Network Transmission Time Research Articles

Enhancing spectrum sensing efficiency in multi‐channel cognitive device‐to‐device networks: Medium Access Control layer strategies and analysis

AbstractThe detection and characterisation of electromagnetic signals within a specific frequency range, known as spectrum sensing, plays a crucial role in Cognitive Radio Networks (CRNs). The CRNs aim to adapt their communication parameters to the surrounding radio environment, thereby improving the efficiency and utilisation of the available radio spectrum. Spectrum sensing is particularly important in device‐to‐device (D2D) communication when operating independently of the cellular network infrastructure. The Medium Access Control (MAC) protocol coordinates device communication and ensures interference‐free operation of the CRN coexisting with the primary cellular network. A spectrum sensing strategy at the MAC layer for cognitive D2D communication. The strategy focuses on reducing the overall sensing period allocated at the MAC layer by having each Cognitive D2D User (cD2DU) sense a smaller subset of available channels while maintaining the same sensing time for cellular user detection at the physical layer. To achieve this, the concept of concurrent groups of D2D devices is introduced in proximity, which are formed by using unique IDs of cD2DUs during the device discovery stage. Each concurrent group senses a specific portion of the cellular user band in a shorter time, resulting in a reduced overall sensing period. In addition to mitigating traffic congestion through data diversion from the cellular network, the proposed strategy facilitates the concurrent sensing of multiple channels by cD2DUs within the underutilised cellular user band. This leads to extended data transmission periods, increased network throughput, and effective offloading of the cellular network. The effectiveness of the proposed work is evaluated by considering factors, such as network throughput and transmission time. Simulation results confirm the effectiveness of the approach in improving spectrum utilisation and communication efficiency in multi‐channel Cognitive D2D Networks (cD2DNs).

Task Offloading in Cloud-Edge Environments

Cloud computing involves transferring data to remote data centers for processing, which consumes significant network bandwidth and transmission time. Edge computing can effectively address this issue by processing tasks at edge nodes, thereby reducing the amount of data transmitted and enhancing the utilization of network bandwidth. This paper investigates intelligent task offloading under the three-layer architecture of cloud-edge-device to fully exploit the cloud-edge collaboration potential. Specifically, an optimization objective function is constructed by modelling the processing cost of all computing tasks. Additionally, asynchronous advantage actor-critic (A3C) algorithm is proposed under cloud-edge collaboration to solve the optimization problem of minimizing the sum of the weights of task offloading delay and energy consumption. Experimental results indicate that the algorithm can effectively utilize the computing resources of the cloud center, reduce task execution delay and energy consumption, and compare favourably with three existing task offloading methods.

Packet Routing in Dynamic Multi-Hop UAV Relay Network: A Multi-Agent Learning Approach

The multi-hop unmanned aerial vehicle (UAV) network can serve as data relays where ground users (GUs) do not have reliable direct connections to the base station (BS). Existing works mainly focus on simple dual-hop system. In this paper, we investigate the packet routing problem in a multi-hop UAV relay network to minimize the data transmission time and enhance the network throughput. However, the dynamic network topology due to UAV mobility makes the packet routing challenging since the limited communication range of each UAV leads to volatile wireless connection. Moreover, the line-of-sight communication links may cause strong interference among UAVs. Towards this end, we propose a novel multi-agent deep reinforcement learning based algorithm, named as multi-agent QMIX (MAQMIX) to: 1) design proper UAVs’ trajectories to serve the moving GUs while maintaining the network connection; 2) allocate frequency resource properly among UAVs to alleviate the impact of interference; and 3) choose a proper next hop UAV for each data packet to reduce the transmission time and probability of network congestion. The proposed MAQMIX has two novel training mechanisms, i.e., intra-UAV and inter-UAV training mechanisms, which can tackle the large action space issue and coordinate the training among UAVs in the multi-hop UAV relay network. Simulation results demonstrate that the MAQMIX outperforms baseline schemes in terms of the network congestion avoidance, throughput, and transmission time.

Secure audio file indexing based on hidden markov model (HMM) on the cloud storage

With the introduction of many social media applications and the exponential growth of the number of people using such applications to exchange Audio files as a main way of conveying confidential messages relaying on public telecommunications and networks. The need arise to secure audio data files and preserve the integrity of the message while traversing public networks and when the data is at rest in the cloud. Therefore, in this research, to ensure confidentiality and integrity of the audio files while reducing storage space several algorithms have been devised. To achieve this, we utilized Public key infrastructure and Hash functions along with Hidden Markov Models (HMM). The results show a significant drop in the storage space needed while remarkable reduction in network transmission time. When comparing the original audio file size with the converted file size after applying HMM, the results show a variation between 0% and 10% only, with over 50% reduction in the storage space in some cases.

