Latency Analysis Research Articles

A Novel Approach of Latency and Energy Efficiency Analysis of IIoT With SQL and NoSQL Databases Communication

A Novel Approach of Latency and Energy Efficiency Analysis of IIoT With SQL and NoSQL Databases Communication

Latency Analysis for Real-Time Sensor Sharing Using 4G/5G C-V2X Uu Interfaces

With the development of communications, various applications of communication technologies, such as remote driving, delivery drones, and telesurgery, are emerging. In particular, in many cases, these applications need real-time video transmission services, and they should support low latency for operation reliability and quick response in emergencies. Sensor sharing is required to support advanced communication services, but the latency analysis of device-to-remote users or remote servers with high data traffic is insufficient. Most related works have device-to-device communication or low data traffic messages for basic device status sharing. However, the latency analysis of sensor sharing between a device and a remote server or remote user is essential to support advanced communication services such as autonomous driving utilizing data offloading and device operation by remote users via the base station and server. Therefore, in this paper, we analyze the end-to-end latency and latency elements for video sharing, which is the most representative sensor in 4G long-term evolution (LTE) and 5G new radio (NR) Uu interfaces. In addition, we derive the supportable video resolution according to the raw video and encoded video transmission in each communication system. For each video resolution level, we analyze which latency elements have a significant effect on the end-to-end latency. Depending on each communication system, we investigate the number of users for the real-time sensor-sharing system that can be supported at the same time. Simulation results show that the LTE Uu interface supports up to full high definition (FHD) video resolution, and the 5G Uu interface supports up to ultrahigh definition (UHD). Additionally, the results show that only a single user can be supported with the FHD resolution level in the LTE Uu interface, whereas up to 19 users can be supported in the 5G Uu interface.

Edge-Cloud Continuum Solutions for Urban Mobility Prediction and Planning

In recent years, there has been an increase in the use of edge-cloud continuum solutions to efficiently collect and analyze data generated by IoT devices. In this paper, we investigate to what extent these solutions can manage tasks related to urban mobility, by combining real-time and low latency analysis offered by the edge with large computing and storage resources provided by the cloud. Our proposal is organized into three parts. The first part focuses on defining three application scenarios in which geotagged data generated by IoT objects, such as taxis, cars, and smartphones, are collected and analyzed through machine learning algorithms (i.e., next location prediction, location-based advertising, and points of interest recommendation). The second part is dedicated to modeling an edge-cloud continuum architecture capable of managing a large number of IoT devices and executing machine learning algorithms to analyze the data they generate. The third part analyzes the experimental results in which different design choices were evaluated, such as the number of devices and orchestration policies, to improve the performance of machine learning algorithms in terms of processing time, network delay, task failure, and computational resource utilization. The results highlight the potential benefits of edge and cloud cooperation in the three application scenarios, demonstrating that it significantly improves resource utilization and reduces the task failure rate compared to other widely adopted architectures, such as edge- or cloud-only architectures.

Distributed Latency Profiling through Critical Path Tracing

CPT can provide actionable and precise latency analysis.

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R.

Many reports in the last 15 years have assessed changes in the auditory brainstem response (ABR) waveform after insults such as noise exposure. Common changes include reductions in the peak 1 amplitude and the relative latencies of the later peaks, as well as increased central gain, which is reflected by a relative increase in the amplitudes of the later peaks compared to the amplitude of peak 1. Many experimenters identify the peaks and troughs visually to assess their relative heights and latencies, which is a laborious process when the waveforms are collected in 5 dB increments throughout the hearing range for each frequency and condition. This paper describes free routines that may be executed in the open-source platform R with the RStudio interface to semi-automate the measurements of the peaks and troughs of auditory brainstem response (ABR) waveforms. The routines identify the amplitudes and latencies of peaks and troughs, display these on a generated waveform for inspection, collate and annotate the results into a spreadsheet for statistical analysis, and generate averaged waveforms for figures. In cases when the automated process misidentifies the ABR waveform, there is an additional tool to assist in correction. The goal is to reduce the time and effort needed to analyze the ABR waveform so that more researchers will include these analyses in the future.

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

Semi Automated Analysis Peak Amplitude Latency For Auditory Brainstem - Video. JoVE publishes peer-reviewed scientific video protocols to accelerate biological, medical, chemical and physical research. Watch our scientific video articles. JoVE is the world-leading producer and provider of science videos with the mission to improve scientific research, scientific journals, and education. Millions of scientists, educators and students at thousands of universities, colleges, hospitals and biopharmaceutical companies worldwide use JoVE for their research, teaching and learning.

