An adaptive enhanced differential evolution strategies for topology robustness in internet of things

2022 ◽  
Vol 18 (1) ◽  
pp. 1
Author(s):  
Talha Naeem Qureshi ◽  
Nadeem Javaid ◽  
Ahmad Almogren ◽  
Asad Ullah Khan ◽  
Hisham Almajed ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Differential Evolution ◽  
Evolution Strategies

An Adaptive Enhanced Differential Evolution Strategies for Topology Robustness in Internet of Things

2021 ◽  
Vol 18 (1) ◽  
pp. 1
Author(s):  
Irfan Mohiuddin ◽  
Hisham Almajed ◽  
Asad Ullah Khan ◽  
Ahmad Almogren ◽  
Talha Naeem Qureshi ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Differential Evolution ◽  
Evolution Strategies

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%.

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