireless sensor networks based on LoRa was provided. Furthermore, analyses of energy consumption of sensor nodes used in agriculture to observe environmental parameters were conducted using the results of real measurements in the field, as well as simulations carried out based on collected data about real equipment. Optimization methods of energy consumption, in terms of choosing the appropriate data collection processes from the conducted field measurements, as well as the settings of network radio parameters imitating real conditions used in conducted simulations were highlighted. In the analyses, special emphasis was placed on choosing the optimal packet size. Unlike in other papers analyzing energy efficiency of LoRa communication, in this paper, it was proven that the adjustment of the transmission speed to the actual size of the packet is important for better energy efficiency of communication and that it can reduce energy consumption considerably. Moreover, in the paper, the contents of a packet that can be used in precision agriculture is suggested in order to prove that the 6-bit packet is sufficient for energy-efficient collection of parameters from the environment, in contrast to the 11-bit packets used in standard commercially available equipment.

A Guide to Data Collection for Computation and Monitoring of Node Energy Consumption

The digital transition that drives the new industrial revolution is largely driven by the application of intelligence and data. This boost leads to an increase in energy consumption, much of it associated with computing in data centers. This fact clashes with the growing need to save and improve energy efficiency and requires a more optimized use of resources. The deployment of new services in edge and cloud computing, virtualization, and software-defined networks requires a better understanding of consumption patterns aimed at more efficient and sustainable models and a reduction in carbon footprints. These patterns are suitable to be exploited by machine, deep, and reinforced learning techniques in pursuit of energy consumption optimization, which can ideally improve the energy efficiency of data centers and big computing servers providing these kinds of services. For the application of these techniques, it is essential to investigate data collection processes to create initial information points. Datasets also need to be created to analyze how to diagnose systems and sort out new ways of optimization. This work describes a data collection methodology used to create datasets that collect consumption data from a real-world work environment dedicated to data centers, server farms, or similar architectures. Specifically, it covers the entire process of energy stimuli generation, data extraction, and data preprocessing. The evaluation and reproduction of this method is offered to the scientific community through an online repository created for this work, which hosts all the code available for its download.

Evaluation of Correlation between Temperature of IoT Microcontroller Devices and Blockchain Energy Consumption in Wireless Sensor Networks.

Blockchain technology is an information security solution that operates on a distributed ledger system. Blockchain technology has considerable potential for securing Internet of Things (IoT) low-powered devices. However, the integration of IoT and blockchain technologies raises a number of research issues. One of the most important is the energy consumption of different blockchain algorithms. Because IoT devices are typically low-powered battery-powered devices, the energy consumption of any blockchain node must be kept low. IoT end nodes are typically low-powered devices expected to survive for extended periods without battery replacement. Energy consumption of blockchain algorithms is an important consideration in any application that combines both technologies, as some blockchain algorithms are infeasible because they consume large amounts of energy, causing the IoT device to reach high temperatures and potentially damaging the hardware; they are also a possible fire hazard. In this paper, we examine the temperatures reached in devices used to process blockchain algorithms, and the energy consumption of three commonly used blockchain algorithms running on low-powered microcontrollers communicating in a wireless sensor network. We found temperatures of IoT devices and energy consumption were highly correlated with the temperatures reached. The results indicate that device temperatures reached 80 °C. This work will contribute to developing energy-efficient blockchain-based IoT sensor networks.

Energy Efficient Region based Source Distributed Routing Algorithm for Sink Mobility in Underwater Sensor Network

The residual nodes energy consumption is a primary factor to extend the lifespan of network. Utilizing a sensor node's very low-powerbattery in Underwater Wireless Sensor Networks (UWSNs) is still a difficult task. To increase the network lifespan, this paper proposes an Energy Efficient Region based Source Distributed Routing Algorithm for Sink Mobility in Underwater Sensor Network (EERSDRA-GCOP-UWSN). The proposed work consists of three Phases that are initialization phase, Routing Phase and Data transmission. For initialization phase, sinks and the sensor nodes are initialized. For Routing phase, Energy EERSDRA with sink mobility is employed to identify the region of sensor nodes. For data transmission phase, Geographic and Cooperative Opportunistic Protocol is proposed to select the best relay node on the basis of depth fitness factor. The proposed EERSDRA-GCOP-UWSN is evaluated with the performance metrics. Then performance of the EERSDRA-GCOP-UWSN proposed method provides 18.409%, 13.843% and 23.768% high Network lifetime, 27.744%, 24.881% and 19.703% low energy consumption, 17.346%, 20.852% and 26.347% high throughput and 16.385%, 20.416% and 22.907% high PDR when compared to the existing methods such as, Energy-efficient regional based cooperative routing protocol with sink mobility for UWSN (EERCRP-UWSN), a safe energy efficient and cooperative routing protocol for UWSN (SEECRP-UWSN), geo-opportunistic routing protocols for improving the residual energy range (EEGORP-UWSN) methods respectively. Finally, the performance of the proposed method achieves better result while compared to the existing methods.

