Noble, W. T., & Dickinson, Z. O., & Sundaram, R. (2023, March), Wireless Sensor Network for Data Mining in Engineering Projects  Paper presented at 2023 ASEE North Central Section Conference, Morgantown, West Virginia. 10.18260/1-2--44939

\bibitem{asee_peer_44939}
  Noble, W. T., \& Dickinson, Z. O., \& Sundaram, R. (2023, March), \emph{Wireless Sensor Network for Data Mining in Engineering Projects}
  Paper presented at 2023 ASEE North Central Section Conference, Morgantown, West Virginia.
  10.18260/1-2--44939

Wesley Thomas Noble, Zachary Owen Dickinson, and Ramakrishnan Sundaram.  "Wireless Sensor Network for Data Mining in Engineering Projects".  2023 ASEE North Central Section Conference, Morgantown, West Virginia, 2023, March.  ASEE Conferences, 2023.  https://peer.asee.org/44939 Internet.  27 May, 2024

\bibitem{asee_peer_44939}
  Wesley Thomas Noble, Zachary Owen Dickinson, and Ramakrishnan Sundaram.
  "Wireless Sensor Network for Data Mining in Engineering Projects".
  \emph{2023 ASEE North Central Section Conference, Morgantown, West Virginia, 2023, March}.
  ASEE Conferences, 2023.
  https://peer.asee.org/44939 Internet.  27 May, 2024

@INPROCEEDINGS{asee_peer_44939
author = "Wesley Thomas Noble, Zachary Owen Dickinson, and Ramakrishnan Sundaram"
title = "Wireless Sensor Network for Data Mining in Engineering Projects"
booktitle = "2023 ASEE North Central Section Conference"
year = "2023"
month = "March"
address = "Morgantown, West Virginia"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/44939}
number = {10.18260/1-2--44939}
}

TY  - CPAPER
AB  - This paper describes the setup of a wireless sensor network to collect and analyze data associated with engineering projects in the real world. The sensor network comprises WiFi modules, configured in a grid, to transmit and receive radio frequency signals. These signals are recorded at a command center and analyzed for activity within the grid. Typical radio tomographic imaging systems are based on wireless sensor networks comprising transmitting and receiving nodes. Radio frequency signals are used to perform non-invasive and device-free localization of objects or entities in space. The image of each object or entity is formed from the attenuation in the received signal strengths along each path or link between the transmitting node and the receiving node. The attenuations characterize the loss of or drop in the radio frequency signal strength within the space being sensed and monitored. The radio tomographic imaging system determines the location information from the reconstructed image by solving the inverse problem using the attenuations. Accurate localization of the entities in indoor and outdoor environments is one of the critical requirements of the radio tomographic imaging system. The inverse problem of image reconstruction from the changes in the received signal strength or link values is ill-conditioned due to the extremely small singular values of the weight matrix. Regularization is included to offset the non-invertible nature of the weight matrix. This is achieved by adding a regularization term such as the matrix approximation of derivatives in each dimension based on the difference operator. This operation yields a smooth least-squares solution for the measured data by suppressing the high energy or noise terms in the derivative of the image. Instances of the engineering projects discussed in this paper relate to (a) room surveillance (b) environmental studies (c) vehicle identification and classification.
AU  - Wesley Thomas Noble
AU  - Zachary Owen Dickinson
AU  - Ramakrishnan Sundaram
CY  - Morgantown, West Virginia
DA  - 2023/03/24
PB  - ASEE Conferences
TI  - Wireless Sensor Network for Data Mining in Engineering Projects
UR  - https://peer.asee.org/44939
DO  - 10.18260/1-2--44939
ER  -