Using Soft-line Recursive Response to Improve Query Aggregation in Wireless Sensor Networks

1. Using Soft-line Recursive Response to Improve Query Aggregation in Wireless Sensor Networks High-Speed Networking Lab. Dept. of CSIE, Fu-Jen Catholic University Adviser: Jonathan C. Lu, Ph.D. Speaker: Tzung-Lin Yu

2. Outline • Abstract • Introduction • Related Work • SRR protocol • Evaluation • Conclusion • Reference

3. k k k Query fork-type data BS I. Abstract • BS query for k-type data • sensors response the k-type packets waiting with a delay time Response waiting with a delay time

4. I. Abstract • In WSNs, aggregated or compressed data along the way toward the BS is desirable for saving energy . • Each hop might incur varying delays due to medium access contention, transmission and computation delays. • The common method to aggregate data uses a hard-line precomputed timer. • We develop a novel method, Soft-line Recursive Response (SRR), that bases response-wait on actual response times to previous queries using a history buffer.

5. 10 21 transmissions 8 3 10 transmissions I. Abstract An example to motivate SRR • Once aggregation misses, downstream data will not be able to catch up with the upstream traffic.

6. II.Introduction • Sensors in some areas might have different transmission abilities from sensors in other areas therefore introducing different transmission delay. • Under the presence of network failure or delay fluctuations, we want to maximize the amount of traffic going back to the sink in the least amount of time. • Our goal is to have query responses flow from the network edge towards the BS while maximizing aggregation using shortest paths. • the event of failure or temporal delay, the aggregation process should skip those nodes for more aggregation

7. III. Related Work - iBubble Infrastructure • SRR adapt to the iBubble infrastructure with minimal modification. • iBubble routing protocol allows efficient query in heterogeneous networks • Keywords for describing sensor data type and sending the keywords towards the BS to guide and restrict queries. • original: not consider aggregation

8. {type of sensing device} {keywords} hopcount can sense type k 8 III. Related Work - iBubble Infrastructure iBubble in Heterogeneous WSN • since N7has received keyword k from downstream when BS queries for k, N7 will forward the query to its children (N3, N6) announced kwaitsfor both nodes to reply • N3, N4: forward the query • N5, N6: senddata upstream immediately

9. IV. SRR protocol A. When Network Connection Is Perfect(using Basic Recursive Response) • Homogenous Network1) edge nodes  respond to a query immediately2) others  waitforresponses from all of its downstream • Heterogeneous Network • using iBubble Infrastructure

10. IV. SRR protocol B. When the Network is Not Perfect) (Using the Soft-lineThreshold to Override Recursion) • When a node receives a query request, it will calculate how long it should wait for the downstream response using the α– percentile of the previous downstream response delay values it tracks in its two history buffers. • 1st buffer : • store all history response-times • record even time-out last time • for calculating the next • if buffer is full: FIFO queue • 2nd buffer : • same values as 1st but a sorted list • binary search to insert a new response time

11. IV. SRR protocol Ex: node x, sorted list (1, 2, 3, 4) the next response comes in  x computes the response-time = 2.5 (response - query)  list = (1, 2, 2.5, 3, 4)a new query comes in (with α– percentile = 80%)  5 (elements) × 80% = 4,  index = 4,it will wait for downstream query responses for 3 seconds • insertion and deletion into FIFO (1st buffer): constant time • insertion and deletion into sorted list (2st buffer): logarithmic time • computation of α–percentile: constant time  SRR operation times at most O (log b) computation time (b= history buffer size)

12. V. Evaluation • 500 nodes randomly in a WSN • 1000 × 1000m2area • BS centered at the middle of the area. • Node’s radio transmission range: 240m

13. α=10% α=100% V. Evaluation • Larger buffer reduce moreaggregation misses • fixing buffer size:α↑, aggregation miss↓

14. V. Evaluation Average end to end node response delay to BS Average response delays from nodes situated at network edge to BS Contrast aggregation miss between the hard-line and SRR SRR’s two advantage over hard-line • when α increases  SRR: aggregation miss tolerance increases& end to end delay decreasing DelaySaving Soft / Hard Saving Miss Saving RSS response delays of three distributions (buffers)

15. VI. Conclusion • Our simulations show that SRR can improve aggregation opportunities up to 120% over the hard-line approach, while increasing response delay less than 5%. • SRR reduces query response traffic and data redundancy in both homogeneous and heterogeneousstatic and mobile WSNs with a maximum O(N)transmission overhead in large WSNs of N nodes and O (log b) update cost. (b= history buffer size)

16. VII. Reference • Using Soft-Line Recursive Response to Improve Query Aggregation in Wireless Sensor NetworksXiaoming Lu; Spear, M.; Levitt, K.; Matloff, N.S.; Wu, S.F.;Communications, 2008. ICC '08. IEEE International Conference on19-23 May 2008 Page(s):2309 - 2316 Digital Object Identifier 10.1109/ICC.2008.440