Communication Latency Research Articles

Practical Byzantine Fault Tolerance-Enhanced Blockchain-Enabled Data Sharing System: Latency and Age of Data Package Analysis

Data timeliness, privacy, and security are key enablers for data-sharing systems to support time-sensitive and mission-critical systems and applications. While blockchain-enabled data sharing frameworks can offer reliable security and privacy when properly implemented, the timeliness of data and the related latency are important issues that can limit the adoption of blockchain in large-scale mission-critical applications. This paper thus carried out a performance analysis of the blockchain-enabled data-sharing framework from latency and data age perspectives to investigate the suitability of blockchain technology in data sharing systems. To achieve this, the uniqueness of such systems such as transactions validation latency, transaction generation rate, waiting time, blockchain-appending rate, and overall communication latency were jointly studied. The communication latency was characterized following the spatiotemporal modeling approach. We further adopted the practical Byzantine fault tolerance (PBFT) consensus protocol due to its well discussed suitability in large-scale data sharing applications and captured the validation stages of such a PBFT scheme using the Erlang distribution of order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> . Simulations results show that various influential system parameters must be carefully considered when adopting blockchain technology in time-sensitive data sharing applications. This will guide the adoption of blockchain technology in various data sharing applications and systems.

Reconfigurable Neuromorphic Neural Network Architecture

Neural network (NN), also known as artificial neural network (ANN), provides efficient results in every computation, whether it is pattern recognition, forecasting, customer research, data validation, risk management, data mining, etc. In each computation, NN requires a specific dataset, and according to the datasets, a new NN structure is used to compute the results. Thus, these NN structures mostly depend upon the type of datasets. The current study presents a method that comprises a dedicated architecture known as neuromorphic neural network (NNN), which is made up of optical waveguides that enable high communication and data processing speed at the same time. We have also proposed three algorithms for configuring and building distinct ANN structures from the same architecture. These dedicated structures are not dependent on the datasets and employ the necessary processing element (PE) nodes to function as neurons in the hidden layer. Because specialized resources will be employed to perform operations in the hidden layer, these designs may produce more efficient outcomes than the present logical NN. Furthermore, we assessed our proposed architecture in terms of communication latency, deadlock prevention, energy consumption, and power usage. The simulation results show that deadlocks are avoided to the greatest extent possible, power consumption is reduced by up to 95%, and communicational latency is accomplished in the order of femtoseconds while conversing among PE nodes. The proposed architecture and simulation results promise an alternative for logical NN as well as improved results in terms of speed and efficiency.

Scalable and Low-Latency Federated Learning With Cooperative Mobile Edge Networking

Federated learning (FL) enables collaborative model training without centralizing data. However, the traditional FL framework is cloud-based and suffers from high communication latency. On the other hand, the edge-based FL framework that relies on an edge server co-located with mobile base station for model aggregation has low communication latency but suffers from degraded model accuracy due to the limited coverage of edge server. In light of high-accuracy but high-latency cloud-based FL and low-latency but low-accuracy edge-based FL, this paper proposes a new FL framework based on cooperative mobile edge networking called cooperative federated edge learning (CFEL) to enable both high-accuracy and low-latency distributed intelligence at mobile edge networks. Considering the unique two-tier network architecture of CFEL, a novel federated optimization method dubbed cooperative edge-based federated averaging (CE-FedAvg) is further developed, wherein each edge server both coordinates collaborative model training among the devices within its own coverage and cooperates with other edge servers to learn a shared global model through decentralized consensus. Experimental results based on benchmark datasets show that CFEL can largely reduce the training time to achieve a target model accuracy compared with prior FL frameworks.

Partial observation learning-based task offloading and spectrum allocation in UAV collaborative edge computing

Capable of flexibly supporting diverse applications and providing computation services, the Mobile Edge Computing (MEC)-assisted Unmanned Aerial Vehicle (UAV) network is emerging as an innovational paradigm. In this paradigm, the heterogeneous resources of the network, including computing and communication resources, should be allocated properly to reduce computation and communication latency as well as energy consumption. However, most existing works solely focus on the optimization issues with global information, which is generally difficult to obtain in real-world scenarios. In this paper, fully considering the incomplete information resulting from diverse types of tasks, we study the joint task offloading and spectrum allocation problem in UAV network, where free UAV nodes serve as helpers for cooperative computation. The objective is to jointly optimize offloading mode, collaboration pairing, and channel allocation to minimize the weighted network cost. To achieve the purpose with only partial observation, an extensive-form game is introduced to reformulate the problem, and a regret learning-based scheme is proposed to achieve the equilibrium solution. With retrospective improvement property and information set concept, the designed algorithm is capable of combating incomplete information and obtaining more precise allocation patterns for diverse tasks. Numerical results show that our proposed algorithm outperforms the benchmarks across various settings.

