Data Transmission Time Research Articles

A fog assisted intelligent framework based on cyber physical system for safe evacuation in panic situations

In the current scenario of the COVID-19 pandemic and worldwide health emergency, one of the major challenges is to identify and predict the panic health of persons. The management of panic health and on-time evacuation prevents COVID-19 infection incidences in educational institutions and public places. Therefore, a system is required to predict the infection and suggests a safe evacuation path to people that control panic scenarios with mortality. In this paper, a fog-assisted cyber physical system is introduced to control panic attacks and COVID-19 infection risk in public places. The proposed model uses the concept of physical and cyber space. The physical space helps in real time data collection and transmission of the alert generation to the stakeholders. Cyberspace consists of two spaces, fog space, and cloud-space. The fog-space facilitates panic health and COVID-19 symptoms determination with alert generation for risk-affected areas. Cloud space monitors and predicts the person’s panic health and symptoms using the SARIMA model. Furthermore, it also identifies risk-prone regions in the affected place using Geographical Population Analysis. The performance evaluation acknowledges the efficiency related to panic health determination and prediction based on the SARIMA with risks mapping accuracy. The proposed system provides an efficient on time evacuation with priority from risk-affected places that protect people from attacks due to panic and infection caused by COVID-19.

Optimal data placement strategy considering capacity limitation and load balancing in geographically distributed cloud

At present, the amount of data from users is increasing exponentially, and most of the data is stored in data centers distributed in different geographic locations. The cost of transferring large amounts of data across geographically distributed data centers can become prohibitive. Therefore, to shorten the data transmission time and reduce the cost of data transmission bandwidth and maintain the load balance of the geographically distributed cloud system, an optimal data placement strategy considering capacity limitation and load balancing in a geographically distributed cloud is proposed. Firstly, the capacity limitation, load balancing, and bandwidth cost of each cloud data center in the geographically distributed cloud are considered, and the data placement problem in the geographically distributed cloud is mathematically modeled. Secondly, the Floyd algorithm is used to model the cost of data transmission bandwidth and find the minimum transmission bandwidth cost. Finally, the Lagrangian relaxation method is used to obtain the optimal data placement scheme for the transmission time. To show the performance advantages of the proposed algorithm, comparative experiments are carried out. When the bandwidth is 15 Mbps, in terms of the Load Balancing Degree (LBD), the proposed algorithm is 40.3% higher than the Hash on average, 35.6% higher than the Closest on average, and 25.7% higher than the CRANE on average. Moreover, the experimental results show that the proposed algorithm can reduce the data transmission cost, and improve the load balancing in a geographically distributed cloud system.

Feasibility Study of an Earthquake Early Warning System in Eastern Central Italy

An earthquake early warning system (EEWS) is a monitoring infrastructure that allows alerting strategic points (targets) before the arrival of strong shaking waves during an earthquake. In a region like Central Italy, struck by recent and historical destructive earthquakes, the assessment of implementation of an EEWS is a significant challenge due to the proximity of seismic sources to many potential targets, such as historical towns, industrial plants, and hospitals. In order to understand the feasibility of an EEWS in such an area, we developed an original method of event declaration simulation (EDS), a tool for assessing the effectiveness of an EEWS for existing seismic networks, improving them with new stations, and designing new networks for EEW applications. Values of the time first alert (TFA), blind zone radius (BZ), and lead time (LT) have been estimated with respect to selected targets for different network configurations in the study region. Starting from virtual sources homogeneously arranged on regular mesh grids, the alert response was evaluated for actual and improved seismic networks operating in the area, taking into account the effects of the transmission and acquisition systems. In the procedure, the arrival times of the P wave picks, the association binder, the transmission latencies, and the computation times were used to simulate the configuration of PRESTo EEWS, simulating both real-time and playback elaborations of real earthquakes. The NLLOC software was used to estimate P and S arrival times, with a local velocity model also implemented in the PRESTo EEWS. Our results show that, although Italy’s main seismic sources are located close to urban areas, the lead times calculated with the EDS procedure, applied to actual and to improved seismic networks, encourage the implementation of EEWS in the study area. Considering actual delays due to data transmission and computation time, lead times of 5–10 s were obtained simulating real historical events striking some important targets of the region. We conclude that EEWSs are useful tools that can contribute to protecting people from the harmful effects of earthquakes in Italy.

