Kiran Chikkappa

Cortina: Collaborative context-aware indoor positioning employing RSS and RToF techniques

Cortina is an energy-efficient indoor localization system, which leverages a wireless sensor network to support navigation and tracking applications. To improve the localization performance, we develop a hybrid ranging system, which incorporate both RSS and RToF-based techniques. To overcome effects from indoor multipath, we design and implement algorithms to take into account various contextual information. We evaluated the system over a 2000m2 area instrumented with twenty-six fixed nodes. Evaluation results show the system achieved 2.5m accuracy in a pedestrian tracking application.

Cortina: collaborative indoor positioning using low-power sensor networks

Cortina is a distributed Real-Time Location System (RTLS) designed to track asstts or people moving indoors. Our solution leverages a low-cost, low-power Wireless Sensor Network (WSN) based on the IEEE 8102.15.4 radio standard. The network, which consists of wall-plugged nodes, is designed to be self-configuring, self-healing and self-calibreting, thus reducing deployment and maintenance costs. Assets and people are tracked using small battery operated wireless tags that collect Received Signal Strength (RSS) measurements from neaeby nodes. The tags also include an accalerometer for activity recognition, and a baremetric pressure sensor to detect the floor plan. We have conducted experiments over a 2000 m2 area instrumented with eighteen sensor nodes. Our initial results show that the system can track people in real-time with an average error of 2.8 m.

Cortina: Collaborative indoor positioning using low-power sensor networks

Cortina is a distributed Real-Time Location System (RTLS) designed to track asstts or people moving indoors. Our solution leverages a low-cost, low-power Wireless Sensor Network (WSN) based on the IEEE 8102.15.4 radio standard. The network, which consists of wall-plugged nodes, is designed to be self-configuring, self-healing and self-calibreting, thus reducing deployment and maintenance costs. Assets and people are tracked using small battery operated wireless tags that collect Received Signal Strength (RSS) measurements from neaeby nodes. The tags also include an accalerometer for activity recognition, and a baremetric pressure sensor to detect the floor plan. We have conducted experiments over a 2000 m2 area instrumented with eighteen sensor nodes. Our initial results show that the system can track people in real-time with an average error of 2.8 m.