Biased Sink Mobility with Adaptive Stop Times for Low Latency Data Collection in Sensor Networks

1. Biased Sink Mobility with Adaptive Stop Times forLow Latency Data Collection in Sensor Networks Athanasios Kinalis∗, Sotiris Nikoletseas∗, Dimitra Patroumpa∗, Jose Rolim† ∗University of Patras and Computer Technology Institute, Patras, Greece †Centre Universitaire d’ Informatique, Geneva, Switzerland IEEE Globecom2009

2. Outline • Introduction • Network model • The proposed scheme • Simulation • Conclusion

3. Introduction • As the sensed data are forwarded to the sink node in the WSN • Settings have increased implementation complexity • Sensor devices consume significant amounts of energy Sensor node Sink node

4. Introduction • A approach has been introduced that shifts the burden of acquiring the data, from the sensor nodes to the sink. • Help conserve energy since data is transmitted over fewer hops. • Many apparent difficulties arise as well since traversing the network in a timely and efficient way is critical. • high density of sensors in an area • some sensors have recorded a significant amount of data

5. Introduction • High delivery delays • Even data loss B A

6. Goal • They propose biased sink mobility with adaptive stop times, as a method for efficient data collection in wireless sensor networks. • reduces latency • delivery success rate

7. Network model • The sink can accurately calculate its position • The sink can aware of the dimensions and boundaries of the network area • The sensor of sensing range R D j j D Sensor node Sink node

8. Scheme • Network Traversal • Deterministic Walk • Biased Random Walk • Calculation of Stop Time

9. Deterministic Walk j j A Sensor node Sink node

10. Biased Random Walk • The probability p(f)v of visiting a neighboring vertex v Sensor node Sink node 2 2 3 3 E 2 3 3 0 D A B 2 1 2 2 4 C 2 1 2 1

11. Calculation of Stop Time • Etotal is the total initial energy of all the sensors in the network • Ttotal_stoprepresents the maximum total amount of time the sink can remain stationary. n is the number of sensors of the network εithe initial energy of each sensor i. Tsimis the total time that the experiment is performed Etotal the energy spent when the sensors remain idle the maximum amount of energy consumed when sending a message the average event generation rate

12. Calculation of Stop Time • Ttotal_stop_round is the maximum amount of time that thesink will remain static in each round. represents the maximum total amount of time the sink canremain stationary the algorithm evolves in r rounds

13. Calculation of Stop Time • Constant stop time. • Adaptive stop time. the maximum amount of time that thesink will remain static in each round the density in cell i

14. Calculation of Stop Time • Example d = 1 dA = 1.5 dB = 1.2 dC = 0.5 dD = 0.8 C D 0.8 0.5 A B 1.5 1.2

15. Simulation

16. Simulation • SCD (Stop to Collect Data), one of the algorithms proposed in [6]. In SCD, the mobile sink stops when receiving new data. A. Kansal, A. Somasundara, D. Jea, M. Srivastava, and D. Estrin, “Intelligent fluid infrastructure for embedded networks,” in 2nd ACM/USENIX International Conference on Mobile Systems, Applications, and Services (MobiSys04), 2004.

17. Simulation • SCD (Stop to Collect Data), one of the algorithms proposed in [6]. In SCD, the mobile sink stops when receiving new data. A. Kansal, A. Somasundara, D. Jea, M. Srivastava, and D. Estrin, “Intelligent fluid infrastructure for embedded networks,” in 2nd ACM/USENIX International Conference on Mobile Systems, Applications, and Services (MobiSys04), 2004.

18. Conclusion • They propose both randomized mobility and deterministic traversals. • They adaptive stop times lead to significantly reduced latency and keeping the delivery success rate.

19. Thank you ~