Network Delay Research Articles

Network Delay Forecast and Master-Slave Consistency Enhancement for Remote Surgical Robots.

The inevitable network delay can directly impact the process of remote surgeries and affect the master-slave motion consistency, and sudden changes in delay can compromise surgical safety. Firstly, real-time calibration of unidirectional network delays is performed. Subsequently, the network delay is forecasted with a real-time training parallel recurrent neural network for safety warnings, and the real-time forecast of slave manipulator position is performed to enhanced the master-slave motion consistency. Finally, the forecast accuracy across multiple scales is assessed to provide feedback. The programme can operate on standard computers at distances of at least 630km. Our forecast method meets the real-time requirement, demonstrates strong generalisation capabilities and reduces the impact of network delay on master-slave motion consistency to approximately 20%-80% of its original level. The proposed forecast method enables real-time delay forecast for remote surgeries, reducing the impact of delay on master-slave motion consistency.

Understanding bus network delay propagation: Integration of causal inference and complex network theory

Understanding bus network delay propagation: Integration of causal inference and complex network theory

Optimized Cluster-Based Routing Protocol for Improved Bandwidth and Reduced Delay in Vehicular Ad-Hoc Networks

Vehicular Ad Hoc Network (VANET) is a cutting-edge technology that enables communication between vehicles and the surrounding road infrastructure, paving the way for intelligent transportation systems. Ensuring stable connections in VANETs requires a robust and reliable routing protocol, as these networks lack central coordination, exhibit high node mobility, and possess highly dynamic topologies, making routing a significant challenge. Many existing mobility-based routing protocols fall short in addressing Quality of Service (QoS) requirements, as their performance is heavily influenced by vehicle speed and driving conditions. On the other hand, QoS-based approaches often fail to account for the challenges posed by high-speed mobility, leading to frequent connection failures due to the increasing mobility of nodes within a given area. To overcome these challenges, this paper introduces a novel Cluster-Based Congestion Control Routing (CBCCR) protocol designed to enhance overall network efficiency by improving route throughput, optimizing bandwidth utilization, and minimizing end-to-end latency. The CBCCR protocol addresses the limitations of repetitive route detection and frequent Cluster Head (CH) reelection processes, thereby enhancing route stability and network performance.The proposed approach involves several key steps. First, the network is divided into stationary clusters. Next, a new distributed CH selection method is introduced, leveraging specific parameters to optimize the selection process. To further improve efficiency, an Enhanced BAT Algorithm (EBA) is employed for CH selection, and a novel routing method is developed to identify the most suitable CH based on the destination's position and the locations of neighboring CHs.Simulation results highlight the effectiveness of the CBCCR protocol, showcasing significant improvements in bandwidth utilization, increased throughput, and reduced transmission delays. These findings demonstrate that the CBCCR protocol offers a robust and efficient solution to the routing challenges in VANETs, making it a promising approach for advancing intelligent transportation systems.

RWA-BFT: Reputation-Weighted Asynchronous BFT for Large-Scale IoT.

This paper introduces RWA-BFT, a reputation-weighted asynchronous Byzantine Fault Tolerance (BFT) consensus algorithm designed to address the scalability and performance challenges of blockchain systems in large-scale IoT scenarios. Traditional centralized IoT architectures often face issues such as single points of failure and insufficient reliability, while blockchain, with its decentralized and tamper-resistant properties, offers a promising solution. However, existing blockchain consensus mechanisms struggle to meet the high throughput, low latency, and scalability demands of IoT applications. To address these limitations, RWA-BFT adopts a two-layer blockchain architecture; the first layer leverages reputation-based filtering to reduce computational complexity by excluding low-reputation nodes, while the second layer employs an asynchronous consensus mechanism to ensure efficient and secure communication among high-reputation nodes, even under network delays. This dual-layer design significantly improves performance, achieving higher throughput, lower latency, and enhanced scalability, while maintaining strong fault tolerance even in the presence of a substantial proportion of malicious nodes. Experimental results demonstrate that RWA-BFT outperforms HB-BFT and PBFT algorithms, making it a scalable and secure blockchain solution for decentralized IoT applications.

