Location data is an important piece of information in many Internet of Things (IoT) applications. Global Navigation Satellite Systems (GNSS) have been established as the standard for large-scale localization. However, the rapidly increasing need to locate IoT devices in recent years has exposed several shortcomings of traditional GNSS approaches. These limitations include the weak signal propagation in indoor and dense environments, the inability to calculate or obtain a location remotely, and a high energy consumption. Therefore, several industries have shown an increasing demand for alternative and innovative positioning solutions that are more suited in an IoT context. Hence, we conduct a survey on state-of-the-art, large-scale and energy-efficient positioning techniques for IoT applications. More specifically, we analyze the performance of terrestrial-based Low Power Wide Area Network (LPWAN) techniques, novel GNSS solutions, and innovative positioning techniques leveraging Low Earth Orbit (LEO) satellite constellations. A comparison is made in terms of 16 dimensions including energy consumption, positioning accuracy, coverage, and scalability. The analysis shows that interoperability between technologies is key to enable energy-efficient communication and positioning applications in the emerging market of satellite IoT.
1 Hz GPS data recorded by the GNSS network of the Consejería de Agricultura y Agua of the Murcia Region during the Mw 5.1 Lorca earthquake on May 11th 2011 is used as a test case. A Precise Point Positioning (PPP) approach is applied to analyse the earthquake-induced motion of the station LORC, located close to the epicenter. The results are validated using a conventional Double Differences (DD) processing. After applying sidereal and regional filters, the detected transient motion is about 20 millimeters in each component and clearly above noise level. The results from the two different processings are compared in view of the accuracy and applicability. The PPP approach described here can potentially be used for real-time analysis e.g. based on NTRIP streaming data. It may be used to set up an early warning system, as well as to gain real-time knowledge of ongoing earthquakes, extending the already-existing seismic information obtained from classical measurements.
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Blue force tracking represents an essential task in the field of military applications. A blue force tracking system provides the location information of their own forces on a map to commanders. For the command post, this results in more efficient operation control with increasing safety. In underground structures (e.g., tunnels or subways), the localisation is challenging due to the lack of GNSS signals. This paper presents a localisation system for military or emergency forces tailored to usage in complex underground structures. In a particle filter, position changes from a dual foot-mounted INS are fused with opportunistic UWB ranges and data from a 3D tunnel model to derive position information. A concept to deal with the absence of UWB infrastructure or 3D tunnel models is illustrated. Recurrent neural network methodologies are applied to cope with different motion types of the operators. The evaluation of the positioning algorithm took place in a street tunnel. If a fully installed infrastructure was available, positioning errors under one metre were reached. The results also showed that the INS can bridge UWB outages. A particle-filter-based approach to UWB anchor mapping is presented, and the first simulation results showed its viability.
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