Traffic Scheduling For Energy Sustainable Vehicular Infrastructure

1. Traffic Scheduling For Energy Sustainable Vehicular Infrastructure Abdulla A. H., Ghada H. B., Terence D. T., Amire A. S. and Dongmei Z. IEEE Globecom 2010 proceeding Presented by 劉美妙

2. Outline • Introduction • System model and optimal energy bound • Nearest fastest set (NFS) scheduler • Performance evaluation • Conclusions

3. Introduction • Vehicular network infrastructure will eventually evolve into a platform which will permit an even larger variety of mobile applications. • In many highway locations, setting up this type of infrastructure is difficult due to the unavailability or excess expense of wired electrical power. • An alternative to wired power connections is to operate some of the APs using an energy sustainable source such as solar or wind power. • Since multiple vehicles may be present in the AP coverage area, the question arises as to the order with which vehicles should be served. • As the energy available for the AP is limited, it is important to design energy efficient scheduling algorithms.

4. System model and optimal energy bound • Single roadside AP • Serving vehicles passing by in one direction • Single radio transceiver • Connect to one vehicle at any given time for a time slot,Δt • Pdoze: power consumption in Doze mode • the AP’s radio is placed into a Doze mode when traffic is not being processed • Pt : AP transmission power at time t • transmit to vehicles using as small a transmit power as possible • subject to satisfying the communication requests issued by the passing vehicles

5. Minimize the total energy • A standard exponential distance-dependent path loss model is used. power consumption in Doze mode Energy used by AP to serve the demands of the vehicles

6. Nearest fastest set (NFS) scheduler • Nearest Fastest Set (NFS) Scheduler that uses received knowledge of vehicle locations and velocities over a given time window • NFS: schedule vehicle requests when they are as close as possible to the AP. • same distance: determined by velocity of the vehicles • In a given time slot, NFS decides the order with which vehicles will be served for a short duration of time • NFS consists of two execution phases • Preparation of candidate communication opportunities. • Scheduling phase

7. NFS Scheduler-phase I • Preparation of candidate communication opportunities. • NCLNv × tws= • Vehicle v1, R1=4, ws=5 • Select nearest locations Time window t1 … tws V1 … … VNv 1 1 1 1 AP Distance: Dv1, t3 < Dv1, t4 < Dv1, t5 < Dv1, t2 < Dv1, t1 Dv1,t4 Dv1,t1 Dv1,t2 Dv1,t3 Dv1,t5

8. NFS Scheduler-phase II • Scheduling phase • resolves contention between vehicle requests • In time slot t, • If , then , • else if • Compute weight: AP 1 1 1 t

9. Performance evaluation • NFS scheduler performance for different traffic density • The simulation time for each run is 600 time steps and the number of vehicles is 48. • The window size for the NFS and NO Schedulers is 10 time steps.

10. Performance evaluation • NFS scheduler performance for different σ • NFS schedule performance for different α Density: 8 Vehicles per KM (125m apart) Average speed: 72KM/h

11. Compare with other scheduler • Different demand • Different traffic density Standard deviation σ = 4, Propagation path loss exponent α = 2, Density: 4 Vehicles per KM (125m apart) Demand level R= 6, Average speed: 108KM/h

12. Compare with other scheduler (cont.) 20 times • Different σ • Different α 5 times

13. Conclusions • In this paper, we have considered the problem of satisfying vehicular communication requirements while minimizing the energy needed by the roadside access point. • proposed a Nearest Fastest Set (NFS) scheduler that uses vehicle location and velocity inputs to perform causal scheduling.

14. since multiple vehicles may be present in the AP coverage area, the question arises as to the order with which vehicles should be served. • phrase • as to something;   as regards something • 關於;至於

15. AP Dv1,t Dv2,t Dv3,t