Statistical Evaluation of TCP Packet Loss Rate Estimation

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

Wireless Communications and Mobile Computing ◽

10.1155/2019/7080249 ◽

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.

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