PROJECT TITLE :
Maximizing Broadcast Throughput Under Ultra-Low-Power Constraints - 2018
Wireless object-tracking applications are gaining popularity and can soon utilize emerging ultra-low-power deviceto-device communication. But, severe energy constraints need abundant additional careful accounting of energy usage than what prior art provides. In specific, the available energy, the differing power consumption levels for listening, receiving, and transmitting, similarly because the restricted management bandwidth should all be considered. Thus, we formulate the matter of maximizing the throughput among a group of heterogeneous broadcasting nodes with differing power consumption levels, every subject to a strict ultra-low-power budget. We tend to get the oracle throughput (i.e., most 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 mode to respectively maximize 2 various throughput measures. We have a tendency to show that EconCast approaches the oracle throughput. The performance is additionally evaluated numerically and via in depth simulations and it's shown that EconCast outperforms prior art by half-dozen×-17× under realistic assumptions. Moreover, we tend to evaluate EconCast's latency performance and contemplate style tradeoffs when operating in groupput and anyput modes. Finally, we have a tendency to implement EconCast using the TI eZ430-RF250zero-SEH energy harvesting nodes and experimentally show that in realistic environments it obtains fifty seven%-77% of the achievable throughput.
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