Latency Problem Research Articles

Edge Computing vs. Cloud Computing: A Comparative Analysis for Real-Time AI Applications

AbstractReal-time AI has been evidenced to be supported by two models, namely edge computing and cloud computing. This research seeks to compare the two methods: Latency, security features, and data processing capabilities will be among the most critical aspects of comparison. Real-time AI applications are envisioned to be robust through 2020, most of which will be used in applications such as autonomous automobile systems and IoT devices that demand low latency for data processing. As data processing is outsourced in clouds, it is elastic and centralized, but this deal faces latency problems when the distance between the source of data and the processing center is large. On the other hand, edge computing refers to conducting computation closer to the data source, which could reduce latency and enhance everyday real-time performance.This research assesses the above models based on the literature review, technical papers, and case studies from industries that depend more on real-time AI. According to the research, edge computing is typically more effective for latency-sensitive workloads, while cloud computing outperforms throughput-intensive applications. Security concerns, however, present themselves as having dual effects; while advancing the privacy of handling data through edge computing, it elevates new risks. As such, this study concludes that no clear winner depends on the necessary application, therefore recommending a symmetric mode for the requirements, where both latency time and computational needs are required. Further studies should be conducted to establish the coordinated implementation approach of these models.

Design and Implementation of an IOT-based Smart Home Automation System in Real World Scenario

INTRODUCTION: Automation developments have significantly increased general convenience in modern technology. Our study focuses on developing and deploying an Internet of Things (IoT)–enabled smart home automation system that maximizes energy efficiency and improves convenience in residential settings. Our technology provides homes with an intelligent environment by smoothly combining several sensors, actuators, and communication protocols. We explore the challenges of creating a reliable and useful smart home systemthat works well in everyday situations. We investigate the difficulties, architectural issues, communication protocols, and security features specific to these systems.OBJECTIVES: This project aims to use a database server and Wi-Fi module to develop an effective and reasonably priced smart home automation system. It improves accessibility and convenience by enabling smartphone voice control of household appliances.METHODS: This project was developed using various implementation techniques, including wireless home automation via GSM technology, speech recognition-based systems, Blynk, and the Internet of Things. These techniques added up to a reliable and effective smart home system.RESULTS: Although it improved user routines, the IoT-based smart home system had latency problems. Interoperability issues are still present. Improvements were guided by user input. Using Blynk, our software manages loads remotely. CONCLUSION: A home automation system that is inexpensive and locally supplied can effectively operate household appliances. It is adaptable, scalable, and a component of future smart homes. IoT applications in real life revolutionize convenience and efficiency in living environments.

A dynamic pricing scheme for secure offloading and resource allocation based on the internet of vehicles

With the development of sixth-generation (6G) wireless communication technology, billions of vehicles will access the network in the future, and the number of vehicle applications and user data will also increase dramatically. Traditional cloud computing faces serious problems of latency and energy consumption in handling massive data. Mobile edge computing (MEC) has emerged to dramatically improve computing efficiency and reduce energy consumption by placing computing servers at network edge locations close to vehicles. However, the service range of MEC servers is limited and cannot fully satisfy user requirements. In addition, the task offloading process has security risks. The current research focuses on how to reduce the energy consumption and latency overhead of task offloading and neglects the economic cost or data transmission security of vehicles. To solve the above problems, we propose a cooperative security offloading (CSO) scheme for auxiliary vehicles and MEC servers. Firstly, we propose a dynamic pricing mechanism for computing resources by considering the credibility of MEC servers and auxiliary vehicles, the urgency of the task, and the number of users competing for auxiliary vehicles. Secondly, to prevent malicious MEC servers and eavesdroppers from attacking, we employ homomorphic encryption to protect user privacy. Meanwhile, efficient and secure computing services are achieved by optimizing user selection decisions, offloading decisions, and resource allocation decisions. Finally, the optimal decisions are obtained by the dueling DQN-based resource allocation and pricing strategy (DDRP) and the cost-minimizing security offloading (CMSO) algorithm, which minimizes the economic cost of users while maximizing security. Simulation results show that, compared with some existing schemes, the CSO scheme effectively reduces the economic cost of users while ensuring the security of data transmission.

A Machine Learning-Based Tropospheric Prediction Approach for High-Precision Real-Time GNSS Positioning.

