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

Abstract

Purpose – This paper is an extension of the previous study on “A Heuristic Approach to Classifying Different Multiple Urban Settings for Ambient RF Energy Harvesting Potential using TV Technology as an RF Energy Source.” The study now focuses on FM technology which was quite different from the TV technology in terms of frequency range that was previously published by the authors on February 9, 2022. Thus, it aims to expand a Graphical Footprint Model (GFM) that will verify 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.

Method – The Budget link from the Communications Engineering Formula was utilized  to get the Power Received Level of Radio Frequency potentials in dBm, in order to know the classified multi-settings collectively with the RF sources for RF harvesting capabilities. A scoring scheme called GFM model was developed in order to know the energy capabilities based on -20 dBm and up regarding Power Received Level (PRL) at various classified multiple urban settings, such as Line of Sight (LOS), Rural (R), Suburban (S), Urban High (UH), Urban Very High (UVH), and Non-Line of Sight (NLOS) settings.

Result – The outcome of the study revealed a percentage acceptability range of 84%  with a Mean Square Error (MSE) of 14 along with a mean average distance accuracy range of 1451 meters. To establish whether the GFM can anticipate heuristically with the use of a weighted mean, the analysis comprised spread-out data points over a large range of values, regard as a standard deviation of 3091. The grand mean achieved an intensity score of 3, with the initial voltage range from 70mV to 125 mV. This shows a remarkably good level for GFM to forecast heuristically the RF potential. In determining a benchmark for the GFM, a review of twenty-one research studies undertaken for data analytics.

Conclusion – The Graphical Footprint Model will advance future design engineers and installers of RF harvesters. Graphical Footprint Model can offer the essential information to know the locations and distances for Radio Frequency potential coming from FM technology as an energy source in various classified multiple settings.

Recommendation – The Radio Frequency harvester designers may use the model for their design specification because it shows preliminary evaluations of the selected urban setting for Radio Frequency harvesting potentials and provides a map to distinguish the best sites for energy harvesting. 

Practical Implication – The Graphical Footprints Model study will serve as a map to illustrate the best sites for Radio Frequency harvesting potentials in which in turn can be utilized for design considerations and specifications even in an off scheme versus the traditional surveying on-site to get the PRL and the only effort needed is the reading and following the values of the Energy Graphical Footprints Model.