As buildings become larger and more complex, there becomes a greater need for localization systems that are capable of functioning quickly with high accuracy. Location Based Services (LBS) are a general application where a person's location data can be used to easily control devices in one's everyday life. However, before LBS can be achieved, location information must be gathered. This work investigates a Wireless Sensor Network (WSN) used for performing indoor localization under a variety of wireless technologies, localization techniques, and filtering algorithms. The system was required to accurately locate devices with low error, have high precision, be cost-effective, and easy to deploy in an environment. Based on experiments performed, WiFi was determined to have the lowest error overall, while Bluetooth Low Energy (BLE) consumed the lowest amount of power. Fingerprinting using K-Nearest Neighbor (KNN) with k=4 processing was determined to be the most accurate and precise technique. In addition, by using six anchors in an environment and applying nonlinear least squares processing localization accuracy could be improved.