1. 5/4/2006EE228A – Communication Networks 1 Congestion Control to Reduce Latency in Sensor Networks for Real-Time Applications Presented by Phoebus Chen

2. EE228A – Communication Networks2 5/4/2006 Outline Motivation: Sensor Network Surveillance Background: Congestion Control Difficulties with Addressing Latency Design Guidelines for Latency Congestion Control Policies

3. EE228A – Communication Networks3 5/4/2006 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

4. EE228A – Communication Networks4 5/4/2006 Sample Surveillance Scenario Multiple targets on linear trajectories One centralized estimator per cell Ultimate scenario: Pursuit-Evasion Games with mobile robots

5. EE228A – Communication Networks5 5/4/2006 Study focused on design of network congestion control Wireless, multi-hop channel Fixed routing Multiple sources, one sink Estimation Sensing and Data Aggregation Sensing and Data Aggregation (sink) (source) (network)

6. EE228A – Communication Networks6 5/4/2006 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

7. EE228A – Communication Networks7 5/4/2006 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.

8. EE228A – Communication Networks8 5/4/2006 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.

9. EE228A – Communication Networks9 5/4/2006 Primal Algorithm and Controller Primal Algorithm (Lyapunov Function) Flow Controller  k r (x r ) > 0 is a non-decreasing, continuous function Assume prices react instantaneously

10. EE228A – Communication Networks10 5/4/2006 Dual Algorithm and Controller Dual Algorithm Price Controller  h l (p l ) > 0 is a non-decreasing continuous function Assume flows react instantaneously

11. EE228A – Communication Networks11 5/4/2006 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

12. EE228A – Communication Networks12 5/4/2006 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

13. EE228A – Communication Networks13 5/4/2006 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 over wireless networks: stability analysis,” in Proceedings of the 16th IFAC World Congress, 2005. [5] A. Abate, M. Chen, and S. Sastry, “New congestion control schemes over wireless networks: delay sensitivity analysis and simulations,” in Proceedings of the 16th IFAC World Congress, 2005.

14. EE228A – Communication Networks14 5/4/2006 Properties of Utility and Pricing Functions Assumptions on U r (x r ),  r  is a non-decreasing, continuously differentiable, strictly concave function  U r (x r )  -  as x r  0 Assumptions on prices p l (  )  l  is a non-decreasing, continuous function such that

15. EE228A – Communication Networks15 5/4/2006 Incorporating Latency into Utility Assign a utility to each packet  Sigmoidal function for differentiability

16. EE228A – Communication Networks16 5/4/2006 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 U r (x r )  -  as x r  0

17. EE228A – Communication Networks17 5/4/2006 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

18. EE228A – Communication Networks18 5/4/2006 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 Source Nodes Relay Nodes D = g({x r }) Congestion Black Box {x r } Delay Noise D D plpl plpl

19. EE228A – Communication Networks19 5/4/2006 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

20. EE228A – Communication Networks20 5/4/2006 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

21. EE228A – Communication Networks21 5/4/2006 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)?

22. EE228A – Communication Networks22 5/4/2006 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

23. EE228A – Communication Networks23 5/4/2006 Extra Slides

24. EE228A – Communication Networks24 5/4/2006 Definition of Max-Min Fair

25. EE228A – Communication Networks25 5/4/2006 What pursuers really see

26. EE228A – Communication Networks26 5/4/2006 Sensor net increases visibility