Green Data Collection for Terrestrial and Near Terrestrial Networks : vTools Events

IEEE.org | IEEE Xplore Digital Library | IEEE Standards | IEEE Spectrum | More Sites

Green Data Collection for Terrestrial and Near Terrestrial Networks

# #Digital #Twins #5G #6G #communications #comsoc #radio #network #smart #infrastructure

IEEE Central Texas (Austin) ComSoc Joint chapter jointly with Texas State – Ingram School of Engineering invites you to a special Lecture on

Green Data Collection for Terrestrial and Near Terrestrial Networks

Dr. Sumit Chakravarty

Kennesaw State University

Date: Thursday April 18, 2024

Time: 11:00 am – 12:30 pm Central Time

Location: Ingram School of Engineering, Texas State at San Marcos (INGM 4301)

RSVP: vTools Registration:

https://events.vtools.ieee.org/m/417731

Zoom Info
https://zoom.us/j/96742912306?pwd=emoxRHB0M3hYengvM1dGTlpCSkVhdz09

Meeting ID: 917 0805 5112
Passcode: 928018

Zoom is provided to those out of town

Topic Title: Green Data Collection for Terrestrial and Near Terrestrial Networks

Distinguished Lecturer: Dr. Sumit Chakravarty, Kennesaw State University

Abstract:

With the widespread adoption and maturity of the Internet of Remote Things (IoRT) in the commercial sectors, diverse applications of new IoT technologies can be implemented in almost every environment. Data-driven applications in remote areas, like environmental monitoring and surveillance applications, pose immense challenges as terrestrial access networks are not capable of serving these IoT devices due to their geographical locations.

Apart from the communication challenge, there is the issue of maintaining a stable power supply to many low-power sensor devices with minimum human intervention. The critical point is that IoT devices must be able to fulfill their duties autonomously, which is the prime objective of IoT. The feasibility of the use of Energy Harvesting (EH) IoT devices by utilizing ambient energy (RF, solar, etc.) as the power supply might provide the solution for these energy-constrained sensor devices to operate under self-sustainable conditions. This thesis discusses the applicability of Unmanned Aerial Vehicles (UAV) in remote Wireless Sensor Networks for data gathering and energy harvesting. In this presentation, we propose an online UAV hovering time allocation and data transmission or energy harvesting frame selection algorithm in a dynamic cluster-based sensor network. A combined approach of reinforcement learning and Lyapunov optimization is considered to overcome the energy scarcity problem and maintain queue stability of the sensor nodes to achieve a stable system with a prolonged lifetime.

Bio: Dr. Sumit Chakravarty is an Electrical and Computer Engineering Associate Professor with more than 15 years of experience in academia and industry. He has spent five years as a tenured faculty performing teaching and research in Communications/ signal processing and Deep Learning at Kennesaw State University. He has collaborated with multiple experts, students, and other collaborators in this position to publish cutting-edge research articles, research projects, and grant proposals. His long-term goal is to bring my Communications and Machine Learning expertise to the next generation of communication systems and standards.

If help is needed, please connect with:

Prof. Semih Aslan, aslan@txstate.edu or

Fawzi Behmann, f.behmann@ieee.org