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Towards Optimal Operation State Scheduling in RF-Powered Internet of Things | IEEE Conference Publication | IEEE Xplore

Towards Optimal Operation State Scheduling in RF-Powered Internet of Things


Abstract:

RF power transfer is becoming a reliable solution to energy supplement of Internet of Things (IoT) in recent years, thanks to the emerging off-the-shelf wireless charging...Show More

Abstract:

RF power transfer is becoming a reliable solution to energy supplement of Internet of Things (IoT) in recent years, thanks to the emerging off-the-shelf wireless charging and sensing platforms. As a core component of IoT, sensor nodes mounted with these platforms can not work and harvest energy simultaneously, due to the low-manufacture-cost requirement. This leads to a new design challenge of optimally scheduling sensor nodes' operation states: working or recharging, to achieve a desirable network utility. We show that the operation state scheduling problem is quite challenging, since the time-varying network topology leads to spatiotemporal coupling of scheduling strategies. We first consider a single-hop special case of small-scale networks. We employ geometric programming to transfer it into a convex optimization problem, and obtain an optimal analytical solution. Then a general case of large-scale multi-hop networks is investigated. Based on Lyapunov optimization technique, we design a State Scheduling Algorithm (SSA) with a proved performance guarantee. Our algorithm decouples the primal problem by defining a dynamic energy threshold vector, which successfully schedules each sensor node to the desirable state according to its energy level. To verify our design, the SSA is implemented on a Powercast wireless charging and sensing testbed, achieving about 85% of the theoretical optimal with quite low time complexity. Furthermore, numerous simulation results demonstrate that the SSA outperforms the baseline algorithms and achieves good performance under different network settings.
Date of Conference: 11-13 June 2018
Date Added to IEEE Xplore: 28 June 2018
ISBN Information:
Electronic ISSN: 2155-5494
Conference Location: Hong Kong, China

References

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