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Advanced Transport Protocols for Efficient Congestion Control in Wireless Sensor Networks

This document explores the design and implementation of advanced transport protocols aimed at improving congestion control within Wireless Sensor Networks (WSNs) and Wireless Multimedia Sensor Networks (WMSNs). It delves into essential topics such as reliability, loss recovery, congestion detection, and fairness issues, addressing the unique challenges posed by limited resources of sensor nodes. Key strategies discussed include feedback mechanisms for congestion control, dynamic reporting rates based on congestion levels, and the efficacy of various transport protocols like TCP and UDP in WSN environments, with a focus on in-network data processing and aggregation techniques.

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Advanced Transport Protocols for Efficient Congestion Control in Wireless Sensor Networks

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  1. Transport protocols in Wireless Sensor Networks Congduc PHAM University of Pau LIUPPA laboratory

  2. Transport layer for WSN/WMSN • Reliability/loss recovery • Congestion control • Congestion detection • Fairness issues • Higher semantic than packet level • Multipoint communication • Data aggregation, data dissemination

  3. feedback Congestion Control Feedback should be frequent, but not too much otherwise there will be oscillations Can not control the behavior with a time granularity less than the feedback period Closed-loop control

  4. Congestion window doubles every round-trip time packet ack Sequence No Time TCP congestion control cwnd grows exponentially (slow start), then linearly (congestion avoidance) with 1 more segment per RTT If loss, divides threshold by 2 (multiplicative decrease) and restart with cwnd=1 packet From Computer Networks, A. Tanenbaum

  5. WSN vs Internet • Small fraction of time dealing with impulses, but data of greatest importance! • As in wireless transmission, difficult to distinguish congestions from node failures or bad channel quality • Sensors have limited resources • Simplicity in congestion detection and control algorithms • Great interest of in-network processing: hop-by-hop CC more efficient than E2E? • WSN are collaborative in nature. Fairness issues less important?

  6. Reference architecture

  7. CC scenario in WSN • Densely deployed sensors • Persistent hotspots • Congestion occur near the sources • Sparsely deployed sensors, low rate • Transient hotspots • Congestion anywhere but likely far from the sources, towards the sink • Sparsely deployed sensors, high rate • Both persistent and transient hotspots • Hotspot distributed throughout the network

  8. Some ideas for CC in WSN • Congestion detection • Monitor buffer/queue size • Monitor channel busy time, estimate channel’s load • Monitor the inter-packet arrival time (data, ctrl) • Congestion notification • Explicit congestion notification in packet header, then broadcast (but then energy-consuming!) • Congestion control • Dynamic reporting rate depending on congestion level • In-network data reduction techniques (agressive aggregation) on congestion

  9. TCP or UDP? • TCP • Connection-oriented, 3-way handshake • Assumes segment losses results from congestion • E2E reliability, congestion mechanism • Fairness as a function of RTT • UDP • No flow control nor congestion control • No reliability

  10. TCP or specialized approach? • TCP with appropriate modifications is better than UDP is standardized protocols are to be used. • Specialized approaches • allow for a specific preference between reliability and congestion control • Application awareness is also possible

  11. Proposals for WSNs • ESRT • CODA • PSFQ • RMST • HDA • GARUDA • SenTCP • STCP See the reference section for complete references of the papers

  12. Which CC for WMSN? (1) • WSN: scalar data • Wireless Multimedia Sensor Networks add video, audio for • Enlarging the view • Field of View of single camera is limited • Multiples camera overcome occlusion effects • Enhancing the view • Can help disambiguate cluttered situations • Enabling multi-resolution views

  13. Which CC for WMSN? (2) • Reliability should be enforced at the packet level • Some packets are more important than others in most of video coding schemes • Collaborative in-network processing • Reduce amap the amount of (redundant) raw streams to the sink

  14. Related projects • SensEye, UMass • http://sensors.cs.umass.edu/projects/senseye/ • Cyclops, UCLA • Panoptes, Portland State University • VSAM team at Carnegie Mellon Univ.

  15. Test-case in TCAP Grid-based localization? Lower priority, backup sensor?

  16. Design issues & directions • Cross-layering • Use low-level information • Collaborative in-network processing • Distributed algorithms • Router-assisted, node-assisted, ad-hoc • Simple congestion detection mechanism

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