For high-precision positioning applications, various GNSS errors need to be mitigated, including the tropospheric error, which remains a significant error source as it can reach up to a few meters. Although some commercial GNSS correction data providers, such as the Quasi-Zenith Satellite System (QZSS) Centimeter Level Augmentation Service (CLAS), have developed real-time precise regional troposphere products, the service is available only in limited regional areas. The International GNSS Service (IGS) has provided precise troposphere correction data in TRO format post-mission, but its long latency of 1 to 2 weeks makes it unable to support real-time applications. In this work, a real-time troposphere prediction method based on the IGS post-processing products was developed using machine learning techniques to eliminate the long latency problem. The test results from tropospheric predictions over a year using the proposed method indicate that the new method can achieve a prediction accuracy (RMSE) of 2 cm, making it suitable for real-time applications.

Module lattice based post quantum secure blockchain empowered vehicle to vehicle communication in the internet of vehicles

The importance of vehicle-to-vehicle (V2V) communication is increasing due to high rate of accidents and advancements in information exchange system. The V2V communication facilitates vehicles to exchange real-time informations about their surrounding, such as nearby vehicle, pedestrian, and road hazard. These type of informations are very helpful to detect potential collision, warn driver of imminent danger, and take advanced measure to avoid accident. Both security and privacy of user participating in the vehicular networking system depends upon reliable data transmission system. The suggested approaches must have minimal processing complexity and be unaffected by latency problems due to the scalability of V2V connection. This paper contains module lattice based quantum safe blockchain empowered authentication framework for autonomous vehicle communication system. This framework uses blockchain that is always contains multiple nodes, then we need an aggregation technique to provide authenticity to the system. The proposed framework provides authenticity to the system based on two assumptions, (1) Module Learning With Error and (2) Module Short Integer Solution. The proposed framework is quantum safe, provides high latency and throughput.

Credit Card Fraud Detection Challenges and Solutions: A Review

Credit card fraud has become an increasing problem due to the growing reliance on electronic payment systems and technological advances that have improved fraud techniques. Numerous financial institutions are looking for the best ways to leverage technological advancements to provide better services to their end users, and researchers used various protection methods to provide security and privacy for credit cards. Therefore, it is necessary to identify the challenges and the proposed solutions to address them. This review provides an overview of the most recent research on the detection of fraudulent credit card transactions to protect those transactions from tampering or improper use, which includes imbalance classes, concept drift, and verification latency problems using machine learning and deep learning. It also provides valuable information for academic and industrial researchers and opens new avenues for research aimed at developing robust fraud detection systems.

Enhancing 5G communication in business networks with an innovative secured narrowband IoT framework

Abstract These days, 5G mobile technology highly influences the wireless communication process, which is more responsive than the other generation. However, the 5G communication process handles the speed and latency issues while transmitting data over the network. Especially in rural areas, most people face this speed and latency problem while connecting to the network. Therefore, the network uses massive amounts of data to analyse this problem by integrating 5G with the Internet of things (IoT). The IoT-based data transmission and collection helps manage the data capacity, connection, and transmission rate. Therefore, this study uses the novel secured narrowband IoT framework for 5G communication and operational goals based on their position in the market. The 5G network uses business connectivity and access information to manage the data transmission. The network uses cellular bandwidth and IoT devices to connect more during this process. According to the connection, the information is transmitted with the minimum delay. Thus, experimental results have proposed these parameters: an accuracy ratio of 92.5%, a security ratio of 94.4%, a reliability ratio of 93.5%, a latency ratio of 91.8%, a cost-effective ratio of 20.5%, and a throughput ratio of 25.4% compared to other methods.

Mean-Field Stackelberg Game-Based Security Defense and Resource Optimization in Edge Computing

Edge computing brings computation and storage resources to the edge of the mobile network to solve the problems of low latency and high real-time demand. However, edge computing is more vulnerable to malicious attacks due to its open and dynamic environments. In this article, we investigate security defense strategies in edge computing systems, focusing on scenarios with one attacker and multiple defenders to determine optimal defense strategies with minimal resource allocation. Firstly, we formulate the interactions between the defenders and the attackers as the mean-field Stackelberg game model, where the state and the objective functions of the defenders are coupled through the mean-field term, and are strongly influenced by the strategy of the attacker. Then, we analyze the local optimal strategies of the defenders given an arbitrary strategy of the attackers. We demonstrate the Nash equilibrium and the mean-field equilibrium for both the defenders and the attackers. Finally, simulation analysis will illustrate the dynamic evolution of the defense strategy of the defenders and the trajectory of the attackers based on the proposed Stackelberg game model.