A study of identity authentication using blockchain technology in a 5G multi-type network environment

The 5G technology, known for its large bandwidth, high speed, low latency, and multi-connection capabilities, significantly accelerates digital transformation in enterprises, especially in addressing factory automation challenges. It facilitates efficient machine-to-machine (M2M) and device-to-device (D2D) connectivity, ensuring rapid data transfer and seamless process convergence under 5G standards. Although 5G offers substantial communication and low latency benefits, its limited indoor coverage requires the deployment of decentralized antennas or small base stations. In contrast, Wi-Fi 6 seamlessly complements 5G, providing superior indoor mobile connectivity. This integration is crucial for businesses looking to accelerate digital transformation. To optimize 5G, the deployment of devices such as bypass switches, SDN switches, and MEC in the 5G Local Breakout network enables user access control and fast authentication. Real-world validation confirms the effectiveness of these measures, which are expected to lead to the future of 5G mobile networks.

Latency Minimization for Wireless Federated Learning With Heterogeneous Local Model Updates

In this paper, we study the latency minimization problem for a wireless federated learning (FL) system with heterogeneous computation capability, where different edge devices perform different numbers of local model updates in each communication round. We formulate a total latency minimization problem with probabilistic device selection, taking into account both the communication and computation latency in the whole FL procedure. However, it is highly challenging to optimally solve this problem due to the coupling issues of model convergence and latency minimization problem caused by the heterogeneity of local model updates. Through convergence analysis, we reveal that decoupling the resource allocation variables from the model convergence is essential to reduce the problem to a single-round latency minimization problem. To solve this simplified problem, we propose an alternating optimization scheme to jointly consider communication and computation resource allocation and mitigate the straggler effect. We prove that the resulting sub-problems, i.e., bandwidth and computation capacity allocation, are both convex and can be optimally solved in closed form, respectively. Simulation results show that compared with the baseline scheme that allocates the communication and computation resources equally across edge devices, the proposed scheme can achieve up to 47.04% single-round latency reduction.

Haptic Interactions Subject to Variable Latency.

Model-Mediated Teleoperation (MMT) between a haptic device and a remote or virtual environment uses a local model of the environment to compensate for latency of communication. MMT is often case-specific, and requires underlying latency distributions to be known. We propose a novel approach - which we refer to as the DelayRIM - which uses the time-stepping aspect of a Reduced Interface Model for the environment to render an up-to-date force to the haptic device from the delayed information. RIM is applicable to any physical or virtual system, and the DelayRIM itself makes no underlying assumption about the latency distribution. We show that for realistic variable delays, the DelayRIM improves transparency compared to other methods for a virtual drone bilateral teleoperation scenario.

Compensation of communication latency in remote monitoring systems by video prediction

Compensation of communication latency in remote monitoring systems by video prediction

Enhancing Distributed Neural Network Training Through Node-Based Communications.

The amount of data needed to effectively train modern deep neural architectures has grown significantly, leading to increased computational requirements. These intensive computations are tackled by the combination of last generation computing resources, such as accelerators, or classic processing units. Nevertheless, gradient communication remains as the major bottleneck, hindering the efficiency notwithstanding the improvements in runtimes obtained through data parallelism strategies. Data parallelism involves all processes in a global exchange of potentially high amount of data, which may impede the achievement of the desired speedup and the elimination of noticeable delays or bottlenecks. As a result, communication latency issues pose a significant challenge that profoundly impacts the performance on distributed platforms. This research presents node-based optimization steps to significantly reduce the gradient exchange between model replicas whilst ensuring model convergence. The proposal serves as a versatile communication scheme, suitable for integration into a wide range of general-purpose deep neural network (DNN) algorithms. The optimization takes into consideration the specific location of each replica within the platform. To demonstrate the effectiveness, different neural network approaches and datasets with disjoint properties are used. In addition, multiple types of applications are considered to demonstrate the robustness and versatility of our proposal. The experimental results show a global training time reduction whilst slightly improving accuracy. Code: https://github.com/mhaut/eDNNcomm.

