5/4/2006 EE228A – Communication Networks 1

Congestion Control to Reduce Latency in Sensor Networks for Real-Time Applications

Presented by

Phoebus Chen

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Outline

Motivation: Sensor Network Surveillance Background: Congestion Control Difficulties with Addressing Latency Design Guidelines for Latency Congestion

Control Policies

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Sensor Networks for Real-Time Surveillance Event Detection

bursty traffic varying importance

of data for estimation can operate with

incomplete data Low Latency

routing selective packet

delivery congestion control

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Sample Surveillance Scenario

Multiple targets on linear trajectories

One centralized estimator per cell

Ultimate scenario:Pursuit-Evasion Games with mobile robots

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Study focused on design of network congestion control Wireless, multi-hop channel Fixed routing Multiple sources, one sink

EstimationSensing and

Data Aggregation

Sensing and Data

Aggregation

(sink)

(source)

(source)

(network)

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Performance Metric: Estimator Linear System Dynamics

driven by a white noise process Simple linear measurement model

Estimation via Kalman Filter

Check performance under different traffic patterns

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Background on Congestion Control [1] [2] Flow model Network Optimization Problem

[1] R. Srikant, The Mathematics of Internet Congestion Control, ser. Systems & Control: Foundations & Applications. Birkhauser Boston, 2004.

[2] F. P. Kelly, A. K. Maulloo, and D. K. H. Tan, “Rate control for communication networks: shadow prices, proportional fairness and stability,” Journal of the Operational Research Society, vol. 49, no. 3, pp. 237–252, March 1998.

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Various User Utility Functions Weighted Proportional Fairness

Minimum Potential Delay Max-Min Fair General Utility Function [3]

For max-min fairness

[3] J. Mo and J. Walrand, “Fair end-to-end window-based congestion control,” IEEE/ACM Transactions on Networking, vol. 8, no. 5, pp. 556–567, Oct 2000.

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Primal Algorithm and Controller Primal Algorithm (Lyapunov Function)

Flow Controller

kr(xr) > 0 is a non-decreasing, continuous function

Assume prices react instantaneously

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Dual Algorithm and Controller Dual Algorithm

Price Controller

hl(pl) > 0 is a non-decreasing continuous function

Assume flows react instantaneously

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Primal-Dual Algorithms and other variants Can combine primal and dual controllers, and

prove via a Lyapunov function that the algorithm is globally, asymptotically stable

Other variants exist Calculate prices using a weighted average of the flow

at a link over time Setting prices based on fullness of a virtual queue

(Adaptive Virtual Queue, or AVQ) Prices are marking probabilities of packets

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Examples of Congestion Control Analysis Convergence Rate

Linearize about equilibrium Look at smallest eigenvalue of dynamics matrix

Time-delay Stability Analysis Linearize about equilibrium Look at transfer function in the frequency domain and apply

Nyquist stability criterion Stochastic Stability

Linearize about equilibrium Look at Brownian motion perturbations, check induced

covariance of fluctuations

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Applying TCP/IP congestion control to wireless sensor networks Does not account for

wireless networks with: interference from

neighboring paths physical channel errors Hard to address both, first

pass is to treat as constant error disturbance like [4] [5]

[4] M. Chen, A. Abate, and S. Sastry, “New congestion control schemes overwireless networks: stability analysis,” in Proceedings of the 16th IFACWorld Congress, 2005.[5] A. Abate, M. Chen, and S. Sastry, “New congestion control schemesover wireless networks: delay sensitivity analysis and simulations,” inProceedings of the 16th IFAC World Congress, 2005.

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Properties of Utility and Pricing Functions

Assumptions on Ur(xr), r is a non-decreasing, continuously

differentiable, strictly concave functionUr(xr) - as xr 0

Assumptions on prices pl() l is a non-decreasing, continuous function such

that

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Incorporating Latency into Utility Assign a utility to each packet

Sigmoidal function for differentiability

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Incorporating Latency into Utility (2) Integrate delay utility of each packet with

flow

non-decreasing, continuously differentiable, strictly concave (assuming additional flow only come with greater delay)

May not be able to meet constraint

Ur(xr) - as xr 0

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Flow Rate vs. Delay and Packet Drop Rate Delay is a function of

queuing delay Congestion Errors from wireless channel CSMA contention

transmission delay (number of hops)

Do not have a good/simple model of CSMA contention at the MAC layer Without knowing we have a hard time knowing

for our optimization problem

Congestion at merge points In routing tree

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Hope? Congestion control policies as an optimization solver with a black box Some optimization solvers only needs a black box Make delay part of objective function Know general trend D = g(x), delay increases with more flow Treat channel contention, lossy wireless link, inteference, as noise

Lossy Communication

Channel

Lossy Communication

Channel

SourceNodes

RelayNodes

D = g({xr})

Congestion Black Box

{xr} Delay Noise

DD

pl

pl

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Design Guidelines for Packet Drop Policy May want to use a LIFO queue on a node, to get

latest packets delivered (least delay) Fairness for packets from different merging

routes suggests round robin service over many queues May want to prioritize based on time to last delivered

packet Need to design policy on when to purge LIFO

queues, and how many LIFO queues Parameters of policy set by messages from sink Given vehicle dynamics, sink can determine how

many targets it can track well

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Design Guidelines for Congestion Feedback Policy Since low network bandwidth, may not want

end-to-end acknowledgement Sparse end-to-end acknowledgement means cannot

adapt to network changes as quickly Types of Information

Queue lengths Number of hops to congestion point Delay on packets delivered

Interfering nodes may want to share information about their respective flow rates and packet delays

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Design Guidelines for Rate Adaptation Policy Slow start phase? May want evenly spaced samples for

Kalman Filter If within delay constraints, may want to queue

packets to accommodate channel fluctuations How to decode multiple congestion

indicators from relay nodes (queue length, delay, number of hops)?

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Future Work

Fix a model for simulating the network Design a congestion control scheme via

heuristics, and simulate If I can get a mathematical model, analyze

its stability and convergence

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Extra Slides

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Definition of Max-Min Fair

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What pursuers really see

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Sensor net increases visibility