Wireless radio frequency (RF) energy transfer is a promising technology to provide a reliability-guaranteed power supply for wireless sensor networks.

1. By
Mr. Ajit M. Karanjkar
17 /June /20171
Mobile Charging in Wireless-Powered Sensor Networks:
Optimal Scheduling and Experimental Implementation
Guided By
Prof. D.H. Kulkarni
Department of Computer Engineering
STES’s Smt. Kashibai Navale College of
Engineering
Vadgaon Bk, Off Sinhgad Road, Pune
411041

2. CONTENTS:
17/June/20172
 INTRODUCTION
 LITERATURE SURVEY
 ARCHITECTURE
 HARDWARE SETUP AND EMPIRICAL
MODELS
 MOBILE CHARGE SCHEDULING
 APPLICATION
 FUTURE SCOPE
 CONCLUSION
 REFERENCES

3. INTRODUCTION:
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A wireless sensor network (WSN) is a special network system
consisting of autonomous sensors spatially distributed in a
given area to sense and collect the information of interest.
RF wireless power transfer-based hardware has been evaluated
and tested in many existing works.
WET has the potential to fundamentally solve the energy
problems and provide a permanent energy source for future
generation wireless sensor networks.

4. 17/June/20174
Mobile charging vehicle periodically traveling inside the
sensor network and charging each sensor node’s battery
wirelessly. a circuit model for renewable energy cycle and
corresponding RF charging time, and derived the node
lifetime expressions.
RF energy transfer-based wireless sensor networks in
which each sensor is installed with an antenna or antenna-
array that can convert RF signals into electrical energy.
Cont…….

5. WHAT IS WIRELESS CHARGING
17/June/20175
Wireless Charging (Wireless power transfer) uses electromagnetic
field to safely power transfer power from a transmitting source to a
receiving device for the purpose of charging a battery.

6. HOW DOES WIRELESS CHARGING
WORK?
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Wireless Charging is based on the principle of magnetic
resonance, or inductive power Transfer (IPT).
This is the process of transferring an electrical current between
two object through the use of coils to induce an electromagnetic
field.

7. 17/June/20177
Fig :1 The diagram below simplifies the process of wireless power
transfer into 5 key steps.

8. 17/April/20178
AUTHOR TITLE
NAME
YEAR SUMMARY
Z. Wang, L. Duan,
and R. Zhang
Adaptively
directional wireless
power transfer for
large-scale sensor
networks.
May 2016.
•Optimal charging radius and
maximized average received
power decrease with the
increased energy beam width
and density of the SNs.
M. Y. Naderi, K. R.
Chowdhury, and S.
Basagni
Wireless sensor
networks with RF
energy harvesting:
Energy models and
analysis.
March 2015
•2D and 3D placement
of multiple Radio Frequency,
Energy Transmitters for
recharging the nodes of a
wireless sensor network
X. Lu, D. Niyato, P.
Wang, D. I. Kim, and
Z. Han
Wireless charger
networking for
mobile devices:
fundamentals,
standards, and
applications.
Apr 2015
•Two major standards, Qi
and A4WP, have been
reviewed, with the focus on
their data communication
protocols.
•wireless charger networking
to support inter-charger data
communication
LITERATURE SURVEY:

9. 17/June/20179
AUTHOR TITLE
NAME
YEAR SUMMARY
X. Ren, W. Liang,
and W. Xu.
Maximizing charging
throughput in
rechargeable sensor
networks.
Aug 2014.
•Mobile charger, the base
station may receive many
recharging requests from
different sensors, depending
on the network.
S. Guo, C. Wang, and
Y. Yang,
Joint mobile data
gathering and
energy provisioning
in wireless
rechargeable sensor
networks.
Feb 2014
•A distributed Algorithm
composed of cross-layer data
control, scheduling and
routing sub algorithm for
each sensor nodes.
•Mobile collector at different
anchor points.
Cont……

10. ARCHITECTURE
17/June/201710
Fig:2 An Architecture For the Wireless charger network.

11. 17/June/201711
HARDWARE SET-UPAND EMPERICAL
MODELS
Fig: 3 Our developed hardware platform for RF energy transfer-based wireless sensor
networks.