(Re)deployment of Smart Algorithms in Cyber–Physical Production Systems Using DSL4hDNCS

Intelligent algorithms and learning are the basis for evolving smart cyber-physical production systems (CPPSs) and Industrie 4.0. A smart image detection algorithm shall be added to reduce downtime due to the extended operation of glass bottles in a yogurt producing plant. To support the engineers in doing so, a comprehensive domain-specific language (DSL), DSL4hDNCS, is introduced, enabling rapid analysis, addressing hardware/software architectures and network-related delays and uncertainties. DSL4hDNCS is defined by a metamodel to avoid ambiguity and enriched by aspects, such as safety, calculation power, and network transmission time. DSL4hDNCS is used to compare (re)deployment alternatives using different technologies, such as edge, fog, and cloud to implement the additional smart algorithm. The evaluation of DSL4hDNCS using an acknowledged Industrie 4.0 demonstrator plant as a case study confirmed the benefit for engineers during the redesign.

IoTSim-Osmosis: A framework for modeling and simulating IoT applications over an edge-cloud continuum

The osmotic computing paradigm sets out the principles and algorithms for simplifying the deployment of Internet of Things (IoT) applications in integrated edge-cloud environments. Various existing simulation frameworks can be used to support integration of cloud and edge computing environments. However, none of these can directly support an osmotic computing environment due to the complexity of IoT applications and heterogeneity of integrated edge-cloud environments. Osmotic computing suggests the migration of workload to/from a cloud data center to edge devices, based on performance and security trigger events. We propose ‘IoTSim-Osmosis– a simulation framework to support the testing and validation of osmotic computing applications. In particular, our detailed related work analysis demonstrates that IoTSim-Osmosis is the first simulation framework to enable unified modeling and simulation of complex IoT applications over heterogeneous edge-cloud environments. IoTSim-Osmosis is demonstrated using an electricity management and billing application case study, for benchmarking various run-time QoS parameters, such as IoT battery use, execution time, network transmission time and consumed energy.

BOOST: A User Association and Scheduling Framework for Beamforming mmWave Networks

The millimeter wave (mmWave) band offers vast bandwidth and plays a key role for next generation wireless networks. However, the mmWave network raises a great challenge for user association and scheduling, due to the limited power budget and beamformers, diverse user traffic loads, user quality of service requirement, etc. In this paper, we propose a novel framework for user association and scheduling in multi-base station mmWave networks, termed the clustering Based dOwnlink UE assOciation, Scheduling, beamforming with power allocaTion (BOOST). The objective is to reduce the downlink network transmission time, subject to the base station power budget, number of beamformers, user traffic loads, and the quality of service requirement at users. We compare BOOST with three state-of-the-art user scheduling schemes. On average, BOOST reduces the transmission time by 37, 30, and 26 percent, and achieves a sum rate gain of 56, 43, and 34 percent, respectively.

Nucleotide Archival Format (NAF) enables efficient lossless reference-free compression of DNA sequences.

SummaryDNA sequence databases use compression such as gzip to reduce the required storage space and network transmission time. We describe Nucleotide Archival Format (NAF)—a new file format for lossless reference-free compression of FASTA and FASTQ-formatted nucleotide sequences. Nucleotide Archival Format compression ratio is comparable to the best DNA compressors, while providing dramatically faster decompression. We compared our format with DNA compressors: DELIMINATE and MFCompress, and with general purpose compressors: gzip, bzip2, xz, brotli and zstd.Availability and implementationNAF compressor and decompressor, as well as format specification are available at https://github.com/KirillKryukov/naf. Format specification is in public domain. Compressor and decompressor are open source under the zlib/libpng license, free for nearly any use.Supplementary information Supplementary data are available at Bioinformatics online.

