David Murray

While LoRa overcomes the high-power consumption and deployment costs of GPS and mobile networks, it faces challenges in accuracy. This paper presents a method for LoRa-based localization and tracking. It uses unsupervised symbolization to analyze received signal features. We use partitioning, D-Markov machines for symbolization and the Chinese restaurant process to achieve unsupervised symbolization. In particular, a novel adaptive feature extraction technique is proposed in partitioning to overcome the problems of over-tracking and under-tracking. Mean spectral kurtosis analysis is performed across several partitioning techniques to assess their symbolization effectiveness. This enables the selection of the most appropriate partitioning technique. This enhances the localization and tracking accuracy of target objects by focusing on robustness to noise and multipath effects. The proposed method learns and estimates the distance range simultaneously, thereby eliminating the need for a separate offline training phase and the storage of reference coordinates. Experimental results using LoRa highlight the proposed method's efficacy in real-time localization, tracking, and superiority over the state-of-the-art method.

Global communications without reliance on an engineered communications network make the ionosphere an attractive medium for wireless sensors in remote deployments. However, ionospheric circuits’ temporary availability is a challenge in scheduling transmissions for a sensor with limited power, communications and computational capacity, particularly where cost and antenna constraints limit operation to a single frequency. We describe a technique for scheduling transmissions based on precomputed propagation models. The models predict the time-varying Signal to Noise Ratio (SNR) at the receiver. We describe methods to determine threshold SNR values, using the Weak Signal Propagation Reporter (WSPR) database to determine if a time slot is suitable for transmission.Two techniques are investigated to quantify the failed receptions: the Inverse Square Law method uses a statistical approach and a sampling measurement technique called Goldilocks. The two approaches yielded threshold SNR values of −21 dB and −19 dB, respectively, for a time slot with a 90% successful reception goal. Applying these thresholds to the modelled SNR, we generate a precomputed hourly transmission schedule. With the schedule determined monthly, a 12-month plan requires 36 bytes of wireless sensor storage. A six-day experiment, using a 1677 km path, found that the schedule resulted in an 83% reception rate when used with a power level of 200 mW.