Providing Application QoS through Intelligent Sensor Management

1. Providing Application QoS through Intelligent Sensor Management Proceedings of the 1st IEEE International Workshop on SNPA '03 M. Perillo and W. Heinzelman Nov. 11, 2003 Presented by Sookhyun, Yang

2. Contents • Introduction • Multihop Sensor Network Management Problem • Problem Formalization • Modeling • Simulation • Limitation • Conclusion

3. Introduction • Wireless sensor network • Tight energy and bandwidth constraints • Tradeoff between power consumption and data reliability • Type of application QoS • Balancing the application reliability with energy-efficiency • In this paper • Turning off redundant sensors • Energy-efficient routing with joint scheduling • Intelligent management • Work with knowledge of future traffic patterns in the network • Maximizing lifetime while minimum level of reliability

4. Multihop Sensor Network Management Problem Problem Formalization (1/3) • Application activation • Perform an acceptable level of QoS using data from a number of different sensor sets • Strategy • Schedule the sets to maximize the sum of time that all sensor sets are used • Determine route selection in conjunction with the sensor schedule • Critical nodes for use as sensors • Length of time • Nodes that are active in the set • Nodes used in the chosen paths to the data sink • Feasible sensor sets • Bandwidth • Schedulable • Reliability

5. Turn off Multihop Sensor Network Management Problem Problem Formalization (2/3) • Matter of scheduling • Which sensor combinations should be used to monitor the environment • How long sensor is turned on • How the data from these sensors should be routed to application • Multihop network Sensor field • Multimode sensors F2 F1 • F: feasible set F3 • T: scheduled time Active sensor Sink T1 T2 • Power consumption T3

6. : Feasible sensor set , MAX : length of scheduled time < Multihop Sensor Network Management Problem Problem Formalization (3/3) • Constraints • Time (Node can route other node’s data) • Sensor cannot realistically operate in multiple modes within a single sensor set • Data forwarding is needed for the entire duration of each of its sensor set’s scheduled time if a sensor is not in direct communication • Objective of management problem + Time (Node’s initial energy) Time (Node can be a active sensor)

7. Energy Time Multihop Sensor Network Management Problem Modeling (1/3) F1 F2 s S1 S2 • Generalized maximum flow problem d S3 P211 R21 S1 F1 E1 S2 E2 F2 d s E3 Application Energy bank S3 E4 F3 S4 P431 R43 P432

8. Energy Time Multihop Sensor Network Management Problem Modeling (2/3) F1 F2 s S1 S2 • Generalized maximum flow problem d 1/(# of intermediate node) S3 1/(power consumption) P211 R21 S1 F1 E1 1/(# of active sensor+ # of active sensors requiring data routing) S2 E2 F2 1/(power consumption) d s E3 Application Energy bank S3 E4 F3 S4 P431 R43 P432

9. Energy Time Multihop Sensor Network Management Problem Modeling (3/3) • Extension to multi-state applications P211 R21 S1 State1 F1 State2 S2 F2 d s Application Energy bank … S3 F3 Staten S4 P431 R43 P432

10. 1 packet/sec 1J 15µJ 10µJ sensor Simulation (1/5) • Metric : lifetime • Optimal scheduling/routing from the feasible sensor sets • Randomly chosen from the feasible sensor sets • Shortest path routing • Shortest cost routing : energy consumption • Factors on lifetime improvement • Path length or transmission range • Sensor node density • Size of environment • Simulation setting • Feasible sensor sets are founded by determining which combinations of sensors would allow 100% of a predetermined portions of area to be monitored • # of feasible sensor sets = 50 • Sensor node

11. Simulation (2/5) 100*100m 25m Sink • Result • Transmission range (Fig.2.) • Normalized to the optimal solution’s lifetime • Size of the benefit should remain relatively constant • Average shortest path length (Fig.3.) • Random set selection with shortest path/cost routing performs poorly 100 nodes Fig. 2. Fig. 3.

12. Simulation (3/5) 100*100m 25m Sink • Result (cont’d) • Sensor node density (Fig.4.) • As more energy is distributed, network lifetime is extended • Sensor node density seems to have a small effect on the size of relative improvement Fig. 4. (b) Fig. 4. (a)

13. Simulation (4/5) 0.01node/m^2 Sink • Result (cont’d) • Size of environment (Fig.5.) • Since sensor location is random, the possibility of a lightly covered area increases • Average power consumption in the network should increase as the sensor data needs to be forwarded along more hops on average Fig. 5. (a) Fig. 5. (b)

14. Simulation (5/5) • Result (cont’d) • Lifetime improvement (Table 1) • From nothing to more than a factor of 4

15. Limitation • Overhead not considered • Setting up traffic schedules • Setting up and tearing down routes • Considered only routes in which each successive hop moves toward the base station to be valid • Require global information about the neighborhoods of each node • Not scale well for larger networks

16. Conclusion • Sensor scheduling and routing improves liftetime larger than a factor of 4 when compared with more random methods • Paper’s model represent some typical networks that are likely to be used in sensor