| dc.description.abstract | Wireless sensor network (WSN) consists of a large number of sensor nodes interconnected by wireless communication links. The sensor nodes have limited power, low memory and processing capability. The nodes can sense the environment conditions and send the data to a sink. Therefore, WSN can be used in many applications such as control the temperature, humidity, monitor the contamination, pollution etc.
Localization is widely used in Wireless Sensor Networks (WSNs) to identify the current location of the sensor nodes. A WSN consist of thousands of nodes that make the installation of GPS on each sensor node expensive. Moreover, GPS may not provide exact localization estimation in an indoor environment. Most of the building nowadays are covered by Wi-Fi network. In this thesis, we utilize the Wi-Fi signal to estimate the locations of the unknown nodes. The range-based methods are used in calculations.
We first study the localization methods in range-based approach. Then, two methods are concentrated: 1) Euclidean and trilateration localization method, and 2) finger-printing method. We also present an additional method, i.e., Bluetooth Low Energy devices, which is used combined with the two methods to improve the localization accuracy.
We measure the received RSSI at the unknown nodes via extensively testbed experiments. The error calculations shown that estimation is in 0.141421±0.424264 (m) to 2.140492±1.009106 (m) error.
Keywords: indoor localization, Wi-Fi fingerprint, location estimation, RSSI | en_US |