Wireless Communication on PLC Using Access Point TP-Link TL-WN722N

Technological developments are a requirement for more practical system operation. One example is in data transmission. Wireless data communication is currently very popular. In today's revolution 4.0, of course, the use of cables in data transmission media is rarely used, but not every device supports wireless data communication. One of them is the PLC (Programable Logic Controller). As for additional extensions for PLCs to communicate wirelessly, they are sold separately and, of course, the price is quite expensive. Therefore, a solution for PLCs to communicate wirelessly using the TL-WN722N access point is widely available on the market. Measurements are made with the concept of point to point by looking at the results of modbus scans using Modscan32 on a PC server. The results show that the optimal maximum distance is 80 meters, with an average data transmission time of 1 second.

Exact and Approximation Algorithms for Minimizing Energy in Wireless Sensor Data Gathering Network with Data Compression

This article studies the problem of minimizing the total energy consumed in a heterogeneous wireless sensor data gathering network with data compression. In a wireless sensor data gathering network, a set of sensors is used to collect data and all the data are required to be transmitted to a single base station. Whether the base station is working in data receiving or idle mode, it consumes energy. To reduce the data transmission time, each sensor has the option to compress its collected data to decrease the original size before sending the data to the base station. However, compressing data takes some time delaying the data transmission starting time and also consuming energy. The task is to choose which sensors should compress their data and determine the data transmission order between the sensors and the base station with the goal of minimizing the total energy consumed. We prove that the studied problem is NP-hard, and propose a pseudo-polynomial dynamic programming exact algorithm. Furthermore, we present an approximation algorithm with the performance ratio that depends on the given energy consuming parameters for each unit time in different energy consuming activities.

A chaos theory inspired, asynchronous two-way encryption mechanism for cloud computing.

Data exchange over the Internet and other access channels is on the rise, leads to the insecurity of consequences. Many experiments have been conducted to investigate time-efficient and high-randomized encryption methods for the data. The latest studies, however, have still been debated because of different factors. The study outcomes do not yield completely random keys for encryption methods that are longer than this. Prominent repetition makes the processes predictable and susceptible to assaults. Furthermore, recently generated keys need recent algorithms to run at a high volume of transactional data successfully. In this article, the proposed solutions to these two critical issues are presented. In the beginning, one must use the chaotic series of events for generating keys is sufficient to obtain a high degree of randomness. Moreover, this work also proposes a novel and non-traditional validation test to determine the true randomness of the keys produced from a correlation algorithm. An approximate 100% probability of the vital phase over almost infinitely long-time intervals minimizes the algorithms’ complexity for the higher volume of data security. It is suggested that these algorithms are mainly intended for cloud-based transactions. Data volume is potentially higher and extremely changeable 3% to 4% of the improvement in data transmission time with suggested algorithms. This research has the potential to improve communication systems over ten years by unblocking decades-long bottlenecks.

Design of Multimedia Vocal Music Education Data Integration System Based on Adaptive Genetic Algorithm

In order to solve the problems of low accuracy of data integration results, low integration efficiency, and easy confusion between different types of data in traditional methods, a multimedia vocal education data integration system based on adaptive genetic algorithm was designed. Specifically, the designed system is divided into three parts: data source management module, system administrator module, and database management module. The synchronized multimedia vocal education data are first processed by the synchronous multimedia vocal education data processing and then integrated by an adaptive genetic algorithm. The experimental results show that the longest data transmission time of the system is 2.3 s, which is much lower than that of the traditional method, and the accuracy of the integration result is higher, and the probability of data integration confusion is lower, which all indicate that the designed system has better application performance.

Cloud data security and various security algorithms

The term Cloud computing is a most trending and promisingtechnology. It is the most basic need for many organizations. Cloud technology has many advantages as user can access the files from anywhere over the internet. It has also a very known feature of using the resources according to their demand and user does need to pay extra charges for the unused resources. In Cloud Computing user do not need to carry all their documents with them as cloud access is global. But as Cloud computing means sending data over internet so sometimes there may be security issues and threat of intruder attack at the time of data transmission. As traditional banking industry is totally changed and now the total control in the hand of customer rather than the bank. In banking industry cloud computing provides various features viz; cost saving, business continuity, business agility and time saving. but as data in bank is the most crucial data so cloud security is the first priority for every bank before adopting cloud. security at cloud is the biggest obstacle for the banking industry to adopt this trending technology and its services. So there is need to provide security to the cloud. This paper aims on cloud basic methodology and analyze some security algorithms and their applicability in cloud banking environment.