Analysis and Design of Wind Turbine Monitoring System Based on Edge Computing

INTRODUCTION: A wind turbine data analysis method based on the combination of Hadoop and edge computing is proposed. OBJECTIVES: Solve the wind turbine health status monitoring system large data, time extension, energy consumption and other problems. METHODS: By analysing the technical requirements and business processes of the system, the overall framework of the system was designed and a deep reinforcement learning algorithm based on big data was proposed. RESULTS: It solves the problem of insufficient computing resources as well as energy consumption and latency problems occurring in the data analysis layer, solves the problems in WTG task offloading, and improves the computational offloading efficiency of the edge nodes to complete the collection, storage, and analysis of WTG data. CONCLUSION: The data analysis and experimental simulation platform is built through Python, and the results show that the application of Hadoop and the edge computing offloading strategy based on the DDPG algorithm to the system improves the system's quality of service and computational performance, and the method is applicable to the distributed storage and analysis of the device in the massive monitoring data.

Криминологическая обоснованность внесения изменений в уголовный закон: использование статистики и роль средств массовой информации

Introduction. The foundation of the state’s strategic activity in the fight against crime (criminal policy) is a set of initial data of a criminological and social nature, the objectivity (compliance with the truth) of which is often rightly questioned, and therefore the issues of searching and selecting sources for the formation of criminal policy in order to achieving the requirement of criminological validity. Methods. In work based on the dialectical-materialistic method of scientific knowledge, the legal-dogmatic method, formal-logical and systemic methods are used. Results. Despite the fact that the scale of criminologically significant information is quite large, an obstacle to the formation of a criminologically based criminal policy is the lack of the required volume of objective data on crime. Recognizing that certain indicators themselves must be correctly interpreted (including in their interrelation and interdependence), it can be argued that correct criminological forecasting requires an appropriate methodological basis that clearly indicates how it is necessary to process what is significant for the fight against crime information. Recognizing the problem of crime latency, it is worth emphasizing the need to find tools for objectifying crime data. In particular, it is worth paying attention to the promise of an integrated approach to the analysis of criminal phenomena, including, at a minimum, “cross-reconciliation” of data from various government bodies and non-state structures (subject to confidence in the objectivity of the information they provide). When making changes to the criminal law, the use of official statistics as a basic source of information is recognized as justified. Of particular importance is the issue of searching for alternative sources of information, which are now increasingly referred to as social expectations.

Energy and latency-balanced osmotic-offloading algorithm for healthcare systems

Healthcare has become an on-the-fly service that is available at one’s fingertips through smartphones, smart wearables, etc. Many a task related to health are solved on these devices. However, certain tasks require higher computational resources, leading to high energy consumption. Moreover, various latency-intensive tasks demand immediate actions and need to be completed within a stipulated time or a certain deadline. Offloading tasks to nearby edge devices or moving them to the cloud could be a viable solution to address the problem of latency and energy consumption. We present an offloading algorithm for healthcare systems by leveraging osmotic computing to handle compute-intensive, energy-consuming and latency-intensive tasks. The proposed algorithm is simulated and evaluated using various synthetic test cases to show its efficacy. We have analysed the performance of our proposed energy and latency-balanced osmotic-offloading (ELBO) algorithm in terms of average latency, success ratio, task failure rate and energy consumption.

Generalized extrapolation methods based on compositions of a basic 2nd-order scheme

We propose new linear combinations of compositions of a basic second-order scheme with appropriately chosen coefficients to construct higher order numerical integrators for differential equations. They can be considered as a generalization of extrapolation methods and multi-product expansions. A general analysis is provided and new methods up to order 8 are built and tested. The new approach is shown to reduce the latency problem when implemented in a parallel environment and leads to schemes that are significantly more efficient than standard extrapolation when the linear combination is delayed by a number of steps.

