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

The Problem... Sensor Resources Network Resources Missions/ Applications “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 Perimeter monitoring Gunfire localization Mobile insurgent tracking Surveillance... Image fusion Correlation

In-network Processing In-network processing is an attractive option conserving bandwidth and energy o Compression o 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 o With variable compression ratios Forwarding nodes can fuse multiple streams o the location of these fusion points can be determined on the fly Dual trade-off o Bandwidth vs. loss of information o Communication cost vs. computation cost

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 12 A C M B

Our Approach  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. Network Utility Maximization (NUM) A Distributed, Utility-Based Formulation of Resource Sharing Objective: “Joint Congestion and Energy Control for Network Utility Maximization”

Optimization Problem

Energy Model Energy expended on o Reception: proportional to rate of received flows o Transmission: proportional to rate of transmitted flows o Computation: proportional to received rate and amount of compression

Background: WSN-NUM Model  Airtime constraint over “transmission-specific” cliques  Cliques => “contention region”  No two transmissions in a clique can occur simultaneously Connectivity graph Multicast trees (with broadcast transmissions) Transmission-based Conflict graph m1m2m3

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)

Distributed Solution for INP-NUM Impact on utility 12 A C M B At each source: Energy cost Congestion cost At each forwarding node: Impact on utilityEnergy cost Congestion cost Two penalty values: - Congestion cost, µ - Energy cost, η

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 12 A C M B

1 A m 2 B C Flow 1: x 1 Flow 2: x 2 ` Illustration of INP-NUM Fused flow f

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

Illustration of INP-NUM Feedback 1 A m B C fAfA fBfB Cumulative Info fAfA Rate InfoEnergy InfoCongestion Info Rate InfoEnergy InfoCongestion Info 1 Rate InfoEnergy InfoCongestion Info Cumulative Info 2 Rate InfoEnergy InfoCongestion Info Cumulative Info

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

Evaluation High UtilityMedium Utility Low Utility

Utility Gain

Effect of Discretization

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

Thank You!