Prediction of nodes mobility in 3-D space

<span>Recently, mobility prediction researches attracted increasing interests, especially for mobile networks where nodes are free to move in the three-dimensional space. Accurate mobility prediction leads to an efficient data delivery for real time applications and enables the network to plan for future tasks such as route planning and data transmission in an adequate time and a suitable space. In this paper, we proposed, tested and validated an algorithm that predicts the future mobility of mobile networks in three-dimensional space. The prediction technique uses polynomial regression to model the spatial relation of a set of points along the mobile node’s path and then provides a time-space mapping for each of the three components of the node’s location coordinates along the trajectory of the node. The proposed algorithm was tested and validated in MATLAB simulation platform using real and computer generated location data. The algorithm achieved an accurate mobility prediction with minimal error and provides promising results for many applications.</span>

AI-enabled IoT-Edge Data Analytics for Connected Living

As deep learning, virtual reality, and other technologies become mature, real-time data processing applications running on intelligent terminals are emerging endlessly; meanwhile, edge computing has developed rapidly and has become a popular research direction in the field of distributed computing. Edge computing network is a network computing environment composed of multi-edge computing nodes and data centers. First, the edge computing framework and key technologies are analyzed to improve the performance of real-time data processing applications. In the system scenario where the collaborative deployment tasks of multi-edge nodes and data centers are considered, the stream processing task deployment process is formally described, and an efficient multi-edge node-computing center collaborative task deployment algorithm is proposed, which solves the problem of copy-free task deployment in the task deployment problem. Furthermore, a heterogeneous edge collaborative storage mechanism with tight coupling of computing and data is proposed, which solves the contradiction between the limited computing and storage capabilities of data and intelligent terminals, thereby improving the performance of data processing applications. Here, a Feasible Solution (FS) algorithm is designed to solve the problem of placing copy-free data processing tasks in the system. The FS algorithm has excellent results once considering the overall coordination. Under light load, the V value is reduced by 73% compared to the Only Data Center-available (ODC) algorithm and 41% compared to the Hash algorithm. Under heavy load, the V value is reduced by 66% compared to the ODC algorithm and 35% compared to the Hash algorithm. The algorithm has achieved good results after considering the overall coordination and cooperation and can more effectively use the bandwidth of edge nodes to transmit and process data stream, so that more tasks can be deployed in edge computing nodes, thereby saving time for data transmission to the data centers. The end-to-end collaborative real-time data processing task scheduling mechanism proposed here can effectively avoid the disadvantages of long waiting times and unable to obtain the required data, which significantly improves the success rate of the task and thus ensures the performance of real-time data processing.

Fully Distributed Event-Triggered Vehicular Platooning With Actuator Uncertainties

The goal of this paper is to design a fully distributed event-triggered control policy with intermittent communication to reach leader-follower consensus of vehicular platoons with nonzero leader's input and actuator uncertainties. The time-dependent event-triggered condition in a fully distributed manner is proposed for checking the necessary data transmission time. Meanwhile, a distributed control law based on estimated state is developed when the event-triggered condition is violated, which ensures the platoon can reach bounded consensus with an exponential convergence rate. Compared with existing related results, the fully distributed event-triggered mechanism is independent of any global information or leader's information. Moreover, Zeno behavior can be strictly ruled out for each vehicle. Finally, the effectiveness of the proposed event-triggered control policy is validated by numerical examples.

Hierarchical Data Transmission and Processing Analysis of Sensor Network System

Hierarchical structure is the method and means of data transmission in sensor network system. The large-scale application of sensor network system and a large number of data acquisition will produce massive data. In order to reduce the load of sensor network processor system and make data transmission more reliable and convenient. For the transmission and processing of streaming and structured data, the data can be processed hierarchically and dimensionally. Hierarchical processing can effectively reduce the load of the network system; Dimensionality reduction can effectively compress the amount of data to reduce the time of data transmission and processing. Hierarchical operation is a necessary step for data transmission and processing in sensor networks, and has been applied in large-scale data transmission and processing algorithms.

Acceleration of shooting and bouncing ray method based on OptiX and normal vectors correction.

The present paper deals with a new efficient shooting and bouncing ray (SBR) method based on OptiX and normal vectors correction. The basic idea is to make full use of the computing resources of the RTX series graphics cards. For ray tracing, the algorithm uses OptiX to invoke the built-in RT Cores of hardware. Thus, a fast intersection test can be implemented. To reduce the error of ray tracing caused by the facetted surface characterizing the curved surface, the direction of the reflected ray is corrected by normal vectors correction. Additionally, multiple GPU cores are invoked to accelerate the calculation of far-field integration of millions of ray tubes, which can improve the efficiency of the algorithm while reducing the data transmission time of heterogeneous devices. Simulation results show that the ray path after normal vectors correction is consistent with the theoretical results, and the algorithm can predict the RCS of arbitrary facetted geometries, which is 60 times faster than the SBR method based on kd-tree.

