Asee peer logo

Prototype Development for Adaptive Solar Tracking and Optimization of Data Communication Protocol

Download Paper |

Conference

2021 ASEE North Central Section Conference

Location

University of Toledo, Ohio

Publication Date

March 19, 2021

Start Date

March 19, 2021

End Date

March 20, 2021

Page Count

8

Permanent URL

https://peer.asee.org/36349

Download Count

9

Request a correction

Paper Authors

biography

James D. Kaul Marshall University CCSE

visit author page

Graduated from Woodrow Wilson High School & Raleigh County Academy of Careers & Technology in 2003 with a concentration in Computer Networking and Electronics.
Graduated with Honors from Mountwest Community & Technical College in Applied Engineering Design Technology in Spring 2014.
Joined Marshall University Fall 2014, and graduated with a Bachelors of Electrical/Computer Engineering in Dec. 2020.

visit author page

author page

Gregory Dovel Weed

author page

Jared Cunningham

author page

Imtiaz Ahmed

biography

Wook-Sung Yoo Marshall University

visit author page

Dr. Wook-Sung Yoo is chair and professor in the Department of Computer Sciences and Electrical Engineering at Marshall University. He received his Ph.D. from the Florida Institute of Technology in 1995. He has a diverse academic background in Computer Science, Electrical and Computer Engineering, Information Systems, Cybersecurity, Data Science, Software Engineering, Health Informatics, Bioinformatics, Dentistry, Dental Informatics programs at various public and private universities with more than 15 years of administrative experience as a chair/program director and 25 years of teaching, research, service, and industrial experiences.

visit author page

Download Paper |

Abstract

To provide maintenance-free operation and a longer system lifetime of internet-of-things (IoT) network, utilizing solar energy for powering data transceivers in IoT device is important. A prototype for an adaptive solar tracking and efficient data communication system empowered by the harvested solar energy was developed by the capstone project team at Marshall University. In general, the harvested renewable (solar) energy can be random in time and in amount. The team tackled the randomness of the harvested energy with an efficient algorithm assisted by artificial intelligence techniques. The prototype is designed on Raspberry Pi and Arduino development boards, and the overall system comprises a solar tracking module, data transmitter, and receiver supported by omni-directional antennas, power banks, web-portal at the receiver, etc. The harvested energy powers up an integrated sensor-mounted data collection and wireless transmitter module. A long short-term memory (LSTM) layer incorporated deep neural network (DNN) accurately predicts the optimal time instants at which the data should be transmitted to a receiver module over a wireless channel in order to maximize the data throughput. The algorithm for the optimal control of solar tracking was developed in Python using open source DNN modules such as TensorFlow (Lite) to implement LSTM-assisted DNN for optimization of data transmission instances. A web-portal with a database shows the real-time data collection at the receiver end and exhibits dynamic performance analysis based on the collected information. The experimental results show the developed prototype significantly enhanced the efficiency of energy harvesting as an atomic part of a massive internet-of-things (IoT) network, an integral part of the fifth-generation (5G) cellular communication systems. Future enhance is discussed.

Kaul, J. D., & Weed, G. D., & Cunningham, J., & Ahmed, I., & Yoo, W. (2021, March), Prototype Development for Adaptive Solar Tracking and Optimization of Data Communication Protocol Paper presented at 2021 ASEE North Central Section Conference, University of Toledo, Ohio. https://peer.asee.org/36349

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2021 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015