Minimum Latency Research Articles

Nerve Conduction Study in Healthy Elderly Subjects in Central India: A Cross-Sectional Study.

BackgroundThere are many physiological changes that accompany aging. Slowing of muscle contraction, alteration of muscle metabolism and neuromuscular junction, and reduction of nerve conduction velocity (NCV) are among these physiological changes. The present study was conducted to elucidate the effect of physiological factors like gender, height, and Body Mass Index (BMI) on motor and sensory nerve conduction study (NCS) of the upper limb and find out the normal data for healthy elderly subjects in central India.MethodsA cross-sectional study among 382 healthy adult participants of central India aged 60 years and above. The study was carried out in the department of Physiology, Acharya Vinoba Bhave Rural Hospital, Wardha, India, from July 2017 to June 2022. An NCS was performed using the Neuron Spectrum 5 machine (Neurosoft, Ivanovo, Russia). A Microsoft Excel spreadsheet (Microsoft Corporation, Redmond, Washington, United States) was used to tabulate the information gathered. For statistical analysis, IBM SPSS Statistics for Windows, Version 22.0 (Released 2013; IBM Corp., Armonk, New York, United States) was used.ResultsAll NCS parameters were greater in males as compared to females. As age advances, longer distal motor latency (DML) and F-wave minimum latency, decreasing amplitude, and slowing of conduction velocity (CV) were observed. As height increases, increasing DML and F-wave minimum latency, decreasing amplitude, and slowing of CV were observed. Higher BMI was found to be associated with lower amplitudes and slowing of CVs.ConclusionAge and height have a negative influence on amplitude and CV is a positive influence on DML and F-min latency. Higher BMI has a negative influence on amplitude and CV.

Taxonomy of Load Balancing Practices in the Cloud Computing Paradigm

Rapid growth in communication technology allows users location-independent access to IT infrastructure at pay-per-use via cloud computing. This has paved a new paradigm in information processing for the consumers. Due to Cloud's inherent characteristics, most service providers shift to the Cloud and its data centers. To retain Cloud's services' reliability, it's essential to carry out the minimum latency tasks and cost-effectively. Various techniques to improve performance and use of assets are focused on load control, task management, resource management, service quality, and workload management. Data load balancing helps data centers to avoid overload/underload of virtual machines, a difficulty in the world of cloud computing. This study reports a state-of-the-art analysis of current load balancing approaches', problems, and complexities to design more successful algorithms.

An Edge-Based Architecture for Offloading Model Predictive Control for UAVs

Thanks to the development of 5G networks, edge computing has gained popularity in several areas of technology in which the needs for high computational power and low time delays are essential. These requirements are indispensable in the field of robotics, especially when we are thinking in terms of real-time autonomous missions in mobile robots. Edge computing will provide the necessary resources in terms of computation and storage, while 5G technologies will provide minimal latency. High computational capacity is crucial in autonomous missions, especially for cases in which we are using computationally demanding high-level algorithms. In the case of Unmanned Aerial Vehicles (UAVs), the onboard processors usually have limited computational capabilities; therefore, it is necessary to offload some of these tasks to the cloud or edge, depending on the time criticality of the application. Especially in the case of UAVs, the requirement to have large payloads to cover the computational needs conflicts with other payload requirements, reducing the overall flying time and hindering autonomous operations from a regulatory perspective. In this article, we propose an edge-based architecture for autonomous UAV missions in which we offload the high-level control task of the UAV’s trajectory to the edge in order to take advantage of the available resources and push the Model Predictive Controller (MPC) to its limits. Additionally, we use Kubernetes to orchestrate our application, which runs on the edge and presents multiple experimental results that prove the efficacy of the proposed novel scheme.