Millimeter-Wave Beam Selection in Time-Varying Channels With User Orientation Changes

The use of many narrow beams to overcome the adverse propagation conditions in millimeter-wave channels leads to large training durations and overheads in 5G systems. This causes the beam measurements to become outdated by different extents at the time the transmit and receive beams are selected. The rapid changes in user device orientation exacerbate this problem. We first present a novel modified bivariate Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> (MBN) model to tractably and accurately characterize the joint, non-stationary statistics of the channel gains seen at the times of measurement and data transmission. We then derive a novel and optimal beam selection rule that maximizes the average rate of the system. We use the MBN model to propose a near-optimal, practically amenable bound-based selection (PABS) rule. Our approach captures several pertinent aspects about the spatial channel model and 5G, such as transmission of periodic bursts of reference signals, feedback from the user to enable the base station to select its transmit beam, and the faster pace of updating the data rate compared to the transmit-receive beam pair. The PABS rule markedly outperforms the widely used conventional power-based selection rule and is less sensitive to user orientation changes.

Energy-Efficient Fuzzy Management System for Internet of Things Connected Vehicular Ad Hoc Networks

Many algorithms use clustering to improve vehicular ad hoc network performance. The expected points of many of these approaches support multiple rounds of data to the roadside unit and constantly include clustering in every round of single-hop data transmission towards the road side unit; however, the clustering in every round maximizes the number of control messages and there could be the possibility of collision and decreases in network energy. Multi-hop transmission prolongs the cluster head node’s lifetime and boosts the network’s efficiency. Accordingly, this article proposes a new fuzzy-clustering-based routing algorithm to benefit from multi-hop transmission clustering simultaneously. This research has analyzed the limitation of clustering in each round, different algorithms were used to perform the clustering, and multi-hop routing was used to transfer the data of every cluster to the road side unit. The fuzzy logic was used to choose the head node of each cluster. Three parameters, (1) distance of each node, (2) remaining energy, and (3) number of neighbors of every node, were considered as fuzzy criteria. The results of this research were compared to various other algorithms in relation to parameters like dead node in every round, first node expire, half node expire, last node expire, and the network lifetime. The simulation results show that the proposed approach outperforms other methods. On the other hand, the vehicular ad hoc network (VANET) environment is vulnerable at the time of data transmission. The NS-2 software tool was used to simulate and evaluate the proposed fuzzy logic opportunistic routing’s performance results concerning end-to-end delay, packet delivery, and network throughput. We compare to the existing protocols, such as fuzzy Internet of Things (IoT), two fuzzy, and Fuzzy-Based Driver Monitoring System (FDMS). The performance comparison also emphasizes an effective utilization of the resources. Simulations on the highway environment show that the suggested protocol has an improved Quality of Service (QoS) efficiency compared to the above published methods in the literature.

Machine Learning Based Secured Data Transmission For Banking Application

Security in the Internet of Things emphasizes on securing the Internet-enabled gadgets that link to wireless networks. IoT Safety attempts to safeguard IoT devices and systems against cybercrime and it is considered to be a vital security element linked to the Internet of Things. Conversely, banking applications are progressively being supervised for their failure to provide a sufficient degree of customer support and to protect themselves against and respond to cyber-attacks. One of the main factors for this is the vulnerability of fintech systems and networks to malfunctioning. Therefore, wireless networks covering these IoT products are extremely unprotected. IoT is a lightweight system and it is optimal when using lightweight and energy-efficient cryptography for protection. Deep learning is an efficient technique to analyze threats and respond to attacks and security incidents. So this work addresses both security and energy efficiency in IoT using two novel techniques carried out through deep learning. This work contributes to the most innovative way of saving energy in IoT devices through decreasing the use of energy-expensive ‘1’ values in the interface of Dynamic RAM. This can be done by using Base + XOR encoding of data during data transmission. Also, the security of data is incorporated using chaotic XOR encryption (CXE) algorithm which is proved to perform faster and stronger encryption using XOR operation. Using Conditional Generative Adversarial Network (CGAN) based deep learning technique, the Base + XOR encoding technique and CXE are trained well in the banking application. The data generation in CGAN is carried out based on criteria produced using generator model. This work is proved to be consuming less energy, less data transmission time, and provides more security when compared to the existing systems.