ALTO-assisted Peer Selection in Bitcoin P2P Network

Blockchain-based applications rely on a decentralized structure wherein the transactions are recorded on a public ledger that is maintained by every node in the peer-to-peer (P2P) network. The transactions and blocks are propagated using a multi-hop broadcast and verified by every node in the network. Application Layer Traffic Optimization (ALTO), on the other hand, is a network protocol developed and maintained by the Internet Engineering Task Force (IETF) to provide network related information to the P2P applications to increase their performance. In this study, a novel peer selection method based on the network information provided by ALTO protocol is proposed to decrease the block propagation delay of the Bitcoin P2P network. The simulations show that the proposed peer selection method can effectively decrease the block propagation time and fork rate compared to Bitcoin’s random peer selection and region-based peer selection methods.

Cluster-based Data Sharing for Web 3.0 in Intelligent Transportation Systems

Intelligent transportation system (ITS) applications are dependent on secure and robust wireless data sharing among vehicles and roadside units (RSUs). Multiple types of data are shared among the ITS devices which include safety information, road services, web based information retrieval and task computation. Web 3.0 offers a decentralized, distributed and secure data sharing mechanism for ITSs. Allocation of wireless channel resources are critical to enable an efficient ITS system. In this paper, a novel data sharing technique for Web 3.0 based ITS is presented that relies on an intelligent clustering algorithm. In the first step, the proposed technique uses a K-means algorithm to find groups of vehicles with similar speeds. In the second step, each cluster is assigned an RSU which has the highest average data rate with all vehicles in the cluster. This is achieved by using a stable matching technique so that there is no contention and each cluster is assigned a separate RSU. The algorithm periodically updates the clusters and RSU allocation for web data sharing between vehicles and RSUs. Simulation results show that the proposed clustering-based data sharing technique improves sum-rate by 20% and reduces network delay by 23%.

Memory-Non-Linearity Trade-Off in Distance-Based Delay Networks.

The performance of echo state networks (ESNs) in temporal pattern learning tasks depends both on their memory capacity (MC) and their non-linear processing. It has been shown that linear memory capacity is maximized when ESN neurons have linear activation, and that a trade-off between non-linearity and linear memory capacity is required for temporal pattern learning tasks. The more recent distance-based delay networks (DDNs) have shown improved memory capacity over ESNs in several benchmark temporal pattern learning tasks. However, it has not thus far been studied whether this increased memory capacity comes at the cost of reduced non-linear processing. In this paper, we advance the hypothesis that DDNs in fact achieve a better trade-off between linear MC and non-linearity than ESNs, by showing that DDNs can have strong non-linearity with large memory spans. We tested this hypothesis using the NARMA-30 task and the bitwise delayed XOR task, two commonly used reservoir benchmark tasks that require a high degree of both non-linearity and memory.

Synchronization of Neural Networks With Unbounded and Non‐Differentiable Delays via Decentralized Adaptive Control

ABSTRACTSynchronization of delayed neural networks has been investigated in recent years via decentralized adaptive control methods. However, the effectiveness of the reported results heavily depends on the assumptions that network delays are bounded or differentiable. For more general cases involving unbounded and non‐differentiable delays, it remains unclear whether the existing analytical methods and controller designs are still effective. To this end, in this article, a novel method is established to analyze the asymptotical convergence of the controlled error system with adaptive parameters by employing the differential inequality techniques for unbounded delay and Barbalat's lemma, which can effectively overcome the limitations of traditional methods in handling general delay scenarios. The theoretical results demonstrate that traditional decentralized adaptive controller for network synchronization remains effective even if the boundedness and differentiability of delay are removed. A numerical simulation further validates the effectiveness of the proposed theories.

Blockchain Integration in UAV Networks: Performance Metrics and Analysis.