Towards a blockchain-SDN-based secure architecture for cloud computing in smart industrial IoT

Some of the significant new technologies researched in recent studies include BlockChain (BC), Software Defined Networking (SDN), and Smart Industrial Internet of Things (IIoT). All three technologies provide data integrity, confidentiality, and integrity in their respective use cases (especially in industrial fields). Additionally, cloud computing has been in use for several years now. Confidential information is exchanged with cloud infrastructure to provide clients with access to distant resources, such as computing and storage activities in the IIoT. There are also significant security risks, concerns, and difficulties associated with cloud computing. To address these challenges, we propose merging BC and SDN into a cloud computing platform for the IIoT. This paper introduces “DistB-SDCloud”, an architecture for enhanced cloud security for smart IIoT applications. The proposed architecture uses a distributed BC method to provide security, secrecy, privacy, and integrity while remaining flexible and scalable. Customers in the industrial sector benefit from the dispersed or decentralized, and efficient environment of BC. Additionally, we described an SDN method to improve the durability, stability, and load balancing of cloud infrastructure. The efficacy of our SDN and BC-based implementation was experimentally tested by using various parameters including throughput, packet analysis, response time, bandwidth, and latency analysis, as well as the monitoring of several attacks on the system itself.

Analysis of read speed latency in 6T‐SRAM cell using multi‐layered graphene nanoribbon and cu based nano‐interconnects for high performance memory circuit design

AbstractIn this study, we designed a 6T‐SRAM cell using 16‐nm CMOS process and analyzed the performance in terms of read‐speed latency. The temperature‐dependent Cu and multilayered graphene nanoribbon (MLGNR)‐based nano‐interconnect materials is used throughout the circuit (primarily bit/bit‐bars [red lines] and word lines [write lines]). Here, the read speed analysis is performed with four different chip operating temperatures (150K, 250K, 350K, and 450K) using both Cu and graphene nanoribbon (GNR) nano‐interconnects with different interconnect lengths (from 10 μm to 100 μm), for reading‐0 and reading‐1 operations. To execute the reading operation, the CMOS technology, that is, the16‐nm PTM‐HPC model, and the16‐nm interconnect technology, that is, ITRS‐13, are used in this application. The complete design is simulated using TSPICE simulation tools (by Mentor Graphics). The read speed latency increases rapidly as interconnect length increases for both Cu and GNR interconnects. However, the Cu interconnect has three to six times more latency than the GNR. In addition, we observe that the reading speed latency for the GNR interconnect is ~10.29 ns for wide temperature variations (150K to 450K), whereas the reading speed latency for the Cu interconnect varies between ~32 ns and 65 ns for the same temperature ranges. The above analysis is useful for the design of next generation, high‐speed memories using different nano‐interconnect materials.

Role of attention in the associative relatedness effect in verbal working memory: Behavioral and chronometric perspectives.

Long-term semantic memory (LTM) is known for affecting recall during working memory (WM) tasks. However, the way LTM intervenes in WM remains unknown. Moreover, the available findings are incongruent concerning how attention modulates the impact of LTM on WM. To examine this issue, the involvement of LTM representations in a complex span task was manipulated through variations of the associative relatedness of memory items, while the attentional demand of the concurrent task was varied. Children and young adults were also compared, because children are less efficient in using refreshing for maintenance than adults. Despite the impact of the three main factors on recall performance, which was better for related than unrelated words, with the low rather than the high demanding concurrent task and for adults than children, there was no interaction between associative relatedness and attentional demand, neither in children nor in adults. We replicated these results in a second experiment with a more attention-demanding concurrent task. Moreover, analyses of recall latency showed that adults were faster than children at recalling words and both age groups were faster for related (vs. unrelated) words, but there was no effect of the concurrent attentional demand on recall latency and no interaction. Finally, errors were mostly omissions and transpositions, both more prevalent under high concurrent attentional demand. The present findings suggest that the availability of attention does not modulate the effect of LTM on WM. We discuss how WM models can account for this finding and how LTM can act on WM functioning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

End-to-End Latency Analysis and Optimal Block Size of Proof-of-Work Blockchain Applications

Due to the increasing interest in blockchain technology for fostering secure, auditable, decentralized applications, a set of challenges associated with this technology need to be addressed. In this letter, we focus on the delay associated with Proof-of-Work (PoW)-based blockchains, whereby participants validate the new information to be appended to a distributed ledger via consensus to confirm transactions. We propose a novel end-to-end latency model based on batch-service queuing theory that characterizes timers and forks for the first time. Furthermore, we derive an estimation of the optimal block size analytically. Endorsed by analytical and simulation results, we show that the optimal block size approximation is a consistent method that leads to close-to-optimal performance by significantly reducing the overheads associated with blockchain applications.

