scholarly journals Service-Oriented Real-Time Smart Job Shop Symmetric CPS Based on Edge Computing

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1839
Author(s):  
Chuang Wang ◽  
Yi Lv ◽  
Qiang Wang ◽  
Dongyu Yang ◽  
Guanghui Zhou
Keyword(s):  
Data Processing ◽  
Production Process ◽  
Real Time ◽  
Packet Loss ◽  
Loss Rate ◽  
Job Shop ◽  
Packet Loss Rate ◽  
Response Delay ◽  
Function Modules ◽  
Service Oriented

Symmetry is one of the most important notions in the digital twins-driven manufacturing cyber–physical system (CPS). Real-time acquisition of production data and rapid response to changes in the external environment are the keys to ensuring the symmetry of the CPS. In the service-oriented production process, in order to solve the problem of the service response delay of the production nodes in a smart job shop, a CPS based on mobile edge computing (MEC) middleware is proposed. First, the CPS and MEC for a service-oriented production process are analyzed. Secondly, based on MEC middleware, a CPS architecture model of a smart job shop is established. Then, the implementation of MEC middleware and application layer function modules are introduced in detail. By designing an MEC middleware model and embedding function modules such as data cache management, redundant data filtering, and data preprocessing, the ability of data processing is sunk from the data center to the data source. Based on that, the network performances, such as network bandwidth, packet loss rate, and delay, are improved. Finally, an experiment platform of the smart job shop is used to verify different data processing modes by comparing the network performance data such as bandwidth, packet loss rate, and response delay.

scholarly journals The real-time big data processing method based on LSTM or GRU for the smart job shop production process

2020 ◽  
Vol 14 ◽  
pp. 174830262096239 ◽  
Author(s):  
Chuang Wang ◽  
Wenbo Du ◽  
Zhixiang Zhu ◽  
Zhifeng Yue
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Production Process ◽  
Real Time ◽  
Job Shop ◽  
Production Data ◽  
Time Data ◽  
Big Data Processing ◽  
The Real ◽  
Data Processing Method

With the wide application of intelligent sensors and internet of things (IoT) in the smart job shop, a large number of real-time production data is collected. Accurate analysis of the collected data can help producers to make effective decisions. Compared with the traditional data processing methods, artificial intelligence, as the main big data analysis method, is more and more applied to the manufacturing industry. However, the ability of different AI models to process real-time data of smart job shop production is also different. Based on this, a real-time big data processing method for the job shop production process based on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) is proposed. This method uses the historical production data extracted by the IoT job shop as the original data set, and after data preprocessing, uses the LSTM and GRU model to train and predict the real-time data of the job shop. Through the description and implementation of the model, it is compared with KNN, DT and traditional neural network model. The results show that in the real-time big data processing of production process, the performance of the LSTM and GRU models is superior to the traditional neural network, K nearest neighbor (KNN), decision tree (DT). When the performance is similar to LSTM, the training time of GRU is much lower than LSTM model.

scholarly journals An Intelligent Packet Loss Control Heuristic for Connectionless Real-Time Voice Communication

2010 ◽  
Vol 2010 ◽  
pp. 1-9
Author(s):  
Murat Şensoy ◽  
Burcu Yılmaz ◽  
Erdoğan Yılmaz
Keyword(s):  
Real Time ◽  
Packet Loss ◽  
Loss Rate ◽  
Packet Loss Rate ◽  
Video Communication ◽  
Voice Communication ◽  
Remarkable Improvement ◽  
Voice Packets ◽  
Time Critical ◽  
Loss Control

Time critical nature of the real-time communication usually makes connection-oriented protocols such as TCP useless, because retransmission of old and probably expired packets is not desired. However, connectionless protocols such as UDP do not provide such packet loss control and suitable for real-time communication such as voice or video communication. In this paper, we present an adaptive approach for the intelligent packet loss control for connectionless real-time voice communication. Instead of detecting and resending lost voice packets, this heuristic estimates the packet loss rate adaptively using a modified version of reinforcement learning and resends the most critical packets before they are expired. Our simulations indicate that this approach is promising for a remarkable improvement in QoS of real-time voice communication.

