Maximizing Broadcast Throughput Under Ultra-Low-Power Constraints

TL;DR

This paper introduces EconCast, a distributed protocol that maximizes broadcast throughput under ultra-low-power constraints, approaching theoretical limits and outperforming prior methods significantly in simulations and real-world tests.

Contribution

The paper formulates a novel throughput maximization problem for ultra-low-power wireless broadcasting and proposes EconCast, a distributed protocol that dynamically manages node states to optimize performance.

Findings

EconCast approaches the oracle maximum throughput.

EconCast outperforms prior art by 6x to 17x in simulations.

Real-world experiments achieve 57% to 77% of theoretical throughput.

Abstract

Wireless object tracking applications are gaining popularity and will soon utilize emerging ultra-low-power device-to-device communication. However, severe energy constraints require much more careful accounting of energy usage than what prior art provides. In particular, the available energy, the differing power consumption levels for listening, receiving, and transmitting, as well as the limited control bandwidth must all be considered. Therefore, we formulate the problem of maximizing the throughput among a set of heterogeneous broadcasting nodes with differing power consumption levels, each subject to a strict ultra-low-power budget. We obtain the oracle throughput (i.e., maximum throughput achieved by an oracle) and use Lagrangian methods to design EconCast - a simple asynchronous distributed protocol in which nodes transition between sleep, listen, and transmit states, and dynamically change the transition rates. EconCast can operate in groupput or anyput modes to respectively maximize two alternative throughput measures. We show that EconCast approaches the oracle throughput. The performance is also evaluated numerically and via extensive simulations and it is shown that EconCast outperforms prior art by 6x - 17x under realistic assumptions. Moreover, we evaluate EconCast's latency performance and consider design tradeoffs when operating in groupput and anyput modes. Finally, we implement EconCast using the TI eZ430-RF2500-SEH energy harvesting nodes and experimentally show that in realistic environments it obtains 57% - 77% of the achievable throughput.