Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/SAHCN.2018.8397136 | 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) |
Keywords | Field | DocType |
IoT,low-manufacture-cost requirement,time-varying network topology,convex optimization problem,large-scale multihop networks,Lyapunov optimization technique,SSA,dynamic energy threshold vector,RF power transfer,time complexity,optimal operation state scheduling,state scheduling algorithm,RF-powered Internet of Things,off-the-shelf wireless charging,energy harvesting,sensor node operation states,spatiotemporal coupling,geometric programming,powercast wireless charging | Sensor node,Job shop scheduling,Computer science,Scheduling (computing),Network topology,Inductive charging,Lyapunov optimization,Schedule,Wireless sensor network,Distributed computing | Conference |
ISSN | ISBN | Citations |
2473-0440 | 978-1-5386-4282-5 | 0 |
PageRank | References | Authors |
0.34 | 17 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Songyuan Li | 1 | 3 | 3.87 |
| Shibo He | 2 | 1494 | 78.37 |
| Lingkun Fu | 3 | 200 | 10.45 |
| Shuo Chen | 4 | 199 | 33.91 |
| Jiming Chen | 5 | 4389 | 238.91 |