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Dynamic Data Compression in Multi-hop Wireless Networks

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.

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Dynamic Data Compression in Multi-hop Wireless Networks

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

  2. 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. SIGMETRICS/Performance'09

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

  4. 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 Transmission vs. Compression energy trade-off. SIGMETRICS/Performance'09

  5. Data compression:Exploring the 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. SIGMETRICS/Performance'09

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

  7. Compression decision in a dynamic environment • Compression decision: “When to compress?” • Must adapt to system dynamics. • Network dynamics: Link quality, topology • Application-level: sampling rate • 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

  8. 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

  9. Stochastic Network Optimization:Lyapunov Optimization technique1 Arrival process “Backpressure” based transmission decisions Lyapunov drift analysis Link dynamics Stability “Backpressure” based transmission decisions Lyapunov Optimization: Arrival process Lyapunov drift analysis + Utility (energy cost) joint decision Link dynamics Compression at the source Energy- efficient Compression decision algorithm Stability 1Georgiadis, Neely and Tassiulas. Resource Allocation and Cross Layer Control in Wireless Networks, Foundations and Trends in Networking.

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

  11. Our contributions • Stochastic network optimization formulation • First to consider data compression for multi-hop networks in a dynamic environment. • Derive a “joint” congestion and transmission decision algorithm. • Prove stability and analytical performance bounds. • Propose and evaluate a distributed version. • Works with CSMA MACs: 802.11, 802.15.4 SIGMETRICS/Performance'09

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

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

  14. 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. Sink SIGMETRICS/Performance'09

  15. 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

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

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

  18. Distributed version:Implementing SEEC’s transmission decision • Finding the optimum transmission schedule is NP-complete. • Approximation algorithms are known. • Global vs. Local information. • 802.11, 802.15.4 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

  19. Evaluation using Simulations • Determining the model parameters • Compression ratio and energy cost, transmission energy cost • Measurements on real hardware: LEAP2 • Radio: 802.11b • Compressed real-world sensor data from a bridge vibrations monitoring deployment (Paek et al.’ 06). • Compression algorithm: zlib compression libraries. • Simulator: Qualnet SIGMETRICS/Performance'09

  20. dSEEC: Summary of simulation results. • 10-30% energy savings compared to “always compress”. • Tree-topology impacts the savings. SIGMETRICS/Performance'09

  21. Compare with “Always compress” 30 % reduction Cluster-Tree topology1 Never compress Always compress dSEEC Periodic application data arrival Link quality did not change. 1Used in several deployments: Paek (WCSCM’06), Hicks (ImageSense’08)

  22. dSEEC: Summary of simulation results. • 10-30% energy savings compared to “always compress”. • Tree-topology impacts the savings. • Able to adapt to system dynamics. • Sensitivity of energy savings to V Lots of simulation results in the paper SIGMETRICS/Performance'09

  23. Conclusion • 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. • Energy vs. Delay trade-off • Proved Analytical bounds • dSEEC: distributed version suited for CSMA MACs • Significant energy savings compared to simple policies. • Future direction: • Consider in-network data aggregation and compression. SIGMETRICS/Performance'09

  24. Algorithm derivation; proofs available in technical report. http://enl.usc.edu/~abhishek Questions? SIGMETRICS/Performance'09

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