Lotus effect optimization algorithm (LEA): a lotus nature-inspired algorithm for engineering design optimization

Here we introduce a new evolutionary algorithm called the Lotus Effect Algorithm, which combines efficient operators from the dragonfly algorithm, such as the movement of dragonflies in flower pollination for exploration, with the self-cleaning feature of water on flower leaves known as the lotus effect, for extraction and local search operations. The authors compared this method to other improved versions of the dragonfly algorithm using standard benchmark functions, and it outperformed all other methods according to Fredman's test on 29 benchmark functions. The article also highlights the practical application of LEA in reducing energy consumption in IoT nodes through clustering, resulting in increased packet delivery ratio and network lifetime. Additionally, the performance of the proposed method was tested on real-world problems with multiple constraints, such as the welded beam design optimization problem and the speed-reducer problem applied in a gearbox, and the results showed that LEA performs better than other methods in terms of accuracy.

The improvement of wavefront cellular learning automata for task scheduling in fog computing

AbstractThe rapid advancement of the “Internet of Things” (IoT) devices has led to the emergence of different types of IoT applications that need immediate response and low delay to operate. The emergence of fog computing has provided a proper platform to process fast‐emerging IoT applications. Nevertheless, to name the disadvantages of fog computing devices, it can be said that they are typically distributed, dynamic, and resource‐limited. Therefore, it seems a substantial challenge to schedule fog computational resources effectively to perform heterogeneous and delay‐sensitive IoT tasks. The problem of scheduling tasks aimed at minimizing the energy consumption of fog nodes is formulated in this article, while meeting the requirements of the quality of service (QoS) of IoT tasks, including response time. Minimizing the deadline time and balancing the network load are also considered in the mathematical model. In the next stage, a new algorithm is introduced based on a wavefront cellular learning automata (WCLA) called the wavefront cellular learning automata improved by genetic algorithm (WCLA + GA). WCLA + GA is indeed a modified version of WCLA that has been improved using the genetic algorithm. In this version, the WCLA reinforcement signal is regulated by a genetic algorithm that accelerates the automata convergence rate. WCLA + GA is then utilized to schedule fog tasks. Simulating the proposed method followed by comparing it with other methods demonstrates that WCLA + GA performs task scheduling significantly better in terms of response time, energy consumption, and percentage of tasks that meet their deadline.

Node Scheduling Scheme for Wireless Sensor Networks with Partial Coverage

Sensor nodes in a wireless sensor network are distributed across an area for data collection. These nodes have basic capabilities in terms of interfaces and components, and they often operate in dynamic, hostile environments. Sensor networks present numerous challenges: they are dispersed, generate constant high rates data streams, function in dynamic and time-changing situations; and may involve a large number of sensors. Sensor nodes have enough power to transmit their readings to a central high-performance computing unit for processing. Sensor networks generate data streams, which are sequences of real-time data records characterized by their high data rates that consume significant network computing resources. However, only a few studies address the issue of collecting highly redundant data, leading to nodes wasting energy by sending redundant information to a central high-performance computing unit. Improved scheduling tactics can help reduce energy consumption in sensor nodes. This research developed a Node Scheduling Scheme for Wireless Sensor Networks with Partial Coverage (NSPC). Partial coverage can be obtained by dividing the area of interest into smaller sub-regions and determining the monitoring intensity for each sub-region by sensor nodes. Various strategies, such as clustering and scheduling, can be employed to accomplish partial coverage. Considering partial coverage when designing a WSN is crucial, as it can enhance network stability and reliability while reducing the cost and energy consumption of each sensor nodes.

