1. Is There Light at the Ends of the Tunnel? Wireless Sensor Networks for Adaptive Lighting in Road Tunnels IPSN 2011 Sean

2. Outline Goal Challenge Contribution System Architecture Hardware & Software Testbed Evaluation Conclusion

3. Goal WSN-based Close-loop adaptive lighting in road tunnel – Improve tunnel safety – Reduce power consumption State-of-the-art solutions – Pre-set lighting based on date and time – Relying only on external sensor Testbed evaluation Real deployment – Project TRITon – 630m, two-way, two-lane tunnel

4. Challenge Peculiarities of Tunnels – harsh environment, relatively studied on WSN – Vehicular traffic – dirt and dust accumulation – Periodic tunnel cleaning – Limited deployment & debugging – Light variation Need filtering – Better connectivity Robustness Packet collision o. Interference with WSN radio o. Occlusion & noise to light sensor direct sunlight Variation caused by vehicle

5. Challenge Real-world constraints – Extended lifetime : at least 1-year by tunnel operators – WSN cannot fail due to continuous operation – Sensed data must arrive timely – Quality of sensing – Integration with conventional, industrial-strength equipment

6. Contribution Verify WSN-based solution to adaptive lighting is feasible Understand what extent the mainstream WSN technology can achieve Real testbed implement Gaining practical insight into tunnel scenario – Real-world lesson asset

7. System Architecture 3 components – An external sensor – A grid of light sensor along the tunnel length – A control algorithm Measure the veil luminance Compute error between legislated curve and actual lighting Drive above error to zero Determine the legislated curve HPS in Testbed LED for project

8. Hardware & Software Collection tree – Use LQI as path cost – Periodically reconstructed every 3min Light Sensing – Average 4 sensor value into S(i) – Average all S(i) into S(all) – if |S(all) – S(i)| differs from S(all) by 50%, discard it – Recompute S(all)

9. Testbed 40 nodes, 260m-long, two-way, two-lane tunnel PLC relies only on first 15 node 7-month experiments More dense than TRITon – 44 nodes, 630m Light sensor sample every 5s, PLC collects data every 30s

10. Evaluation Light adaptive effect Loss rate Timely delivery Resilience to gateway failures Retransmission cost Expected lifetime

11. Light adaptive effect Artificial step response Node position relative to lamps bears great influence Behavior of other node is closer to node 7 than node 4 Still follow the reference trend

12. Light adaptive effect Real-world reference Bound by the dynamic range of light actuator Only 150 lx maximum

13. Loss rate Time spent transmitting and waiting for receiver to wake up becomes significant

14. Timely delivery 30~60s: PLC may loss a sample in its cycle > 60s: PLC will loss more than one sample in its cycle

15. Resilience to gateway failures

16. Retransmission cost

17. Expected lifetime Battery discharge profile – Temperature – Voltage – Discharge current Underestimate – Use average discharge current of 100mA – LPL-like MAC only consume a few mA 250ms LPL is better – Power consumed in channel check – Packet strobe time Trade-off

18. Conclusion Reach the goal of close-loop adaptive lighting Provide real-world insights and experience by using WSN in road tunnel

19. Any Problem?