| dc.description.abstract | As a node of a wireless sensor network (WSN) with an embedded omni-directional antenna receives signals emitted from a directional antenna, the received signal strength indication(RSSI) varies with the angle of arrival (AoA) of the received signal. From experiments,we have the following two observations about the RSSI values of signals which a sensor node receives from a directional antenna. (1) If the distance of the node and the antenna is fixed, RSSI varies like a parabola function of AoA between −90◦ and 90◦ with a symmetry axis at AoA=0◦. (2) If we put two same-type directional antennas with perpendicular orientation at the same position, then the difference of the signal RSSI values which the node receives from the two antennas varies like a linear function of AoA between 0◦ and 90◦. Based on the above observations, we design and implement a novel localization scheme, called ALRD, for a sensor node to estimate AoA and then its position by RSSI value difference of two perpendicular directional antennas. The proposed localization scheme consists of two phases: the learning phase and the localizing phase. In the learning phase, we measure the RSSI values of signals received from a directional antenna at different distances and angles. For a fixed distance d, we perform regression analysis on the measured RSSI values to obtain two approximation functions: a quadratic function Rd = f( ) and a linear functions Dd = g( ), where is AoA, Rd is RSSI, and Dd is the RSSI difference of two signals received from two perpendicular directional antennas at the same position. These approximation functions, rather than all measured RSSI values, are then loaded into the limited storage of the sensor nodes for them to calculate AoA values to locate themselves. In the localizing phase, two location-known beacon nodes, either of which is equipped with two same-type perpendicular directional antennas, are deployed to transmit beacon signals periodically. By the Rd functions and the RSSI values received from two antennas of one beacon node, a sensor node can roughly estimate the distance d′ to the beacon node. By the Dd′ functions and d′, the sensor node can estimate AoA. With estimated AoA values of two distinct beacon nodes, a sensor node can then calculate its position. We have implemented ALRD and apply it to a WSN in a 10 by 10 meters indoor square area with two beacon nodes being installed at two ends of an area edge. Our experiments demonstrate that the average localization error is 124 centimeters. We further propose two methods, namely maximum-point-minimumdiameter(MPMD) and maximum-point-minimum-rectangle (MPMR), to improve localization accuracy by removing outliers from positioning results. The experiment results demonstrate the two methods can reduce the average localization error to be 89 centimeters, i.e., about 29% improvement in accuracy.
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