Handover mechanisms in VMC systems: Evaluating the reliability of V2X as an alternative to fiber networks in handover areas

Despite advances in communication ways in autonomous driving, including Vehicle-to-Vehicle Communication (V2V), Vehicle-to-Everything Communication (V2X) and Vehicular Micro-clouds (VMC). In this paper, the potential of V2X as a substitution for the artificial connections (mainly optical-fiber cables) between base stations and edge servers are discussed. The quality and latency of communication in Handshake--referring to the scenario of cars moving from one stations operation scope to another ones, is mainly concerned, within the frame of VMC. Additionally, the communication performance, referring to latency performance and data rate performance, for both pure V2X and a combination of LTE and V2X are simulated and analyzed using a Python simulator. Finally, the test results showed that the performance of pure V2X Communication is always superior compared with any combination in every categories as the density of cars gradually increase, which confirms that replace physical inter-station-cables as V2X communication is serious possibility.

Novel reaching law based predictive sliding mode control for lateral motion control of in‐wheel motor drive electric vehicle with delay estimation

AbstractThe lateral motion control of an in‐wheel motor drive electric vehicle (IWMD‐EV) necessitates an accurate measurement of the vehicle states. However, these measured states are always affected by delays due to sensor measurements, communication latencies, and computation time, which results in the degradation of the controller performance. Motivated by this issue, a novel reaching law based predictive sliding mode control (NRL‐PSMC) is proposed to maintain the lateral motion control of the IWMD‐EV subjected to unknown time delay. Initially, a PSMC framework is built, in which a predictor integrating with the sliding mode control is designed to eliminate the effect of time delay and generate the virtual control signals. Further, to alleviate the chattering phenomenon, a novel‐reaching law is developed, enabling the vehicle to track the desired states effectively. Subsequently, a dynamic control allocation technique is presented to optimally allocate the virtual control input to the actual control input. The accurate estimation of the aforementioned unknown delay is realized through a delay estimator. Finally, simulation and hardware‐in‐the‐loop experiments are performed for three specific driving manoeuvres, and the results demonstrate the effectiveness of the proposed controller design.

Simultaneous upstream and inter optical network unit communication for long reach PON using single transmitter and self-phase modulation

The conventional passive optical network (PON) architecture requires optical line terminal (OLT) arbitration for communication between the optical network units (ONUs). This approach increases OLT processing load, it may also increase security hazards and causes higher communication latency. Moreover this also leads to wastage of bandwidth that can be saved if ONUs are able to communicate directly. The direct communication of ONUs without involving OLT is known as inter ONU communication (IOC). Earlier reported IOC proposals either do not support simultaneous upstream communication or require dedicated or tunable transmitters and receivers for simultaneous communication. This increases the overall cost of the system and also degrades the system performance because of additional tuning delays of the tunable transmitter and receiver. Therefore, this study proposes an IOC enabled physical layer design for PON that supports simultaneous upstream and IOC communication using single transmitter and self-phase modulation mechanism at each ONU. A 1310 nm wavelength, return to zero signal is generated by the transmitter which passes through the self-phase modulator resulting in an additional side lobe signal generation that carries the IOC data without impacting the upstream transmission. These two signals are coupled at the remote node where IOC signal gets reflected back, while the upstream signal gets transmitted through it towards the OLT at the same time. A long reach PON scenario is assumed by keeping the OLT at a distance of 80 km or more. The simulation results show that the proposed design supports a maximum reach of 118 km for upstream link between remote node and OLT with bit error rate of 1.52 × 10−12 for 25 Gbps and 90 km with bit error rate of 7.1 × 10−12 for 30 Gbps data rate. The IOC communication also works well up to a split ratio of 32 with a maximum distance of approximately 5 km between the ONUs and remote node with an end to end bit error rate of 8.60 × 10−12. for 25 Gbps and 7.1 × 10−12 for 30 Gbps. Overall, the proposed design shows acceptable performance, in compliance to ITU G 987.1 recommendations for a maximum end to end reach of 122 km.

