@article{dargie_senL-25,
  author = {Dargie, Waltenegus},
  title = {Estimation of Motion Statistics From Statistics of Received Power in Low-Power IoT Sensing Nodes},
  journal = {IEEE Sensors Letters},
  number = {12},
  volume = {8},
  pages = {1-4},
  year = {2024},
  abstract = {Low-power Internet of Things (IoT) sensing nodes can be embedded into various physical environments to monitor vital parameters. Some of these environments impose rough and extreme operation conditions, severely limiting the performance of these nodes. Modeling these environments is vital to make the nodes adaptive. In this letter, we propose a model to estimate the complex motion of nodes deployed on the surface of different water bodies. The model relies on received power statistics only. Experimental results confirm that the model is reliable, achieving an estimation accuracy of 93\%.},
  keywords = {Sensors;Sea surface;Mathematical models;Accuracy;Adaptation models;Wireless sensor networks;Peer-to-peer computing;Optical surface waves;Wireless communication;Three-dimensional displays;Sensor applications;3-D water motion;Inertial measurement unit (IMU);Internet of Things (IoT);Mean square (MS) estimation;Received signal strength indicator (RSSI);received power;wireless sensor networks},
  doi = {10.1109/LSENS.2024.3486582},
}