UNIVERSITY OF SOUTHERN CALIFORNIA 1 ELECTION: Energy-efficient and Low- latEncy sCheduling Technique for wIreless sensOr Networks S. Begum, S. Wang, B. Krishnamachari, A. Helmy Electrical Engineering-Systems University of Southern California

UNIVERSITY OF SOUTHERN CALIFORNIA 2 Motivation Sensor network of homogenous active sensors Monitor some phenomenon to detect abnormalities Application: chemical monitoring, machine fault detection Exhibits spatio-temporal correlation Phases of operation: Phase1 (normal operation): Energy efficiency Phase2 (event detection+): Latency and responsiveness R BS r

UNIVERSITY OF SOUTHERN CALIFORNIA 3 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) 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 Motivation R BS r

UNIVERSITY OF SOUTHERN CALIFORNIA 4 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

UNIVERSITY OF SOUTHERN CALIFORNIA 5 Outline Motivation Description of Algorithms Performance Analysis Conclusion

UNIVERSITY OF SOUTHERN CALIFORNIA 6 System Parameters Initial sleep cycles: S in Data threshold: D th Gradient threshold: G th Gradient: rate of change of the phenomenon Sleep reduction function: F sr

UNIVERSITY OF SOUTHERN CALIFORNIA 7 Basic Algorithms Phase0:Synchronization Phase1:Monitor (sense only: with phenomenon dependant scheduling) Phase2: Report (sense + communication) CH formationTDMA aggregation CH CM Sleep g(t) < G th  s(t+1) = s(t) D(t) G th  s(t+1) = F sr (s(t), g(t)) Active d(t) > D th CH Selection CH Advertisement Init Synch d(t) < D th Phase 1: Radio offPhase 2 CH CM Timing Diagram State Transition Diagram Point at which threshold crosses

UNIVERSITY OF SOUTHERN CALIFORNIA 8 Adapting Sleep Cycles System Parameters: S in = 250 sec, D th = 95 degrees s(t+1) = F sr (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 Geared Sleep Reduction Function (F sr ) s(t) g(t) < 0.0 ½ s(t) 0.0 < g(t) < ¼ s(t) < g(t) < 0.01 s(t+1) =......

UNIVERSITY OF SOUTHERN CALIFORNIA 9 Performance Metrices 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)

UNIVERSITY OF SOUTHERN CALIFORNIA 10 ELECTION =  AE s T 1 / s +  AE s T 2 /T r +  A/  E c +  A/  E r T 2 /T r LEACH =  AE s T/T r +  A/  E c T/T c +  A/  E r T/T r TEEN =  AE s T +  A/  E c T/T c +  A/  E r T 2 /T r Energy Analysis E s : Energy dissipation of a single sensing operation E c : Energy dissipation in a single cluster formation E r : energy dissipation in a single report T: Network life T 1, T 2 : duration of phase 1, phase 2 T r : Reporting interval T c : Cluster formation interval (Le, Te)  : Node density  : Average node degree A: Total area of the network  : Percentage of node CH (Le, Te) s : Expected sleep duration (El) E c >> E s  Savings in cluster formation E s > E c  Savings in sensing (w.r.t. TEEN)

UNIVERSITY OF SOUTHERN CALIFORNIA 11 Latency and Responsiveness ProtocolLatency (Avg): L Latency (Worst):  Respons. (Worst):  ELECTION½ Last sleep duration Last sleep duration G max S in LEACH½ T r TrTr G max *  TEEN½ SSG max * S G max : Max gradient threshold it responds to (El) S in : Initial sleep duration (El) S: Fixed sleep cycle (Le, Te)

UNIVERSITY OF SOUTHERN CALIFORNIA 12 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

UNIVERSITY OF SOUTHERN CALIFORNIA 13 Simulation Parameters Simulation time: 600K seconds ELECTION Geared sleep reduction function Initial sleep cycle (S in ): 256 secs TEEN Cluster formation interval (T c ): 6K secs Fixed sleep cycle: 50 secs Hybrid Cluster formation: on demand Fixed sleep cycle: 50 secs

UNIVERSITY OF SOUTHERN CALIFORNIA 14 Remaining Energy Analysis Average Remaining Energy (in unit): Phenomenon 1 (changes 100 times): E s /E tx = 10% Phenomenon 2 (changes 20 times): E s /E tx = 10% Phenomenon 1 (changes 100 times): E s /E tx = 1%

UNIVERSITY OF SOUTHERN CALIFORNIA 15 Delay and Responsiveness Delay (in seconds) Responsiveness (in degrees)

UNIVERSITY OF SOUTHERN CALIFORNIA 16 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

UNIVERSITY OF SOUTHERN CALIFORNIA 17 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