Communication-Efficient Federated Edge Learning via Optimal Probabilistic Device Scheduling

Federated edge learning (FEEL) is a popular distributed learning framework that allows privacy-preserving collaborative model training via periodic learning-updates communication between edge devices and server. Due to the constrained bandwidth, only a subset of devices can be selected to upload their updates at each training iteration. This has led to an active research area in FEEL studying the optimal device scheduling policy for communication time minimization. However, owing to the difficulty in quantifying the exact communication time, prior work in this area can only tackle the problem partially and indirectly by minimizing either the iteration rounds or per-round latency, while the total communication time is determined by both metrics. To close this research gap, we make the first attempt in this paper to formulate and solve the communication time minimization problem. We first derive a tight bound to approximate the remaining communication time through cross-disciplinary effort that combines the learning theory for convergence rate analysis and communication theory for per-round latency analysis. Building on the novel analytical result, an optimized probabilistic device scheduling policy is derived in closed-form by solving the approximate communication time minimization problem. It is found that the optimized policy gradually turns its priority from suppressing the remaining communication rounds to reducing per-round latency as the training process evolves. Extensive experiments based on real-world dataset and a use case on collaborative 3D objective detection in autonomous driving are provided to verify the superiority of the proposed policy over three benchmark policies based on the indirect solution approaches.

Latency Analysis of Blockchain-Based SSI Applications

Several revolutionary applications have been built on the distributed ledgers of blockchain (BC) technology. Besides cryptocurrencies, many other application fields can be found in smart systems exploiting smart contracts and Self Sovereign Identity (SSI) management. The Hyperledger Indy platform is a suitable open-source solution for realizing permissioned BC systems for SSI projects. SSI applications usually require short response times from the underlying BC network, which may vary highly depending on the application type, the used BC software, and the actual BC deployment parameters. To support the developers and users of SSI applications, we present a detailed latency analysis of a permissioned BC system built with Indy and Aries. To streamline our experiments, we developed a Python application using containerized Indy and Aries components from official Hyperledger repositories. We deployed our experimental application on multiple virtual machines in the public Google Cloud Platform and on our local, private cloud using a Docker platform with Kubernetes. We evaluated and compared their performance benchmarked by Read and Write latencies. We found that the local Indy ledger reads and writes 30–50%, and 65–85% faster than the Indy ledger running on the Google Cloud Platform, respectively.

Latency Analysis for Mobile Cellular Network uRLLC Services

The fifth generation (5G) mobile network technologies include ultra-Reliable Low Latency Communications (uRLLC) capability. To fully exploit uRLLC, distributed Multi- access Edge Computing (MEC) is being developed and introduced at the network edge with an architecture that supports applications and services. Some of the MEC applications will benefit from uRLLC, including virtual reality, augmented reality, education, health, online gaming, automatic manufacturing and Vehicle-to-everything. However, unique challenges and opportunities exist for 5G cellular networks and MEC due to a range of factors, including end-user device mobility and the implementation of the network Control Plane (CP) and User Plane (UP). In this regard, there is a need to optimize protocols and network architecture. This paper investigates latency and related network elements in the next generation mobile cellular network. We also analyze the 5G network latency in the CP and UP. Finally, the paper identifies protocol optimization considerations for MEC integration with 5G to achieve low end-to-end latency.

HopliteML: Evolving Application Customized FPGA NoCs with Adaptable Routers and Regulators

