Indoor wireless sensor localization using mobile robot and RSSI

Abstract

Reliable sensor node localization is a critical and difficult task in a large number of wireless sensor networks applications. Received signal strength indication (RSSI) measurements are a simple and inexpensive way to localize mobile robots, but they suffer from large errors due to noise, occlusions, and multi-path specially in indoor environments. Kalman filters and their variations are widely adopted in the community, although the inherent nonlinearity of the problem suggests the use of more general Bayesian techniques. In this paper, we take on the range-only probabilistic localization problem by a mobile robot that depends on RSSI measurements its only information of a sensor node's location. We propose a method based on a general Probabilistic Graphical Model and our main contribution is the definition of a reasonable, albeit simple, probabilistic RSSI likelihood model connecting distance to the observed RSSI values. Sensor position estimation is performed by integrating a standard robot's position estimator to our Bayesian estimator. Preliminary experimental evaluation indicates that our methodology leads to low error in sensor node's position estimation. Experiments were performed both by simulation, in the Player/Stage platform, and in a real world scenario, using a 802.11g sensor node and a Pioneer 3AT mobile robot.

Prediction-Based Localization for Mobile Wireless Sensor Networks

“…The method proposed in [7] uses a mobile robot to predict the position of nodes in an indoor environment. The method is based on a Probabilistic Graphical Model (PGM) that estimates the sensor node position using range-only measurements of the received signal strength indicator (RSSI).…”

An Improved WiFi/PDR Integrated System Using an Adaptive and Robust Filter for Indoor Localization

“…The WiFi/MEMS inertial measurement unit (IMU) is based on an optimized adaptive version of a mixture particle filtering algorithm for state estimation [3]. Position information was calculated by integrating a standard robot's position estimator with a Bayesian estimator using the received signal strength indicator (RSSI) [4]. A foot-mounted IMU-based position estimation method was aided by the RSSI obtained from several active radio frequency ID (RFID) tags placed at known locations in a building.…”