Data Transmission Delay Research Articles

A novel hierarchical distributed vehicular edge computing framework for supporting intelligent driving

Recently, various infrastructure-assisted or onboard driving assistant applications have been proposed as a component of intelligent transportation systems (ITS) to improve the transportation system’s efficiency and release public concern about road safety. However, such AI-assisted intelligent applications are mainly data-driven and put great demands on the computing power of the ITS systems. Therefore, in the highly dynamic Internet-of-Vehicles environment in ITS, how to effectively coordinate the limited computing power of the various components of the system and realize reliable support for such resource-consuming applications through efficient resource allocation methods is the focus of our research. Accordingly, a novel joint computing and communication resource scheduling method is proposed to fulfill those ITS applications’ inherent heterogeneous quality of service (QoS) requirements. By fully exploiting the computing resources provided by the onboard computing device, the edge computing device located in the vehicle’s proximity and remote data center, we designed a hierarchical three-layer Vehicular Edge Computing (VEC) framework. Briefly, an onboard joint computation offloading and transmission scheduling policy is designed to assign corresponding offloading decisions to the locally generated computing tasks by considering the vehicle’s computing resources and real-time network link status. Additionally, a new distributed resource allocation policy is developed for the edge devices, in which we derive a server selection policy and allocate communication time based on our proposed metric. To evaluate the performance and validate the effectiveness of our proposed method, we conduct extensive simulation tests and ablation experiments, respectively. The results show that our approach can achieve stable performance in various experimental settings. Also, compared to the state-of-the-art algorithms, our joint resource allocation policy significantly reduces the scheduling overhead, improves the utilization of system resources, and minimizes the data transmission delay caused by vehicle motion.

VANETs MAC Protocol Without RSU Assistance

The aim of MAC protocol research without RSU is to solve some of the challenges and problems that exist in VANETs without RSU support. For example, Rsus cannot be deployed in certain regions or scenarios due to various reasons (such as cost, lack of infrastructure, etc.). In this case, the MAC protocol without RSU assistance provides a solution that does not rely on RSU for communication. In addition, in traditional VANETs, the RSU acts as an important relay node, helping vehicles communicate with each other. However, without a sufficiently dense RSU deployment, network coverage will be limited. Through the MAC protocol without RSU assistance, vehicles can communicate directly, expanding the coverage of the network. Through the MAC protocol without RSU assistance, vehicles can communicate directly, expanding the coverage of the network. Improve the reliability and throughput of VANETs networks: Due to the mobility of vehicles and the uncertainty of the communication environment, problems such as message collisions and data transmission delays are common in VANETs. By designing an effective MAC protocol without RSU assistance, message collision can be reduced, system throughput can be improved, and network reliability can be enhanced. Finally, in some application scenarios, such as vehicle safety and traffic management, real-time communication is very important. Without the support of RSU, traditional VANETs may suffer from large communication delays. By studying the MAC protocol without RSU assistance, the communication delay can be minimized to meet the needs of real-time communication.

Design of tractor virtual test system based on high-level architecture technology.

The limitations of the tractor virtual test system are evident in various aspects, including model reuse, system expansion, offsite interconnection, and virtual reality verification. To address these challenges, a distributed virtual test system for tractors based on the high-level architecture (HLA) is proposed. Involve analyzing the hardware structure and the tractor virtual test system, constructing the system federation and its members, and designing the federated object model (FOM) and simulation object model (SOM) tables. The system integrates multi-domain commercial software and enables real-time virtual testing through TCP/IP interconnection of multiple machines. To evaluate the system's performance, a virtual test of the tractor's reversing clutch engagement performance is conducted. The system's simulation performance and data transmission delay are thoroughly tested and analyzed. The results indicate that when the system's data volume reaches 5000KB, the data delay is 9.7ms, which satisfies the requirement of not exceeding 10ms for tractor virtual testing delay. The virtual test of the reversing clutch power reversal process demonstrates that it lasts 0.7s, with the vehicle speed changing from -3.5km/h to 3.5km/h, the forward gear piston oil pressure increasing from 0MPa to 5MPa, and the peak impact degree reaching 17m/s3. The slip work during the reversing process is measured to be 21kJ. Furthermore, the gray correlation method is employed to compare the virtual test results with the bench test results, confirming their consistency. The power reversal process exhibits relatively smooth speed changes overall. Therefore, the tractor power shift transmission (PST) reversing clutch virtual test model operates effectively within the HLA-based tractor virtual test system.

