Adaptive Quantization for Key Generation in Low-Power Wide-Area Networks

TL;DR

This paper introduces an adaptive quantization method for physical layer key generation in LPWANs, dynamically adjusting parameters based on RSSI measurement complexity to improve key rates.

Contribution

It presents a novel adaptive quantization scheme that uses Lempel-Ziv complexity and pre-trained models to optimize key generation from RSSI in LPWANs, specifically LoRa.

Findings

Outperforms fixed quantization in key rate by up to 2.35 times

Uses Lempel-Ziv complexity to adapt quantization parameters

Includes a guard band calibration scheme during real-time operation

Abstract

Physical layer key generation based on reciprocal and random wireless channels has been an attractive solution for securing resource-constrained low-power wide-area networks (LPWANs). When quantizing channel measurements, namely received signal strength indicator (RSSI), into key bits, the existing works mainly adopt fixed quantization levels and guard band parameters, which fail to fully extract keys from RSSI measurements. In this paper, we propose a novel adaptive quantization scheme for key generation in LPWANs, taking LoRa as a case study. The proposed adaptive quantization scheme can dynamically adjust the quantization parameters according to the randomness of RSSI measurements estimated by Lempel-Ziv complexity (LZ76), while ensuring a predefined key disagreement ratio (KDR). Specifically, our scheme uses pre-trained linear regression models to determine the appropriate quantization level and guard band parameter for each segment of RSSI measurements. Moreover, we propose a guard band parameter calibration scheme during information reconciliation during real-time key generation operation. Experimental evaluations using LoRa devices show that the proposed adaptive quantization scheme outperforms the benchmark differential quantization and fixed quantization with up to 2.35$\times$ and 1.51$\times$ key generation rate (KGR) gains, respectively.