Utility-driven Energy-aware In-network Processing for Mission-oriented Wireless Sensor Networks

1. Annual Conference of ITA (September 24, 2009) Utility-driven Energy-aware In-network Processing for Mission-oriented Wireless Sensor Networks Sharanya Eswaran, Penn State University Matthew Johnson, CUNY Archan Misra, Telcordia Technolgoies Thomas La Porta, Penn State University

2. The Problem Missions/ Applications Network Resources Sensor Resources Perimeter monitoring Gunfire localization . . . Mobile insurgent tracking Correlation Image fusion . . Surveillance . • “How to share the network resources (bandwidth, energy) to maximize the • effectiveness of sensor-enabled applications (missions)?” • Limited bandwidth • Limited energy • Heterogeneous missions utilizing multiple types of sensors • Variable degrees of in-network processing • - Forwarding nodes may compress or fuse data

3. In-network Processing • In-network processing is an attractive option conserving bandwidth and energy • Compression • Fusion • Non-negligible energy footprint for streaming applications • Stream-oriented data comprise sophisticated DSP-based operations (e.g., MPEG compression, wavelet coefficient computation) • Forwarding nodes can compress on the fly • With variable compression ratios • Forwarding nodes can fuse multiple streams • the location of these fusion points can be determined on the fly • Dual trade-off • Bandwidth vs. loss of information • Communication cost vs. computation cost

4. Adaptive In-network Processing • Variable quality compression • Each forwarding node compresses data to different ratios, depending on • Residual energy at that and downstream nodes • Congestion in the region • Effect of compression on application • Dynamic fusion operator placement • Select best node in the path each time for fusion, depending on • Residual energy at that and downstream nodes • Congestion in the region • Variable source rate 1 2 A B C M

5. Our Approach Network Utility Maximization (NUM) A Distributed, Utility-Based Formulation of Resource Sharing • Each mission has a “utility”: • A measure of how “happy” the mission is • A function of rates received from all its sensors • Allocate WSN resources (bandwidth and energy of nodes) to maximize cumulative utility. • Objective: • “Joint Congestion and Energy Control for Network Utility Maximization”

6. Optimization Problem

7. 2 1 3 4 5 m1 m2 m3 Background: WSN-NUM Model • Airtime constraint over “transmission-specific” cliques • Cliques => “contention region” • No two transmissions in a clique can occur simultaneously Transmission-based Conflict graph Multicast trees (with broadcast transmissions) Connectivity graph

8. WSN-NUM Protocol • Price-based, iterative, receiver-centric scheme • Solve two independent sub-problems • Network nodes: • Aim to maximize “revenue” • Compute Clique cost: degree of congestion in the clique • Flow cost = sum of costs of all cliques along the flow • Mission (sink): • Aims to maximize its utility minus the cost • Sends path cost to each source • Sends ‘willingness to pay’ for each source • Sensor (source): • Adjusts rate to drive gradient to zero (1) (2) (3) (4)

9. Distributed Solution for INP-NUM • Two penalty values: • - Congestion cost, µ • - Energy cost, η At each source: 1 2 A Impact on utility Energy cost Congestion cost B At each forwarding node: C M Impact on utility Energy cost Congestion cost

10. Adaptive Operator Placement • We assume that fusion can be shared across multiple nodes • Can be thought of as time-sharing • Each candidate node fuses a fraction (θ) of the flow • Sink receives multiple sub-flows, each fused at a different node • Optimize θ such that fusion is most efficient 1 2 A B C M

11. Illustration of INP-NUM 1 2 Flow 2: x2 Flow 1: x1 A Fused flow f ` B C m

12. Challenges in INP-NUM Protocol • Missions do not know about original flow and the transformations (compression and fusion) • Fusion placement and compression ratio adaptation require different sets of data. • Feedback received and processed by each forwarding node in the path • It is modified before forwarding upstream • If it is a fusion point, it updates the feedback to include the effect of fusion • Based on chain rule of differentiation

13. Illustration of INP-NUM Feedback 1 2 1 2 A Cumulative Info Cumulative Info 1 2 2 B fA Cumulative Info 1 C 2 2 fA fB Cumulative Info m

14. Addressing Practical Constraints • Often in reality, fully elastic compression may not be possible • Only discrete levels of compression • E.g., JPEG allows 100 discrete values for compression ratio, video may be encoded in a finite set of bitrates depending on the encoding technique • Similarly, partial fusion may not be feasible • Fusion operation may need to take place at a solitary node. • NP-hard to solve both problems without these assumptions • We can use approximation heuristics • Determine nearest valid compression ratio • Pick node with most responsibility for solitary fusion

15. Evaluation Low Utility High Utility Medium Utility

16. Utility Gain

17. Effect of Discretization

18. Conclusion • Protocol for adaptive compression and fusion placement • Fully distributed • Low overhead • Provably optimal utilization of bandwidth and energy • Heuristics for realistic constraints provide near-optimal solution • In future, we will develop a model taking lifetime requirements of missions into account

19. Thank You!