Energy-Efficient and Low-Latency Scheduling for Wireless Sensor Networks

1. ELECTION - Energy-efficient and low-latency scheduling technique for wireless sensor networks S. Begum, S. Wang, B. Krishnamachari, A. Helmy Department of Electrical Engineering-Systems University of Southern California Presented by Hung-Shi Wang

2. Outline • Introduction and Related work • Protocol Details • Assumption • Description of algorithms • Performance analysis • Mathematical Analysis • Simulation Results • Conclusion

3. S-MAC • Four sources of energy inefficiency • Collision – by using RTSand CTS • Overhearing – by switching the radio off when the transmission is not meant for that node • Control overhead – by message passing • Idle listening – by periodic listen and sleep • Drawback • Fixed duty cycle

4. T-MAC • Basic idea • To utilize an active and a sleep cycle, similar to S-MAC • To introduce an adaptive duty cycle by dynamically ending the active part • Difference in the duty cycle • S-MAC - fixed duty cycle • T-MAC – Dynamic duty cycle

5. 1 2 3 4 5 6 1 2 3 4 5 6 time 1 2 3 4 5 6 1 2 3 4 5 6 Sleep Latency • Largest source of energy consumption is keeping the radio on (even if idle). Particularly wasteful in low-data-rate applications. • Solution: regular duty-cycled sleep-wakeup cycles. E.g. S-MAC • Another Problem: increased latency (Data forwarding interruption problem)

6. Special Case Solution: D-MAC - Staggered sleep wake cycles minimize latency for one-way data gathering. Gang Lu, Bhaskar Krishnamachari and Cauligi Raghavendra, "An Adaptive Energy-Efficient and Low-Latency MAC for Data Gathering in Sensor Networks," IEEE WMAN 2004.

7. Low-energy adaptive clustering hierarchy (LEACH) • Randomly select sensor nodes as cluster-heads, so the high energy dissipation in communicating with the base station is spread to all sensor nodes in the sensor network. • Set-up phase • each sensor node chooses a random number between 0 and 1 • If this random number is less than the threshold T(n), the sensor node is a cluster-head.

8. Low-energy adaptive clustering hierarchy (LEACH) • Set-up phase • The cluster-heads advertise to all sensor nodes in the network • The sensor nodes inform the appropriate cluster-heads that they will be a member of the cluster. (base on signal strength) • Afterwards, the cluster-heads assign the time on which the sensor nodes can send data to the cluster-heads based on a TDMA approach.

9. Low-energy adaptive clustering hierarchy (LEACH) • Steady phase • the sensor nodes can begin sensing and transmitting data to the cluster-heads. • The cluster-heads also aggregate data from the nodes in their cluster before sending these data to the base station. • After a certain period of time spent on the steady phase, the network • goes into the set-up phase again and • enters into another round of selecting the cluster-heads.

10. Threshold-Sensitive Energy Efficient Protocols(TEEN) • Terminology • Hard Threshold (HT) • A threshold value for the sensed attribute • The absolute value of the attribute • Soft Threshold (ST) • A small change in the value of the sensed attribute which triggers the node to switch on its transmitter • Feature • Cluster-based routing protocol based on LEACH • Time critical application • The user can control the trade-off between energy efficiency and accuracy • A smaller value of the ST • more accurate picture of the network • increased energy consumption

11. Threshold-Sensitive Energy Efficient Protocols(TEEN) • Basic scheme • A gain of sensing value • Decision whether to report it or not • Based on the values of HT and ST • Data are reported only • When the sensed value exceeds HT • When the value’s change is bigger than ST • Drawback • Cannot allocate the time slot • Each node turn on its transmitter all the time • Cannot distinguish a node which does not sense a “big” change from a dead or failed node • Collision occurrence in the cluster

12. Motivation • LEACH: Heinzleman et. al., HICSS 2000 • Data driven, passive sensor • Achieves energy efficiency • Periodic clustering • Rotation of cluster head • High latency • TEEN: Manjeshwar et. al., IPDPS 2001 • Event driven, passive sensor • Periodic cluster and rotation of cluster head • Sleeps with fixed sleep cycle • Achieves low latency • Sense continuously • Stay awake when the event is detected (threshold reached)

13. R r BS Motivation • ELECTION: • Event driven, active sensor • Takes advantage of the spatio-temporal correlation to adaptively adjust sleep cycle • Achieve energy efficiency in phase 1: turn radios off • Ensures low latency and high responsiveness in phase2