We can overcome the pessimism in worst-case routing latency analysis of timing-predictable Network-on-Chip (NoC) workloads by single-digit factors through the use of a hybrid field-programmable gate array (FPGA)–optimized NoC and workload-adapted regulation. Timing-predictable FPGA-optimized NoCs such as HopliteBuf integrate stall-free FIFOs that are sized using offline static analysis of a user-supplied flow pattern and rates. For certain bursty traffic and flow configurations, static analysis delivers very large, sometimes infeasible, FIFO size bounds and large worst-case latency bounds. Alternatively, backpressure-based NoCs such as HopliteBP can operate with lower latencies for certain bursty flows. However, they suffer from severe pessimism in the analysis due to the effect of pipelining of packets and interleaving of flows at switch ports. As we show in this article, a hybrid FPGA NoC that seamlessly composes both design styles on a per-switch basis delivers the best of both worlds, with improved feasibility (bounded operation) and tighter latency bounds. We select the NoC switch configuration through a novel evolutionary algorithm based on Maximum Likelihood Estimation (MLE). For synthetic ( RANDOM , LOCAL ) and real-world ( SpMV , Graph ) workloads, we demonstrate ≈2–3× improvements in feasibility and ≈1–6.8× in worst-case latency while requiring an LUT cost only ≈1–1.5× larger than the cheapest HopliteBuf solution. We also deploy and verify our NoC (PL) and MLE framework (PS) on a Pynq-Z1 to adapt and reconfigure NoC switches dynamically. We can further improve a workload’s routability by learning to surgically tune regulation rates for each traffic trace to maximize available routing bandwidth. We capture critical dependency between traces by modelling the regulation space as a multivariate Gaussian distribution and learn the distribution’s parameters using Covariance Matrix Adaptation Evolution Strategy (CMA-ES). We also propose nested learning, which learns switch configurations and regulation rates in tandem. Compared with stand-alone switch learning, this symbiotic nested learning helps achieve ≈ 1.5× lower cost constrained latency, ≈ 3.1× faster individual rates, and ≈ 1.4× faster mean rates. We also evaluate improvements to vanilla NoCs’ routing using only stand-alone rate learning (no switch learning), with ≈ 1.6× lower latency across synthetic and real-world benchmarks.

Reorganizations of latency structures within the white matter from wakefulness to sleep

Previous resting-state functional magnetic resonance imaging (fMRI) studies have revealed highly reproducible latency structures, reflecting the lead/lag relationship of BOLD fMRI signals in white matter (WM). With simultaneous electroencephalography and fMRI data from 35 healthy subjects who were instructed to sleep during imaging, we explored alterations of latency structures in the WM across wakefulness and nonrapid eye movement (NREM) sleep stages. Lagged cross-covariance was computed among voxelwise time series, followed by parabolic interpolation to determine the actual in-between latencies. WM regions, including the brainstem, internal capsule, optic radiation, genu of corpus callosum, and corona radiata, inconsistently changed temporal dynamics with respect to the rest of the WM across wakefulness and NREM sleep stages, as demonstrated when these regions were used as seeds for seed-based latency analysis. Latency analysis of resting-state networks, obtained by applying K–means clustering to a group-level functional connectivity matrix, identified a dominant direction of signaling, starting from the brainstem up to the internal capsule and then the corona radiata during wakefulness, which was reorganized according to stage transitions, e.g., the temporal organization of the internal capsule and corona radiata switched from unidirectional to bidirectional in the wakefulness to N3 transition. These findings suggest that WM BOLD signals are slow, dynamically modulated across wakefulness and NREM sleep stages and that they are involved in maintaining different levels of consciousness.

Performance Analysis Using Full Duplex Discovery Mechanism in 5G-V2X Communication Networks

In the past few years, the industry and academia have been working hard to establish and standardise vehicle-to-everything (V2X) communications, which is one of the vital emerging services for next generation wireless networks. Therewith, to control radio resource properly with balanced implementation complexity, a full-duplex V2V discovery mechanism in a 5G-V2X network based on a random backoff procedure is proposed to better utilize radio resources on which nearby vehicles establish their links. The main aims are to reduce complexity, latency, improve throughput and guarantee stability for V2V communication. The unemployed cellular network channel operates in full-duplex mode and leaves the channel as soon as they are informed that someone is in place through latency and throughput analysis technique. The channel sensing and identification are done in a cooperative manner before transmission. Furthermore, two effective resource distribution algorithms are proposed that grant the optimum resource distribution for V2V and V2I-Users. The detection and the throughput of full-duplex V2V communication in the proposed scheme have been formulated and the performance of the proposed scheme and validity of corresponding analysis are verified through simulation experiments.

End-to-End Latency Analysis for Data Transmission via Optimum Path Allocation in Industrial Sensor Networks

In a sensor network, packet transmission is easy, but achieving an effective routing path is difficult. The packet information is modified by the intruder node. Initial node capacity is not monitored, so it does not filter out the individual status of each and every node in the routing path. It causes a network that utilizes more energy and minimum packet delivery ratio. This work has implemented the enhanced centrum path allotment-based shielded communication (ECSC) scheme to achieve the shielded packet broadcasting from the sender node to the destination node in the network environment. The quality of packet transmission is improved by using the spatial uniqueness node selection algorithm. It is designed to select the routing node based on its uniqueness; priority-based communication is carried out by the uniqueness process. It improves the packet delivery ratio and network lifetime. It also minimizes packet drop rate and end-to-end delay.

