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Distributed Hop-by-Hop Rate Adjustment for Congestion Control in Sensor Networks

Distributed Hop-by-Hop Rate Adjustment for Congestion Control in Sensor Networks. Presented by: Vipul Bhasin Sapna Dixit Vishal Kumar Singh Manmohan Voniyadka. Goal. Implement a distributed rate adjustment strategy to minimize congestion

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Distributed Hop-by-Hop Rate Adjustment for Congestion Control in Sensor Networks

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  1. Distributed Hop-by-Hop Rate Adjustment for Congestion Control in Sensor Networks Presented by: Vipul Bhasin Sapna Dixit Vishal Kumar Singh Manmohan Voniyadka

  2. Goal • Implement a distributed rate adjustment strategy to minimize congestion • Adjust the source rate using congestion factor for better response

  3. Design • Calculate congestion factor • Used queue length, channel load, and retransmission counter • Adjust source rate when consecutive congestion factor found to increase • Propagate cumulative congestion factor upstream • Propagated only when threshold exceeded

  4. Code Changes • Suppress Message Behavior • sendSupressMsg • suppressMsgRcvd • calcCongestionFactor • Source Rate Adjustment (via AIMD) • suppressMsgRcvd • NAK Implementation (packet dropped from queue as criteria to post NAK) • sendNakMsg • NakMsgRcvd

  5. Experiments • Simple Model of VC vs. EC • Varied source rates from 5 to 20 • EC outperformed VC • Lossy Model of VC vs. EC • Two models – one-hop and generated (from lossy builder) • Comparable results in generated and EC outperformed VC in one-hop • Lossy Model Topology of VC vs. EC • EC outperformed VC in new lossy topology

  6. Results

  7. Pros/Cons • Pros • Higher Responsiveness • Local decisions can be made based on global congestion scenario • NAK counter better estimates congestion • Cons • More suppress messages due to greater propagation of congestion factor

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