TY - GEN
T1 - Sensor network topology estimation using time-series data from infrared human presence sensors
AU - Watanabe, Yuta
AU - Kurihara, Satoshi
AU - Sugawara, Toshiharu
PY - 2010
Y1 - 2010
N2 - We describe a method for accurately estimating the topology of sensor networks from time-series data collected from infrared proximity sensors. Our method is a hybrid combining two different methodologies: ant colony optimization (ACO), which is an evolutionary computation algorithm; and an adjacency score, which is a novel statistical measure based on heuristic knowledge. We show that, using actual data gathered from a real-world environment, our method can estimate a sensor network topology whose accuracy is approximately 95% in our environment. This is an acceptable result for real-world sensor-network applications.
AB - We describe a method for accurately estimating the topology of sensor networks from time-series data collected from infrared proximity sensors. Our method is a hybrid combining two different methodologies: ant colony optimization (ACO), which is an evolutionary computation algorithm; and an adjacency score, which is a novel statistical measure based on heuristic knowledge. We show that, using actual data gathered from a real-world environment, our method can estimate a sensor network topology whose accuracy is approximately 95% in our environment. This is an acceptable result for real-world sensor-network applications.
UR - http://www.scopus.com/inward/record.url?scp=79951936514&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79951936514&partnerID=8YFLogxK
U2 - 10.1109/ICSENS.2010.5690090
DO - 10.1109/ICSENS.2010.5690090
M3 - Conference contribution
AN - SCOPUS:79951936514
SN - 9781424481682
T3 - Proceedings of IEEE Sensors
SP - 664
EP - 667
BT - IEEE Sensors 2010 Conference, SENSORS 2010
T2 - 9th IEEE Sensors Conference 2010, SENSORS 2010
Y2 - 1 November 2010 through 4 November 2010
ER -