V1-Origin Bidirectional Plasticity in Visual Thalamo-Ventral Pathway and Its Contribution to Saliency Detection of Dynamic Visual Inputs.

Visual neural plasticity and V1 saliency detection are vital for efficient coding of dynamically changing visual inputs. However, how does neural plasticity contribute to saliency detection of temporal statistically distributed visual stream remains unclear. Therefore, we adopted randomly presented but unevenly distributed stimuli with multiple orientations and examined the single-unit responses evoked by this biased orientation-adaptation protocol by single-unit recordings in the visual thalamo-ventral pathway of cats (of either sex). We found neuronal responses potentiated when the probability of biased orientation was slightly higher than other nonbiased ones and suppressed when the probability became much higher. This single neuronal short-term bidirectional plasticity is selectively induced by optimal stimuli but is interocularly transferable. It is inducible in LGN, Area 17, and Area 21a with distinct and hierarchically progressive patterns. With the results of latency analysis, receptive field structural test, cortical lesion, and simulations, we suggest this bidirectional plasticity may principally originate from the adaptation competition between excitatory and inhibitory components of V1 neuronal receptive field. In our simulation, above bidirectional plasticity could achieve saliency detection of dynamic visual inputs. These findings demonstrate a rapid probability dependent plasticity on the neural coding of visual stream and suggest its functional role in the efficient coding and saliency detection of dynamic environment.SIGNIFICANCE STATEMENT Novel elements within a dynamic visual stream can pop up from the context, which is vital for rapid response to a dynamically changing world. Saliency detection is a promising bottom-up mechanism contributing to efficient selection of visual inputs, wherein visual adaptation also plays a significant role. However, the saliency detection of dynamic visual stream is poorly understood. Here, we found a novel form of visual short-term bidirectional plasticity in multistages of the visual system that contributes to saliency detection of dynamic visual inputs. This bidirectional plasticity may principally originate from the local balance of excitation inhibition in primary visual cortex and propagates to lower and higher visual areas with progressive pattern change. Our findings suggest the excitation-inhibition balance within the visual system contributes to visual efficient coding.

INVESTIGATING THE IMPACT OF TREE-BASED NETWORK TOPOLOGY ON THE SDN CONTROLLER PERFORMANCE

Software Defined Networking (SDN) is an important technology that enables a new approach to how we develop and manage networks. SDN divides the data plane and control plane and promotes logical centralization of network control so that the controller can schedule the data in the network effectively through the OpenFlow protocol. The performance and capabilities of the controller itself are important. The impact of network topology type on controller performance can be very significant. In order to have better communication in SDN, it is essential to have an analysis of the performance of specific network topologies. In this paper, we simulate ONOS and RYU controllers and compare their different network parameters under the proposed complex custom Tree-based topology. A network topology has been designed using a Mininet emulator, and the code for topology is executed in Python. From the throughput, packet transmission rate, and latency analysis, the ONOS controller displayed better results than RYU, showing that it can respond to requests more efficiently under complex SDN topologies and traffic loads. On the contrary, the RYU controller provides better results for the less complex SDN networks.

Medical-Waste Chain: A Medical Waste Collection, Classification and Treatment Management by Blockchain Technology

To prevent the spread of the COVID-19 pandemic, 2019 has seen unprecedented demand for medical equipment and supplies. However, the problem of waste treatment has not yet been given due attention, i.e., the traditional waste treatment process is done independently, and it is not easy to share the necessary information. Especially during the COVID-19 pandemic, the interaction between parties is minimized to limit infections. To evaluate the current system at medical centers, we also refer to the traditional waste treatment processes of four hospitals in Can Tho and Ho Chi Minh cities (Vietnam). Almost all hospitals are handled independently, lacking any interaction between the stakeholders. In this article, we propose a decentralized blockchain-based system for automating waste treatment processes for medical equipment and supplies after usage among the relevant parties, named Medical-Waste Chain. It consists of four components: medical equipment and supplies, waste centers, recycling plants, and sorting factories. Medical-Waste Chain integrates blockchain-based Hyperledger Fabric technology with decentralized storage of medical equipment and supply information, and securely shares related data with stakeholders. We present the system design, along with the interactions among the stakeholders, to ensure the minimization of medical waste generation. We evaluate the performance of the proposed solution using system-wide timing and latency analysis based on the Hyperledger Caliper engine. Our system is developed based on the hybrid-blockchain system, so it is fully scalable for both on-chain and off-chain-based extensions. Moreover, the participants do not need to pay any fees to use and upgrade the system. To encourage future use of Medical-Waste Chain, we also share a proof-of-concept on our Github repository.