A novel framework for capacitated SDN controller placement: Balancing latency and reliability with PSO algorithm

Software Defined Networking (SDN) is a new network architecture theoretical framework, which based on the partition of the control plane and the data plane. Some of the important features of SDN are centralized network management, open code, scalability, effective utilization of network resources, effective traffic management, security etc. All these administrative decisions taken by the controller which lies at the top of the data plane. Efficient placing of controllers is essential to enhance the network’s reliability, which ultimately improves the performance of the network. Much of the work done in the SDN has been aimed on controller placement problems (CPP) by considering latency and reliability of the network separately whereas few of them acknowledged both latency and reliability simultaneously. We considered both communication latency and reliable communication to solve CPP. To achieve this, we aim to minimize the total average latency of the network with a reliable network model that can work perfectly up to the failure of n-1 controllers out of n deployed. We first formulate an efficient algorithm “Capacitated Controllers Arrangement (CCA)” to define the problem and used nature-based metaheuristic technique, Particle Swarm optimization (PSO) to achieve the solution. By intelligently allocating controllers, this dynamic system minimizes network latency and optimizes switch assignments. Three controllers deliver excellent reliability and latency performance even despite controller breakdowns, according to experiments conducted on the Internet2 OS3E architecture. This novel method not only increases SDN efficiency but also establishes a standard for further improvements in controller placement tactics. The outcome shows that the proposed approach achieves its goal efficiently and in future other multi-objective optimization techniques will be explored to solve reliability aware CPP.

Test Bench Solution for ASIC Inspection

"Application Specific Integrated Circuits (ASICs) have been, for a long time, the best, and sometimes only, option when there was a need for high performance electronics while operating with tight constraints regarding communication bandwidth, speed and/or latency, low power consumption and other, more specialized requirements, such as radiation hardness. To make sure that all ASICs that are going to be used in electronics parts installed on experimental physics detectors are working properly, they need to be thoroughly tested and their testing results need to be stored and used for manufacturing traceability purposes. A test bench solution which allows identifying and eliminating sources of errors during the integration of ASICs in detector electronics based on machine vision and test automation procedures has been designed for this purpose and is being presented."

Communication and computational resource optimization for Industry 5.0 smart devices empowered by MEC

Smart devices in Industry 5.0, such as sensors and robots, are often limited by low battery life and finite computational resources, hindering their ability to perform complex tasks. By offloading computation-intensive tasks to Mobile Edge Cloud Computing (MEC) servers at the network’s edge, businesses can achieve real-time data processing and analysis, reducing communication latency, quicker response times, and improved system reliability. This work presents an integrated framework for MEC and Industry 5.0, aimed at enhancing the performance, efficiency, and flexibility of industrial processes. In particular, we propose a joint optimization problem that maximizes computational energy efficiency by optimally allocating resources, such as processing power and computational resources, as well as device association, in the most efficient manner possible. The problem is formulated as nonconvex/nonlinear, which is intractable and poses high complexity. To solve this challenging problem, we first transform and decouple the original optimization problem into a series of subproblems using the block coordinate descent method. Then, we iteratively obtain an efficient solution using convex optimization methods. In addition, our work sheds light on the fundamental trade-off between local computation and partial offloading schemes. The results show that for small data size requirements, the performance is comparable among different schemes. However, as data size increases, our proposed hybrid scheme, which includes a partial offloading scheme, outperforms others, highlighting the effectiveness of the proposed joint optimization scheme.

Q‐learning‐based task offloading strategy for satellite edge computing

SummaryIn this paper, we study the task offloading optimization problem in satellite edge computing environments to reduce the whole communication latency and energy consumption so as to enhance the offloading success rate. A three‐tier machine learning framework consisting of collaborative edge devices, edge data centers, and cloud data centers has been proposed to ensure an efficient task execution. To accomplish this goal, we also propose a Q‐learning‐based reinforcement learning offloading strategy in which both the time‐sensitive constraints and data requirements of the computation‐intensive tasks are taken into account. It enables various types of tasks to select the most suitable satellite nodes for the computing deployment. Simulation results show that our algorithm outperforms other baseline algorithms in terms of latency, energy consumption, and successful execution efficiency.