Optimization of Delay Using Killer Whale Algorithm (KWA) on NB-IoT

Abstract: NB-IoT is designed to connect IoT devices with low-power, wide-area coverage and efficient costs. Ensuring optimal data transmission delay is a challenge in NB-IoT implementation. Inadequate coverage can hinder IoT adoption. Optimization balances energy saving and delay trade-off. The Killer Whale Algorithm (KWA) optimizes delay by adjusting repetition variables. KWA addresses dimensions, variable limits. Applying KWA in NB-IoT optimizes transmission, enhancing QoS. Optimizing delay involves reducing latency in uplink data transmission using repetition variables. This study applies KWA to optimize NB-IoT delay. Analysis in Table 4 shows non-linear repetition-distance correlation. Interestingly, delay outcomes exhibit a contrasting relationship. Still, delay remains advantageous, remaining under 1 second even at 10 km, specifically 9.2674 ms (0.0092674 seconds). This thesis aims to optimize delay in NB-IoT network transmission using the Killer Whale Algorithm (KWA), crucial for modern communication networks and IoT applications. Leveraging KWA, the research identifies solutions to reduce transmission delay, enhancing efficiency and meeting IoT communication demands for speed and timeliness

A novel machine learning-based framework for channel bandwidth allocation and optimization in distributed computing environments

Efficient utilization of network resources, particularly channel bandwidth allocation, is critical for optimizing the overall system performance and ensuring fair resource allocation among multiple distributed computing nodes. Traditional methods for channel bandwidth allocation, based on fixed allocation schemes or static heuristics, often need more adaptability to dynamic changes in the network and may not fully exploit the system’s potential. To address these limitations, we employ reinforcement learning algorithms to learn optimal channel allocation policies by intermingling with the environment and getting feedback on the outcomes of their actions. This allows devices to adapt to changing network conditions and optimize resource usage. Our proposed framework is experimentally evaluated through simulation experiments. The results demonstrate that the framework consistently achieves higher system throughput than conventional static allocation methods and state-of-the-art bandwidth allocation techniques. It also exhibits lower latency values, indicating faster data transmission and reduced communication delays. Additionally, the hybrid approach shows improved resource utilization efficiency, efficiently leveraging the strengths of both Q-learning and reinforcement learning for optimized resource allocation and management.

Reliable and Secure Data Transfer in IoT Networks using Knight-Tour and PHLSB Method

Data security has recently become the most pressing problem for corporate owners and even regular people due to the continual development in the speed of data transmission over the entire Internet, and stricter regulations have been made for data protection. Secure key management is now essential for securing information exchange due to the Internet of Things (IoT) and the rapidly advancing mobile device technologies. Smart home and healthcare IoT apps, for instance, offer automated services to users with little user involvement. Data transmission delays and intrusions can affect existing security solutions that use a single link. In this paper, we propose a novel distributed key management scheme for IoT ecosystem. The methods Knight-Tour and polynomial-based hash least significant bit (PHLSB) that take use of flaws in the human visual system (HVS) are combined to form a hybrid approach to image steganography in this research. The Knight's trip path is a chess board layout that follows the route of a horse without stopping at any of the nodes again. In order to create the scrambled image, this travel path pattern is employed to permute the original image's pixel positions. Prior to being masked behind a cover image, this technique makes sure the communication has been encrypted. A strong mathematical tool is a polynomial equation. The cover object's data was hidden using these equations as a secret key. The suggested solution efficiently secures IoT devices by assigning the resource-intensive encryption processing to local organisations. As a result of the proposed framework's simulation findings, network lifetime PDR (Packet Drop Ratio) has improved, throughput has increased, energy consumption has decreased, and latency has decreased.

