Cri$ques Ø 5 cri&ques in total Ø Each with 6 points

Transcription

1 Cri$ques Ø 5 cri&ques in total Ø Each with 6 points 1

2 Distributed Applica$on Alloca$on in Shared Sensor Networks Chengjie Wu, You Xu, Yixin Chen, Chenyang Lu

3 Shared Sensor Network Example in San Francisco ü Smart parking ü Monitor traffic conges&on Ø Air pollu&on monitoring Ø Noise monitoring Streetline - hjp:// 3

4 Shared WSN Smart Building HVAC System Temperature monitoring Humidity monitoring Air quality monitoring Building Health Monitoring n Noise monitoring n Vibra&on monitoring Ligh&ng Control System Light monitoring Occupancy detec&on Surveillance/Safety n n Fire detec&on Intruder detec&on Echelon Building Automa&on System: hjp:// 4

5 QoM Ø Diverse applica&on- level QoM ajributes Detec&on probability - for surveillance applica&ons Variance reduc&on for distributed es&ma&on of spa&al phenomena (e.g., temperature, humidity). Ø Inter- node dependencies QoM provided by a node depends on the other nodes allocated 5

6 Variance Reduc$on Ø Monitor spa&ally correlated phenomena Ø Minimize uncertainty of the es&ma&on (QoM) OFFICE OFFICE QUIET PHONE Uncertainty of es&ma&on CONFERENCE 2.5 STORAGE LAB 2 ELEC COPY KITCHEN SERVER

7 Centralized Solu$on Overview Applica$ons (code, memory cost) Wireless Sensor Network Deploy Applications Per node sensor measurements Compute Applica$on Alloca$ons 7

8 Genera$ng Nodeset- QoM Mapping Captures inter- node QoM dependencies Per node sensing measurements QoM Aggregator Nodeset A QoM(A) 8

9 Nodeset- Variance Reduc$on Mapping Ø Joint Gaussian distribu&on (Σ V ) Per node temperature Ø VR(A) = trace(σ V ) trace(σ V\A A ) Ø Normalize VR(A) VR(A) Nodeset A 9

10 Limita$on of Exis$ng Solu$ons Ø QoM metric: Variance Reduc&on Pro: Capture inter correla&on of sensor readings Con: Not amenable to distributed Requires global informa&on across the network Involves matrix computa&on Ø Centralized architecture Itera&on between server and the sensor network High communica&on overhead Not scalable and fault tolerant 10

11 Our Contribu$on Ø Distributed QoM formula&on: Covariance Cover Facilitate distributed solu&on Effec&ve approxima&on of variance reduc&on in prac&ce Ø Distributed Game Approach Localized communica&on within neighborhood Adap&ve to local dynamics Robust against network disconnec&on Ø Strong performance guarantee 1/2 approxima&on ra&o Fast Convergence Compe&&ve to centralized solu&on in 3 real datasets 11

12 Covariance Cover Ø Covariance Measure of how much two random variables change together K ij Ø Covariance Cover How much variance the allocated sensor can cover K ij Covariance shared by i and j if both allocate the applica&on λ(a) = λ i = (K ii i A i A + βk ij 2 ) j is i's neighbor Ø High Covariance Cover - > Low Uncertainty 12

13 Covariance Cover Ø Proper&es of Covariance Cover Distributed: Localized to neighborhood Monotonic: More nodes host à Higher value Submodular: Diminishing return OFFICE OFFICE QUIET PHONE CONFERENCE STORAGE ELEC COPY A LAB SERVER KITCHEN B C 13

14 Decentralized Solu$on Overview Applica$ons (code, memory cost) Wireless Sensor Network Deploy Applications Applica$on Alloca$ons 14

15 Game Approach Per node sensor measurement, alloca&on A B C 15

16 Monotonicity A B 0.5 C Covariance Cover Covariance Cover dose not decrease as number of nodes increases 16

17 Monotonicity A B 0.5 C Covariance Cover The return of adding a new node diminishes 17

18 Valid Game A B 0.5 C Covariance Cover Covariance Cover always equals to the sum of private covers 18

19 Insights in Game Design Monotonic Submodular Valid U&lity Covariance Cover is non- decreasing Covariance Cover has diminishing return property Covariance Cover= sum of private cover Existence of Nash Equilibrium Convergence to Equilibrium 1/2 Approxima&on Bound 19

20 Evalua$on: three real datasets Ø Three real world datasets Intel climate monitoring system DARPA vehicle detec&on project BWSN water pollu&on monitoring simula&on Ø Different network topology PRR bound: Packet Recep&on Ra&o bound Smaller PRR bound - > denser neighbors High PRR bound may leads to non- connected network 20

21 Achieve 1/2 Approxima$on Ra$o 1 Covariance Cover Ratio PRR Bound Intel DARPA BWSN 21

22 Converge in small number of rounds 6 Number of Rounds PRR Bound Intel DARPA BWSN 22

23 Low communica$on overhead Ø Small number of messages sent per node Number of Messages Intel DARPA BWSN PRR Bound 23

24 Comparison against Centralized Ø Compe&&ve to State- of- the- art Centralized solu&on FRG QoM SG VR FRG VR 10 SG CC FRG CC PRR Bound Y. Xu, A. Saifullah, Y. Chen and C. Lu, Near Op&mal Mul&- Applica&on Alloca&on in Shared Sensor Networks, MobiHoc

25 Conclusion Ø Maximize QoM of applica&ons in shared sensor networks Ø Covariance cover: QoM metric for distributed solu&ons Submodular and monotonic Facilitate distributed solu&on Ø Submodular Game Game - > distributed algorithm with localized communica&on Submodularity à 1/2 approxima&on ra&o. Compe&&ve to centralized solu&on on three real data sets 25

26 References Ø Ø Ø C. Wu, Y. Xu, Y. Chen and C. Lu, Submodular Game for Distributed Applica&on Alloca&on in Shared Sensor Networks, IEEE Interna&onal Conference on Computer Communica&ons (INFOCOM'12), March Y. Xu, A. Saifullah, Y. Chen, C. Lu and S. BhaJacharya, Near Op&mal Mul&- Applica&on Alloca&on in Shared Sensor Networks, ACM Interna&onal Symposium on Mobile Ad Hoc Networking and Compu&ng (MobiHoc'10), September S. BhaJacharya, A. Saifullah, C. Lu and G.- C. Roman, Mul&- Applica&on Deployment in Shared Sensor Networks Based on Quality of Monitoring, IEEE Real- Time and Embedded Technology and Applica&ons Symposium (RTAS'10), April