1. 1 Sensor Relocation in Mobile Sensor Networks Guiling Wang, Guohong Cao, Tom La Porta, and Wensheng Zhang Department of Computer Science & Engineering Pennsylvania State University INFOCOM 2005

2. 2 Outline Introduction Problem Statement Finding the Redundant Sensors Sensor Relocation Experiments Conclusion

3. 3 Introduction Sensor relocation is to moving sensors to overcome the failure of nodes, or to respond to a new event. Difficulties in sensor relocation  Strict response time requirement  Currently performed application should not be affected.  Algorithm must balance energy cost with response time. Two phase relocation solution  First, Grid-Quorum based solution is to find the redundant sensor.  Second, efficient heuristics is proposed to determine moving path.

4. 4 Problem Statement (1) System Model  Grid-based architecture  Tasks of grid heads Determining redundant nodes Monitoring members and initiate relocation process  Grid-based architecture is feasible in relatively regular deployed networks. Cost of organizing sensors into grids is low. It can facilitate data aggregation, routing, etc.

5. 5 Problem Statement (2) Have redundant nodes Needs redundant nodes

6. 6 Finding the Redundant Sensors (1) Background and Motivation  Finding redundant sensors has some similarity to the publish/subscribe problem Grids need more sensor  Subscribers Grids having redundant sensors  Publishers Matching of a request to an advertisement  Matchinmaking  Three solutions for matchmaking Matching occurs at the subscriber: broadcast advertisement Matching occurs at the publisher: broadcast request Matching occurs in the middle

7. 7 Finding the Redundant Sensors (2) Concept of quorum  Non-empty set U, coterie C is a set of U’s subset.  Each subset P in C is called quorum. Property of quorum   Minimality Property:  Intersection Property: By organizing grids as quorums, each advertisement and request can be sent to a quorums of grids. Due to the intersection property, there must be a grid which is the intersection matching the advertisement and request.

8. 8 Finding the Redundant Sensors (3) Simple Quorum Construction  Organize grids in a row and a column into a quorum. Example Grid(0,3) has redundant node. Grid(3,0) is looking for redundant node. The intersection = Grid(0,0) Quorum of Grid(0,3) = {row(3), column(0)} Quorum of Grid(3,0) = {row(0), column(3)}

9. 9 Finding the Redundant Sensors (4) Grid-Quorum Solution  Two coteries supply coterie: Cs, supply quorum: Ps demand coterie: Cd, demand quorum: Pd  Holding properties Minimality Property: Intersection Property:

10. 10 Finding the Redundant Sensor (5) Grid-Quorum Construction  Organize grids in each row into one demand quorum  Organize grids in each column into one supply quorum Grid-Quorum Example Supply Quorum of Grid(1,3) = column(1) Demand Quorum of Grid(3,0) = row(0) Grid(1,3) has redundant node Grid(3,0) needs redundant node Intersection = Grid(1,0)

11. 11 Finding the Redundant Sensor (6) Optimization Sensor Sc dies in Grid(4,0) Grid(3,0) attach redundant node info Grid(2,0) will not forward the request Demand Quorum of Grid(4,0) = row(0) The closest redundant node = Sa The node out of the circle is too far

12. 12 Finding the Redundant Sensor (7) Compare Grid-Quorum to Broadcast-Request

13. 13 Sensor Relocation (1) Moving Methods  Directly moving redundant sensor to destination It may take a longer time than application requirement Moving single sensor for a long distance may deplete its energy  Cascaded movement First exchange message then move at the same time. It helps reduce relocation delay and balance energy consumption

14. 14 Sensor Relocation (2) Consideration for cascading node selection  To ensure the movement of cascading nodes will not affect the performed application, each sensor S i is associated with a recovery delay T i.  Restriction on the spatial relationship and departure time of the cascading nodes from the value T: S j moves to location of S i T i : recovery delay of S i t i : departure time of S i t j : departure time of S j d ji : distance between S j and S i

15. 15 Sensor Relocation (3) The Metrics to Choose Cascading Schedule  Schedule goals 1. Minimize the total energy consumption. 2. Maximize the minimum remaining energy.  Two goals can not be satisfied at the same time

16. 16 Sensor Relocation (4) Observation for Schedule Metric It is possible to balance the total energy consumption and minimal remaining energy. Metric: E2 – Emin2 < E1 – Emin1  Schedule 2 is better

17. 17 Sensor Relocation (5) Reason for observation result  Directly moving S 3 to S 0 is most total energy efficient solution.  But, adding cascading nodes close to the direct line will improve the minimum remaining energy with slight increase of total energy. Cascading schedule  Shortest schedule: least total energy consumption.  Best schedule: min difference between total and remaining energy.

18. 18 Sensor Relocation (6) Use modified Dijkstra’s algorithm to find shortest schedule  The sensor network is modeled a weighted complete graphic G(V, E)  The weight of edge S i S j is the distance between them  The remaining energy of S i is P i ’ = P i – D ji, if S i moves to S j  Modified Dijkstra’s algorithm adds a DeleteEdge() operation to guarantee the recovery delay constraint.

19. 19 Sensor Relocation (7)

20. 20 Sensor Relocation (8) Algorithm to find the best cascading schedule 1. Using Modified Dijkstra’s Algorithm to find the shortest schedule. 2. Record the minimum remaining energy as Emin’ 3. Check if new schedule is better by the metric: yes  delete all edges S i S j if P i – D ij <= Emin’ and go back to step 1. no  return previously calculated schedule

21. 21 Sensor Relocation (9) Distributed Algorithm  Calculate the shortest cascading schedule Grid head broadcasts request message Request message includes T 0, t 0, redundant node S r, E 0 and Emin 0 (∞). Each node receives the message, if it can be the successor of the sender, it updates the message then broadcast. Finally, the redundant sensor S r can determine the shortest schedule from the arrived messages.  Calculate the best cascading schedule Execute distributed calculation iteratively. Message attaches previous Emin’. When node S i receives message letting P i – D ji < Emin’, S i can not be the successor of S j, S i ignores the received message.

22. 22 Experiments (1) Simulation environment  Nodes num: 48  Target filed: 60m x 60m  Initial deployment: VOR  Moving speed: 2 m/s  Recovery delay: 10 sec  Energy consumption: 27.96 J/m  Initial energy of nodes: 1900J – 2000J

23. 23 Experiments (2) Effectiveness compared with VOR  Number of sensor moved

24. 24 Experiments (3)  Total energy consumption

25. 25 Experiments (4)  Minimum remaining energy

26. 26 Experiments (5) Cascaded Movement vs Direct Movement  Relocation time

27. 27 Experiments (6)  Total energy consumption

28. 28 Experiments (7)  Minimum remaining energy

29. 29 Experiments (8) The metric compares to max min remaining energy  Comparisons when the remaining energy is very different

30. 30 Experiments (9)  Comparison when the remaining energy is similar

31. 31 Conclusion The sensor relocation can be used to deal with sensor failure or response to new events. The Grid-Quorum solution can quickly locate the closest redundant with low message complexity. The cascaded movement can reduce the relocation time. The proposed algorithm can find best cascading schedule to balance total energy consumption and minimum remaining energy.