Multi‐objectivetask scheduling in fog computing using improved gaining sharing knowledge based algorithm

SummaryIn the modern Internet of Things (IoT) era, several applications generate a vast amount of data and that needs to be handled appropriately. The conventional cloud computing system delivers us with enormous resources to manage such voluminous data. Despite that, the growing demands of IoT applications on minimal energy consumption, minimal latency, the privacy of data, data processing based on location, and maximum Quality of Service(QoS) impels the advent of fog computing. As the devices in the fog layer are heterogeneous, distributed, and resource‐constrained, how the fog resources are effectively utilized for executing latency and time‐sensitive applications is a primary challenge. The data generated by the IoT devices are voluminous and produce overhead in network bandwidth during transmission and slow down the response time. This article addresses the task scheduling problem in fog computing to minimize the makespan and energy consumption. The proposed work consists of two phases. In the first phase, the tasks are ordered based on the heuristic method, heterogeneous earliest finish time (HEFT). Then the ordered tasks are scheduled by applying the improved gaining sharing knowledge (IGSK) based algorithm. To reduce the energy consumption dynamic voltage frequency scaling (DVFS) is applied in the proposed scheme. To evaluate the proposed scheduling method the simulations are performed on different workflows with varying sizes. The performance evaluation results exhibit that the proposed work outperforms the task scheduling with similar methods in terms of makespan and energy consumption.

Role of fog‐assisted Industrial Internet of Things: A systematic review

AbstractThe manufacturing industry is benefiting a lot since the Industrial Internet of Things (IIoT) deploys sensors and actuators to calibrate, audit, and analyze data to enhance efficiency and profitability, thereby automating systems in the industrial sector. The exponential growth of smart devices in the industrial sector requires real‐time connectivity, low latency, nearby storage, secure communication, and reliability. To meet this demand, fog computing is effectively used in IIoT applications. Fog computing solves most of the problems in the fourth industrial revolution, which is the interface between end‐user devices and cloud/web services. Fog also allows for local processing with minimal acceptable latency. This article focuses on how fog computing becomes part of IIoT and also explores the architecture of fog‐assisted IIoT applications. The taxonomy is built by classifying the existing literature to reveal the salient features of fog computing in IIoT. This article also discusses IIoT security solutions as well as research problems of fog‐assisted IIoT. This article also outlines the research challenges of fog‐assisted IIoT and also discusses the security solutions of IIoT.

An Intelligent delay efficient data aggregation scheduling for distributed sensor networks

Data aggregation is the most prominent task in many wireless sensor network (WSN) applications, that enables all data from the network to be collected at the sink node. The most critical issue in data aggregation is data aggregation scheduling. This problem becomes even cumbersome when considered to obtain minimum latency and collision in the networks where fixed amount of data can be aggregated. Many researches have been conducted to resolve the issue of data delivery with low latency and collision. Though, some methods nearly achieved this objective but at the cost of increased complexity. Also, many works assume the system to be centralized having knowledge of every related device in the network. Practically, WSNs are likely to be distributed where each device’s information is dissociated from the other. In this context, a data aggregation scheduling algorithm is proposed to aggregate the data from a tree-based distributed sensor network. An effective aggregation tree construction based on Dijkstra’s algorithm is adopted which avoids the chance of re-transmission of data. Furthermore, a new collision prevention scheduling (CPS) algorithm is designed which ensures the data aggregation through non-collision schedules using minimum time slots. The proposed method shows 54.74% less aggregation latency compared to its competitor data aggregation scheduling approach. The outcome of extensive simulations confirms the efficiency and good performance of the proposed algorithm over previous works.

Blockchain Technology-Based Electronic Payment Strategy for City Mobile Pass Cards

Currently, there has been a paradigm shifted in the storing of electronic payment records (EPRs) on mobile distributed systems, wherein mobile devices are coupled with cloud computing to ease financial data transfers between consumers and stakeholders. This enhanced architecture allows low-cost online payment services with high versatility and EPR accessibility. Nevertheless, for e-payment platforms, such a new paradigm poses worries about data privacy and network security. A difficult challenge is how to reliably disseminate EPRs across mobile users while maintaining high-security security standards in the mobile cloud. This paper investigates and demonstrates the electronic payment approach of urban mobile pass cards’ dependability and excellence. Furthermore, on a mobile cloud platform, our technology integrates blockchain with the decentralized interplanetary file system (IPFS). We specifically create a reliable access control method on smart contracts to ensure secure EPR sharing across various clients and stakeholders. Our empirical results indicate that our concept provides a viable solution for data transfers on mobile clouds while protecting critical payment information from potential attacks. When compared to previous data sharing models, the system evaluation and security analysis show performance gains in lightweight access control architecture, minimal network latency with excellent security and data confidentiality levels.