Blockchain technology has revolutionized the management of Unmanned Aerial Vehicle (UAV) networks by enhancing security, enabling decentralized control, and improving operational efficiency. This study assesses the efficiency of private blockchain architectures in UAV networks, specifically examining important performance metrics such as throughput, latency, scalability, and packet size. Furthermore, we evaluate the effectiveness of UAV networks when integrating private blockchain technologies, focusing particularly on key performance indicators such as area, altitude, and data rate. The scope of our work includes extensive simulations that employ a private blockchain to assess its impact on UAV operations. In the blockchain network, throughput decreased as the number of UAVs and transactions increased, while delay remained constant up to a certain point. In contrast, the UAV network saw improved throughput but increased delay with more UAVs and transactions. Changes in area and altitude had little impact on the blockchain network but increased delays in the UAV network. Higher data rates enhanced the UAV network by reducing latency and improving throughput, though this effect was less pronounced in the blockchain network. The aforementioned results highlight the potential and limitations of private blockchains in enhancing the durability and efficiency of UAV networks.

Blockchain Double Spending with Low Mining Power and Network Delays

Traditional blockchain systems offer a secure way of tracking the ownership of digital assets as long as the attacker does not control a large portion of the overall computational or mining power. They typically require participants to generate a proof-of-work before proposing a block at a given index of the chain. To choose one block among the candidate blocks at the same index, Nakamoto’s consensus, Ghost , and the original Ethereum’s consensus select, respectively, the longest branch, the heaviest subtree and the branch with the most difficult crypto-puzzles. This allows an attacker who can generate proofs-of-work faster than others to double spend by overwriting any given branch. In this article, we present a double spending attack, called the Balance attack, that simply needs to delay some messages. This result sheds new lights on an important, often implicit, assumption of the blockchain, synchrony , under which the transmission delay of any message should be within a known upper bound. We show that the attack succeeds with high probability on the protocols of the two largest blockchain systems in market capitalization, Bitcoin and Ethereum. To quantify the impact of our attack, we replicated the blockchain network run by 50 financial institutions and achieved double spending in less than 20 minutes. Finally, we demonstrate the success of the attack empirically by modifying the geth software and hijacking BGP in a controlled distributed system whose distribution of mining power is set to the distribution observed on the Ethereum main blockchain.

Real-time teleoperation of magnetic force-driven microrobots with a motion model and stable haptic force feedback for micromanipulation

Microrobots powered by an external magnetic field could be used for sophisticated medical applications such as cell treatment, micromanipulation, and noninvasive surgery inside the body. Untethered microrobot applications can benefit from haptic technology and telecommunication, enabling telemedical micro-manipulation. Users can manipulate the microrobots with haptic feedback by interacting with the robot operating system remotely in such applications. Artificially created haptic forces based on wirelessly transmitted data and model-based guidance can aid human operators with haptic sensations while manipulating microrobots. The system presented here includes a haptic device and a magnetic tweezer system linked together using a network-based teleoperation method with motion models in fluids. The magnetic microrobots can be controlled remotely, and the haptic interactions with the remote environment can be felt in real time. A time-domain passivity controller is applied to overcome network delay and ensure stability of communication. This study develops and tests a motion model for microrobots and evaluates two image-based 3D tracking algorithms to improve tracking accuracy in various Newtonian fluids. Additionally, it demonstrates that microrobots can group together to transport multiple larger objects, move through microfluidic channels for detailed tasks, and use a novel method for disassembly, greatly expanding their range of use in microscale operations. Remote medical treatment in multiple locations, remote delivery of medication without the need for physical penetration of the skin, and remotely controlled cell manipulations are some of the possible uses of the proposed technology.

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.

Networked control systems induced delay modeling and stability analysis using Markov-regime-switching GARCH model

This article focuses on the stability analysis of networked control systems (NCSs). It is well-known that network-induced delays pose many control challenges due to their non-uniform and multifractal nature. With the growing integration of communication networks in control systems, the impact of time-varying and random delays on system performance becomes critical. Hence, it is essential to study their impact on the system’s stability. There are several network-induced delay modeling methods, resulting in constant, random, and probabilistic random delays, which provide the most realistic representation according to recent studies. This article introduces a new network-induced delay modeling method based on the Markov-regime-switching generalized autoregressive conditional heteroskedasticity (MRS-GARCH) process, which captures the intricate temporal dependence of random network delays. The resulting model offers a more accurate depiction of delay volatility behavior, accommodating sudden shifts between volatility regimes. Simulation results demonstrate that MRS GARCH-type modeling enables a more realistic representation of delay dynamics. We also conduct a stability analysis of NCSs modeled with the proposed method using linear matrix inequalities LMIs criteria derived from the Lyapunov-Krasovskii theorem. The simulation results show less conservatism than conventional models and provide a larger maximum allowable upper delay bound MAUDB.

