Proximity detection is to determine whether an IoT receiver is within a certain distance from a signal transmitter. Due to its low cost and high popularity, Bluetooth low energy (BLE) has been used to detect proximity based on the received signal strength indicator (RSSI). To address the fact that RSSI can be markedly influenced by device carriage states, previous works have incorporated RSSI with inertial measurement unit (IMU) using deep learning. However, they have not sufficiently accounted for the impact of multipath. Furthermore, due to the special setup, the IMU data collected in the training process may be biased, which hampers the system's robustness and generalizability. This issue has not been studied before. We propose PRID, an IMU-assisted BLE proximity detection approach robust against RSSI fluctuation and IMU data bias. PRID histogramizes RSSI to extract multipath features and uses carriage state regularization to mitigate overfitting due to IMU data bias. We further propose PRID-lite based on a binarized neural network to substantially cut memory requirements for resource-constrained devices. We have conducted extensive experiments under different multipath environments, data bias levels, and a crowdsourced dataset. Our results show that PRID significantly reduces false detection cases compared with the existing arts (by over 50%). PRID-lite further reduces over 90% PRID model size and extends 60% battery life, with a minor compromise in accuracy (7%).
翻译:近似度检测是确定IOT接收器是否在信号发射器的某一距离之内。 由于其成本低、广受欢迎程度高, 蓝牙低能(Bleothouse)已被用于根据收到的信号强度指标(RSSI)探测近距离。 为解决RSSI明显受到设备运输状态影响的问题,先前的工程已利用深层学习将RSSI与惯性测量单位(IMU)结合为惯性测量单位(IMU),然而,它们没有充分地说明多路路路路路差的影响。 此外,由于特殊设置,在培训过程中收集的IMU数据可能存在偏差,这妨碍了系统的稳健性和可概括性。 这一问题以前尚未研究过。 我们提议采用IMU辅助的近距离探测方法(RISI)能够受到设备运载状态的显著影响。 IMU数据定位将RSISI与惯性测量单元(IMU数据偏差)相适应。 我们进一步提议基于二进制神经网络的精度, 大大削减了对资源受限制设备的记忆要求。 在不同的多路路面环境下,我们进行了广泛的实验, 将现有数据偏差率水平降低了我们现有的50号数据。