Resource Management for Intelligent Vehicular Edge Computing Networks

To overcome the inherent defect of centralized data processing in cloud computing, the mobile edge computing (MEC) brings data storage and computing capacities, to the edge closer to end users. However, the uneven distribution of access vehicles, as well the volume of computing data, cause the workload diversity among various mobile edge computing servers (MECSs). In this paper, we propose a hierarchical model with quality of service (QoS)-aware and power-aware resource management for the cooperative edge-computing-based intelligent vehicular network (CEC-IoV), and the system latency and energy efficiency at MECSs are respectively optimized. Specifically, considering the changing response times versus MECSs’ workloads, the Minimum Latency with Migration Loads (MLML) scheme is developed for workload balance among multiple MECSs. By selecting the appropriate response time threshold and migration loads from overloading MECSs to idle MECSs simultaneously, the load-balancing problem can be efficiently solved for multiple MECSs with unbalanced workloads. On the other hand, through performing workload redistribution and dynamic reconfiguration of virtual machines (VMs) instantiated onto the parallel computing platform at one MECS, the energy-efficiency can be also optimized while guaranteeing the QoS requirement on the processing delay. With the latency constraint, the power minimization problem is formulated to be a convex one, and the semi-closed forms for optimal solutions of VMs’ workloads and processing rates are provided using KKT conditions. Compared with the performance obtained by benchmark schemes, numerical results exhibit that our resource management schemes gain lower system latency and higher energy efficiency.

Epidemiology of Thyroid Cancer.

Epidemiology of Thyroid Cancer.

Micro-Safe: Microservices- and Deep Learning-Based Safety-as-a-Service Architecture for 6G-Enabled Intelligent Transportation System

In this paper, we propose a microservices and deep learning-based scheme, termed as Micro-Safe, for provisioning Safety-as-a-Service (Safe-aaS) in a 6G environment. A Safe-aaS infrastructure provides customized safety-related decisions dynamically to the registered end-users. As the decisions are time-sensitive in nature, the generation of these decisions should incur minimum latency and high accuracy. Further, scalability and extension of the coverage of the entire Safe-aaS platform are also necessary. Considering road transportation as the application scenario, we propose Safe-aaS, which is a microservices- and deep learning-based platform for provisioning ultra-low latency safety services to the end-users in a 6G scenario. We design the proposed solution in two stages. In the first stage, we develop the microservices-enabled application layer to improve the scalability and adaptability of the traditional Safe-aaS platform. Moreover, we apply the state space model to represent the decision parameters requested and the decision delivered to the end-users. During the second stage, we use deep learning models to improve the accuracy in the decisions delivered to the end-users. Additionally, we apply an assortment of activation functions to analyze and compare the accuracy of the decisions generated in the proposed scheme. Extensive simulation of our proposed scheme, Micro-Safe, demonstrates that latency is improved by 26.1 – 31.2%, energy consumption is reduced by 22.1 – 29.9%, throughput is increased by 26.1 – 31.7%, compared to the existing schemes.

