A rate adaption algorithm for backscatter networks in mobile scenes
by Jia Liu; Baofeng Zhao
International Journal of Sensor Networks (IJSNET), Vol. 42, No. 2, 2023

Abstract: As one of the crucial technologies of intelligent sensing in the internet of things (IoT), a backscatter network has been applied extensively due to realising low-power data transmission in various scenarios, especially in mobile scenes. The complex and changeable environment leads to the dynamic change in the transmission demand of nodes. This study aimed to propose a rate adaptation algorithm extra-trees rate adaptation (ETRA), to overcome the limitation on the throughputs of backscatter nodes in mobile scenes. By studying the channel indexes of both RSSI and packet loss rate, the collision model was used to estimate the packet loss rate accurately and efficiently, which reflected the channel quality better. In addition, the generalisation ability of the rate decision module was improved by the extreme decision tree algorithm, and the accuracy of rate prediction of mobile nodes was further improved. Finally, the design results were evaluated using a commercial reader to collect multi-group tag data and simulate experiments. The results showed that the mobile node throughput of ETRA is 1.5 times compared with the existing algorithms.

Online publication date: Wed, 21-Jun-2023

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Sensor Networks (IJSNET):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com