A Heuristic Approach to Classifying Different Multiple Urban Settings for Ambient RF Energy Harvesting Potential using TV Technology as an RF Energy Source

Abstract

Purpose – This paper aims to develop a Graphical Footprint Model (GFM) that will validate radio frequency (RF) energy harvesting capabilities of RF energy sources, such as frequency modulation (FM), television (TV), and cellular technology (Cell) at different classified multiple urban settings. In this paper, the researchers focus more on TV technology sources.

Method – The Communications Engineering Formula Budget link was used to get the PRL for RF potentials in dBm, to determine the classified multi-settings together with the RF sources for RF harvesting capabilities. The GFM is a scoring scheme for energy capabilities based on a -20dBm and up of Power Received Levels (PRL) at different classified multiple urban settings, which includes Line of Sight (LOS), Rural (R), Suburban (S), Urban High (UH), Urban Very High (UVH), and Non-Line of Sight (NLOS) settings.

Result – The result of the exercise showed a percentage acceptability range of 84.00 % with a Mean Square Error (MSE) of 14 along with a mean average distance accuracy range of 1451 meters. To determine whether the GFM can predict heuristically with the use of a weighted mean, the analysis covered spread-out data points over a large range of values, considering a standard deviation of 3091. The grand mean obtained was an intensity score of 3, with the initial voltage range from 70mV to 125 mV. This presents a particularly good level for GFM to predict heuristically the RF potential. In establishing a benchmark for the GFM, a review of twenty-one research studies was undertaken for data analytics.

Conclusion – The GFM will benefit future design engineers and installers of RF harvesters. GFM can provide them the necessary information to determine the locations and distances for RF potential emitted from TV technology as an energy source in different classified multiple settings.

Recommendation – The RF harvester designers may use the model for their design considerations because it entails easy preliminary assessments of the selected urban setting for RF energy harvesting capabilities and serves as a map to identify the best locations for harvesting.

Practical Implication – The study on GFM will act as a map to show the best place for RF harvesting capabilities in which can be used for design considerations even in an off scheme compared to tedious surveying on-site to get the Power Received Level and the only effort required is the reading and following the values of the Energy GFM.