Reduce Network Latency Research Articles

Enhancing the Scalability of Blockchain Networks using a Data Partitioning Technique

The scalability limitations of current blockchain systems slow down their broad adoption. This issue arises because transactions are processed sequentially, limiting throughput and increasing network delays. Additionally, even with advanced multicore technology, the Proof-of-Work (PoW) process is generally performed in a linear fashion. To address these challenges, this study proposes a static analysis-based data partitioning technique to enhance transaction performance and reduce network latency by allowing parallel processing of transactions, called Simultaneous Block-Level Transaction Execution in a Distributed Setting. This framework utilizes a master-slave system within a trusted node community. The master node analyzes transactions and partitions non-conflicting ones into separate groups, or shards, which are then distributed among slave nodes for parallel execution. Once transactions are completed, the community's combined computing power is used to perform PoW simultaneously. The miner subsequently broadcasts the newly created block to other network peers for validation, which can be performed either sequentially or in parallel. Validators ensure that they achieve the same state as specified in the block. Implementing this framework on a workload can result in a maximum speedup of 1.81x for miners and 1.80x for validators, with each block containing between 150 and 550 transactions and involving six community members. PoW is a consensus mechanism in which miners solve complex cryptographic puzzles to validate transactions. It ensures network security but is resource-intensive due to its high computational demands. In the proposed framework, the master node coordinates transactions, while the slave nodes process them in parallel. This approach maximizes resource utilization across nodes.

Efficient Edge Computing: A Survey of High-Throughput Concurrent Processing Strategies for Graph Data

This paper reviews the strategies for high-throughput concurrent processing of graph data in edge computing environments. As information technology rapidly advances, particularly in areas such as the Internet of Things (IoT), smart cities, and autonomous driving, the need for real-time and efficient data processing continues to grow. Edge computing is a distributed computing paradigm that can process data at or near its origin, thereby reducing network latency, improving application performance, and reducing the load on central data centers. The discussion includes the application of edge computing in graph data processing, highlighting parallel computing models such as the Bulk Synchronous Parallel (BSP) model and other parallel optimization techniques like data partitioning and task scheduling. The paper also addresses specific challenges of parallel processing in edge computing, such as resource constraints, data communication delays, and strategies for security and privacy protection. Despite the significant potential edge computing has demonstrated in processing graph data, numerous challenges remain. Future research directions involve the development of new resource optimization algorithms, low-latency communication protocols, and technologies to enhance data security, aiming to achieve more efficient and secure graph data processing. This paper provides clear directions and a foundation for the efficient implementation of graph data processing in edge computing environments, positioning edge computing to play an increasingly significant role in real-time data analysis and intelligent applications.

Resource-aware fog service placement using deferred acceptance in edge computing

Fog computing aims at reducing network latency, bandwidth and processing delay by bringing computation resources closer to the data sources. Modern IoT applications, such as smart surveillance systems, remote health monitoring applications, ambient-aware applications etc., are time-critical and computationally intensive. For such applications, it is ideal to consider an intermediate layer of processing i.e a fog layer in addition to cloud. However, processing the workload of such IoT applications with an intermediate layer of fog nodes has several challenges. The significant challenges include the mobility of end-user nodes, dynamic patterns of workload generation leading to highly-variant resource requirements and sensitive quality of service (QOS) requirements. Hence a dynamic approach is required to precisely map the service requests from end-user devices to a suitable fog node. The optimality of a fog node for service placement depends on several factors, including location, resource utilization rates such as CPU, Memory and Disk utilization % and energy consumption. Several meta-heuristic techniques and evolutionary algorithms have been proposed in the past for optimal service placement among fog nodes. This paper investigates the applicability of a game theory-based approach called Deferred acceptance (DA) for the same. The DA algorithm, originally designed as a stable matching mechanism for the marriage market is adapted for service placement problems in fog-based IoT systems. The effectiveness of optimized service placement through the DA approach is validated experimentally. The experiment is carried out by creating a test bed that mimics a fog computing-based intelligent traffic management application. The proposed approach's performance is evaluated through parameters such as the startup time for the DA approach to produce outcomes, total execution time and handshake latency. The numerical results of the DA approach are compared with those of a simple scheduling algorithm ( FIFO) and a basic genetic algorithm-based approach. The DA approach appears to perform on par with the genetic algorithm in terms of start-up time, but exceeds the genetic algorithm with respect to stability of the outcomes produced.

