dynamic data compression in multi-hop wireless networks abhishek b. sharma (usc) collaborators:...

Dynamic Data Compressionin Multi-hop Wireless NetworksAbhishek B. Sharma (USC)

Collaborators:Leana GolubchikRamesh 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.

2SIGMETRICS/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 sameenergy as 1 byte transmittedProcessor: 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


System dynamics


SinkAB

Energy

w/o comp. comp.

SinkAB

w/o comp. comp.

Energy

Don’t compress Compress

System dynamics impact the energy savings from compression.

Sink

A B

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.


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.


Stochastic Network Optimization:Lyapunov Optimization technique1

Lyapunov driftanalysis

Arrival process

Link dynamics

Stability

“Backpressure” basedtransmission decisions

Compressionat the source

Arrival process

Link dynamics

Lyapunov driftanalysis +

Utility (energy cost)

StabilityEnergy-efficient

“Backpressure” basedtransmission decisions

Compression decisionalgorithm

Lyapunov Optimization:

joint decision

1Georgiadis, Neely and Tassiulas. Resource Allocation and Cross Layer Control in Wireless Networks, Foundations and Trends in Networking.

“Joint” compression and transmission decisions


TransmissionDecision

Algorithm

Compression DecisionAlgorithm

Data transfer rate

Lots of retransmissionsApplication 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: 802.11, 802.15.4



SEEC: Stable and Energy Efficient CompressionSystem Model

Compression Module

Transmission Module

Application Data

l[t] = C(link quality, trans. power)

Data from othernodes Un[t]

Un[t]: Queue backlog

Maintains a table of avg. compression ratio and avg. energy costfor each comp. option k.

Node n

mUl [t] = Un[t] - Um[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


Differential backlog

Transmission rate

Control parameter

Transmit power

Transmission schedulerLink

weights

Positive weight links on whichdata transfer isallowed

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.


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.


Example: SEEC in action

Transmit power = P (fixed) Link quality: “Good”= 2 Mbps, “Bad” = 1 Mbps


SinkAB

time timeNode A Node B

Queuebacklog

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


V (control parameter)

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.

2. 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.



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

dSEEC: Summary of simulation results.

1. 10-30% energy savings compared to “always compress”. Tree-topology impacts the savings.


Compare with “Always compress”

Cluster-Tree topology1

1Used in several deployments: Paek (WCSCM’06), Hicks (ImageSense’08)

Periodic application data arrivalLink quality did not change.

Nevercompress

dSEEC Always compress

30 % reduction

dSEEC: Summary of simulation results.

1. 10-30% 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


Lots of simulation results in the paper

Conclusion1. 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.



Algorithm derivation; proofs available in technical report. http://enl.usc.edu/~abhishek

Questions?

dynamic data compression in multi-hop wireless networks abhishek b. sharma (usc) collaborators:...

Documents

compression energy tradeoff

energy cost of compression

energy savings

dynamic data compression

energy dont compress

total energy expenditure

sink ab energy wo

energy hog figure