Wireless object tracking applications are gaining popularity within the realm of Internet-of-Things (IoT), and will soon utilize emerging ultra-low-power device-to-device communication. However, severe energy constraints (e.g., an ambient light source or an energy harvesting source) 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.
In this talk, I will first present the design, analysis, and evaluation of Panda – a neighbor discovery protocol tailored for energy harvesting nodes with extremely limited power budgets. Panda is centralized protocol designed for homogeneous nodes with equal power consumption levels. In order to understand the fundamental communication limits in a more general network setting, 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 Lagrange 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 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.
The results are based on joint work with Rob Margolies, Guy Grebla, Javad Ghaderi, Dan Rubenstein, and Gil Zussman.
Tingjun Chen is currently a Ph.D. student in the Electrical Engineering Department at Columbia University. He received the M.S. degree in Electrical Engineering from Columbia University in 2015, and the B.Eng. degree in Electronic Engineering from Tsinghua University in 2014. His research interests are in PHY/MAC layer algorithms, optimization, and system design and implementation in Internet-of-Things, energy harvesting networks, full-duplex networks, and millimeter-wave and 5G networks. He received the Wei Family Private Foundation Fellowship, the Columbia University Electrical Engineering Armstrong Memorial Award, the Columbia Engineering Oscar and Verna Byron Fellowship, and the ACM CoNEXT 2016 Best Paper Award.