A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Abstract

Abstract-Location information for events, assets, and individuals, mostly focusing on two dimensions so far, has triggered a multitude of applications across different verticals, such as consumer, networking, industrial, health care, public safety, and emergency response use cases. To fully exploit the potential of location awareness and enable new advanced location-based services, localization algorithms need to be combined with complementary technologies including accurate height estimation, i.e., three dimensional location, reliable user mobility classification, and efficient indoor mapping solutions. This survey provides a comprehensive review of such enabling technologies. In particular, we present cellular localization systems including recent results on 5G localization, and solutions based on Wireless Local Area Networks (WLAN), highlighting those that are capable of computing 3D location in multi-floor indoor environments. We overview range-free localization schemes, which have been traditionally explored in Wireless Sensor Networks (WSN) and are nowadays gaining attention for several envisioned Internet of Things (IoT) applications. We also present user mobility estimation techniques, particularly those applicable in cellular networks, that can improve localization and tracking accuracy. Regarding the mapping of physical space inside buildings for aiding tracking and navigation applications, we study recent advances and focus on smartphone-based indoor Simultaneous Localization and Mapping (SLAM) approaches.The survey concludes with service availability and system scalability considerations, as well as security and privacy concerns in location architectures, discusses the technology roadmap, and identifies future research directions.

3D Trilateration Localization using RSSI in Indoor Environment

“…The location of user can be estimated to the position of access point for connection based on wireless communication. This method was utilized by the Cell-ID method standardized in GSM cellular systems, followed by RFID and Bluetooth-based systems [14].…”

Calibration of Wi-Fi-Based Indoor Tracking Systems for Android-Based Smartphones

“…The authors of [19,20] obtain a single parameter value for the entire environment, however, this approach cannot describe the signal propagation in complex real-world scenarios. This is due to LDPLM not being able to accurately represent the real attenuation of Wi-Fi signal in environments with the presence of NLOS situations between receiver and transmitter devices [21]. To overcome this problem, Carrera et al [22] define two different situations in which a particular model is obtained: one for LOS situations and another one for NLOS situations.…”

Low-Power LoRa Signal-Based Outdoor Positioning Using Fingerprint Algorithm

“…The proximity method estimates the location of an end-device by using the known location of the gateway; however, the positioning accuracy depends on the density of gateways in the network [10]. The path-loss model estimates the location of an end-device by mathematically formulating the characterization of radio wave propagation as a function of distance and other conditions.…”

“…The path-loss model estimates the location of an end-device by mathematically formulating the characterization of radio wave propagation as a function of distance and other conditions. The fingerprint algorithm, also known as scene analysis, estimates the location of an end-device by matching the observed RSSI with the radio map containing RSSI data using a pattern matching method [10]. Usually, probabilistic methods, such as maximum likelihood estimator (MLE), are used for estimating the location [11,12].…”