Latency and Alphabet Size in the Context of Multicast Network Coding

We study the relation between <i>latency</i> and <i>alphabet size</i> in the context of Multicast Network Coding. Given a graph <inline-formula> <tex-math notation="LaTeX">$G = (V, E)$ </tex-math></inline-formula> representing a communication network, a subset <inline-formula> <tex-math notation="LaTeX">$S \subseteq V$ </tex-math></inline-formula> of sources, each of which initially holds a set of information messages, and a set <inline-formula> <tex-math notation="LaTeX">$T \subseteq V$ </tex-math></inline-formula> of terminals; we consider the problem in which one wishes to design a communication scheme that eventually allows all terminals to obtain all the messages held by the sources. In this study we assume that communication is performed in rounds, where in each round each network node may transmit a single (possibly encoded) information packet on any of its outgoing edges. The objective is to minimize the communication latency, i.e., number of communication rounds needed until all terminals have all the messages of the source nodes. For sufficiently large alphabet sizes (i.e., large block length, packet sizes), it is known that traditional linear multicast network coding techniques (such as random linear network coding) minimize latency. In this work we seek to study the task of minimizing latency in the setting of limited alphabet sizes (i.e., finite block length), and alternatively, the task of minimizing the alphabet size in the setting of bounded latency. We focus on the establishing the computation complexity of the problem and present several intractability results. In particular, through reductive arguments, we prove that it is NP-hard to (i) approximate (and in particular to determine) the minimum alphabet size given a latency constraint; (ii) approximate (and in particular to determine) the minimum latency of communication schemes in the setting of limited alphabet sizes.

UltrARsound: in situ visualization of live ultrasound images using HoloLens 2

PurposeAugmented Reality (AR) has the potential to simplify ultrasound (US) examinations which usually require a skilled and experienced sonographer to mentally align narrow 2D cross-sectional US images in the 3D anatomy of the patient. This work describes and evaluates a novel approach to track retroreflective spheres attached to the US probe using an inside-out technique with the AR glasses HoloLens 2. Finally, live US images are displayed in situ on the imaged anatomy.MethodsThe Unity application UltrARsound performs spatial tracking of the US probe and attached retroreflective markers using the depth camera integrated into the AR glasses—thus eliminating the need for an external tracking system. Additionally, a Kalman filter is implemented to improve the noisy measurements of the camera. US images are streamed wirelessly via the PLUS toolkit to HoloLens 2. The technical evaluation comprises static and dynamic tracking accuracy, frequency and latency of displayed images.ResultsTracking is performed with a median accuracy of 1.98 mm/1.81^circ for the static setting when using the Kalman filter. In a dynamic scenario, the median error was 2.81 mm/1.70^circ . The tracking frequency is currently limited to 20 Hz. 83% of the displayed US images had a latency lower than 16 ms.ConclusionsIn this work, we showed that spatial tracking of retroreflective spheres with the depth camera of HoloLens 2 is feasible, achieving a promising accuracy for in situ visualization of live US images. For tracking, no additional hardware nor modifications to HoloLens 2 are required making it a cheap and easy-to-use approach. Moreover, a minimal latency of displayed images enables a real-time perception for the sonographer.

Privacy-Aware Cloud Auditing for GDPR Compliance Verification in Online Healthcare

Emerging multitenant cloud computing ecosystems allow multiple applications to share virtualized pool of computing and networking resources. As a result, such ecosystems are becoming increasingly prone to data privacy concerns (personal data leakages and unauthorized access). While cloud computing providers support robust security and privacy mechanisms (e.g., public key cryptography, firewalls, and virtual private networks, among many others), they lack mechanisms and frameworks to monitor, audit, and verify these data privacy concerns. The emergence of data protection regulations around the world, such as General Data Protection Regulation in Europe and the Data Protection Act in the U.K., further emphasizes the need to overcome these privacy limitations. In this article, a novel technique for monitoring, auditing, and verifying the operations carried out on a user’s personal data in cloud computing ecosystems is proposed. Our research methodology leverages distributed ledger technologies (e.g., blockchain and smart contracts) for developing an immutable recording technique, which transparently logs, monitors, and verifies the operations carried out on user data. Using a healthcare pharmacy scenario and extensive real-world experiments, we validate the feasibility of the proposed technique. The proposed work handles a large pool of requests ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 13K) ensuring minimal latency ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 50–60 ms) and overheads for three different service packages varied with respect to the number of actors and operations.

