Is There Light at the Ends of the Tunnel?Wireless Sensor Networks for Adaptive Lighting in Road

Tunnels

IPSN 2011

Sean

Outline

• Goal • Challenge• Contribution• System Architecture• Hardware & Software• Testbed• Evaluation• Conclusion

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

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 radioo. Occlusion & noise to light sensor

direct sunlight

Variation caused by vehicle

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

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

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 TestbedLED for project

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)

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

Evaluation

• Light adaptive effect• Loss rate• Timely delivery• Resilience to gateway failures• Retransmission cost• Expected lifetime

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

Light adaptive effect• Real-world reference

Bound by the dynamic range of light actuatorOnly 150 lx maximum

Loss rateTime spent transmitting and waiting for receiver to wake up becomes significant

Timely delivery

30~60s:PLC may loss a sample in its cycle

> 60s:PLC will loss more than one sample in its cycle

Resilience to gateway failures

Retransmission cost

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

Conclusion

• Reach the goal of close-loop adaptive lighting• Provide real-world insights and experience by

using WSN in road tunnel

Any Problem?