Delay-aware Routing in Low Duty-Cycle Wireless Sensor Networks Guodong Sun and Bin Xu Computer Science and Technology Department Tsinghua University, Beijing, China IEEE Wicom 2011

Outline Introduction Network model Algorithm design Simulation evaluation Conclusion

Introduction Advances in microelectronics, wireless networking make wireless sensor networks applicable – Civilian – Military

Introduction Sensor nodes work relying on – Capacity-small – Unrechargeable batteries

Introduction Energy consumption of Sensor nodes – Idle listening – Packet overhearing

Introduction To save sensors’ energy and then prolong the system lifetime – Low duty-cycle active sleep Sensor sleep

Introduction Problem experienced by low duty-cycle sensor networks – Long delivery delay caused by the sleep latency of sensors The delay is critical to the performance of systems – Military surveillance – Target tracking – Monitoring

Goal Designing a delay-aware routing algorithm for low duty-cycle sensor networks – Reduce the network delay – Data packet drop rate

Network model N sensor nodes L*L square Communication range = r Multi-hop

Network model Duty cycle of sensor – Active – Sleep active sleep Sensor A sleep EX: duty cycle = 40% (5|1,5)

Network model Channel access – CSMA/CA like method REQ/CLR – Successful transmission Locations of node A,B and other node K

Network model Delay model – Queuing delay – Transmission delay – Propagation delay Node A’s queue packet1 packet2 packet3 active ….……… Sensor B active Queuing delay:

Algorithm design Two phases – Network initializing – Dynamic forwarding

Network initializing ID Working schedule Layer number A S C D B Broadcast message

Network initializing Layered topology A S C D B

Dynamic forwarding Forwarding set – F A ={S} – F B ={S} – F C ={S} – F D ={B,C} A S C D B B : (100|5,30,62) C : (100|3,24,30)

Dynamic forwarding Forwarding sequence – S D ={C,B,C,C,B,B} Node D’s queue packet1 packet2 packet3 CBCCBBCBCCBB A S C D B B : (100|5,30,62) C : (100|3,24,30)

Performance analysis Simulation setup Comparison – Static shortest-path routing(SSPR) parametervalue Square area of side150 Work schedule length150τ / 1τ=20ms Packet generate rate3 packets / 5minutes

Simulation result 150 sensors

Simulation result 150 sensors

Simulation result 150 sensors

Simulation result Duty cycle = 5%

Simulation result Duty cycle = 5%

Simulation result Duty cycle = 5%

Simulation result Sensor density = 8, duty cycle = 5%

Simulation result Sensor density = 8, duty cycle = 5%

Simulation result Sensor density = 8, duty cycle = 5%

Conclusions The authors proposed a delay-aware routing algorithm for low duty-cycle sensor networks – Achieves shorter delay by dynamically selecting forwarders Simulation results demonstrate that our algorithm improves – delivery delay – Reduces the network drop rate – Saving the energy of sensors

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