Link Packet Loss Rate Inference Algorithm Based on Network Characteristics in Carrier Network

2021 ◽  
pp. 1392-1401
Author(s):  
Zhan Shi ◽  
Ying Zeng ◽  
Yutu Liang ◽  
Weichao Gong
Keyword(s):  
Packet Loss ◽  
Loss Rate ◽  
Packet Loss Rate ◽  
Inference Algorithm ◽  
Network Characteristics

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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