On the Use of Wireless Technologies for Wildlife Monitoring: Wireless Sensor Network Routing Protocols

Traditional methods for wildlife monitoring are labor-intensive and time-consuming. Therefore, advanced technologies and remote monitoring methods are becoming increasingly popular. This paper presents a study on wireless technologies for wildlife monitoring. In the study, a review of the literature was done to identify the most commonly used wireless technologies. Various technologies were explored including unmanned aerial vehicles (UAVs), Internet of Things (IoT), wireless sensor networks (WSNs), artificial intelligence (AI), global positioning system (GPS), and very high frequency (VHF) radio. Then, a more detailed study was done on WSN technology. Investigations were done to observe the performance of routing protocols in WSNs. The use of source location privacy (SLP) routing protocols was considered for secure wildlife monitoring in areas such as game reserves. In such areas, sensor node energy consumption minimization and battery lifetime maximization are crucial. Hence, energy-efficient SLP protocols are more suitable for deployment. Using MATLAB simulation environment, performance analysis of various phantom- based SLP protocols was done to identify effective and energy-efficient SLP protocols. Simulation results show that two-level phantom with a backbone route protocol (TBP) and phantom with angle protocol (PAP) exhibit advantageous performance features in terms of SLP protection, energy efficiency, effective long-term SLP protection, and scalability. Thus, TBP and PAP are suitable for deployment in wildlife monitoring WSNs.

Energy Efficient Node Selection in Edge-Fog-Cloud Layered IoT Architecture

Internet of Things (IoT) architectures generally focus on providing consistent performance and reliable communications. The convergence of IoT, edge, fog, and cloud aims to improve the quality of service of applications, which does not typically emphasize energy efficiency. Considering energy in IoT architectures would reduce the energy impact from billions of IoT devices. The research presented in this paper proposes an optimization framework that considers energy consumption of nodes when selecting a node for processing an IoT request in edge-fog-cloud layered architecture. The IoT use cases considered in this paper include smart grid, autonomous vehicles, and eHealth. The proposed framework is evaluated using CPLEX simulations. The results provide insights into mechanisms that can be used to select nodes energy-efficiently whilst meeting the application requirements and other network constraints in multi-layered IoT architectures.

Energy-Efficient Data Transmission for Underwater Wireless Sensor Networks: A Novel Hierarchical Underwater Wireless Sensor Transmission Framework

The complexity of the underwater environment enables significant energy consumption of sensor nodes for communication with base stations in underwater wireless sensor networks (UWSNs), and the energy consumption of nodes in different water depths is unbalanced. How to improve the energy efficiency of sensor nodes and meanwhile balance the energy consumption of nodes in different water depths in UWSNs are thus urgent concerns. Therefore, in this paper, we first propose a novel hierarchical underwater wireless sensor transmission (HUWST) framework. We then propose a game-based, energy-efficient underwater communication mechanism in the presented HUWST. It improves the energy efficiency of the underwater sensors personalized according to the various water depth layers of sensor locations. In particular, we integrate the economic game theory in our mechanism to trade off variations in communication energy consumption due to sensors in different water depth layers. Mathematically, the optimal mechanism is formulated as a complex nonlinear integer programming (NIP) problem. A new energy-efficient distributed data transmission mode decision algorithm (E-DDTMD) based on the alternating direction method of multipliers (ADMM) is thus further proposed to tackle this sophisticated NIP problem. The systematic simulation results demonstrate the effectiveness of our mechanism in improving the energy efficiency of UWSNs. Moreover, our presented E-DDTMD algorithm achieves significantly superior performance to the baseline schemes.

Low energy split-cluster wireless acceleration sensors for motion and posture change recognition in sports

The rise of the national fitness trend has led more and more people to join the ranks of physical fitness, and the current physical education resources and human resources have not been able to keep pace with the development of sports. Due to the lack of guidance, many beginners of sports are not standardized, and their movements are not effective and even cause injuries. Sports action recognition technology can make up for this lack and realize the action recognition and guidance for sports players. In this paper, we build a sports movement and posture recognition model based on acceleration sensors, introduce Selectable Mode Vocoder (SMV) algorithm to optimize the performance, and ensure the effectiveness of sensor data transmission through a low energy multi-layer clustered wireless sensor network. The experiment mainly compares the generation results of the first energy depleted node in the entire network with the number of rounds with energy depleted nodes reaching 10 %. The experimental results show that the low-energy multi-layer clustered wireless sensor network effectively shortens the data transmission path, reduces the energy consumption of nodes, extends the network life time, ensures the data transmission effectiveness, and provides network guarantees for sports action recognition. In addition, in the comparison experiments, the action recognition model in this paper shows better recognition effect and efficiency with obvious performance advantages.