Inner-Distance Measurement and Shape Recognition of Target Object Using Networked Binary Sensors

The inner distance is defined as the length of a line segment whose end points are arbitrary taken within the area occupied by the target object. Since the statistics (e.g. average and/or variance) of the inner distance depend on the shape of the object, they provide us useful information on the shape of the object. In this paper, we propose an inner-distance-measurement-based algorithm for shape recognition of the target object going across the field, where binary sensors are densely deployed. The inner distance of the target object can be obtained from the distance between two binary sensors concurrently detecting the object, if the positions of sensors are known. To estimate the position of each binary sensor, we apply a range-free localization using a moving landmark, which wanders around the field of watch. Each sensor periodically sends the data on whether it detects the moving landmark to the central server. The server estimates the physical location of a sensor based on the positions at which the moving landmark is detected by the corresponding sensor. We theoretically and numerically study the accuracy of the shape recognition based on the inner distance when it is combinedly used with the range-free sensor localization based on the moving landmark, and find that the proposed shape recognition is very promising when the positions of sensors are accurately estimated.