A deep asynchronous actor‐critic learning‐based event‐triggered decentralized load frequency control of power systems with communication delays

AbstractThis article proposes a novel asynchronous advantage actor‐critic (A3C) learning‐based dynamic event‐triggered mechanism for the decentralized load frequency regulation to alleviate the local‐area communication burden and influence of the load fluctuations. The proposed dynamic event‐triggered mechanism applies the A3C algorithm to optimally adjust the threshold of the event‐triggered function in real time. In the A3C algorithm framework, the long short‐term memory (LSTM) network is used to estimate the policy function and value function. First, for each control area, a novel model of the decentralized load frequency control (LFC) system is established to design the event‐triggered communication mechanism and deal with the communication delay simultaneously. Then, based on the Lyapunov stability theory, the controller gain parameters of the decentralized LFC system and the margins of the even‐triggering thresholds are derived by solving a series of linear matrix inequalities (LMIs). Finally, a three‐area and four‐area power systems are used to evaluate the proposed decentralized LFC method. Simulation results show that the proposed method can greatly reduce the data transmission times and preserve a satisfactory system performance.

A Secured and Reliable Continuous Transmission Scheme in Cognitive HARQ-Aided Internet of Things

The Internet of Things (IoT) is considered a key enabler for a wide range of smart applications. In IoT, a large number of heterogeneous devices form an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> connection with each other. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> infrastructure is considered an integral part of IoT-empowered applications because of its efficient, cost-effective, and dynamic nature. These networks need to ensure the quality of service using their limited resources, particularly in multihop communication. Because multihop communication can be an easy target of attackers, it needs a secure and reliable data transmission scheme. In this article, we propose a secured and reliable continuous transmission scheme for cognitive hybrid automatic repeat request (HARQ)-aided IoT (SRCT-HARQ) capable of maintaining high throughput and lower delay. The SRCT-HARQ scheme is analytically modeled using a probability-based approach. The mathematical formulas are derived for delay and throughput using a probability-based analysis, and the results are verified using the Monte Carlo simulations. The performance results elaborate that the network throughput and delay are improved, mainly due to the proposed authentication scheme. Using our experimental results, we evaluated the optimal time for data transmission to protect the legal rights of primary users that resulted in improved performance.

Fusion Application of Big Data and Cloud Computing In the Internet of Things

The Internet of Things can be simply understood as the mode of connecting things in the Internet environment. It is mainly the organic combination of computer, Internet technology and other information technology means. At the same time, the organic combination of the Internet of Things, big data, cloud computing and other advanced information thinking and computing methods is the direction of the future smart city construction. In the experiment, due to the characteristics of real-time and massive data of the Internet of Things, the fusion processing of big data cannot be carried out well. Based on the Internet of Things technology, cloud computing technology and fusion algorithm are selected for research. Experimental data show that the number of four types of nodes is set as 30, and the system has 100 nodes in total, and the length of one rotation of the system is 50s. The ordinary Leacha algorithm is used first, and then the improved Leach algorithm is used in the same environment. The experimental results show that there are four different types of nodes in the monitoring environment obtained by the test, and the initial data transmission rates of each node are 60, 90, 110 and 80 respectively. Therefore, the adjusted and improved LEACH algorithm can allocate the required data transmission time according to the different failure levels of the data. If the aging level is higher, the data transmission time of node data will be relatively longer. The improved strategy is in line with the characteristics of the Internet of Things and has the feasibility to complete the experiment.

Energy-Efficient Resource Allocation in Massive MIMO-NOMA Networks With Wireless Power Transfer: A Distributed ADMM Approach

In multicell massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks, base stations (BSs) with multiple antennas deliver their radio frequency energy in the downlink, and Internet-of-Things (IoT) devices use their harvested energy to support uplink data transmission. This paper investigates the energy efficiency (EE) problem for multicell massive MIMO NOMA networks with wireless power transfer (WPT). To maximize the EE of the network, we propose a novel joint power, time, antenna selection, and subcarrier resource allocation scheme, which can properly allocate the time for energy harvesting and data transmission. Both perfect and imperfect channel state information (CSI) are considered, and their corresponding EE performance is analyzed. Under quality-of-service (QoS) requirements, an EE maximization problem is formulated, which is non-trivial due to non-convexity. We first adopt nonlinear fraction programming methods to convert the problem to be convex, and then, develop a distributed alternating direction method of multipliers (ADMM)- based approach to solve the problem. Simulation results demonstrate that compared to alternative methods, the proposed algorithm can converge quickly within fewer iterations, and can achieve better EE performance.