Title: A rate adaption algorithm for backscatter networks in mobile scenes

Authors: Jia Liu; Baofeng Zhao

Addresses: Mechanical and Vehicle Engineering College, Taiyuan University of Technology, Taiyuan, 030024, China; Electronic Information Engineering Department, Shanxi Polytechnic College, Taiyuan, 030006, China ' Mining Engineering College, Taiyuan University of Technology, Taiyuan, 030024, China

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.

Keywords: internet of things; IoT; backscatter network; mobile node; rate adaptation.

DOI: 10.1504/IJSNET.2023.131652

International Journal of Sensor Networks, 2023 Vol.42 No.2, pp.137 - 144

Received: 17 Apr 2023
Accepted: 18 Apr 2023
Published online: 21 Jun 2023 *

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