The Selection and Placement Method of Materialized Views on Big Data Platform of Equipment Condition Assessment

With the formation of electric big data environment, more and more big data analyses emerge. In the complicated data analysis on equipment condition assessment, there exist many join operations, which are time-consuming. In order to save time, the approach of materialized view is usually used. It places part of common and critical join results on external storage and avoids the frequent join operation. In the paper we propose the methods of selecting and placing materialized views to reduce the query time of electric transmission and transformation equipment, and make the profits of service providers maximal. In selection method we design a computation way for the value of non-leaf node based on MVPP structure chart. In placement method we use relevance weights to place the selected materialized views, which help reduce the network transmission time. Our experiments show that the proposed selection and placement methods have a high throughput and good optimization ability of query time for electric transmission and transformation equipment.

Two rapid test methods for assessing transmission time reliability of a multiple-channel network with Poisson arrival and service rates

To shorten the duration of the network transmission time reliability test, two rapid methods are proposed for a multiple-channel network with Poisson arrival and service rates. For a network whose service rate is given and can be increased in practice, a rapid test can be conducted on a similar network with higher arrival and service rates. The test conditions are derived using the similarity theory, and the data collected in the test can be used to estimate the reliability of the original network. For a network whose service rate is unknown, a rapid test analogous to accelerated test can be applied, which only increases the arrival rate. The analytical expression of transmission time reliability is used to derive the accelerated model, and the data collected in the test can be used to extrapolate the network reliability under normal arrival rate. Our numerical study shows that the two proposed methods are efficient and the reliability estimates are quite accurate compared to the empirical estimate obtained from verification tests. In the rapid tests, the data collected in a given period of time increases along with the arrival rate, and the test duration can be shortened without affecting the sample size of packets.

희소행렬 기반 NetCDF 파일의 압축 방법

Like many types of scientific data, results from simulations of volcanic ash diffusion are of a clustered sparse matrix in the netCDF format. Since these data sets are large in size, they generate high storage and transmission costs. In this paper, we suggest a new method that reduces the size of the data of volcanic ash diffusion simulations by converting the multi-dimensional index to a single dimension and keeping only the starting point and length of the consecutive zeros. This method presents performance that is almost as good as that of ZIP format compression, but does not destroy the netCDF structure. The suggested method is expected to allow for storage space to be efficiently used by reducing both the data size and the network transmission time.

Implementation and Application of QoS in Power Ethernet Switch

Smart grid based on Ethernet technology is the development direction of power transmission network, power Ethernet carried different data communication service, which have different needs for network transmission time delay and bandwidth guarantee, which is reflected in the quality of service of network QoS (Quality of Service) requirements, in power Ethernet, QoS which can provided differentiated services (Diff-Serv) is essential. This paper outlines QoS implementation, QoS feature and related technologies in the power Ethernet switch, and analyses the flow characteristics of digital substation network, and proposes classification and scheduling scheme of service traffic based on network nodes, and elaborates an important effect that QoS has in ensuring substation communication network transmission of high quality service.

Predictive Method of Networked Control of Industrial Automation Systems

A New method for real-time prediction of uncertain network transmission time delay and the closed-loop control method through the network manufacturing and industrial plant are introduced. Put forward delay prediction method is based on the multilayer perceptron neural model. To reduce the amount of the first layer of neurons network, thus reducing the computational burden a real-time implementation, a method for the determination of Markov delay sequence order. Used to predict delay and a zero order equivalent discrete time model of the plant, a kind of time-varying state feedback control algorithm is put forward a strategy to obtain real-time update. The stability of the closed-loop switch sufficient conditions is derived using the theorem of linear system. It shows that the method studies of industrial network by two cases, i.e., a DC motor driven transport roll paper and a milling machine. Simulation study describes the effectiveness of the method to control these challenging problems.

Research on Optimization Selection Algorithm of Track and Field Based on Adaptive Wireless Sensor Network Node Speed

By using the adaptive search algorithm of particle swarm layout, we optimize 3D space nodes of wireless network, and obtain the optimum mathematical model of particle swarm optimization distribution of nodes in wireless network. Using the tree structure and the wireless network hardware equipment we design algorithm the optimization platform of track and field technique. In order to verify the stability and reliability of platform running, we use the wireless network node division method and transmission time groups of control network to control athlete’s technical movement. And we use Java technology to calculate the athlete’s action to verify the stability. Through calculation, for the same optimization task, the time of adaptive search algorithm is short, and the search is less, which is a rapid and effective optimization algorithm.

