An Approach for Efficient Object Tracking for Indoor Localization

Positioning systems plays an important role in finding location information. Location based services are increasing very rapidly and hence the demand for efficient positioning system is also increasing day by day. For positioning Global Positioning System (GPS) is mostly used all over the world but it's very power consuming and it not very effective in built-up indoor environments. So positioning techniques like Global System for Mobile Communications (GSM) and Wi-Fi based on wireless sensor networks are used. We implemented the localization system using Received Signal Strength (RSS) for Location Defining Phase, by collecting the different values of the Defined Area which is used for the localization. It includes Basic Artificial Neural Network (ANN) Mapping Algorithm to transform the RSS Integration to the Mapping of Longitude and Latitude of the Database. Location Estimation Phase uses the supervised dataset for Determining the current Location of the device. The system overcomes drawbacks of GPS and reduce transmission load, computational overhead, computational iteration and efficiently localize the device.