Name
Abstract | ||
|---|---|---|
Wireless Power Transfer (WPT) has emerged into an inspiringly commercial and applicable era to charge devices. Existing studies mainly focus on general charging patterns and metrics while overlooking the collaborated task execution, which incurs charging inefficiency among nodes. In this paper we first advocate the collaborated tasks-driven mobile charging and scheduling to respect the energy requirement diversity. Specially, the mobile charging scheduling strategy is considered to maximize the overall task utility which concerns sensor selection and task cooperation. Unfortunately, solving this problem is non-trivial, because it involves solving two coupling NP-hard problems. In tackling with this difficulty, we construct a surrogate function with specific theoretical analysis of its submodularity and gap property. Then, we approximate the traveling cost to transform the formulated problem into an essentially monotone submodular function optimization subject to a general routing constraint, where we propose $a (1-\ 1/e)/4$-approximation algorithm. Extensive simulations are conducted and the results show that our algorithm can achieve a near-optimal solution covering at least S4.9% of the optimal result achieved by the OPT algorithm. Furthermore, field experiments in office room and soccer field environment with 10 and 20 sensors are implemented respectively to validate our proposed algorithm. |
Year | Venue | Keywords |
|---|---|---|
2019 | ieee international conference computer and communications | Task analysis,Optimization,Resource management,Sensors,Energy consumption,Measurement,Couplings |
Field | DocType | ISSN |
Resource management,Wireless power transfer,Task analysis,Scheduling (computing),Computer science,Inefficiency,Submodular set function,Energy consumption,Monotone polygon,Distributed computing | Conference | 0743-166X |
ISBN | Citations | PageRank |
978-1-7281-0515-4 | 2 | 0.37 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Wu | 1 | 3 | 2.73 |
| panlong yang | 2 | 458 | 62.73 |
| Dai Haipeng | 3 | 419 | 55.44 |
| Wanru Xu | 4 | 47 | 14.23 |
| Mingxue Xu | 5 | 7 | 0.75 |