Hybrid learning based service migration for cost minimization with deadlines in multi-user mobile edge computing systems

Mobile edge computing, a new computing model, aims to reduce network latency and improve user experience by placing computing resources closer to the users. However, when users move away from the current service areas, the quality of services will decline. To guarantee the quality of services, service migration technique is proposed and studied. Due to the wireless resource contention among users, there is service migration decision-making coupling among multiple users. Meanwhile, making real-time service migration decisions for multiple users to minimize service cost with application deadlines remains an open challenge because of the huge state space. In this paper, we formalize the service migration problem with deadlines as a Markov decision process (MDP) and propose a hybrid learning based service migration strategy. The proposed approach integrates Monte Carlo method and DQN learning, which adopts online training without Q-values and neural network approximation to generate service migration decisions for multiple users. Experimental results demonstrate that the proposed hybrid learning based service migration strategy outperforms the existing migration strategies in terms of service success ratio and service cost.

A Temporal Deep Q Learning for Optimal Load Balancing in Software-Defined Networks.

With the rapid advancement of the Internet of Things (IoT), there is a global surge in network traffic. Software-Defined Networks (SDNs) provide a holistic network perspective, facilitating software-based traffic analysis, and are more suitable to handle dynamic loads than a traditional network. The standard SDN architecture control plane has been designed for a single controller or multiple distributed controllers; however, a logically centralized single controller faces severe bottleneck issues. Most proposed solutions in the literature are based on the static deployment of multiple controllers without the consideration of flow fluctuations and traffic bursts, which ultimately leads to a lack of load balancing among controllers in real time, resulting in increased network latency. Moreover, some methods addressing dynamic controller mapping in multi-controller SDNs consider load fluctuation and latency but face controller placement problems. Earlier, we proposed priority scheduling and congestion control algorithm (eSDN) and dynamic mapping of controllers for dynamic SDN (dSDN) to address this issue. However, the future growth of IoT is unpredictable and potentially exponential; to accommodate this futuristic trend, we need an intelligent solution to handle the complexity of growing heterogeneous devices and minimize network latency. Therefore, this paper continues our previous research and proposes temporal deep Q learning in the dSDN controller. A Temporal Deep Q learning Network (tDQN) serves as a self-learning reinforcement-based model. The agent in the tDQN learns to improve decision-making for switch-controller mapping through a reward-punish scheme, maximizing the goal of reducing network latency during the iterative learning process. Our approach-tDQN-effectively addresses dynamic flow mapping and latency optimization without increasing the number of optimally placed controllers. A multi-objective optimization problem for flow fluctuation is formulated to divert the traffic to the best-suited controller dynamically. Extensive simulation results with varied network scenarios and traffic show that the tDQN outperforms traditional networks, eSDNs, and dSDNs in terms of throughput, delay, jitter, packet delivery ratio, and packet loss.

Komodo Mlipir Algorithm-based optimal route determination mechanism for improving Quality of Service in Vehicular ad hoc network

In Vehicular ad hoc network (VANETs), Quality of Service (QoS)- aware protocols helps in handling the necessitated demand of delay sensitive applications for facilitating intelligent transportation. The fundamental challenge of VANETs lies in the process of establishing vehicle to infrastructure and vehicle-to-vehicle communication that are prone to link failure. Bio-inspired algorithms are identified to provide reliable solutions for securing the links of VANETs. In this paper, Komodo Mlipir Algorithm-based Optimal Route Determination Mechanism (KMAORDM) is proposed for achieving best optimal route that guarantees enhanced QoS. This QoS aware routing protocol utilizes the exceptional potentiality of Komodo Mlipir Algorithm attributed in terms of exploration and exploitation for the objective of evaluating and memorizing the impactful factors that helps in exchanging the necessitated messages to its neighbours. It adopts Mlipir as a best mode of modelling the behaviours of vehicular nodes during the process of data routing in VANETs. It uses a cache strategy which discovered reliable traversed routing paths such that the pre-cached route is used for data transmission. This cache strategy paves the way for preventing the route identification and maintenance process with minimized routing overhead. It also identifies the fittest vehicular node from its one hop distance as the successive forwarder in the absence of pre-cached route for addressing link failure during reliable packet transmission. The simulation results of this proposed KMAORDM approach with different vehicular nodes confirmed minimized communication overhead of 19.32 %, reduced network latency of 18.64 %, maximized throughput of 18.54 % better than benchmarked approaches.

