Self-Error Detecting and Correcting Algorithm for Accurate Occupancy Tracking using a Wireless Sensor Network

For a variety of smart building applications, the accurate occupancy counting is regarded as one of the most important factors. This is because it becomes the basic information of smart building applications. Most of the applications for smart building have been operated based on their occupancy. So, many researchers have been trying to discover reliable occupancy counting system. But, most of their studies are focused on reducing the device error caused by sensors and counting algorithm, using machine learning algorithms. But, we are focusing on the accumulated error problem of the counting system. As the operating time increased, the accumulated error problem becomes more serious continuously. To solve this problem, we propose a Self-Error Detecting and Correcting Algorithm (SEDCA), consists of a Self-Error Detecting Algorithm (SEDA) and Self Error Correcting algorithm (SECA). All of the devices counting persons transfer and receive the counting information to other neighbor devices in order to sharing counting information within wireless network. Using this wireless network property, a SEDCA could detect and correct the counting error. In order to poof our proposed algorithms, we consisted an experimental environment with Passive RFID counters in our testbed. As a result, we will expect to avoid the accumulated error problem of occupancy counting.