Packet loss rate monitoring model of IOT based on differential evolution algorithm

2021 ◽  
pp. 1-12
Author(s):  
Yinghua Feng ◽  
Wei Yang
Keyword(s):  
Internet Of Things ◽  
Differential Evolution ◽  
Packet Loss ◽  
Loss Rate ◽  
Differential Evolution Algorithm ◽  
Packet Loss Rate ◽  
Data Space ◽  
Monitoring Model ◽  
Evolution Algorithm ◽  
The Internet Of Things

In order to overcome the problems of high energy consumption and low execution efficiency of traditional Internet of things (IOT) packet loss rate monitoring model, a new packet loss rate monitoring model based on differential evolution algorithm is proposed. The similarity between each data point in the data space of the Internet of things is set as the data gravity. On the basis of the data gravity, combined with the law of gravity in the data space, the gravity of different data is calculated. At the same time, the size of the data gravity is compared, and the data are classified. Through the classification results, the packet loss rate monitoring model of the Internet of things is established. Differential evolution algorithm is used to solve the model to obtain the best monitoring scheme to ensure the security of network data transmission. The experimental results show that the proposed model can effectively reduce the data acquisition overhead and energy consumption, and improve the execution efficiency of the model. The maximum monitoring efficiency is 99.74%.

scholarly journals Sensing Coverage Algorithm of Sparse Mobile Sensor Node with Trade-Off between Packet Loss Rate and Transmission Delay

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Kehua Zhao ◽  
Yourong Chen ◽  
Siyi Lu ◽  
Banteng Liu ◽  
Tiaojuan Ren ◽  
...  
Keyword(s):  
Packet Loss ◽  
Data Transmission ◽  
Loss Rate ◽  
Packet Loss Rate ◽  
Transmission Delay ◽  
Sensor Nodes ◽  
Mobile Sensor ◽  
Sensing Coverage ◽  
Mobile Sensor Node ◽  
Monitoring Area

To solve the problem of sensing coverage of sparse wireless sensor networks, the movement of sensor nodes is considered and a sensing coverage algorithm of sparse mobile sensor node with trade-off between packet loss rate and transmission delay (SCA_SM) is proposed. Firstly, SCA_SM divides the monitoring area into several grids of same size and establishes a path planning model of multisensor nodes’ movement. Secondly, the social foraging behavior of Escherichia coli in bacterial foraging is used. A fitness function formula of sensor nodes’ moving paths is proposed. The optimal moving paths of all mobile sensor nodes which can cover the entire monitoring area are obtained through the operations of chemotaxis, replication, and migration. The simulation results show that SCA_SM can fully cover the monitoring area and reduce the packet loss rate and data transmission delay in the process of data transmission. Under certain conditions, SCA_SM is better than RAND_D, HILBERT, and TCM.

scholarly journals Wireless Body Area Network (WBAN)-Based Telemedicine for Emergency Care

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2153 ◽  
Author(s):  
Latha R ◽  
Vetrivelan P
Keyword(s):  
Packet Loss ◽  
Loss Rate ◽  
Bayes Theorem ◽  
Data Rate ◽  
Packet Loss Rate ◽  
Medical Decision ◽  
Area Network ◽  
Body Area ◽  
Delay Sensitive ◽  
Emergency Monitoring

This paper is a collection of telemedicine techniques used by wireless body area networks (WBANs) for emergency conditions. Furthermore, Bayes’ theorem is proposed for predicting emergency conditions. With prior knowledge, the posterior probability can be found along with the observed evidence. The probability of sending emergency messages can be determined using Bayes’ theorem with the likelihood evidence. It can be viewed as medical decision-making, since diagnosis conditions such as emergency monitoring, delay-sensitive monitoring, and general monitoring are analyzed with its network characteristics, including data rate, cost, packet loss rate, latency, and jitter. This paper explains the network model with 16 variables, with one describing immediate consultation, as well as another three describing emergency monitoring, delay-sensitive monitoring, and general monitoring. The remaining 12 variables are observations related to latency, cost, packet loss rate, data rate, and jitter.

Network Performance Comparison between Linear and Hybrid Topology Structure of WSN in Coal Mining Equipment

2012 ◽  
Vol 548 ◽  
pp. 775-779
Author(s):  
Hong Liang Gao ◽  
Bing Wen Wang ◽  
Chao Gao ◽  
Xiao Ya Hu
Keyword(s):  
Coal Mine ◽  
Packet Loss ◽  
Network Performance ◽  
Loss Rate ◽  
Packet Loss Rate ◽  
Mine Safety ◽  
Mining Equipment ◽  
Linear Topology ◽  
Hybrid Topology ◽  
Average Transmission

This paper analyzes the characteristics of current monitoring wireless sensor networks for coal mine safety and two kinds of typical system network architecture of mining working face in coal mine firstly, and then analyzes the network performance of the two kinds of network system theoretically. In order to compare the performance of WSN adopting linear topology and hybrid topology, we build the simulation model using NS2 to evaluate the performance through three indexes, i.e. total energy consumption, packet loss rate and average transmission latency. The research results show that the network adopting hybrid topology has better energy efficiency, and the network adopting linear topology has better performance in packet loss rate and average transmission latency.

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