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

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

Introduction Wireless sensor network Type of application QoS 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 Turn off 방법을 채택하지만 실제로 통신량도 줄일수 있다(라우팅을 intelligent하게 함으로써)

Problem Formalization (1/3) 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 Schedule - sensor set = active sensors in the set + sensors used in the chosen paths to the data sink

Problem Formalization (2/3) Multihop Sensor Network Management Problem Problem Formalization (2/3) Multihop network 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 Sensor field Multimode sensors F1 F2 F: feasible set F3 T: scheduled time Turn off Sink T1 Active sensor T2 Power consumption T3 Multihop networks Multimode sensors

Problem Formalization (3/3) 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) : Feasible sensor set : length of scheduled time , MAX

Modeling (1/3) Generalized maximum flow problem s d Multihop Sensor Network Management Problem Modeling (1/3) F1 s F2 S1 S2 Generalized maximum flow problem d S3 P211 R21 S1 F1 E1 E2 S2 F2 s d Energy bank E3 Application S3 Si included in F (Si, F): once flow arrives at one of the nodes in F  Si-> (one of Fi -> one of Fj: because of multihop)  d For multihop, R노드와 P노드 존재 RP multipath routing 각 multiplier가 의미하는 것에 대해서 설명해야함 E4 F3 S4 P431 R43 P432 Energy Time

Modeling (2/3) Generalized maximum flow problem s d Multihop Sensor Network Management Problem Modeling (2/3) F1 s F2 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) E2 S2 F2 s 1/(power consumption) d Energy bank E3 Application S3 E4 F3 S4 P431 R43 P432 Energy Time

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

Simulation (1/5) Metric : lifetime Factors on lifetime improvement 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 1J sensor 10µJ 15µJ 1 packet/sec

Simulation (2/5) Result Transmission range (Fig.2.) 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.

Simulation (3/5) Result (cont’d) Sensor node density (Fig.4.) 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. (a) Fig. 4. (b)

Simulation (4/5) Result (cont’d) Size of environment (Fig.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)

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

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

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 현재의 실험과 실제 환경에서와의 차이점과 현재 실험의 한계