Acoustic Backscatter Network for Vehicle Body-in-White
Weiguo Wang, Yuan He, Yadong Xie, Chuyue Xie, Yi Kai, Chengchen Hu
Abstract
We present a novel approach to monitor the Body in White (BiW), the fundamental metallic structure of a vehicle. Existing monitoring methods, including both wired and wireless sensor systems, face significant challenges due to integration complexity, weight considerations, material costs, and signal blockage within the metallic environment. To overcome these limitations, we introduce Arach-Net, an acoustic backscatter network that leverages the conductive properties of the BiW to propagate vibration signals for energy transfer and data communication. This system comprises battery-free tags that harvest energy from BiW vibrations and utilize a backscatter technique for efficient communication, thereby eliminating the need for external power sources and reducing the power consumption. We address key challenges such as power sufficiency for tag activation and sustained operation, and collision reduction in network communication, by designing an ultra-low power backscatter tag and a distributed slot allocation protocol. We implement ArachNet, and deploy 12 tags onto the BiW of an electric SUV car. The evaluation results show that the power consumption of the tag is 51.0 μW for uplink packet transmission, and 24.8 μW for downlink packet reception. With our network protocol, the slot utilization can be up to 81.2%.
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