Predictive Display With Perspective Projection of Surroundings in Vehicle Teleoperation to Account Time-Delays

Teleoperation provides human operator sophisticated perceptual and cognitive skills into an over the network control loop. It gives hope of addressing some challenges related to vehicular autonomy which is based on artificial intelligence by providing a backup plan. Variable network time delays in data transmission is the major problem in teleoperating a vehicle. On 4G network, variability of these delays is high. Due to this, both video streaming and driving commands encounter variable time delay. This paper presents an approach of providing the human operator a forecast video stream which replicates future perspective of vehicle field of view accounting the delay present in the network. Regarding the image transformation, perspective projection technique is combined with correction given by smith predictor in the control loop. This image transformation accounts current time delay and tries to address both issues, time delays as well as its variability. For experiment sake, only frontward field of view is forecast. Performance is evaluated by performing online vehicle teleoperation on street edge case maneuvers and later comparing the path deviation with and without perspective projection.

Securing Medical Information Transmission Between IoT Devices: An Innovative Hybrid Encryption Scheme Based on Quantum Walk, DNA Encoding, and Chaos

The healthcare industry has undergone a transformation due to the widespread use of advanced communication technologies and wireless sensor networks such as the Internet of Medical Things (IoMT), Health Information Exchange Technology (HIET), Internet of Healthcare Things (IoHT) and Health IoT (HIoT). These technologies have led to an increase in the transmission of medical data, particularly medical imaging data, over various wireless communication channels. However, transmitting high-quality color medical images over insecure internet channels like the Internet and communication networks like 5G presents significant security risks that could threaten patients’ data privacy. Furthermore, this process can also burden the limited bandwidth of the communication channel, leading to delayed data transmission. To address security concerns in healthcare data, researchers have focused a lot of attention on medical image encryption as a means of protecting patient data. This paper presents a color image encryption scheme that integrates multiple encryption techniques, including alternate quantum random walks, controlled Rubik’s Cube transformations, and the integration of the Elliptic Curve Cryptosystem with Hill Cipher (ECCHC). The proposed scheme divides various plaintext images by creating a regular cube by layering planes of a fixed size. Each plane is rotated in an anticlockwise direction, followed by row, column and face swapping, and then DNA encoding is performed. The image cube encoded with DNA is combined with the chaotic cube through DNA addition, and a couple of random DNA sequences are chosen for DNA mutation. After undergoing DNA mutation, the encoded cube is then decoded using DNA. The proposed method has the theoretical capability of encrypting 2D images of unlimited size and number by utilizing an infinitely large cube. The proposed image encryption scheme has been rigorously tested through various experimental simulations and cyberattack analysis, which shows the efficiency and reliability of the proposed encryption scheme.

Energy-Saving Adaptive Routing for High-Speed Railway Monitoring Network Based on Improved Q Learning.

In high-speed railway operational monitoring network systems targeting railway infrastructure as its monitoring objective, there is a wide variety of sensor types with diverse operational requirements. These systems have varying demands on data transmission latency and network lifespan. Most of the previous research focuses only on prolonging network lifetime or reducing data transmission delays when designing or optimizing routing protocols, without co-designing the two. In addition, due to the harsh operating environment of high-speed railways, when the network changes dynamically, the traditional routing algorithm generates unnecessary redesigns and leads to high overhead. Based on the actual needs of high-speed railway operation environment monitoring, this paper proposes a novel Double Q-values adaptive model combined with the existing reinforcement learning method, which considers the energy balance of the network and real-time data transmission, and constructs energy saving and delay. The two-dimensional reward avoids the extra overhead of maintaining a global routing table while capturing network dynamics. In addition, the adaptive weight coefficient is used to ensure the adaptability of the model to each business of the high-speed railway operation environment monitoring system. Finally, simulations and performance evaluations are carried out and compared with previous studies. The results show that the proposed routing algorithm extends the network lifecycle by 33% compared to the comparison algorithm and achieves good real-time data performance. It also saves energy and has fewer delays than the other three routing protocols in different situations.

