1. Information Quality Aware Routing in Event-Driven Sensor Networks Hwee-Xian TAN 1, Mun Choon CHAN 1, Wendong XIAO 2, Peng-Yong KONG 2 and Chen-Khong THAM 2 1 School of Computing, National University of Singapore (NUS) 2 Institute for Infocomm Research (I 2 R), Singapore

2. Overview Introduction Related Work and Motivation System Model Topology-Aware Histogram-Based Aggregation IQAR – Information Quality Aware Routing Protocol Performance Evaluation Concluding Remarks 2 Information Quality Aware Routing in Event-Driven Sensor Networks

3. Introduction Event-driven sensor networks – Deployed specifically for detection of Phenomenon of Interest (PoIs) – Converge-cast traffic characteristics – Sensory data is generated by multiple sensors only when PoI is detected Severe data implosion and redundancy 3 Information Quality Aware Routing in Event-Driven Sensor Networks v0v0 v1v1 v2v2 v3v3 v4v4 v5v5 v6v6 v7v7 v8v8 v9v9 PoI Node that does not detect PoI Node that detects PoI Fusion center v 0

4. Introduction Data aggregation and/or fusion – Mitigates congestion and medium access contention – Suppresses data to reduce traffic load and energy consumption by exploiting spatio-temporal correlation among sensory data – Comes at expense of loss in information quality (IQ) of data collected at fusion center – Results in reduction of (event) detection accuracy 4 Information Quality Aware Routing in Event-Driven Sensor Networks

5. Considers information content of data during aggregation/fusion and forwarding. Neighbor with highest information gain is selected to be next-hop. Fused data is transmitted to fusion center when IQ threshold is satisfied. Typically query-based rather than event-based. Alleviates medium contention, reduces transmission costs and reduces e2e delays. All sensory data is forwarded to fusion center, resulting in high data redundancy and energy costs. Related Work and Motivation Information Quality Aware Routing in Event-Driven Sensor Networks 5 Aggregation/Fusion based Routing IQ Aware Routing IQAR considers information content, and addresses both event-detection and multi-hop networks.

6. System Model Network has n sensors and fusion center v 0 H 1 denotes presence of PoI H 0 denotes absence of PoI P(H 1 ) = p, P(H 0 ) = 1-p0 < p < 1 Sensor observations are assumed to be i.i.d. at each sensor as well as across sensors. Information Quality Aware Routing in Event-Driven Sensor Networks 6

7. System Model Information Quality Aware Routing in Event-Driven Sensor Networks 7 Event Detection at Sensor Independent signal y i observed by node v i is: where w i is noise and r i is distance between v i and PoI. For each sampled signal y i, v i makes a per-sample binary decision b i  {0,1}: where T i is the per-sample threshold. Event Detection at Fusion Center

8. System Model Information Quality Aware Routing in Event-Driven Sensor Networks 8 Event Detection at Sensor Fusion center v 0 detects presence of PoI by making a global binary decision H={H 0,H 1 } based on data received. Optimal fusion rule is the Likelihood Ratio Test (LRT): where B={b 1,b 2,..,b |V a | } is the set of per-sample binary decisions received; and V a is set of activated nodes. Event Detection at Fusion Center Independent signal y i observed by node v i is: where w i is noise and r i is distance between v i and PoI. For each sampled signal y i, v i makes a per-sample binary decision b i  {0,1}: where T i is the per-sample threshold.

9. System Model Information Quality Aware Routing in Event-Driven Sensor Networks 9 Sequential Detection Data acquisition can terminate at earliest subsequence of data which satisfies a pre-determined IQ threshold. Reduces amount of data required to make an accurate global binary decision H = {H 0,H 1 }. Cumulative log-likelihood ratio at fusion center v 0 is: where V a is set of activated nodes. Cumulative log-likelihood at v i is: vivi vivi v1v1 v1v1 v2v2 v2v2 v3v3 v3v3 upstream nodes of v i IQ of node v i

10. Topology-Aware Histogram-based Aggregation Information Quality Aware Routing in Event-Driven Sensor Networks 10 v0v0 0.4 1.0 0.5 0.3 0.2 0.8 0.3 0.1 1.7 1.2 0.4 0.6 v1v1 v4v4 v9v9 v2v2 v5v5 v6v6 v7v7 v8v8 v3v3 v 10 v 11 v 12 With Global View & Topological Knowledge Required IQ I T Min-cost aggregation tree 1.0{v 3 } 2.0{v 3, v 7, v 8 } {v 2, v 3, v 8 } {v 2, v 3, v 5 } {v 1, v 3, v 8 } {v 1, v 4, v 9 } 4.5{v 2, v 3, v 5, v 6, v 8, v 12 } {v 1, v 2, v 3, v 4, v 5, v 9 } High communication costs and overheads!

