Ambient Intelligent Wireless Sensor Network for Environmental Monitoring and Indoor Localization Using an Extended Kalman Filter

Abstract

Ambient Intelligent (AmI) Wireless Sensor Networks (WSN) provide intelligent services based on users and environment data obtained by sensors. In this paper, we propose AmI WSN that could sense environment parameters like temperature, humidity around the entity, and also estimate the position of an entity in indoor environments. Digital Humidity Temperature (DHT) sensor is used to sense temperature and humidity around the entity. Zigbee which is an IEEE802.15.4 based wireless specification is used for localization, that uses the Received Signal Strength Indicator (RSSI) values for locating the position of an entity. In addition, Extended Kalman Filter (EKF) is adapted to filter the RSSI values influenced by multipath fading. Graphical User Interface (GUI) is developed to display temperature, humidity, and position coordinates in the MATLAB platform. Performances of the proposed system are evaluated by deriving the Cramer Rao Lower Bound (CRLB) variance for the position estimate. It is found that the variance of the position coordinates obtained by the proposed system is close to the CRLB variance, that makes the proposed system to be efficient.