14. Assumptions • Active/smart sensors • Able to sense the environment in a responsive and timely manner • Schedules sensors and communication radios independently • The underlying phenomenon exhibits spatio-temporal correlation

15. System Parameters • Initial sleep cycles: Sin, Sin0 • Data threshold: Dth • Gradient threshold: Gth • Gradient: rate of change of the phenomenon • Sleep reduction function: Fsr

16. Basic Algorithms Timing Diagram CH formation TDMA aggregation Phase0:Synchronization Phase1:Monitor (sense only: with phenomenon dependant scheduling) Phase2: Report (sense + communication) State Transition Diagram CH CH g(t) < Gth s(t+1) = s(t) CH Selection Active Init Sleep Synch d(t) > Dth CM d(t) < Dth CM D(t) < Dth, g(t) > Gth s(t+1) = Fsr(s(t), g(t)) CH Advertisement Phase 1: Radio off Phase 2 Point at which threshold crosses

17. Geared Sleep Reduction Function (Fsr) s(t) g(t) < 0.0 ½ s(t) 0.0< g(t) < 0.005 s(t+1) = ¼ s(t) 0.005< g(t) < 0.01 . . . Adaptive Sleep Cycles s(t+1) = Fsr(s(t), g(t)) • Adjust sleep cycle based previous sleep cycle and gradient • Temporal correlation a node wakeup at the event of threshold crossing • Spatial correlation All sensors measuring same phenomenon wake up at the same time System Parameters: Sin= 250 sec, Dth= 95 degrees

18. Performance Metrics • Energy • Total energy dissipation • Sensing energy • Communication: Cluster formation + Reporting • Latency • Delay between report generation and actual time of threshold being reached • Responsiveness • Difference between reported data value and threshold (e.g. degree of temperature)

19. Mathematical Analysis ELECTION = AEsT1/s + AEsT2/Tr + A/Ec + A/ Er T2/Tr LEACH = AEsT/Tr + A/EcT/Tc + A/ErT/Tr TEEN = AEsT + A/EcT/Tc + A/ErT2/Tr Ec >> Es Savings in cluster formation Es > Ec  Savings in sensing (TEEN) Es: Energy dissipation of a single sensing operation Ec: Energy dissipation in a single cluster formation Er: energy dissipation in a single report T: Network life T1, T2: duration of phase 1, phase 2 Tr: Reporting interval Tc: Cluster formation interval : Node density : Average node degree A: Total area of the network : Percentage of node CH s: Expected sleep duration )

20. Latency and Responsiveness Gmax: Max gradient threshold it responds to Sin: Initial sleep duration S: Fixed sleep cycle

21. Simulation Setup • High level simulation • ELECTION • TEEN • Hybrid • Fixed sleep cycle (like TEEN) • On demand cluster formation (like ELECTION) • Network simulated • 36 uniformly distributed sensors • Network divided into 4 quadrant • Each quadrant is assigned a sensing pattern • Phenomenon simulated • Phenomenon 1: changes 100 times during entire simulation • Phenomenon 2: changes 20 times

22. Simulation Parameters • Simulation time: 600K seconds • ELECTION • Geared sleep reduction function • Initial sleep cycle (Sin): 256 secs • TEEN • Cluster formation interval (Tc): 6K secs • Fixed sleep cycle: 50 secs • Hybrid • Cluster formation: on demand • Fixed sleep cycle: 50 secs

23. Remaining Energy Analysis Average Remaining Energy (in unit): Phenomenon 1 (changes 100 times): Es/Etx = 10% Phenomenon 1 (changes 100 times): Es/Etx = 1% Phenomenon 2 (changes 20 times): Es/Etx = 10%

24. Delay and Responsiveness Delay (in seconds) Responsiveness (in degrees)

25. Limitations • Dependency on the underlying phenomenon • A priori information of the environment may not be available • Not suitable for phenomenon that does not exhibit spatio-temporal correlation (e.g. seismic monitoring) • Synchronization problem in phase 1(clock skew)

26. Conclusion • New sleep scheduling scheme for wireless active sensor networks • Exploit spatio-temporal correlation of physical phenomenon • Adaptively adjust sleep cycle • Outperforms LEACH and TEEN with respect to energy, latency and responsiveness

27. The End Thank you for your attention.