Bilateral teleoperation of multiple agents with formation control

This paper studies the formation problem for multi-slave teleoperation system over general communication networks, where multiple mobile slave agents are coupled with a single master robot. The forward and backward network transmission time delays are assumed to be asymmetric and time-varying. Due to the quantization in the network, a dynamic quantization strategy is provided to quantize the output signals of the master robot and slave agents before transmitting. Then, a novel master-slave protocol is designed to achieve the formation task under variable time delays and quantization. Additionally, the sufficient conditions for stability are presented to show that the formation protocol can stabilize the master-slave system under variable time delays and quantization. Finally, simulation are performed to show effectiveness of the main results.

Time Synchronization in Tandem Hot Strip Line Based on Improved Broadcast Mode

In order to solve the time synchronization problem in tandem hot strip line, the traditional algorithm of time synchronization was analyzed, and the server-client mode was compared with the broadcast mode using multithread technology, then the present paper improved the broadcast mode to improve its precision. The new method can estimate total delay including clock offset and network transmission time. A simple, easy and little loading time synchronization method was designed for the distributed process control system of tandem hot strip line. The new method was applied in domestic some tandem hot strip line successfully which showed that it could meet the demands of process control system with little expenses, simple structure and high precision.

A novel real-time fuzzy adaptive auto-tuning scheme for cascade PID controllers

This paper presents a novel fuzzy auto-tuning methodology to continuously adapt PID control actions without interrupting the normal process operation. New auto-tuning rules are introduced to schedule a gradual and guided activation of each individual P, I and D control mode in three adjustable functional control zones. An auto-scaling procedure has been incorporated to generalize the autotuning scheme to efficiently respond to any set-point change outside a pre-defined operating span. In contrast to existing auto-tuning algorithms, the proposed scheme is not an on-demand auto-tuning methodology and hence does not require alertness of an experienced engineer to initiate and supervise its initial operation in a separate commissioning identification pre-test. This interesting feature provides a new perspective on PID auto-tuning approaches. Performance of the proposed auto-tuning scheme is practically evaluated in a real pilot plant within a networked control system (NCS) configuration, realized by industrial Ethernet and Foundation Fieldbus technologies. An extensive series of test scenarios has been conducted to explore efficiency of the proposed auto-tuning methodology to cope with fixed and varying operating set-points under uncertain and variable network transmission time delays and external disturbance.

Networked control of industrial automation systems—a new predictive method

A new method for real-time prediction of uncertain network transmission time delays and a method for closed-loop control of manufacturing and industrial plants through networks are introduced. The proposed delay prediction method is based on the multilayer perceptron neural model. In order to minimize the number of neurons in the first layer of the network and hence reducing the computational burden in a real-time implementation, a method for determination of the Markov order of the time delay sequence is presented. Using the predicted delay, and a zero-order hold equivalent discrete-time model of the plant, a time varying state feedback control algorithm with a real-time gain updating strategy is proposed. A sufficient condition for closed-loop stability is also derived using the switching theorem for linear systems. The proposed method is shown, through two industrial networked case studies, namely, a DC motor driving a transportation roller for paper sheets and a milling machine. Simulation studies depict the efficacy of the proposed method in controlling such challenging problems.

Research and Design of Transmission Rate Adaptive Control in Public Video Monitoring System

In order to solve the practical technology problems in the field of public video monitoring system engineering, in this paper, a real-time video transmission rate adaptive control programming in public video monitoring system is introduced. The system can monitor network congestion status according to the parameters such as network transmission time, transmission delay and data packet loss rate, and dynamically adjust the output rate of the video transmitter according to a certain algorithm of adaptive control of transmission rate, it has certain robustness, and improved the quality of video output. Experimental test results show that the contrast effect is better in reducing video packet loss rate, improving network bandwidth utilization or avoiding network congestion.

Teleoperation Over the Internet With/Without Velocity Signal

This paper addresses the controller design problem for teleoperation over networks such as the Internet. The forward and backward network transmission time delays are assumed to be asymmetric and time varying, which is the case for computer network communications. We propose a novel master-slave controller, which renders the entire system stable with relatively good steady- and transient-state performances. The relations among the parameters of the controller and the allowable maximum time delays are built in the form of linear matrix inequality. The designed controller is extended to the case that the velocity information is not available. A high-gain observer is designed to estimate the velocities of the master and slave joints online, and the controller is constructed on the basis of the estimated velocities. The stability of the closed-loop system is proved. Both simulations and experiments are performed to verify the correctness and effectiveness of the proposed method.