Enhancing modular application placement in a hierarchical fog computing: A latency and communication cost-sensitive approach

Over the years, cloud computing has been a key enabler for handling complex applications and services for Internet of Things (IoT) devices situated at the edge of the network. Services and applications that are driven by the IoT environment commonly have stringent latency-sensitive requirements and may experience long network delays due to the long physical distance of cloud-based computational resources from IoT devices. Fog computing gained adoption as a solution in this case because it shortens this distance by spreading the computing power around the edge of the network in tiers. This contributes to network latency reduction and response times improvements of applications that have sensitive temporal requirements, besides improving the overall data traffic management in the network. Nevertheless, when certain requirements of an application are prioritized over others during the resource allocation process, a fog tier closer to IoT devices may experience resource depletion, forcing other latency-sensitive applications to use resources from a distant fog level and causing them to become non-responsive. To address this issue, this work proposes an approach for allocating modular applications in a hierarchical tier-based fog computing architecture. The proposed approach, named Least Impact - X (LI-X), aims to minimize the response time of latency-sensitive applications and reduce data traffic on the network by mitigating the idle time of resources at the lower levels of the hierarchical fog. This is achieved by distributing the application modules among the fog tiers in order to minimize the response time of delay-sensitive applications, while also reducing the overall network traffic. The performance of LI-X was compared to previous studies in a simulated iFogSim environment. Results have demonstrated that LI-X outperforms these studies in most of the proposed scenarios, effectively reducing response time and minimizing communication data costs on the network.

Privacy-Preserving Offloading in Edge Intelligence Systems With Inductive Learning and Local Differential Privacy

We address privacy and latency issues in edge-cloud computing environments where the neural network training is centralized. This paper considers the scenario where the edge devices are the only data sources for the deep learning model to be trained on the central server. Improper access to the massive amounts of data generated by edge devices could lead to privacy concerns. As a result, existing solutions for preserving privacy and reducing network latency in the edge environment rely on auxiliary datasets with no privacy risks or pre-trained models to build the client side feature extractor. However, finding auxiliary datasets or pre-trained models is not always guaranteed and may be challenging. To bridge this gap and eliminate the reliance on auxiliary datasets or pre-trained models of existing solutions, this paper presents DeepGuess, a privacy-preserving and latency-aware deep-learning framework. DeepGuess introduces a new learning mechanism enabled by the AutoEncoder architecture: inductive learning. With inductive learning, sensitive data stays on devices and is not explicitly sent to the central server to engage in back-propagations. To further enhance privacy, we propose a new local differential privacy algorithm that allows edge devices to apply random noise to features extracted from their sensitive data before being transferred to the non-trusted central server. The experimental evaluation of DeepGuess with various datasets and in a real-world scenario shows that our solution achieves comparable or even higher accuracy than existing solutions while reducing data transfer over the network by more than 50%.

Federated learning‐based privacy‐preserving electricity load forecasting scheme in edge computing scenario

SummaryIn the era of big data, massive amounts of data hold great value. However, much data exists as isolated islands, and the maximum value of the data cannot be fully utilized. Federated learning allows each client to train local data and then share the training model parameters securely, which can address the isolated data island problem and exploit data value while ensuring data privacy and security. Accordingly, in order to securely complete the electric power load forecasting using existing data, this paper constructs a federated learning‐based privacy‐preserving scheme to support electricity load forecasting in edge computing scenarios. To address the problems of the data‐isolated islands and data privacy in electric power systems, this paper proposes a decentralized distributed solution based on the federated learning technique. Our scheme achieves electricity load forecasting for power systems through the federated learning‐based framework and uses edge computing architecture to improve real‐time data capability and reduce network latency. For the hierarchical scheduling structure in power systems, we divide the system into a cloud‐side‐device three‐layer architecture, which achieves structural coordination and balance, and each layer collects information according to the scheduling control tasks, promoting scheduling effectiveness. Finally, different privacy protection methods are used on the cloud‐edge and edge‐device sides to significantly enhance data security. Moreover, We have conducted extensive experimental simulations for our proposed scheme. The experimental results show that the relative error of electricity load forecasting is around 1.580%. Meanwhile, our scheme achieves high accuracy and low memory usage. The security analysis proves the feasibility and security of our scheme.

Enhanced Dual-Selection Krill Herd Strategy for Optimizing Network Lifetime and Stability in Wireless Sensor Networks.

