105 research outputs found
TDoA-based outdoor positioning in a public LoRa network
The performance of LoRa Geo-location for outdoor tracking purposes has been evaluated on a public LoRaWAN network. Time Difference of Arrival (TDOA) localization accuracy, probability and update frequency were evaluated for different trajectories (walking, cycling, driving) and LoRa spreading factors. A median accuracy of 200m was obtained and in 90% of the cases the error was less then 480m
Experimental performance evaluation of outdoor TDoA and RSS positioning in a public LoRa network
This paper experimentally compares the positioning accuracy of TDoA-based and RSS-based localization in a public outdoor LoRa network in the Netherlands. The performance of different Received Signal Strength (RSS)-based approaches (proximity, centroid, map matching,...) is compared with Time-Difference-of-Arrival (TDoA) performance. The number of RSS and TDoA location updates and the positioning accuracy per spreading factor (SF) is assessed, allowing to select the optimal SF choice for the network. A road mapping filter is applied to the raw location estimates for the best algorithms and SFs. RSS-based approaches have median and maximal errors that are limited to 1000 m and 2000 m respectively, using a road mapping filter. Using the same filter, TDoA-based approaches deliver median and maximal errors in the order of 150 m and 350 m respectively. However, the number of location updates per time unit using SF7 is around 10 times higher for RSS algorithms than for the TDoA algorithm
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
GNSS-free outdoor localization techniques for resource-constrained IoT architectures : a literature review
Large-scale deployments of the Internet of Things (IoT) are adopted for performance
improvement and cost reduction in several application domains. The four main IoT application
domains covered throughout this article are smart cities, smart transportation, smart healthcare, and
smart manufacturing. To increase IoT applicability, data generated by the IoT devices need to be
time-stamped and spatially contextualized. LPWANs have become an attractive solution for outdoor
localization and received significant attention from the research community due to low-power,
low-cost, and long-range communication. In addition, its signals can be used for communication
and localization simultaneously. There are different proposed localization methods to obtain the
IoT relative location. Each category of these proposed methods has pros and cons that make them
useful for specific IoT systems. Nevertheless, there are some limitations in proposed localization
methods that need to be eliminated to meet the IoT ecosystem needs completely. This has motivated
this work and provided the following contributions: (1) definition of the main requirements and
limitations of outdoor localization techniques for the IoT ecosystem, (2) description of the most
relevant GNSS-free outdoor localization methods with a focus on LPWAN technologies, (3) survey
the most relevant methods used within the IoT ecosystem for improving GNSS-free localization
accuracy, and (4) discussion covering the open challenges and future directions within the field.
Some of the important open issues that have different requirements in different IoT systems include
energy consumption, security and privacy, accuracy, and scalability. This paper provides an overview
of research works that have been published between 2018 to July 2021 and made available through
the Google Scholar database.5311-8814-F0ED | Sara Maria da Cruz Maia de Oliveira PaivaN/
Outdoor node localization using random neural networks for large-scale urban IoT LoRa networks
Accurate localization for wireless sensor end devices is critical, particularly for Internet of Things (IoT) location-based applications such as remote healthcare, where there is a need for quick response to emergency or maintenance services. Global Positioning Systems (GPS) are widely known for outdoor localization services; however, high-power consumption and hardware cost become a significant hindrance to dense wireless sensor networks in large-scale urban areas. Therefore, wireless technologies such as Long-Range Wide-Area Networks (LoRaWAN) are being investigated in different location-aware IoT applications due to having more advantages with low-cost, long-range, and low-power characteristics. Furthermore, various localization methods, including fingerprint localization techniques, are present in the literature but with different limitations. This study uses LoRaWAN Received Signal Strength Indicator (RSSI) values to predict the unknown X and Y position coordinates on a publicly available LoRaWAN dataset for Antwerp in Belgium using Random Neural Networks (RNN). The proposed localization system achieves an improved high-level accuracy for outdoor dense urban areas and outperforms the present conventional LoRa-based localization systems in other work, with a minimum mean localization error of 0.29 m
Combining TDoA and AoA with a particle filter in an outdoor LoRaWAN network
Internet of Things (IoT) applications that value long battery lifetime over accurate location-based services benefit from localization via Low Power Wide Area Networks (LPWANs) such as LoRaWAN. Recent work on Angle Of Arrival (AoA) estimation with LoRa enables us to explore new optimizations that decrease the estimation error and increase the reliability of Time Difference Of Arrival (TDoA) methods. In this paper, particle filtering is applied to combine TDoA and AoA measurements that were collected in a dense urban environment. The performance of this particle filter is compared to a TDoA estimator and our previous grid-based combination. The results show that a median estimation error of 199 m can be obtained with a particle filter without AoA, which is an error reduction of 10 % compared to the grid-based method. Moreover, the median error is reduced with 57 % if AoA measurements are used. Hence, more accurate and reliable localization is achieved compared to the performance of other baseline methods
Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation
The Internet of Things (IoT) has started to empower the future of many
industrial and mass-market applications. Localization techniques are becoming
key to add location context to IoT data without human perception and
intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN)
technologies have advantages such as long-range, low power consumption, low
cost, massive connections, and the capability for communication in both indoor
and outdoor areas. These features make LPWAN signals strong candidates for
mass-market localization applications. However, there are various error sources
that have limited localization performance by using such IoT signals. This
paper reviews the IoT localization system through the following sequence: IoT
localization system review -- localization data sources -- localization
algorithms -- localization error sources and mitigation -- localization
performance evaluation. Compared to the related surveys, this paper has a more
comprehensive and state-of-the-art review on IoT localization methods, an
original review on IoT localization error sources and mitigation, an original
review on IoT localization performance evaluation, and a more comprehensive
review of IoT localization applications, opportunities, and challenges. Thus,
this survey provides comprehensive guidance for peers who are interested in
enabling localization ability in the existing IoT systems, using IoT systems
for localization, or integrating IoT signals with the existing localization
sensors
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