LoRaWAN geo-tracking using map matching and compass sensor fusion

In contrast to accurate GPS-based localization, approaches to localize within LoRaWAN networks offer the advantages of being low power and low cost. This targets a very different set of use cases and applications on the market where accuracy is not the main considered metric. The localization is performed by the Time Difference of Arrival (TDoA) method and provides discrete position estimates on a map. An accurate "tracking-on-demand" mode for retrieving lost and stolen assets is important. To enable this mode, we propose deploying an e-compass in the mobile LoRa node, which frequently communicates directional information via the payload of the LoRaWAN uplink messages. Fusing this additional information with raw TDoA estimates in a map matching algorithm enables us to estimate the node location with a much increased accuracy. It is shown that this sensor fusion technique outperforms raw TDoA at the cost of only embedding a low-cost e-compass. For driving, cycling, and walking trajectories, we obtained minimal improvements of 65, 76, and 82% on the median errors which were reduced from 206 to 68 m, 197 to 47 m, and 175 to 31 m, respectively. The energy impact of adding an e-compass is limited: energy consumption increases by only 10% compared to traditional LoRa localization, resulting in a solution that is still 14 times more energy-efficient than a GPS-over-LoRa solution

Predicting lorawan behavior. How machine learning can help

Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation. In this work, we discuss how machine learning approaches can be used to improve network performance (and if and how they can help). To this aim, we describe a methodology to process LoRaWAN packets and apply a machine learning pipeline to: (i) perform device profiling, and (ii) predict the inter-arrival of IoT packets. This latter analysis is very related to the channel and network usage and can be leveraged in the future for system performance enhancements. Our analysis mainly focuses on the use of k-means, Long Short-Term Memory Neural Networks and Decision Trees. We test these approaches on a real large-scale LoRaWAN network where the overall captured traffic is stored in a proprietary database. Our study shows how profiling techniques enable a machine learning prediction algorithm even when training is not possible because of high error rates perceived by some devices. In this challenging case, the prediction of the inter-arrival time of packets has an error of about 3.5% for 77% of real sequence cases

Geolocation with LoRa and machine learning using TDOA algorithm

Capstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Computer Engineering, May 2021A vehicle tracking system is a crucial system used in fields such as transportation, delivery services, and criminal investigations. GPS and GSM system is the most popular means of determining vehicle location. It is accurate; however, it is costly and consumes a lot of power due to the need for frequent battery replacement. Implementing it in local public transportation, especially in African countries is not feasible. This is because installation and maintenance are expensive. Various research has been going on to improve geolocation with LoRa. However, one of the issues that remain is the time accuracy in collecting the time of arrival which is crucial in determining the vehicle position in the Time of Difference of Arrival (TDOA) algorithm. This project designed a low-cost and low-power LoRa vehicle tracking system and researched ways to improve LoRa geolocation using the TDOA algorithm using machine learning. A multilateration algorithm was used in positioning station gateways for the TDOA algorithm to provide an accurate measurement, it required a nanoseconds precision, however, only a millisecond precision was achieved from the hardware setup. The machine learning model used RandomForest for training and prediction advantages. The lack of a microcontroller with a nanoseconds precision, limited resources to build a full multilateration system affected the accuracy of the TDOA algorithm.Ashesi Universit