Wireless sensor networks (WSNs) enable communication among sensor nodes and require efficient energy management for optimal operation under various conditions. Key challenges include maximizing network lifetime, coverage area, and effective data aggregation and planning. A longer network lifetime contributes to improved data transfer durability, sensor conservation, and scalability. In this paper, an enhanced dual-selection krill herd (KH) optimization clustering scheme for resource-efficient WSNs with minimal overhead is introduced. The proposed approach increases overall energy utilization and reduces inter-node communication, addressing energy conservation challenges in node deployment and clustering for WSNs as optimization problems. A dynamic layering mechanism is employed to prevent repetitive selection of the same cluster head nodes, ensuring effective dual selection. Our algorithm is designed to identify the optimal solution through enhanced exploitation and exploration processes, leveraging a modified krill-based clustering method. Comparative analysis with benchmark approaches demonstrates that the proposed model enhances network lifetime by 23.21%, increases stable energy by 19.84%, and reduces network latency by 22.88%, offering a more efficient and reliable solution for WSN energy management.

The low latency networking method for task-driven MEC-enabled UAV swarm

Unmanned Aerial Vehicles (UAVs) are increasingly being used for special missions as Mobile Edge Computing (MEC) devices, and the utilization of multiple UAVs who are integrated into formations for collaboratively executing tasks that cannot be performed by single aircraft has also been a recent research hit. In this paper, we present a low latency networking method aims to shorten the preparation time so that MEC-enabled swarm can as quick as possible concentrate on the task execution. For reducing the time of swarm initialization, we consider applying the Leader-Follower (L-F) topology model in networking and formation control. Firstly, by combining the nature of wireless communication and correlation analysis, we improve the traditional L-F model to avoid too many nodes accessing the same node and raise the communication efficiency. Secondly, in order to choose the topology that can provide better performance in network, we adopt an improved Particle Swarm Optimization (PSO) algorithm to customize the appropriate factor coefficients during leader selection for swarms with different densities and communication initial conditions. Experiments and evaluations demonstrate that the proposed algorithm has significant effect in reducing network latency.

Opportunistic Digital Twin: an Edge Intelligence enabler for Smart City

Although Digital Twins (DTs) became very popular in industry, nowadays they represent a pre-requisite of many systems across different domains, by taking advantage of the disrupting digital technologies such as Artificial Intelligence (AI), Edge Computing and Internet of Things (IoT). In this paper we present our “opportunistic” interpretation, which advances the traditional DT concept and provides a valid support for enabling next-generation solutions in dynamic, distributed and large scale scenarios as smart cities. Indeed, by collecting simple data from the environment and by opportunistically elaborating them through AI techniques directly at the network edge (also referred to as Edge Intelligence), a digital version of a physical object can be built from the bottom up as well as dynamically manipulated and operated in a data-driven manner, thus enabling prompt responses to external stimuli and effective command actuation. To demonstrate the viability of our Opportunistic Digital Twin (ODT) a real use case focused on a traffic prediction task has been incrementally developed and presented, showing improved inference performance and reduced network latency, bandwidth and power consumption.

Edge computing-based Generative Adversarial Network for photo design style transfer using conditional entropy distance

In recent years, cell phone photography systems have made significant advancements due to their widespread adoption. However, processing the large amount of data generated by these systems on the cloud often leads to high network latency and bandwidth requirements, limiting computational efficiency. To address this challenge, this study introduces a novel design style transfer method for generating high-quality stylized photos in cell phone photography systems using Generative Adversarial Networks (GANs) with an edge computing-based approach. The proposed method aims to enhance the visual appeal of cell phone images by mimicking the artistic style of paintings through abstraction and emphasis on key features. To achieve this objective, a conditional GAN is trained on meticulously labeled data pairs that identify crucial regions in images. This approach offers an efficient solution for computationally intensive tasks by utilizing inference on edge computing nodes. This effectively reduces network latency and bandwidth requirements while improving computational efficiency. Additionally, a penalty is introduced using conditional entropy distance to ensure consistent entropy levels across different photo samples, resulting in higher quality generated photos. Experimental results demonstrate that the proposed CEDGAN model achieves a detection accuracy ranging from 95% to 98%, with consistent results, thus laying a solid foundation for effective photo enhancement. When collecting important portions of materials, CEDGAN maintains a detection accuracy of around 90% to 95% without any significant difference in overall performance. As a result, this method opens up new possibilities for cell phone photography enthusiasts who desire to effortlessly create artistic and stylized photos using edge computing-based GANs. It offers a practical solution for combining technological innovation with creativity in photography.

