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Transport protocols in Wireless Sensor Networks. Congduc PHAM University of Pau LIUPPA laboratory. Transport layer for WSN/WMSN. Reliability/loss recovery Congestion control Congestion detection Fairness issues Higher semantic than packet level Multipoint communication

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Transport protocols in Wireless Sensor Networks

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Transport protocols in wireless sensor networks l.jpg

Transport protocols in Wireless Sensor Networks

Congduc PHAM

University of Pau

LIUPPA laboratory

Transport layer for wsn wmsn l.jpg

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

Congestion control l.jpg


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

Tcp congestion control l.jpg

Congestion window

doubles every round-trip




Sequence No


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

Wsn vs internet l.jpg

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?

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Reference architecture

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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

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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

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  • 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

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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

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Proposals for WSNs

  • ESRT

  • CODA

  • PSFQ

  • RMST

  • HDA


  • SenTCP

  • STCP

See the reference section for complete references of the papers

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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

Which cc for wmsn 2 l.jpg

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

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Related projects

  • SensEye, UMass


  • Cyclops, UCLA

  • Panoptes, Portland State University

  • VSAM team at Carnegie Mellon Univ.

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Test-case in TCAP

Grid-based localization?

Lower priority, backup sensor?

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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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