Name
Title | ||
|---|---|---|
Wirelessly Powered Crowd Sensing: Joint Power Transfer, Sensing, Compression, and Transmission. |
Abstract | ||
|---|---|---|
Leveraging massive numbers of sensors in user equipment as well as opportunistic human mobility,
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mobile crowd sensing</italic>
(MCS) has emerged as a powerful paradigm, where prolonging battery life of constrained devices and motivating human involvement are two key design challenges. To address these, we envision a novel framework, named
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">wirelessly powered crowd sensing</italic>
(WPCS), which integrates MCS with
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">wireless power transfer</italic>
for supplying the involved devices with extra energy and thus facilitating user incentivization. This paper considers a multiuser WPCS system where an
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">access point</italic>
(AP) transfers energy to multiple
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mobile sensors</italic>
(MSs), each of which performing data sensing, compression, and transmission. Assuming lossless (data) compression, an optimization problem is formulated to simultaneously maximize data utility and minimize energy consumption at the operator side, by jointly controlling wireless-power allocation at the AP as well as sensing-data sizes, compression ratios, and sensor-transmission durations at the MSs. Given fixed compression ratios, the proposed optimal power allocation policy has the
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">threshold</italic>
-based structure with respect to a defined
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">crowd-sensing priority</italic>
function for each MS depending on both the operator configuration and the MS information. Further, for fixed sensing-data sizes, the optimal compression policy suggests that compression can reduce the total energy consumption at each MS only if the sensing-data size is sufficiently large. Our solution is also extended to the case of lossy compression, while extensive simulations are offered to confirm the efficiency of the contributed mechanisms. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/JSAC.2018.2872379 | IEEE Journal on Selected Areas in Communications |
Keywords | DocType | Volume |
Energy consumption,Resource management,Sensor systems,Wireless sensor networks,Optimization,Array signal processing | Journal | abs/1711.02066 |
Issue | ISSN | Citations |
2 | 0733-8716 | 6 |
PageRank | References | Authors |
0.41 | 21 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoyang Li | 1 | 53 | 10.12 |
| Changsheng You | 2 | 1109 | 36.00 |
| Sergey Andreev | 3 | 410 | 61.79 |
| Yi Gong | 4 | 197 | 28.72 |
| Kaibin Huang | 5 | 3155 | 182.06 |