Research on key technologies for connected vehicle autonomous driving based on 5G big data

Abstract In recent years, with the improvement of computers, automation, and communication technologies, autonomous driving has developed rapidly and has become a research hotspot in transportation. In order to optimize the existing autonomous driving scheme, this paper investigates the key technologies in 5G-based Telematics autonomous driving, mainly including the millimeter wave communication method and automatic obstacle avoidance strategy design, and tests and analyzes them through simulation experiments. In the simulation experiment, the synchronization rate of rear vehicle 1 of lane X 1 is 97.56%, that of rear vehicle X 2 is 98.43%, and that of rear vehicle X 3 is 97.82%, with an average synchronization rate of 97.94%. The synchronization rates of rear vehicle Y 1, Y 2 and Y 3 of lane 2 are 98.27%, 97.84%, and 96.89%, respectively, with an average synchronization rate of 97.67%. For the local observation latency in Telematics, the 5G Big Data-based scheme reduces 10.22% on average compared to the F-DDQL scheme and 9.76% on average compared to the IF-DDQL scheme. Regarding system latency, the 5G Big Data-based scheme reduces 8.67% and 9.21% on average compared to the other two schemes, respectively. The 5G Big Data-based Telematics autopilot can significantly improve the synchronization rate of vehicles and effectively reduce network latency. The research on the key technologies of 5G big data-based connected vehicle autonomous driving in this paper can overcome the shortcomings of traditional autonomous driving technology with unstable networking and help reduce the reliance on high-precision sensors, thus further improving autonomous driving performance.

Workload-Aware WiNoC Design with Intelligent Reconfigurable Wireless Interface

By introducing wireless interfaces in conventional wired routers or hubs, wireless network-on-chip (WiNoC) is proposed to relieve congestion pressure from high volume inter-subnet data transmission. Generally, processing elements on chip receive input data and return feedback through network interface, and data transmission function in Network-on-Chip (NoC) is completed by routers. Hubs equipped with wireless interface are fixed to certain wired routers. While wireless channels may not be fully utilized due to unbalanced workload and constant hub-router connection, e.g., certain nodes processing excess inter-subnet data traffic are far away from hubs. In this paper, we proposed a workload-aware WiNoC design with intelligent reconfigurable wireless interface to improve wireless resources utilization and mitigate congestion. Through multidimensional analysis of traffic flow, a 4-layer neural network is trained offline and applied to analyze workload in each tile, and return three most potential tiles for wireless interface reconfiguration to fully utilize wireless channel and lowing latency. We also implement a historical traffic information-based reconfigurable scheme for comparation. Evaluation results show that in an 8 × 8 hybrid mesh topology, the proposed scheme can achieve 10%–16% reduction in network latency and 5%–11% increment in network throughput compared with fixed-link hub-node connection scheme under several mixed traffic patterns.

A Blockchain Protocol for Real-Time Application Migration on the Edge.

The Internet of Things (IoT) is experiencing widespread adoption across industry sectors ranging from supply chain management to smart cities, buildings, and health monitoring. However, most software architectures for the IoT deployment rely on centralized cloud computing infrastructures to provide storage and computing power, as cloud providers have high economic incentives to organize their infrastructure into clusters. Despite these incentives, there has been a recent shift from centralized to decentralized architectures that harness the potential of edge devices, reduce network latency, and lower infrastructure costs to support IoT applications. This shift has resulted in new edge computing architectures, but many still rely on centralized solutions for managing applications. A truly decentralized approach would offer interesting properties required for IoT use cases. In this paper, we introduce a decentralized architecture tailored for large-scale deployments of peer-to-peer IoT sensor networks and capable of run-time application migration. We propose a leader election consensus protocol for permissioned distributed networks that only requires one series of messages in order to commit to a change. The solution combines a blockchain consensus protocol using Verifiable Delay Functions (VDF) to achieve decentralized randomness, fault tolerance, transparency, and no single point of failure. We validate our solution by testing and analyzing the performance of our reference implementation. Our results show that nodes are able to reach consensus consistently, and the VDF proofs can be used as an entropy pool for decentralized randomness. We show that our system can perform autonomous real-time application migrations. Finally, we conclude that the implementation is scalable by testing it on 100 consensus nodes running 200 applications.