Collaborative on-demand dynamic deployment via deep reinforcement learning for IoV service in multi edge clouds

In vehicular edge computing, the low-delay services are invoked by the vehicles from the edge clouds while the vehicles moving on the roads. Because of the insufficiency of computing capacity and storage resource for edge clouds, a single edge cloud cannot handle all the services, and thus the efficient service deployment strategy in multi edge clouds should be designed according to the service demands. Noticed that the service demands are dynamic in temporal, and the inter-relationship between services is a non-negligible factor for service deployment. In order to address the new challenges produced by these factors, a collaborative service on-demand dynamic deployment approach with deep reinforcement learning is proposed, which is named CODD-DQN. In our approach, the number of service request of each edge clouds are forecasted by a time-aware service demands prediction algorithm, and then the interacting services are discovered through the analysis of service invoking logs. On this basis, the service response time models are constructed to formulated the problem, aiming to minimize service response time with data transmission delay between services. Furthermore, a collaborative service dynamic deployment algorithm with DQN model is proposed to deploy the interacting services. Finally, the real-world dataset based experiments are conducted. The results show our approach can achieve lowest service response time than other algorithms for service deployment.

Spectrum Sharing Toward Delay Deterministic Wireless Network: Delay Performance Analysis

To accommodate Machine-type Communication (MTC) service, the wireless network needs to support low-delay and low-jitter data transmission, realizing delay deterministic wireless network. This paper analyzes the delay and jitter of the wireless network with and without spectrum sharing. When sharing the spectrum of the licensed network, the spectrum band of wireless network can be expanded, such that the delay and jitter of data transmission are reduced. The challenge of this research is to model the relation between the delay/jitter and the parameters such as node distribution, transmit power, and bandwidth, etc. To this end, this paper applies stochastic geometry and queueing theory to analyze the outage probability of the licensed network and the delay performance of the wireless network with and without spectrum sharing. By establishing the M/G/1 queueing model for the queueing of the Base Station (BS) in the wireless network, the downlink delay and jitter are derived. Monte Carlo simulation results show that the spectrum sharing reduces the delay and jitter without causing serious interference to the licensed network, which can lay a foundation for the application of spectrum sharing in delay deterministic wireless network supporting MTC service.

Quantum Particle Swarm Optimization for Task Offloading in Mobile Edge Computing

Mobile Edge Computing (MEC) deploys servers on the edge of the mobile network to reduce the data transmission delay between servers and mobile devices, and can meet the computing demand of mobile computing tasks. It alleviates the problem of computing power and delay requirements of mobile computing tasks and reduces the energy consumption of mobile devices. However, the MEC server has limited computing and storage resources and mobile network bandwidth, making it impossible to offload all mobile computing tasks to MEC servers for processing. Therefore, MEC needs to reasonably offload and schedule mobile computing tasks, to achieve efficient utilization of server resources. To solve the above problems, in this paper, the task offloading problem is formulated as an optimization problem, and Particle Swarm Optimization (PSO) and Quantum Particle Swarm Optimization (QPSO) based task offloading strategies are proposed. Extensive simulation results show that the proposed algorithm can significantly reduce the system energy consumption, task completion time, and running time compared to recent advanced strategies, namely, Ant Colony Optimization (ACO), Multi-Agent Deep Deterministic Policy Gradients (MADDPG), Deep Meta Reinforcement Learning-based Offloading (DRMO), Iterative Proximal Algorithm (IPA), and Parallel Random Forest (PRF).

Predictive Modeling of Delay in an LTE Network by Optimizing the Number of Predictors Using Dimensionality Reduction Techniques

Delay in data transmission is one of the key performance indicators (KPIs) of a network. The planning and design value of delay in network management is of crucial importance for the optimal allocation of network resources and their performance focuses. To create optimal solutions, predictive models, which are currently most often based on machine learning (ML), are used. This paper aims to investigate the training, testing and selection of the best predictive delay model for a VoIP service in a Long Term Evolution (LTE) network using three ML techniques: Multilayer Perceptron (MLP), Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN). The space of model input variables is optimized by dimensionality reduction techniques: RReliefF algorithm, Backward selection via the recursive feature elimination algorithm and the Pareto 80/20 rule. A three-segment road in the geo-space between the cities of Banja Luka (BL) and Doboj (Db) in the Republic of Srpska (RS), Bosnia and Herzegovina (BiH), covered by the cellular network (LTE) of the M:tel BL operator was chosen for the case study. The results show that the k-NN model has been selected as the best solution in all three optimization approaches. For the RReliefF optimization algorithm, the best model has six inputs and the minimum relative error (RE) RE = 0.109. For the Backward selection via the recursive feature elimination algorithm, the best model has four inputs and RE = 0.041. Finally, for the Pareto 80/20 rule, the best model has 11 inputs and RE = 0.049. The comparative analysis of the results concludes that, according to observed criteria for the selection of the final model, the best solution is an approach to optimizing the number of predictors based on the Backward selection via the recursive feature elimination algorithm.

