EH from V2X Communications: the Price of Uncertainty and the Impact of Platooning

TL;DR

This work studies an energy harvesting device (EHD) positioned beside a road that scavenges RF energy from vehicular communications to relay data to a remote AP. It introduces a cycle-based harvesting strategy that uses only local topology, models the EHD battery as a discrete Markov process, and derives a throughput expression under Poisson and platooning traffic, including the blackout probability. Key findings show that regular traffic patterns such as platooning significantly boost average throughput and energy efficiency (e.g., over 30% gains) and reduce the price of uncertainty, while enabling tunable tradeoffs between throughput and reliability. The results highlight the practical potential of platooning as a favorable RF energy source for ambient-energy powered communication devices and provide analytic tools and guidelines for parameter tuning and future extensions.

Abstract

In this paper, we explore how radio frequency energy from vehicular communications can be exploited by an energy harvesting device (EHD) placed alongside the road to deliver data packets through wireless connection to a remote Access Point. Based on updated local topology knowledge, we propose a cycle-based strategy to balance harvest and transmit phases at the EHD, in order to maximize the average throughput. A theoretical derivation is carried out to determine the optimal strategy parameters setting, and used to investigate the effectiveness of the proposed approach over different scenarios, taking into account the road traffic intensity, the EHD battery capacity, the transmit power and the data rate. Results show that regular traffic patterns, as those created by vehicles platooning, can increase the obtained throughput by more than 30% with respect to irregular ones with the same average intensity. Black out probability is also derived for the former scenario. The resulting tradeoff between higher average throughput and lower black out probability shows that the proposed approach can be adopted for different applications by properly tuning the strategy parameters.

Figures (15)

• Figure 1: Graphic representation of the considered scenario. Above, a qualitative graph of the average received power as a function of the vehicles positions is also reported, which shifts as the cars move. Since the EHD harvests energy when the x-coordinate of the closest vehicle is within the interval $[x_{\textrm{ehd}}-\ell,x_{\textrm{ehd}}+\ell]$, the yellow shaded area is proportional to the harvested energy.
• Figure 2: Accuracy of the CDF and cCDF of the harvested energy obtained via saddle point approximation.
• Figure 3: Quantization error as a function of the harvesting threshold. Solid lines are for the platooning scenario, while dashed lines are for the sparse vehicular traffic scenario.
• Figure 4: Average throughput as a function of the harvesting distance $\ell$, with Poisson arrivals. Lines are theoretical results, while markers are simulation results.
• Figure 5: Average throughput as a function of the harvesting distance $\ell$, with fixed inter-vehicle distance. Lines are theoretical results, while markers are simulation results.