Research on cloud-edge AC Power Monitoring Systems based on MODBUS

With the development of the Internet of Things and automation technology in the industry, the types of equipment in the industrial field are increasing, and the data parameters of the industrial process are gradually becoming complex. It puts forward higher requirements for the system perception layer’s real-time performance, reliability, and maintainability. The original data acquisition architecture is not easy to ensure the reliable transmission and real-time processing of massive industrial data. In order to solve these problems, this paper designs an AC Power Monitoring System. It proposes a cloud edge computing framework based on MODBUS: First, the data is collected and sent to the edge server by MODBUS through the RS485 bus. Second, combined with the advantages of the powerful service capability of cloud computing resources and the low transmission delay of edge computing, the edge side and cloud side will work together. Finally, the functions of data reading, uploading and sending, correction, configuration, and fault alarm are realized. The field operation shows that using a cloud edge computing framework can reduce the processing pressure of cloud computing, reduce network latency and lower requirements for network bandwidth and make the system efficient and safe for collecting and analyzing big data. The cooperation of MODBUS protocol enhances the reliability and practicability of communication. The cloud edge computing system based on MODBUS is easy to design and maintain and is suitable for many monitoring systems, which have high promotion value.

A Technique for Approximate Communication in Network-on-Chips for Image Classification

Approximation is an emerging design methodology for reducing power consumption and latency of on-chip communication in many computing applications. However, existing approximation techniques either achieve modest improvements in these metrics or require retraining after approximation. Since classifying many images introduces intensive on-chip communication, reductions in both network latency and power consumption are highly desired. In this paper, we propose an approximate communication technique (ACT) to improve the efficiency of on-chip communications for image classification applications. The proposed technique exploits the error-tolerance of the image classification process to reduce power consumption and latency of on-chip communications, resulting in better overall performance for image classification. This is achieved by incorporating novel quality control and data approximation mechanisms that reduce the packet size. In particular, the proposed quality control mechanisms identify the error-resilient variables and automatically adjust the error thresholds of the variables based on the image classification accuracy. The proposed data approximation mechanisms significantly reduce packet size when the variables are transmitted. The proposed technique reduces the number of flits in each data packet as well as the on-chip communication while maintaining an excellent image classification accuracy. Cycle-accurate simulation results show that ACT achieves 23% in network latency reduction and 24% in dynamic power reduction as compared to existing approximate communication techniques with less than 0.99% classification accuracy loss.

Two-Way Approach for Improved Real-Time Transmission in Fog-IoT-Based Health Monitoring System for Critical Patients

Health monitoring systems are now required, particularly for essential patients, following the COVID-19 pandemic, which was followed by its variants and other epidemics of a similar nature. Effective procedures and strategies are required, though, to react promptly to the enormous volume of real-time data offered by monitoring equipment. Although fog-based designs for IoT health systems typically result in enhanced services, they also give rise to issues that need to be resolved. In this paper, we propose a two-way strategy to reduce network latency and use while increasing real-time data transmission of device gateways used for sensors by making educated judgments for connection setup with BS and task assignment. For this, a simulation using iFogSim in the Eclipse IDE showed how effective the suggested strategy for massive IoT health monitoring systems is. The algorithm is analyzed for network usage and latency, and the results reveal 20%–25% improvements compared to the existing methods regarding network usage and latency.

A transparent virtual channel power gating method for on-chip network routers

Since static power gradually dominates on-chip network power, power gating has been widely studied as a way to mitigate this trend. Even though many power gating methods reduce static power, they introduce new problems such as higher latency and hardware overhead, network disconnections, and low scalability. A new power gating method is therefore needed that can effectively reduce static power without sacrificing the benefits of the on-chip network. In this paper, we propose an efficient power gating method for virtual channels. Firstly, neighboring routers no longer exchange and store each other’s power states with handshake signals, and each router independently decides when to sleep or wake up based on traffic loads. Secondly, in order to eliminate flit stalls and reduce network latency, we propose a modified credit-based flow control and provide a detailed hardware implementation. Finally, bypasses allow packets to reach processing elements (PEs) or downstream routers without waking up sleeping virtual channels, which reduces breakeven time (BET) violations and cumulative wake-up latency while ensuring network connectivity. Based on the evaluation, under real applications, the proposed power gating method reduces static energy by an average of 82.5% compared to the baseline, while increasing latency overhead by 13.7% and area overhead by 6.48%.