Formal Model Engineering of Synchronous CPS Designs in AADL

Many cyber-physical systems (CPSs)-such as aircrafts, cars, robots, and manufacturing plants-have synchronous designs and are realized on platforms with bounded network delays and clock skews. This paper summarizes how we have: (i) defined modeling languages for synchronous CPS designs in the embedded systems modeling standard AADL, and (ii) integrated Maude-based formal model checking ("push-button") analysis of such AADL synchronous designs into the OSATE tool environment for AADL. This enables a "formal model engineering" approach which combines the convenience of domain-specific modeling with automatic "under-thehood" formal analysis. Furthermore, by the PALS synchronizers, the correctness of such synchronous designs implies the correctness of the much more complex and harder-to-analyze asynchronous implementations, greatly simplifying the task of designing and analyzing "virtually synchronous" CPSs.

МОДЕЛЮВАННЯ ПРОЦЕСУ ФОРМУВАННЯ БЛОКІВ У БЛОКЧЕЙНІ ТА ЙОГО ВПЛИВ НА МАСШТАБОВАНІСТЬ

The article investigates the process of block formation in blockchain networks and the impact of node network architecture and consensus algorithms on their scalability and performance. Analysis of blockchain system scalability is important due to problems that arise when network load increases, particularly the increase in the number of block forks and transaction confirmation times. The research focuses on studying the impact of network delays and the choice of consensus algorithm on the performance and scalability of blockchain networks. The main attention is devoted to mathematical models that describe block formation, as well as the analysis of factors affecting transaction processing speed and throughput. The primary consensus algorithms, such as Proof of Work (PoW) and Proof of Stake (PoS), are considered, and their impact on scalability in implementations based on the Ethereum Virtual Machine (EVM) and Bitcoin is compared. Experimental studies using Geth and Amazon cloud services revealed that the application of the Proof of Stake (PoS) consensus algorithm increases network performance by reducing the complexity of the block formation process in blockchain networks by 99% and accelerates consensus achievement by 70% compared to Proof of Work (PoW). It was also established that increasing the number of nodes from 5 to 50 reduces the network's throughput by almost 10%, and the average confirmation time doubles. The obtained results are aimed at solving the scalability issue by reducing transaction confirmation times for the implementation of decentralized technologies in the Internet of Things (IoT) sphere, where processing speed and storage of large volumes of data are critically important. Keywords: blockchain, block formation, consensus algorithms, decentralized technologies, Ethereum Virtual Machine (EVM), Internet of Things (IoT), mathematical modeling, network delays, scalability.

Longitudinal protection scheme of distributed power distribution network based on M_T comprehensive similarity

Abstract To solve the problems of the rapid deterioration of the three-stage overcurrent protection performance after the extensive access of inverter distributed generators, the deterioration of the traditional longitudinal protection performance, and the problems of 0/0 calculation results and insufficient waveform difference measurement of the existing similarity protection algorithms, a longitudinal protection scheme based on M_T comprehensive similarity was proposed. Firstly, the transmission communication delay of the 5G core network is analyzed, and the mini-slot technology is proposed to effectively reduce the communication delay. Secondly, the M-K mutation initiation algorithm adapted to the characteristics of inverter DG faults was used to replace the traditional phase current mutation detection algorithm. The fault self-synchronization with higher precision was realized. Finally, M_T comprehensive similarity is constructed to describe the difference between the short-circuit current waveform of the system side and the inverter-distributed generator side. The changes in the similarity coefficient of faults in and out of the area are analyzed. A new principle of longitudinal protection based on the comprehensive similarity of M_T is formed. The simulation results show that the proposed new protection principle can reliably identify faults in and out of the area, will not have the anomaly of 0/0 of Pearson coefficient and cosine similarity calculation results, and has better anti-transition resistance ability than the Tanimoto similarity algorithm, which is fully suitable for distribution networks with distributed generators.

