Publication Date
Fall 2024
Abstract
A Mobile Ad hoc Network (MANET) is a spontaneous network consisting of wireless nodes which are mobile and self-configuring. Devices in MANET can move freely in any direction independently and change its link frequently to other devices that are within range. Due to its dynamic nature, accurate mapping of radio frequency (RF) signal strength is crucial for optimizing network performance. While path loss models, ray tracing, and radio propagation software are widely employed for predicting RF signal strength, a significant knowledge gap exists in assessing the accuracy of these tools in the context of MANET.
This research project consists of two parts. The first part evaluates the performance of three popular path loss models: the Friis Transmission Equation, Okumura, and Okumura-Hata. Additionally, CloudRF, a cloud-based RF signal strength platform, is evaluated alongside these traditional models using the ITU-R P.1546 model configuration. The evaluation uses measured data from two Persistent Systems MPU5 military radios in two different environments, suburban and urban Champaign, IL, with radios in line-of-sight at distances up to 1000 feet. The second part of the project involves building a dynamic mapping platform that utilizes a WebSocket connection to acquire GPS data from the radios, makes requests to CloudRF API, and saves the calculations in a KML file, which is then uploaded to Google Earth for visualization every second. This visualization enables us to observe the dynamic changes in signal strength as nodes move, which gives us an understanding of their behavior and performance.
Rights
Copyright is owned by Parkland College.
Recommended Citation
Lee, Jeffery; Blankenau, Isaac; and Soylemezoglu, Ahmet, "Visualizing RF Signal Strength in Mobile Ad Hoc Networks using CloudRF API and Known Path Loss Models" (2024). Parkland Science Scholars. 8.
https://spark.parkland.edu/science_scholars/8
