PROJECT TITLE :
Maximizing Broadcast Throughput Under Ultra-Low-Power Constraints - 2018
Wireless object-tracking applications are gaining popularity and will soon utilize rising ultra-low-power deviceto-device communication. However, severe energy constraints require abundant a lot of 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, likewise because the restricted management bandwidth must all be thought of. Therefore, we tend to formulate the problem of maximizing the throughput among a collection of heterogeneous broadcasting nodes with differing power consumption levels, every subject to a strict ultra-low-power budget. We obtain the oracle throughput (i.e., most throughput achieved by an oracle) and use Lagrangian methods to design EconCast-a straightforward asynchronous distributed protocol in which nodes transition between sleep, listen, and transmit states, and dynamically modification the transition rates. EconCast can operate in groupput or anyput mode to respectively maximize two alternative throughput measures. We tend to show that EconCast approaches the oracle throughput. The performance is also evaluated numerically and via extensive simulations and it's shown that EconCast outperforms prior art by half-dozen×-seventeen× below realistic assumptions. Moreover, we evaluate EconCast's latency performance and consider style tradeoffs when operating in groupput and anyput modes. Finally, we tend to implement EconCast using the TI eZ430-RF2500-SEH energy harvesting nodes and experimentally show that in realistic environments it obtains fifty sevenpercent-77p.c of the achievable throughput.
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