Intrinsic Profit Maximization of the Offloading Tasks for Mobile Edge Computing with Fixed Memory Capacities and Low Latency Constraints Using Ant Colony Optimization

Artificial intelligence and the Internet of Things (IoT) have resulted in more computationally demanding and time-sensitive applications. Given the limited processing power of current mobile computers, there is a need for on-demand computing resources with minimal latency. Edge computing has already made a significant contribution to mobile networks, enabling the distribution, scaling, and faster access of computational resources at network margins closer to users, especially in power-constrained mobile devices. Offloading tasks efficiently on the Mobile Edge Computing Server (MECS) is an important part of our proposed method. We propose a method of offloading multiple tasks for Mobile Edge Computing servers that require fixed memory capacities and low latency. We calculate the optimum cumulative intrinsic profit of the number of offloaded tasks efficiently using the Ant Colony Optimization (ACO) model, which is flexible and versatile in the context of real-time applications.

Optimizing QoS-Based Clustering Using a Multi-Hop with Single Cluster Communication for Efficient Packet Routing

Modern day communication systems have gained a revolutionized growth in long-distance wireless data transmission. High speed packet transfer impacts quality requirements. Critical factors that ruin service quality (Qos) are calculated by the primary factors involving power efficiency, packet delivery ratio, and overall transmission and reception delay. A well-developed routing protocol with unique attributes should be deployed to give improved QoS. The drawback of single path routing in delivering a packet at traffic is challenging since it does not have an alternative path in case of path failure. This problem can be targeted by a properly structured protocol with a multipath mechanism. In this article, Multi-hop with single cluster (SCMC) protocol is designed to increase the overall system efficiency by improving bandwidth, packet delivery ratio (PDR), reducing communication delay, and quality improvement. Adopting a single cluster and several hop protocol achieves power conservation for an additional period and balances the energy level. In multi hopping communications, numerous paths with various members can be produced by a single cluster is an added advantage. The proposed work is tested in NS-2 in comparison with equal cost multipath and protocol SPEED. The outcome of the designed protocol outperforms all other protocols in terms of minimum latency, lower power usage and improved packet delivery ratio

A Latency-Aware Offloading Strategy over Fiber-Wireless (FiWi) Infrastructures for Tactile Internet Services

With the emergence of the tactile internet, low-latency, even, real-time data transmission is indispensable for human-agent–robot teamwork. Offloading is considered a feasible approach. Determining the offloading solution according to the dynamic network circumstance is attractive. In this paper, we investigate the resource management issue in a three-tier, heterogeneous, fiber-wireless (FiWi) network with offloading. Based on the model of the wireless link, the fiber link, the data rate, and the offloading, a mixed-integer, non-linear problem is formulated to obtain the minimum total latency for tactile internet services. Through constraint relaxation, MINLP is converted to a linear problem (LP). A Lagrange multiplier method with Karush–Kuhn–Tucker (KKT) conditions is used to solve LP. Using the numerical simulation, the superiority of our work is evaluated and compared with the previous work.

A novel energy aware cluster based routing protocol for emergency message dissemination in vehicular adhoc networks

In Vehicular Adhoc Network (VANET), the typical topology variations produced by the highly mobile nature of vehicle poses major issues in data transmission. The mobility and location based data dissemination techniques could satisfy the requirements of emergency message (EM) dissemination. It faces several issues in highway as well as urban roadways like minimum latency, highly secure, and scalable, which needs to be resolved. Generally, broadcasting protocols for VANET make use of beacon signals, which are distributed amongst the vehicles to attain information about the neighboring vehicles. With this motivation, this paper presents an energy aware cluster based routing protocol for the critical message dissemination (EAC-RPCMD) technique for VANET. The goal of the EAC-RPCMD technique aims to disseminate the EMs in the cluster based VANET environment. The EAC-RPCMD technique primarily designs an equilibrium optimizer based clustering (EOC) technique for the organization of vehicles into clusters. Besides, the manta ray foraging optimization (MRFO) algorithm based route selection (RS) technique, named MRFO-RS technique for optimal choice of routes to destination. Moreover, the EM dissemination scheme is also employed in the VANET. The performance validation of the EAC-RPCMD technique takes place and the results are inspected under several aspects.