Design and Simulation of Efficient Dynamic Data Forwarding Protocol towards Route Optimization in VANET Infrastructure

The VANET, which consists of the Internet and the Internet of Things (IoT), has recently been implemented with the Internet of Vehicles (IoV). Through the use of a car network and an AdHoc approach, it provides the user with environmental traffic information. Combining the networks of cars, known as VANETs, allows for infrastructure-free data transfer, particularly for entertainment and emergency vehicle notification. The data or packet delivery ratio, data communication latency, data transmission faults, and details of vehicle collisions are among the traffic information provided by the internet of vehicles. In this work, an unmanned aerial vehicle (UAV) is used to solve the problem of route optimisation in VANET topologies for effective data transfer. Many routing protocols are used to improve the effectiveness of the packet or data routing process. In order to alleviate the routing issue and generate an effective way for efficient data transfer with strong network stability, the Optimised Link State Routing (OLSR) Protocol is used in this study with energy-saving techniques on many parameters. To evaluate the efficacy of the suggested routing technique on various network sizes, extensive simulations are carried out on the NS2 simulator with various network parameters. When compared to traditional routing protocols along UAV-based protocols for data transmission, the performance results of the suggested data routing solution employing OLSR protocol achieve higher efficient pathways. Ultimately, the suggested routing system produces superior outcomes in terms of message throughput, message delivery ratio, latency and network overhead on comparing with existing routing protocols.

Intelligent and Fair IoV Charging Service Based on Blockchain With Cross-Area Consensus

The emergence of electric vehicles promotes the development of Internet of Vehicle (IoV). However, there are a series of security problems to be solved in the IoV. Firstly, how to allow vehicle users to find the nearest non-queuing charging pile without detours is a challenge. Secondly, applications in the IoV are delay-sensitive, while high communication delays caused by security mechanisms would bring serious consequences to vehicles. Thirdly, the verifiability and fairness between charging and payment are difficult to be guaranteed. Aiming at the efficiency and security problems of the charging service in the IoV, this paper proposes a blockchain-based intelligent and fair IoV charging service system. According to the multi-factor constraints between vehicles and charging piles, a multi-factor IoV branch and bound algorithm is proposed to intelligently recommend charging piles for vehicles and maximize the overall energy saving. We propose the cross-area consensus protocol to achieve low latency in vehicle communication. In addition, we ensure the fairness between charging and payment through a payment channel protocol based on verifiable encrypted signatures. Finally, we implement the proposed scheme, and we put the project prototype on an open source platform is available at: https://github.com/chenruonan/blockchain-based-intelligent-and-fair-IoV-charging-service-protocol. The experimental results demonstrate the low latency and energy-saving advantages of our proposal. When the payment is executed 250 times, the delay of our proposal is only 1.54% of the normal on-chain payment time.

Towards fostering the role of 5G networks in the field of digital health

&lt;div align="center"&gt;&lt;span lang="EN-US"&gt;A typical healthcare system needs further participation with patient monitoring, vital signs sensors and other medical devices. Healthcare moved from a traditional central hospital to scattered patients. Healthcare systems receive help from emerging technology innovations such as fifth generation (5G) communication infrastructure: internet of things (IoT), machine learning (ML), and artificial intelligence (AI). Healthcare providers benefit from IoT capabilities to comfort patients by using smart appliances that improve the healthcare level they receive. These IoT smart healthcare gadgets produce massive data volume. It is crucial to use very high-speed communication networks such as 5G wireless technology with the increased communication bandwidth, data transmission efficiency and reduced communication delay and latency, thus leading to strengthen the precise requirements of healthcare big data utilities. The adaptation of 5G in smart healthcare networks allows increasing number of IoT devices that supplies an augmentation in network performance. This paper reviewed distinctive aspects of internet of medical things (IoMT) and 5G architectures with their future and present sides, which can lead to improve healthcare of patients in the near future.&lt;/span&gt;&lt;/div&gt;

Eliminating the Dependence of GPS or Communication Latency Estimation in Traveling Wave Based Double-Terminal Fault Location

Double-terminal traveling wave-based fault location is one of the most reliable and accurate technologies for power transmission line fault location. However, existing methods require a global positioning system (GPS) or accurate communication latency estimation to synchronize data from the two terminals. In this study, we propose an innovative double-terminal traveling wave-based fault location method that is independent of either GPS or communication latency estimation. The basic principle of the proposed method is that the arrival time difference between initial surges could be roughly calculated through the angle of pre-fault phasors by artificially assigning surge arrival points as the time references. Therefore, an approximate fault distance is obtained. The error could be reduced by detecting the fault-point-reflected surge. Extensive simulation experiments were performed using PSCAD/EMTDC software to assess the performance of the proposed algorithm. The results showed that the proposed method is applicable to different fault circumstances and independent of GPS synchronization and communication latency estimation while maintaining accuracy close to those of state-of-the-art algorithms. Finally, hardware experiments were performed to assess the validity and practicability of the proposed method.