In the bustling world of smart buildings and the Internet of Things (IoT), reliable wireless communication is the backbone that keeps everything connected. Enter IQRF technology, a promising contender known for its low power consumption, cost-effectiveness, and wide coverage. However, deploying these networks indoors, where signals bounce off walls and corners like pinballs, has been a persistent challenge. That’s where the work of Talip Eren Doyan, a researcher from the Department of Electrical and Electronics Engineering at Atilim University in Turkey, comes into play.
Doyan and his team have been exploring ways to model signal propagation in complex indoor environments, aiming to make IQRF network deployment simpler and more accurate. Their recent study, published in the journal ‘Sensors’ (translated from Turkish as ‘Sensors’), investigates the use of a site-specific modeling approach, originally designed for urban street canyons, to characterize IQRF links in typical indoor scenarios.
The team compared the received signal powers with well-known empirical models and ray-tracing simulations. The results were revealing. While the ITU-R P.1238-9 model performed better under line-of-sight conditions, Doyan’s site-specific approach shone in non-line-of-sight scenarios. “The site-specific approach achieves substantially higher accuracy in NLoS scenarios, maintaining RMSE values below 3.9 dB for one- and two-turn links,” Doyan explained. This means more reliable predictions for signal strength in complex indoor environments, which could translate to more efficient and effective network deployments.
The implications for the energy sector are significant. Smart buildings rely on robust IoT networks to manage energy consumption, optimize HVAC systems, and monitor occupancy. Accurate indoor propagation modeling can lead to better network planning, reducing costs, and improving performance. As Doyan noted, “These results demonstrate the potential of site-specific modeling to provide practical, computationally efficient, and accurate insights for IQRF network deployment planning in smart building environments.”
The study also highlighted the limitations of ray-tracing simulations, which exhibited larger deviations from actual measurements. This finding could steer future research and industry practices away from overly complex models and towards more practical, site-specific approaches.
As the demand for smart buildings continues to grow, so does the need for reliable, efficient wireless communication networks. Doyan’s research offers a promising path forward, one that could shape the future of IoT-based smart buildings and the energy sector as a whole. By providing a more accurate and practical way to model signal propagation indoors, this work could help pave the way for smarter, more energy-efficient buildings.