Enhanced Lion Optimization with Efficient Path Routing Equalization Technique against DoS Attack in Wireless Sensor Network

In WSN, DoS (denial of service) attack makes shortcoming system. The packets travel over and over in the sensor network. By that, all the assets like data transmission, memory, and vitality are squandered by this attack. However, the attacker ought to optimize its attacker plan for request to boost the impact on the system performance because of the deficiency of vitality at the aggressor side. Denial of service (DoS) attack on the Internet has become a squeezing issue. By staying away from these sorts of attacks, network performance can be improved. Therefore, security is a fundamental requirement for these networks. Effective routing is necessary in order to overcome the issued faced by the crosslayer in the DOS attack of the WSN network for the purpose of good transmission. This research work mainly focuses on performance evaluation using optimization methods. To establish the efficient path in the crosslayer against DoS attack, this paper has proposed enhanced lion optimization with an efficient path routing equalization technique (LOEPRE). If any failure node occurs in the network, then the node is recognized and the transfer of the data packet is done to the other node. Retransmission of data causes overload in the network. The proposed model focuses on these issues and overcomes these issues by improving the path efficiently with robust security. It consists of three phases: the initial phase includes the route discovery in the network. In the second phase, the transfer of data is done in the high router path for security purposes. Finally, the efficient path routing equalization technique is used for minimizing the overload in the network; it provides the equalized path length in the network and is highly efficient. Hence, the proposed LOEPRE technique is used to achieve energy efficiency in wireless network for prolonged network lifetime and minimum packet latency and minimize consumption of energy. Moreover, the simulation outcome of the proposed LOEPRE method is highly robust while comparing to the existing methods EFCRS, SSPRA ELOER, EFLOR, and TSTP. It achieves better performance than existing algorithms in comparing metric connectivity ratio, end-to-end delay, overhead, throughput, and packet delivery ratio.

Cluster Head Selection and Energy Efficient Multicast Routing Protocol-Based Optimal Route Selection for Mobile Ad Hoc Networks

Wireless networks with a large number of peer nodes are known as mobile ad hoc networks (MANETs). In MANETs, the mobility of nodes causes a number of challenges, including path preservation, battery life, safety, dependability, and unexpected connection characteristics. As a result, the network’s quality of service (QoS) would be compromised (QoS). For the discovery and maintenance of pathways in MANETs, the routing protocol is critical. By implementing the multicast routing protocol, the MANET network’s reliability may be improved significantly. Evaluation of multicast routing for quality of service (QoS) is the primary goal of this study. In multicasting, data packets from one node are transmitted to a set of receiver nodes at a time, simultaneously. Multicasting reduces transmission costs. Cluster head selection is one of the challenges in MANET. This proposed research paper optimal route selection (ORS) provides the cluster head selection and alternate cluster head selection for avoiding the failure of the cluster head, generation of the optimal path between the cluster head and member node based on reliability pair factor and node’s energy, and establishment of the path based on maximum energy and number of hops between the nodes (minimum number of hops). In comparison to existing methods, ORS is more effective in the energy-efficient path between base station and cluster head, and member node is provided by an ORS route. Results show that the proposed ORSMAN has higher throughput, minimum latency, minimum jitter, and maximum packet delivery ratio, when compared to the existing methodologies.

Doppler Modeling and Simulation of Train-to-Train Communication in Metro Tunnel Environment.

The communication system of urban rail transit is gradually changing from train-to-ground (T2G) to train-to-train (T2T) communication. The subway can travel at speeds of up to 200 km/h in the tunnel environment, and communication between trains can be conducted via millimeter waves with minimum latency. A precise channel model is required to test the reliability of T2T communication over a non-line-of-sight (NLoS) Doppler channel in a tunnel scenario. In this paper, the description of the ray angle for a T2T communication terminal is established, and the mapping relationship of the multipath signals from the transmitter to the receiver is established. The channel parameters including the angle, amplitude, and mapping matrix from the transmitter to the receiver are obtained by the ray-tracing method. In addition, the channel model for the T2T communication system with multipath propagations is constructed. The Doppler spread simulation results in this paper are consistent with the RT simulation results. A channel physics modelling approach using an IQ vector phase shifter to achieve Doppler spread in the RF domain is proposed when paired with the Doppler spread model.