Dynamic Data Compression in Multi-hop Wireless Networks Abhishek B. Sharma (USC) Collaborators: Leana Golubchik Ramesh Govindan Michael J. Neely

Data collection application in sensor networks Sensor nodes collect measurements that must be delivered at a sink. Multi-hop routing over a tree.  Radios have limited transmission range Energy constrained  Nodes are battery powered. 2 SIGMETRICS/Performance'09

3 Wireless sensor network platforms: Radio is the energy hog Figure from Sadler and Martonosi (SenSys 2006) Sensor network radios Transmission range: increases # CPU cycles for same energy as 1 byte transmitted Processor: MSP430 Data transmission is expensive.

Energy efficient data collection applications Need to transmit data using small energy budget. Challenge: Transmission costs lots of energy.  Data is transmitted across multiple hops. Solution: Send less.  compress data before transmitting Energy cost of compression.  Not just CPU computations.  Memory access, FLASH access SIGMETRICS/Performance'09 4 Transmission vs. Compression energy trade-off.

Related work:  Single vs. multi-hop routing (Sadler et al., SenSys’06).  Evaluating the energy efficiency of various algorithms. (Barr et al., MobiSys’03).  Designing “light” yet energy efficient compression algorithms (Sadler et al., SenSys’06). Sadler et. al., SenSys’06  Single-hop: data compression does not save energy  Multi-hop: data compression saves energy.  “always compress” is not optimal. Energy trade-off was not explored in a “dynamic” environment. Data compression: Exploring the energy trade-off SIGMETRICS/Performance'09 5

System dynamics SIGMETRICS/Performance'09 6 Sink AB Energy w/o comp.comp. Sink AB w/o comp.comp. Energy Don’t compressCompress System dynamics impact the energy savings from compression. Sink AB w/o comp.comp. Energy Don’t compress

Compression decision in a dynamic environment Compression decision: “When to compress?” Must adapt to system dynamics. 1. Network dynamics: Link quality, topology 2. Application-level: sampling rate 3. Platform upgrade: low power radios, compression algorithm “When to compress” is not straight forward to determine.  “Always compress” policy may not work well. SIGMETRICS/Performance'09 7

Data compression in a dynamic environment: Stochastic Network Optimization The application data arrival process and time varying link qualities are modeled as ergodic stochatic processes. Goal: Minimize the total system energy expenditure.  System energy expenditure: total energy expenditure across all the nodes. Constraint: Network is “stable”  bounded average queue size at all the nodes.  implies finite delay in delivering data to the sink. SIGMETRICS/Performance'09 8

Stochastic Network Optimization: Lyapunov Optimization technique 1 Lyapunov drift analysis Arrival process Link dynamics Stability “Backpressure” based transmission decisions Compression at the source Arrival process Link dynamics Lyapunov drift analysis + Utility (energy cost) Stability Energy- efficient “Backpressure” based transmission decisions Compression decision algorithm Lyapunov Optimization: joint decision 1 Georgiadis, Neely and Tassiulas. Resource Allocation and Cross Layer Control in Wireless Networks, Foundations and Trends in Networking.

“Joint” compression and transmission decisions SIGMETRICS/Performance'09 10 Transmission Decision Algorithm Compression Decision Algorithm Data transfer rate Lots of retransmissions Application data rate

Our contributions 1. Stochastic network optimization formulation  First to consider data compression for multi-hop networks in a dynamic environment. 2. Derive a “joint” congestion and transmission decision algorithm. 3. Prove stability and analytical performance bounds. 4. Propose and evaluate a distributed version.  Works with CSMA MACs: , SIGMETRICS/Performance'09 11

SIGMETRICS/Performance'09 12 SEEC: Stable and Energy Efficient Compression System Model Compression Module Transmission Module Application Data  l [t] = C(link quality, trans. power) Data from other nodes U n [t] U n [t]: Queue backlog Maintains a table of avg. compression ratio and avg. energy cost for each comp. option k. Node n m U l [t] = U n [t] - U m [t] Decisions (every time slot t): Compression decision: whether to compress ? which option? Transmission decision: which nodes should transmit data?

SEEC: Transmission schedule “Queue differential backlog” based Each link is assigned a weight. Negative weight on a link  Either due to a small queue backlog or poor link quality SIGMETRICS/Performance'09 13 Differential backlog Transmission rate Control parameter Transmit power Transmission scheduler Link weights Positive weight links on which data transfer is allowed Scheduling constraints

Transmission decision: Impact on queue backlog A node does not get to transmit till its backlog is greater than transmission threshold  [t] = O (V/  [t]).  Weight on its outgoing link should be positive. Increasing V results in higher queue backlog.  Higher delay in delivering data to the sink. Avg. queue backlog grows will hop-count distance from the sink. SIGMETRICS/Performance'09 14 Sink

Compression decision: Driven by queue backlog A node compresses data only when its queue backlog is greater than compression threshold  [t].  Directly proportional to compression energy cost.  Inversely proportional to the average compression ratio.  Increases as we increase the V. SEEC does not compute these thresholds explicitly. SIGMETRICS/Performance'09 15

Example: SEEC in action Transmit power = P (fixed) Link quality: “Good”= 2 Mbps, “Bad” = 1 Mbps SIGMETRICS/Performance'09 16 Sink AB time Node ANode B Queue backlog  A [t]  A [t]  B [t]  B [t] No compression Both links are “Good”Link from A to sink becomes “Bad” Node B starts compressing data

SEEC’s Performance: Energy vs. Delay trade-off SIGMETRICS/Performance'09 17 V (control parameter) P*P* Theorem:

Distributed version: Implementing SEEC’s transmission decision Finding the optimum transmission schedule is NP- complete.  Approximation algorithms are known. 1. Global vs. Local information , MACs:  CSMA based (no timeslots). Positive queue differential heuristic (Sridharan et al.)  Contend if (outgoing) link weight is positive  Distributed version: dSEEC. SIGMETRICS/Performance'09 18

SIGMETRICS/Performance'09 19 Evaluation using Simulations Determining the model parameters  Compression ratio and energy cost, transmission energy cost Measurements on real hardware: LEAP2 Radio: b Compressed real-world sensor data from a bridge vibrations monitoring deployment (Paek et al.’ 06). Compression algorithm: zlib compression libraries. Simulator: Qualnet

dSEEC: Summary of simulation results % energy savings compared to “always compress”.  Tree-topology impacts the savings. SIGMETRICS/Performance'09 20

Compare with “Always compress” Cluster-Tree topology 1 1 Used in several deployments: Paek (WCSCM’06), Hicks (ImageSense’08) Periodic application data arrival Link quality did not change. Never compress dSEEC Always compress 30 % reduction

dSEEC: Summary of simulation results % energy savings compared to “always compress”.  Tree-topology impacts the savings. 2. Able to adapt to system dynamics. 3. Sensitivity of energy savings to V SIGMETRICS/Performance'09 22 Lots of simulation results in the paper

Conclusion 1. Derived an algorithm for making compression decisions that is stable, energy-efficient, and adapts to system dynamics.  Our work is the first to propose an adaptive algorithm for the multi-hop networks. 2. Energy vs. Delay trade-off  Proved Analytical bounds 3. dSEEC: distributed version suited for CSMA MACs 4. Significant energy savings compared to simple policies. Future direction:  Consider in-network data aggregation and compression. SIGMETRICS/Performance'09 23

SIGMETRICS/Performance'09 24 Algorithm derivation; proofs available in technical report. Questions?