Falls are a significant problem which predominantly affects the life of the elderly population. The probability of falls increases as health of a person detriment, making them dependent on others and restricting their freedom of movement. Therefore, tremendous amount of resources and time is being spent in the healthcare sector to develop systems which performs fall detection in real-time, as well as investigation of the cause and immediate effects. This type of system must be inexpensive, accurate and low power consuming for daily use. Furthermore, a user-friendly interface should be provided on the device. In this paper, an architecture of fall detection system coupled with the Wireless Intelligent Personal Communication Node (W-iPCN) and Android smartphone is presented. Sensor data necessary for detecting falls are received from accelerometers and gyroscopes through the W-iPCN. To increase power efficiency and configuration flexibility, each sensor node is connected to the W-iPCN using 6LoWPAN. A fall detection algorithm performs real-time processing and analysis of the collected data. Additionally, the W-iPCN is used to bridge the processed data analysis results to the Android smartphone through Bluetooth. Adapting 6LoWPAN capability to our design flow enhances the Internet accessibility of the sensor node, increases compatibility with other packet-switched networks, and offers low power consumption on sensor nodes. Our design has the ability to prioritize sensor data in different medical emergency situations by implementing Quality of Service (QoS) framework.