Reducing tail latency for multi-bottleneck in datacenter networks: A compound approach

The effectiveness of network congestion control fundamentally depends on the accuracy and granularity of congestion feedback. In datacenter networks, precise feedback is essential for achieving high performance. Most existing approaches use either Explicit Congestion Notification (ECN) or network delay (e.g., RTT) independently as congestion indicators. However, in multi-bottleneck networks, the limitations of these signals become more pronounced: ECN struggles with large cumulative end-to-end latency, while RTT lacks the precision needed to control queuing delays at individual hops. To address these challenges, we propose Cocktail, a simple yet effective transport protocol for datacenter networks that combines both ECN and RTT congestion signals to more effectively handle multi-bottleneck scenarios. By leveraging the ECN signal, Cocktail bounds per-hop queue lengths, enhancing its ability to control single-hop latency and prevent packet loss. Additionally, by estimating RTT, Cocktail effectively manages end-to-end delay, resulting in lower Flow Completion Time (FCT). Extensive experimental evaluations in Mininet demonstrate that Cocktail significantly reduces the average and 99th-percentile completion times for small flows by up to 20% and 29%, respectively, compared to current practices under production workloads.

GENERALIZED SYNCHRONIZATION OF A CLASS OF HETEROGENEOUS DRIVE RESPONSE NETWORKS WITH TIME DELAY BASED ON VARIABLE STRUCTURE CONTROL

In this study, the problem of synchronization of heterogeneous-driven response networks is introduced by using variable structure control strategy. First, the mathematical model of the heterogeneous-driven response network is described, and the development history of variable structure control is outlined. Second, considering the coupling time delay of the heterogeneous-driven response network, two types of variable structure controls are designed based on sliding mode control and soft variable structure control theories. Finally, the effectiveness of the two variable structure controls is verified through numerical simulation experiments, and a discussion on their advantages and disadvantages is provided.

Automatically Injecting Robustness Statements into Distributed Applications

When developing a distributed application, several issues need to be handled, and software components should include some mechanisms to make their execution resilient when network faults, delays, or tampering occur. For example, synchronous calls represent a too-tight connection between a client requesting a service and the service itself, whereby potential network delays or temporary server overloads would keep the client side hanging, exposing it to a domino effect. The proposed approach assists developers in dealing with such issues by providing an automatic tool that enhances a distributed application using simple blocking calls and makes it robust in the face of adverse events. The proposed devised solution consists in automatically identifying the parts of the application that connect to remote services using simple synchronous calls and substituting them with a generated customized snippet of code that handles potential network delays or faults. To accurately perform the proposed transformation, the devised tool finds application code statements that are data-dependent on the results of the original synchronous calls. Then, for the dependent statements, a solution involving guarding code, proper synchronization, and timeouts is injected. We experimented with the analysis and transformation of several applications and report a meaningful example, together with the analysis of the results achieved.

The Effect of Network Delay and Contagion on Mobile Banking Users: A Dynamical Analysis

This paper analyses how network contagion affects the acceptance or rejection of mobile banking, emphasizing the risk of contagion that hinders the growth of users among bank clients. As a research method, a mathematical model based on the Susceptible, Infected, and Recovered framework is used within a nonlinear dynamic system with a distributed time delay and the optimal control strategy. The approach aims to find the behaviour dynamics of mobile banking users, non-users, and undecided ones. Our model adds value to the existing literature by employing a qualitative dynamic analysis, as opposed to other studies that study contagion empirically. The steady states and their conditions for stability are found, and the critical value for time delay causing oscillatory is determined. The optimal control strategies are identified to enlarge the number of mobile banking users and decrease the number of non-users. Numerical simulations support the theoretical findings. The research has practical implications given that the model predicts mobile banking user behaviour in assisting policy decision-making and addresses the possible policy measures that aim to increase the number of mobile banking users. The conclusions suggest that financial inclusion can contribute to the expansion of mobile banking users.