Dataplane-Based Fast Failover in SDN-Enabled Wide Area Measurement System of Smart Grid

In the wide area measurement system (WAMS) of smart grid, the real-time monitoring and protection applications have stringent requirements for the end-to-end transmission delays between PMUs and PDC, and fast failover is required to ensure the communication performance after link failures. In this work, the software-defined network (SDN) technology is utilized to enable datapath failover upon a link failure with a global view of the communication network. Then, a novel dataplane based fast failover (DFF) mechanism is proposed to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">directly</i> re-route the data packet in dataplane without interacting with the SDN controller. Based on the mathematical analysis over the WAMS topology features, the proposed DFF optimizes two procedures of failover: backup path construction, and backup path installation. Using the proposed backup path construction algorithms, the 3-approximate and (1+2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> )-approximate ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0&lt; \varepsilon &lt; 1$</tex-math></inline-formula> ) backup paths can be constructed, theoretically guaranteeing the data transmission delays both <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">during</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">after</i> failover. Using the proposed LinkID-based FF group table installation method, the conflict of forwarding rules between original and backup paths can be eliminated, while the storage cost is also optimized. The simulation results on six IEEE benchmark test power systems show that the proposed DFF mechanism could achieve lower data transmission delays during and after failover compared to the existing control plane based and dataplane based failover mechanisms.

Multi-sensor network tracking research utilizing searchable encryption algorithm in the cloud computing environment.

Presently, the focus of target detection is shifting towards the integration of information acquired from multiple sensors. When faced with a vast amount of data from various sensors, ensuring data security during transmission and storage in the cloud becomes a primary concern. Data files can be encrypted and stored in the cloud. When using data, the required data files can be returned through ciphertext retrieval, and then searchable encryption technology can be developed. However, the existing searchable encryption algorithms mainly ignore the data explosion problem in a cloud computing environment. The issue of authorised access under cloud computing has yet to be solved uniformly, resulting in a waste of computing power by data users when processing more and more data. Furthermore, to save computing resources, ECS (encrypted cloud storage) may only return a fragment of results in response to a search query, lacking a practical and universal verification mechanism. Therefore, this article proposes a lightweight, fine-grained searchable encryption scheme tailored to the cloud edge computing environment. We generate ciphertext and search trap gates for terminal devices based on bilinear pairs and introduce access policies to restrict ciphertext search permissions, which improves the efficiency of ciphertext generation and retrieval. This scheme allows for encryption and trapdoor calculation generation on auxiliary terminal devices, with complex calculations carried out on edge devices. The resulting method ensures secure data access, fast search in multi-sensor network tracking, and accelerates computing speed while maintaining data security. Ultimately, experimental comparisons and analyses demonstrate that the proposed method improves data retrieval efficiency by approximately 62%, reduces the storage overhead of the public key, ciphertext index, and verifiable searchable ciphertext by half, and effectively mitigates delays in data transmission and computation processes.

Predictor‐based switching control for networked systems

SummaryThis paper concerns a networked predictive control (NPC) for linear systems with data time‐varying delays, packet losses and disordering. By utilizing the received time‐delayed state measurement and control input, a predictive algorithm is firstly proposed for approximating the future state prediction. Afterwards, a probability dependent switching control law is proposed. Although the large data transmission delays emerge in the forward and backward channels, the packet disordering in the backward channel can be naturally excluded thus the actuator always receives valid control command during each sampling period with correct time sequence. The proposed NPC not only guarantees the global uniform exponential stability of the overall networked system but also brings merits in tolerating locally unstable sub‐systems. Numerical examples are provided to demonstrate the effectiveness and improvements of the proposed method.

