Verification of Error-Increasing Factors by Sensor Response-Based Localization Technology Through Real Device Experiments

Abstract

A sensor response-based localization technology that uses the non-geotagged responses of environmental object detection sensors to estimate the positions of sensors in a defined observation area was proposed in a previous study. That method is based on the concept that the proximity relationships among sensors obtained from the closeness of the sensor response times or collected data similarities. However, since that study was limited to simulation-based experiments, this study extends the scheme to experiments involving real devices, simulated environments, and ideal environments to ascertain the accuracy of localization estimates produced using real devices. We also aimed at extracting the problems that must be resolved before the proposed technology can be put into practical use. Even if clock synchronization between the sensors was impossible, the proximity relationships among them could be estimated using before-after detection time relationships, thus confirming that sensor locations could be estimated based on these proximity relationships. In a real device experiment using 20 sensors indoors at 20.25 2 , we confirmed that the average localization error was 0.84 m. Based on those results and other findings resulting from this study, we determined that the primary factor related to increased sensor localization errors was the number of incorrectly constructed sensor pairs. The results of this study are expected to facilitate the realization of physical world sensing using sensor response-based localization technology.

Contact Information-Based Indoor Pedestrian Localization Using Bluetooth Low Energy Beacons

“…• We confirmed that the sensor response-based localization method [10], [11] for WSNs could be applied to indoor localization. • Through simulation experiments, we found that the proposed methods could obtain comparable accuracy to existing methods when the attenuation model is accurate.…”

“…In these simulation experiments, we assumed that the average strength of the radio signal from a pedestrian's smartphone or an anchor node at a distance of 𝑑 is determined by (11). Note that radio signals in an actual environment are affected by fading.…”

“…Thus, proposed method 1 does not consider the distance between the pedestrians. In proposed method 2, which is an improved version of the sensor response-based localization method [10], [11] for WSNs, the distance between other pedestrians is considered, in addition to the distance between the pedestrian and anchor nodes. As a result, proposed method 2 improves the localization accuracy of proposed method 1.…”

“…However, the application of this method to indoor pedestrian localization is limited; thus, it is unclear whether this method would be effective for indoor pedestrian localization because it is assumed that the sensor is fixed. The previously proposed sensor response-based localization technique [10], [11] is based on connectivity-based localization [28], [29] and cooperative localization [30], [31]. Furthermore, we have proposed two indoor pedestrian localization methods based on contact information using BLE beacons [9].…”

“…In a previous study, we proposed two indoor pedestrian localization methods based on contact information using BLE beacons [9]. The first method is multilateration and the second one is cooperative localization, which is an improved version of the sensor response-based localization technique [10], [11] for wireless sensor networks (WSNs). The sensor response-based localization technique uses proximity relations between nodes recognized from sensor responses to estimate the sensor position.…”