* Dalian University of Technology

1. MPF: Prolonging Network Lifetime of Wireless RechargeableSensor Networks by Mixing Partial Charge and Full Charge * Dalian University of Technology +Nanjing University ^ University of North Carolina at Greensboro Chi Lin*, Yanhong Zhou*, Haipeng Dai+, Jing Deng^, and Guowei Wu* IEEE SECON 2018

2. Background Literature Review MPF Scheme Experiments & Simulations Conclusions & Future Work Outline

3. WRSNs : Wireless Rechargeable Sensor Network Background WSNs : Wireless Sensor Networks • Event monitoring in agricultural, industrial, climate applications • Drawbacks: limited power capacity & not feasible for large-scale networks magnetic field Benefiting from the recent breakthrough in Wireless Power Transfer technology (WPT) Inductive Coupling Magnetic Resonant Coupling Electro-magnetic Radiation • Limited energy capacity problem: Solved 2/27

4. WRSN : Wireless Rechargeable Sensor Network Collect sensory data and provide energy for mobile chargers Background Base Station Rechargeable Sensors • Monitor events and send charging requests Mobile Charging Vehicle (MCV) • Replenish energy for sensor nodes • Overview of an on-demand charging architecture 3/27

5. Full Charge: Charge to sensor’s full capacity Strength: • More stable emergence of charging requests Weakness: • Long charging duration Literature Reivew Charging strategies are two-folds: full charge & partial charge. 2. Partial Charge Charge a sensor partially • Strength: • Short charging duration • Weakness: • Heavier charging burdens Motivation: Can we mix full chargeand partial chargetogether? Overcome drawbacks mix both strategies 4/27

6. 3. A charging method feasible for large-scale networks 4. Maximize the energy utilization rate Other Motivations An interesting phenomenon When the MCV is heading towards BS, sometimes it carries extra energy, which is sufficient to serve some nodes. Design return mechanism ( RTM ) Fully use the energy to replenish more sensor nodes and enhance energy efficiency 5/27

7. Problem Formulation • Objective: Minimizing the traveling cost with the maximum survival rate • Formalization: • Constraints: • Variables: 6/27

8. Generating a charging plan to guarantee the maximum node survival rate and minimize the traveling cost in each charging mission. A charging plan is composed of two parts: • a charging path • the corresponding power allocation scheme MPF (Mixed Partial & Full Charge) • MPF has three modules: • Evaluation Module (EM) • Adjustment Module (AM) • Selection Module (SM) MPF diagram 7/27

9. General process of EM, AM and SM: General Process of MPF illustration: 1. : an initial plan, full charge with the shortest Hamiltonnian path. 2. : an adjusted plan, with adjusted alloction power or charging path. 3. : a partial charging plan. 8/27

10. MPF • Evaluation Module (EM): three constraints a. Energy constraint of MCV • An MCV should hold enough residual energy before returning back to the BS. • To quantify the schedulability, we define: b. Energy constraint of nodes • All nodes in the charging mission should keep alive before the MCV’s arrival. schedulable c. Energy constraint of the next charging mission indicates • Guarantee the survival of all nodes belonging to the next charging mission 9/27

11. MPF Adjustment Module (AM) Purpose: To minimize the value of through adjusting the charging plan. Two Alternatives: A1 & A2 • Update the power allocation scheme for the charging path in the original charging plan. A1 • Change node sequence of charging path based on the power allocation scheme in the original charging plan. A2 10/27

12. MPF • Adjustment Module (AM) Output: A schedulable plan Or non-schedualable • Process initial plan not schedulable by A1 Adjust by A2 Adjust by A1 not schedulable by A2 AM • Define the concept of path and power adjustment window (PPA) • Record a set of nodes, positioned in front of the dead node in the charging plan. • Through adjusting PPA, a non-schedualable task can be converted into a scheduable one. 11/27

13. MPF Adjustment Module (AM)  Update the power allocation scheme A1 • Construct PPA windows for each dead node • Update the allocated power of the nodes in the corresponding PPA window. • Testify the schedualability of a charging mission.   12/27

14. MPF • Adjustment Module (AM) Change the node sequence A2 • Construct PPA windows for each dead node • Remove paths with longer dead duration in the charging path set • Choose the shortest path and apply full charge strategy • Evaluate the schedulability by using EM 13/27

15. MPF • Selection Module (SM) Reason: After executing AM, if no schedulable plan is obtained, full charge will inevitably cause node exhaustion, partial charge will be applied to shorten the dead duration. Method: Generate a charging plan by using identical approaches asAM, which is formally expressed as: 14/27

16. RTM • Return Mechanism Problem: MCVs may sometimes come back to BS holding extra energy, low energy usage efficiency. Solution: Charge nodes in the next charging mission or nodes that will request for replenishment in the future on the way back to BS. 15/27

17. RTM • Return Mechanism • Charge nodes in the next charging mission • Charge nodes that will request for replenishment in the future. 16/27

18. A mobile robot arm (GPS + Camera + Wheels + Arm + Transmitting coil) A rechargeable sensor (sensor + receiving coil ) Test-bed Experiments Experiments and Simulations Energy efficiency of WiTricity: 47% 17/27

19. Test-bed experiment in a meeting room Test-bed Experiments Experiments and Simulations 18/27

20. Test-bed Experiments Experiments and Simulations • Three classic charging schemes: • EDF: serves the node with earliest deadline • NJNP: serves the spatial closest node • Heurustic: partial charge • Conclusion: • Partial charge consumes more traveling cost than full charge • Partial charge can reduce dead duration of nodes. 19/27

21. Experiments and Simulations 20/27

22. Survival rate varies a little and the traveling distance reduces gradually. To reduce the computational complexity of calculating the charging path, finally we set the size of charging mission as 5. Simulations for large scale networks Experiments and Simulations One important task: Determine the best size of the charging mission 21/27

23. When the charging threshld is below 40%, the survival rate is the highest, hence we set the threshold as 40%. Whenever the energy of nodes falls below 40%, they will send out charging requests to the MCV. Simulations for large scale networks Experiments and Simulations Another important task: Determine the charging threshold 22/27

24. Experiments and Simulations Survival Rate • Partial charge can effectively improve survival rate. • MPF allocates power in a dynamic way according to the dead duration. Thus, it owns higher survival rate than Heuristic. • MPF has an approximately 13%, 11%, and 7% higher survival rate than those of NJNP, EDF, and Heuristic, respectively. 23/27

25. Experiments and Simulations Energy Utilization • Effective energy utilization increases with speed. • EDF and Heuristic are lack of path planning, MPF performs better than them. • When comparing with NJNP, MPF has a lower effective energy utilization because it adopts full charge and has less traveling cost. 24/27

26. Experiments and Simulations Effectiveness of RTM • When MPF is implemented without RTM, the average traveling distance is 9% longer than that with such a mechanism. Furthermore, the effective energy utilization is higher when RTM is used. 25/27

27. Conclusions Conclusion & Future Work • A mixed charging scheme MPF that computes the feasible charging is proposed • RTM is designed to fully utilize the residual energy of the MCV • Experimental results show the feasibility of partial charging mechanism and disclose practicability of MPF. • Simulation results demonstrate the advantages of MPF in terms of survival rate, traveling cost, etc. Future Work • Focus on how to apply MPF into collaborative charging scenario for multiple MCVs. 26/27

28. Thanks !Any Questions ?c.lin@dlut.edu.cn