An Energy-Efficient and Routing Approach for Position Estimation using Kalman Filter Techniques in Mobile WSNs

Abstract

Mobile Wireless Sensor Networks is being an attractive field due to its applicability to an increasingly amount of mobile scenarios such as wild monitoring, disaster prevention, object guidance and health monitoring. In addition, since the sensors have limited batteries, data routing has to be planned strategically in order to extend the battery lifetime as much as possible [1] [2]. In this paper, we assume GPS free sensor devices, where considering a predictive technique to estimate the sensor position in a circular trajectory scenario can be useful to know when the sensor will be as close as possible to a sink, and then, help us to reduce the energy consumption by the fact of transmitting data at a short distance respect to the sink. In this paper, we propose an predictive algorithm based on Kalman filter techniques to estimate the proper time at which the sensor is close as much as possible to a sink, in order to reduce the energy consumption in the sensor. Specifically, we propose the usage of two Kalman Filters. One Kalman Filter is used for estimating the Received Signal Strength Indicator (RSSI) level based on several control packets received at the sensor device. This RSSI estimation indicates the distance from the mobile sensor device to the sink at a given time. The second Kalman Filter, based on the outputs from the first Kalman Filter, estimates the angular velocity and the angle of the mobile sensor device at a given time. Once this information is processed, it is possible to estimate the mobile sensor position in a circular trajectory in order to determine how much close is the mobile sensor device respect to the sink. In addition, the communication channel noise may affect the packet content, generating non-accurate information measurements at the receptor. For this reason, our proposal is evaluated under different noise channel levels and compared against a traditional technique. Our predictive routing algorithm shows better results in terms of distance accuracy to the sink and energy consumption in noisy communication channels.