11. Topology-Aware Histogram-based Aggregation Information Quality Aware Routing in Event-Driven Sensor Networks 11 v0v0 0.4 1.0 0.5 0.3 0.2 0.8 0.3 0.1 1.7 1.2 0.4 0.6 v1v1 v4v4 v9v9 v2v2 v5v5 v6v6 v7v7 v8v8 v3v3 v 10 v 11 v 12 {0.3, 2.1, 3, {2, 0, 1, 0, 0}} IQ of v 1 Max IQ using subtree rooted at v 1 Max cost using subtree rooted at v 1 histogram

12. Topology-Aware Histogram-based Aggregation Information Quality Aware Routing in Event-Driven Sensor Networks 12 v0v0 0.4 1.0 0.5 0.3 0.2 0.8 0.3 0.1 1.7 1.2 0.4 0.6 v1v1 v4v4 v9v9 v2v2 v5v5 v6v6 v7v7 v8v8 v3v3 v 10 v 11 v 12 {0.4, 3.2, 6, {1, 2, 2, 1, 0}} IQ of v 2 Max IQ using subtree rooted at v 2 Max cost using subtree rooted at v 2 histogram

13. IQ-Aware Routing Protocol Information Quality Aware Routing in Event-Driven Sensor Networks 13 Initialization Aggregation and Update Pruning v0v0 v1v1 v4v4 v9v9 v2v2 v5v5 v6v6 v7v7 v8v8 v3v3 v 10 v 11 v 12

14. IQ-Aware Routing Protocol Information Quality Aware Routing in Event-Driven Sensor Networks 14 Initialization Aggregation and Update Pruning v0v0 0.4 1.0 0.5 0.3 0.2 0.8 0.1 0.4 v1v1 v4v4 v9v9 v2v2 v5v5 v6v6 v7v7 v8v8 v3v3 v 10 v 11 v 12

15. IQ-Aware Routing Protocol Information Quality Aware Routing in Event-Driven Sensor Networks 15 Initialization Aggregation and Update Pruning Objective is to prune off as many nodes as possible from initial distance-based aggregation tree such that: 1.IQ constraint is still satisfied. 2.Total transmission cost is minimized. v0v0 0.4 1.0 0.5 0.3 0.2 0.8 0.1 0.4 v1v1 v4v4 v9v9 v2v2 v5v5 v6v6 v7v7 v8v8 v3v3 v 10 v 11 v 12

16. Performance Evaluation Simulator: Qualnet 4.0 Fusion center near bottom left-hand corner of terrain. Exponential sensing model. Information Quality Aware Routing in Event-Driven Sensor Networks 16 ParameterValue Terrain size100 meters × 100 meters Sensing interval1 second Transmission range~ 8 meters Target detection probability P d 0.9 Target false alarm probability P f 0.001

17. Performance Evaluation Information Quality Aware Routing in Event-Driven Sensor Networks 17 aggTree brute-force walk 1.1 0.8 0.6 0.5 0.3 v0v0 v1v1 v2v2 v3v3 v4v4 v5v5 v6v6 v7v7 v8v8 v9v9 1.1 0.8 0.6 0.5 0.3 v0v0 v1v1 v2v2 v3v3 v4v4 v5v5 v6v6 v7v7 v8v8 v9v9 1.1 0.8 0.6 0.5 0.3 v0v0 v1v1 v2v2 v3v3 v4v4 v5v5 v6v6 v7v7 v8v8 v9v9

18. Performance Evaluation Information Quality Aware Routing in Event-Driven Sensor Networks 18

19. Performance Evaluation Information Quality Aware Routing in Event-Driven Sensor Networks 19

20. Concluding Remarks Considers individual IQ contributions of each sensory data, and collects only sufficient data for PoI to be detected reliably. Utilizes a compact topology-aware histogram to represent the IQ contributions of nodes in the network. Redundant data is suppressed for time interval to reduce traffic load and alleviate medium access contention. Achieves significant energy and delay savings while maintaining IQ. Information Quality Aware Routing in Event-Driven Sensor Networks 20 IQAR