  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.