12. Cont….
17/April/201712
 A. Hardware Setup:
We implement a hardware platform to evaluate the practical
performance of wireless mobile charging systems.
 Hardware Modification:
 Firmware Customization:
 B. Empirical Models
We present empirical models for wireless charging and
discharging process developed based on our proposed wireless
mobile charging platform.
 Empirical Model for Discharging:
 Empirical Models for Wireless Power Transfer:

13. MOBILE CHARGE SCHEDULING
17/June/201713
A. State Estimation
 State space.
 Action space
 State transition function

14. Cont….
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 B. Sequential Path Planning and Charge Scheduling
Fig:4 Simulation setup for wireless-powered sensor
networks with three sensors and one MSE.

15. APPLICATION
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 In recent year, the number of phone manufactures who have
offered wireless power sensor network charging capabilities have
steadily grown and for even those brands which do not have
wireless charging functionality.
 The current technology enables consistent safe charging of single
device when placed accurately on a charging pad characteristic of
inductive systems.
 Instead our focus is on developing the next generation of
solutions for wireless power sensor network charging for smart
phones including greater capability for free placement of devices
foreign object detection and multi-device charging.

16. FUTURE SCOPE
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 Mobile Charging in wireless power sensor network has been a long
time coming.
 Start Samsung mobile is leading way, But future will be the year the
wireless charging really becomes established.
 In Future not just Samsung , but all android Smartphone
manufacturers will include wireless charging is standard.
 In Future there won’t any smart phones shipping without wireless
charging.

17. CONCLUSION :
17/June/201717
 we have defined a wireless-powered sensor network
consisting of an MES installed with RF energy transmitter.
 The MES could travel through a pre-planned path to charge
multiple sensors in a given area.
 We established an empirical model and used the established
model to jointly optimize the path planning and mobile
charge scheduling for the wireless-powered sensor network.
 We derived an optimal policy for the MES to sequentially
optimize the planned path and the subset of sensors to
charge during each time period.

18. REFERENCE:
17/June/201718
[1] Z. Wang, L. Duan, and R. Zhang, “Adaptively directional wireless power
transfer for large-scale sensor networks,” IEEE Journal on Selected Areas in
Communications, vol. 34, no. 5, pp. 1785–1800, May 2016.
[2]D. Mishra and S. De, “Effects of practical recharge ability constraints on
perpetual RF harvesting sensor network operation,” IEEE Access, vol. 4, pp.
750–765, Mar. 2016.
[3] Y. Xiao, Z. Han, D. Niyato, and C. Yuen, “Opportunistic relay selection for
cooperative energy harvesting communication networks,” in IEEE
International Conference on Communications (ICC), London, UK, Jun.
2015.
[4] Y. Xiao, D. Niyato, Z. Han, and L. DaSilva, “Dynamic energy trading for
energy harvesting communication networks: A stochastic energy trading
game,” IEEE Journal on Selected Areas in Communications, vol. 33, 2015,
[5] H. Tabassum, E. Hossain, A. Ogundipe, and D. I. Kim, “Wirelesspowered
cellular networks: Key challenges and solution techniques,” IEEE
Communications Magazine, vol. 53, no. 6, pp. 63–71, Jun. 2015.

19. 17/June/201719
[6]X. Lu, D. Niyato, P. Wang, D. I. Kim, and Z. Han, “Wireless charger
networking for mobile devices: fundamentals, standards, and
applications,” IEEE Wireless Communications, vol. 22, no. 2, pp. 126–
135, Apr. 2015.
[7]D. T. Hoang, D. Niyato, P. Wang, and D. I. Kim, “Performance
optimization for cooperative multiuser cognitive radio networks with rf
energy harvesting capability,” IEEE Trans. Wireless Commun., vol. 14,
no. 7, pp. 3614–3629, Jul. 2015.
[8] S. Guo, C. Wang, and Y. Yang, “Joint mobile data gathering and
energy provisioning in wireless rechargeable sensor networks,” IEEE
Transactions on Mobile Computing, vol. 13, no. 12, pp. 2836–2852,
Feb. 2014.

20. 17/June/201720
THANK YOU