TY - GEN
T1 - Light Intensity Based IoT Device Positioning for Indoor Monitoring
AU - Sun, Gaofei
AU - Xing, Xiaoshuang
AU - Qian, Zhenjiang
AU - Wang, Zhiguo
AU - Zhu, Saide
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - With the prosperous deployment of IoT devices in recent years, more data are collected by ubiquitous sensors. Hence, there are two main challenges needed to be solved. Firstly, how to accommodate the communication links among these devices and collect their data effectively? Secondly, how to track or localize these devices and well organize the IoT network? The common way to track these devices is achieved by registering locations of these devices manually or RSSI measurement. However, these measures suffer from high complexity and inaccuracy, and cause boring reconfiguration process with shift of devices. In this framework, we proposed a novel and low cost IoT monitoring system with self-location awareness. The collected data by sensors, such as the light intensity, can be used for device localizations. By using the proposed algorithm, we derived the critical parameters for light curves, and interpolated for the predicted light intensity inside the whole room. The experiment results indicated the proposed algorithm is useful for inferring device location with low cost, which are suitable for device management without privacy information.
AB - With the prosperous deployment of IoT devices in recent years, more data are collected by ubiquitous sensors. Hence, there are two main challenges needed to be solved. Firstly, how to accommodate the communication links among these devices and collect their data effectively? Secondly, how to track or localize these devices and well organize the IoT network? The common way to track these devices is achieved by registering locations of these devices manually or RSSI measurement. However, these measures suffer from high complexity and inaccuracy, and cause boring reconfiguration process with shift of devices. In this framework, we proposed a novel and low cost IoT monitoring system with self-location awareness. The collected data by sensors, such as the light intensity, can be used for device localizations. By using the proposed algorithm, we derived the critical parameters for light curves, and interpolated for the predicted light intensity inside the whole room. The experiment results indicated the proposed algorithm is useful for inferring device location with low cost, which are suitable for device management without privacy information.
UR - http://www.scopus.com/inward/record.url?scp=85101859036&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85101859036&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-68851-6_21
DO - 10.1007/978-3-030-68851-6_21
M3 - Conference contribution
AN - SCOPUS:85101859036
SN - 9783030688509
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 290
EP - 300
BT - Security, Privacy, and Anonymity in Computation, Communication, and Storage - 13th International Conference, SpaCCS 2020, Proceedings
A2 - Wang, Guojun
A2 - Chen, Bing
A2 - Li, Wei
A2 - Di Pietro, Roberto
A2 - Yan, Xuefeng
A2 - Han, Hao
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage, SpaCCS 2020
Y2 - 18 December 2020 through 20 December 2020
ER -