Short-term prediction of celestial pole offsets with interpretable machine learning

The difference between observed and modelled precession/nutation reveals unmodelled signals commonly referred to as Celestial Pole Offsets (CPO), denoted by dX and dY. CPO are currently observed only by Very Long Baseline Interferometry (VLBI), but there is nearly 4 weeks of latency by which the data centers provide the most accurate, final CPO series. This latency problem necessitates predicting CPO for high-accuracy, real-time applications that require information regarding Earth rotation, such as spacecraft navigation. Even though the International Earth Rotation and Reference Systems Service (IERS) provides so-called rapid CPO, they are usually less accurate and therefore, may not satisfy the requirements of the mentioned applications. To enhance the quality of CPO predictions, we present a new methodology based on Neural Additive Models (NAMs), a class of interpretable machine learning algorithms. We formulate the problem based on long short-term memory neural networks and derive simple analytical relations for the quantification of prediction uncertainty and feature importance, thereby enhancing the intelligibility of predictions made by machine learning. We then focus on the short-term prediction of CPO with a forecasting horizon of 30 days. We develop an operational framework that consistently provides CPO predictions. Using the CPO series of Jet Propulsion Laboratory as the input to the algorithm, we show that NAMs predictions improve the IERS rapid products on average by 57% for dX and 25% for dY under fully operational conditions. Our predictions are both accurate and overcome the latency issue of final CPO series and thus, can be used in real-time applications.Graphical

Edge-Based Real-Time Sensor Data Processing for Anomaly Detection in Industrial IoT Applications

The Industrial Internet of Things (IIoT), which uses devices with sensors to provide real-time insights into crucial processes, has completely changed how industries function. However, there are many problems associated with the sheer volume and speed of data created in industrial environments, particularly when it comes to anomaly detection. The development of edge-based real-time sensor data processing techniques was required because traditional cloud-based solutions frequently experience latency problems and privacy issues. This study suggests a novel method for IIoT applications that focuses on processing sensor data at the edge, close to the data source, for anomaly identification. We offer real-time analysis of sensor data without the need for continuous data transfer to the cloud by utilising the processing capabilities of edge devices, such as industrial gateways and embedded systems. To find anomalies in streams of real-time sensor data, our methodology integrates data pre-processing, feature engineering, and machine learning algorithms. This strategy not only lessens the strain on the network's bandwidth but also ensures quick reaction to urgent situations, cutting downtime and boosting operational effectiveness. Proposed system has adaptive learning features that enable it to continuously adjust to altering ambient factors and sensor properties, enhancing the precision of anomaly detection over time. We provide experimental findings that show how our edge-based anomaly detection system performs well in diverse industrial situations. The results show that, while protecting data privacy and minimising latency, our methodology outperforms conventional cloud-based methods in terms of anomaly detection performance.

Fog Computing Task Scheduling of Smart Community Based on Hybrid Ant Lion Optimizer

Due to the problem of large latency and energy consumption of fog computing in smart community applications, the fog computing task-scheduling method based on Hybrid Ant Lion Optimizer (HALO) is proposed in this paper. This method is based on the Ant Lion Optimizer (ALO. Firstly, chaotic mapping is adopted to initialize the population, and the quality of the initial population is improved; secondly, the Adaptive Random Wandering (ARW) method is designed to improve the solution efficiency; finally, the improved Dynamic Opposite Learning Crossover (DOLC) strategy is embedded in the generation-hopping stage of the ALO to enrich the diversity of the population and improve the optimization-seeking ability of ALO. HALO is used to optimize the scheduling scheme of fog computing tasks. The simulation experiments are conducted under different data task volumes, compared with several other task scheduling algorithms such as the original algorithm of ALO, Genetic Algorithm (GA), Whale Optimizer Algorithm (WOA) and Salp Swarm Algorithm (SSA). HALO has good initial population quality, fast convergence speed, and high optimization-seeking accuracy. The scheduling scheme obtained by the proposed method in this paper can effectively reduce the latency of the system and reduce the energy consumption of the system.

Optimal Computation Resource Allocation in Energy-Efficient Edge IoT Systems With Deep Reinforcement Learning

This paper investigates a computation resource optimization problem of mobile edge computing (MEC)-aided Internet-of-Things (IoT) devices with a reinforcement learning (RL) solution. Specifically, we leverage the stochastic optimization method and formulate the Lyapunov optimization technique to maximize the long-term energy efficiency, taking into account the transmission power, network stability, and transmission latency. Based on the Markov decision process and model-free deep RL (DRL) approach, we propose a double DRL-based online computation offloading method to implement a deep neural network that learns from interactions to solve the computation offloading and transmission latency problem in the dynamic MEC-aided IoT environments. Furthermore, we design an adaptive method for continuous action-state spaces to minimize the completion time and total energy consumption of the IoT devices for stochastic computation offloading tasks. The proposed real-time Lyapunov optimization and DRL algorithms achieve low computational complexity and optimal processing time. Simulation results demonstrate that the proposed algorithm can achieve near-optimal control performance with enhanced energy efficiency performance compared to the baseline policy control algorithms.