Key Technologies of Collaborative Target Detection between Self-propelled Anti-aircraft Weapons

In the case of a lack of target information, a self-propelled anti-aircraft weapon can complete its own combat functions by using the detection information of other vehicles through detection coordination technology. When using the detection coordination technology to process the detection information of other vehicles into information that can be used by the vehicle, the relative position and attitude of the vehicle and data transmission delay will affect the accuracy of the processed target information. With the system of detection collaborative technology expounded, emphasis is attached to the research on the detection coordination method based on pure angles without considering distance. A mathematical model of detection coordination technology is created, the approximate statistical law of the processed detection coordination information is obtained through the Monte Carlo method, and the influence of various factors on transmission error analyzed. In addition, the polynomial curve fitting method is used for time alignment, and the unscented Kalman filter method based on singular value decomposition adopted to reduce the information transfer error. The simulation results show the the method is effective for that end.

РОЗРОБКА МОБІЛЬНОЇ КІБЕРФІЗИЧНОЇ СИСТЕМИ ІНТЕЛЕКТУАЛЬНОГО МОНІТОРИНГУ КЛІМАТИЧНИХ ПАРАМЕТРІВ

The development of Mobile Cyber-Physical Systems (MCPS) is a promising research and development direction for many industries, such as manufacturing, healthcare, home automation, and many others. That is why a prototype MCPS based on a smartphone has been developed to collect, process, and transmit data from various devices and sensors in real-time, anywhere. The architecture of a mobile cyber-physical system for monitoring climatic parameters with telegram notifications has been designed. An algorithm for intelligent analysis and optimization of data transmission processes has been proposed for the developed MCPS prototype. The advantage of the developed system is the ability to determine the priority of monitoring parameters, which allows for a quick response to critical temperature changes at the object where the measurements are taken. Additionally, a unique method for measuring end-to-end data transmission delay using a timestamp in the packet header metadata has been implemented. This method enables the determination of the processing time of each component of the MCPS and, in case of exceeding the norms, automatically notifies about the necessary control decisions. Supporting such a method in MCPS is a particularly effective solution for monitoring the quality of real-time service delivery in critical infrastructure objects. Based on the conducted research, it has been established that the proposed algorithm for intelligent data analysis and optimization has reduced the number of messages by 3 times and the amount of transmitted information by 2.3 times. In the future, the developed system, in combination with artificial intelligence, will ensure reliable and high-quality data transmission even in unpredictable situations, making it a promising solution for improving the quality of life and the efficiency of smart infrastructures in various fields.

Intelligent TCP Congestion Control Policy Optimization

Network congestion control is an important means to improve network throughput and reduce data transmission delay. To further optimize the network data transmission capability, this research suggests a proximal policy optimization-based intelligent TCP congestion management method, creates a proxy that can communicate with the real-time network environment, and abstracts the TCP congestion control mechanism into a partially observable Markov decision process. Changes in the real-time state of the network are fed back to the agent, and the agent makes action commands to control the size of the congestion window, which will produce a new network state, and the agent will immediately receive a feedback reward value. To guarantee that the actions taken are optimum, the agent’s goal is to obtain the highest feedback reward value. The state space of network characteristics should be designed so that agents can observe enough information to make appropriate decisions. The reward function is designed through a weighted algorithm that enables the agent to balance and optimize throughput and latency. The model parameters of the agent are updated by the proximal policy optimization algorithm, and the truncation function keeps the parameters within a certain range, reducing the possibility of oscillation during gradient descent and ensuring that the training process can converge quickly. Compared to the traditional CUBIC control method, the results show that the TCP-PPO2 policy reduces latency by 11.7–87.5%.

Edge computing Helps the Development of Smart Grid

With the rapid development of smart grids, the number of terminals connected to them has sharply increased. Using these terminals, the smart grid management system can monitor real-time information such as power consumption and power quality in the power grid to ensure safe and stable operation of the power grid. However, the access of a large number of terminals has also increased the amount of data that needs to be processed in the power grid. The traditional centralized cloud computing model can no longer meet the needs of rapid data processing, and a new model is urgently needed to solve the problem. As an emerging distributed computing model, edge computing is an ideal solution to realize large-scale distributed management of smart terminals in the future smart grid by providing computing and storage capabilities at the network edge near device terminals and user sides, reducing data transmission delay, improving data reliability, protecting user privacy, and reducing the computing and storage pressure of cloud data centers.