Abstract
Localization in a wireless sensor network (WSN) becomes important for many modern applications, like landslide detection, precision agriculture, health care, and so forth. The more precise the position of an anchor node is, the more accurate the localization of a sensor node can be measured. Since the Global Positioning System (GPS) device cannot work properly indoor, some existing localization methods configure anchor nodes in a manual fashion. However, neither applying GPS modules nor manually configuring anchor nodes is suitable for a WSN and especially artificial errors of manual configuration may be propagated and affect the results of localization. In this paper, we propose an alternative method to estimate anchor node locations in an indoor environment. We collect the Received Signal Strength Indicator (RSSI) data from the anchor node when human is walking around them. Meanwhile, we use a wearable IMU-camera device to assist the moving trajectory estimation. We implement a monocular Visual Odometry with a human walking model to estimate moving trajectories. An Unscented Kalman Filter (UKF) is used to estimate the anchor node location by fusing the RSSI data and moving trajectory. The experiment results show that the proposed method has lower estimation error when locating anchors.
Highlights
Wireless communication and MEMS IC technology have enabled the development of low-cost, low-power, and multifunctional sensor nodes
The simplest way to determine the positions of anchor nodes is to use Global Positioning System (GPS) devices [4, 12,13,14,15,16,17,18] or is to be manually measured [8, 10, 11] in advance
The second step combines the attitude of the scaled Visual Odometry (VO) and calibrated Received Signal Strength Indicator (RSSI) data to simultaneously estimate the inertial measurement unit (IMU)-camera moving trajectory and anchor locations by applying Unscented Kalman Filter (UKF)
Summary
Wireless communication and MEMS IC technology have enabled the development of low-cost, low-power, and multifunctional sensor nodes. A range-based approach relies on the distance or the angle information between anchor nodes to estimate locations by using Trilateration, triangulation, or multilateration algorithms. Designing a reference device that can be carried by human is the most attractive way to localize anchor nodes in indoor environment. When it is applied on human, the signal-noise-ratio of the IMU sensor is small since the walking speed is slow. We propose a sensor fusion method to locate anchors in an indoor environment by using a wearable IMU-camera and a wireless sensor mounted on the center of human’s waist for moving trajectory estimation.
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