Using IGMP Protocol to Improve the Latency of Cloud Computing

To solve the problem of network latency of cloud computing, organizations usually use the edge computing, which means shorter physical distance from the client, or the parallel computing method, which means separate the task to multi cloud servers. However, these two major solutions do not effectively solve the problem of network latency caused by multiple clients accessing the same resources. In this paper, a new strategy is proposed based on the operation mode of Internet Group Management Protocol (IGMP) to solve the networks latency and waste of network resources caused by multiple clients’ access. This paper would perform the comparison tasks by using Amazon Web Services (AWS). To show the differences, there would be a simulated test of 1000 clients who are trying to access cloud resources from one cloud server. By comparing the total time of 1000 clients receiving their resources, the original group takes 5309 seconds for the cloud server to process the tasks. The test group takes 5034 seconds for the cloud server to process the tasks, which is about 5.68% improvement. Through the research, the conclusion is that if cloud resources are partition properly, the grouping strategy could effectively alleviate the networks latency problem of multiple clients.

Low-latency remote-offloading system for accelerator

Specific workloads are increasingly offloaded to accelerators such as a graphic processing unit (GPU) and field-programmable gate array (FPGA) for real-time processing and computing efficiency. Because accelerators are expensive and consume much power, it is desirable to increase the efficiency of accelerator utilization by sharing accelerators among multiple servers over a network. However, task offloading over a network has the problem of latency due to network processing overhead in remote offloading. This paper proposes a low-latency system for accelerator offloading over a network. To reduce the overhead of remote offloading, we propose a system composed of (1) fast recombination processing of chunked data with a simple protocol to reduce the number of memory copies, (2) polling-based packet receiving check to reduce overhead due to interrupts in interaction with a network interface card, and (3) a run-to-completion model in network processing and accelerator offloading to reduce overhead with context switching. We show that the system can improve performance by 66.40% compared with a simple implementation using kernel protocol stack and confirmed the performance improvement with a virtual radio access network use case as a low-latency application. Furthermore, we show that this performance can also be achieved in practical usage in data center networks.

A secure and privacy blockchain-based data sharing scheme in mobile edge caching system

Today, mobile devices provide their users with a range of multiple services and powerful applications. Despite being beneficial, these applications are massively relying on gathering sensitive information from the mobile devices, which creates numerous challenges regarding security and latency problems. For instance, these applications’ stakeholders take their time to select mobile device for gathering necessary resources. Therefore, this process of recruiting mobile devices will not be practical with sensing information which has an expiration date, which the time of collecting and recruiting these types of sensing information are surpassing its expiration date. As device-to-device (D2D) paradigms have been established as relevant communication for sharing sensing information in a low latency and minimum cost, it still suffers from mobility problems. Hence, mobile edge caching (MEC) system has been developed to increase efficiency of information sharing with mobility of high-flexibility. Nonetheless this paradigm has some challenges with regards to constructing a satisfactory resource sharing system concerning security and trust ability. The aim of this paper is to propose a novel security and privacy blockchain sharing (SPB-DS) system based on MEC system. A searchable keyword will be recorded on the system’s blockchain for every sensing information and all requester devices that require to download specific sensing information, it will generate a trapdoor first for looking for the matching keyword in the blockchain system. To incentive provider devices an optimal payment mechanism has been developed for those who wish to partake in the system. An analysis of security demonstrates that the suggested protocol can satisfy many security goals, while an evaluation on performance illustrates high feasibility and scalability of the MEC system’s proposed scheme.

Single-tap Latency Reduction with Single- or Double- tap Prediction

Touch surfaces are widely utilized for smartphones, tablet PCs, and laptops (touchpad), and single and double taps are the most basic and common operations on them. The detection of single or double taps causes the single-tap latency problem, which creates a bottleneck in terms of the sensitivity of touch inputs. To reduce the single-tap latency, we propose a novel machine-learning-based tap prediction method called PredicTaps. Our method predicts whether a detected tap is a single tap or the first contact of a double tap without having to wait for the hundreds of milliseconds conventionally required. We present three evaluations and one user evaluation that demonstrate its broad applicability and usability for various tap situations on two form factors (touchpad and smartphone). The results showed PredicTaps reduces the single-tap latency from 150--500 ms to 12 ms on laptops and to 17.6 ms on smartphones without reducing usability.