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Fault-Tolerant Data Collection inFault-Tolerant Data Collection in Heterogeneous Intelligent Monitoring Heterogeneous Intelligent Monitoring NetworksNetworks

Jing DengDepartment of Computer Science

University of North Carolina at Greensborojing.deng@uncg.edu

http://www.uncg.edu/~j_deng/

Joint work with Profs. Meikang Qiu and Gang Wu

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Wireless Networks

• Networks formed by wireless devices– All communications are sent through wireless channels.– Wireless devices with limited resource

Battery energy, memory space, computation power

• Many interesting problems:– How to lower communication/computation cost for network

activities?Communication takes time/energy.Computation requires memory space and energy.

– How to protect systems from node failure?Small wireless devices could easily fail or run out of battery.

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Failure Models

• Fail-stop– Device simply stops working.– No information will be sent or received.– Similarly to one with dead-battery

• Byzantine failure– Device can virtually do anything that it is capable of

• Dropping packets from others• Sending out fabricate packets• Modifying packets from other nodes• Deviating from communication protocols

– Much more difficult to address

We will use the fail-stop model

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Intelligent Monitoring Networks (IMNs)

• Wireless sensor networks– Networks with (possibly numerous) wireless micro-sensors

• A special type of wireless sensor networks– Likely to be deployed for building structure monitoring, forest

monitoring, levee monitoring, industrial plant monitoring, etc.

• Two key characteristics– Node failures expected– Heterogeneous architecture

• Mostly small devices to collect/report data• Some larger and more powerful devices to process/fusion data• These power nodes send results to observer (data sink).

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Illustration of Large Wireless Networks

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Low-power Low-Cost Devices

• Devices usually use low-power transceivers– Goal: to lower energy consumption and to extend lifetime

• Forming multi-hop communication topology– Relying on other devices’ help to deliver data

• Interference can easily disrupt communication– Network topology changes– Data collection paths change– Data loss

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BitTorrrent - P2P File Sharing Technique

• Swarm: collection ofnodes with the file(even partially).

• Tracker providesswarm information

• Client downloadspieces from nodes inswarm.

• At the same time,uploading pieces toother nodes

• Finished clients serveas seeds (upload only)

Even when some of the nodes inthe swarm fail (or left), filesharing continues.

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BitTorrent Strategies

• Two strategies in BitTorrent make it surprisingly efficient– Optimistic un-choking– Rarest-first

• Optimistic un-choking is the strategy to choose peers todownload pieces– Suppose there are 100 peers (with ever-changing D/L speed).– Which of these 100 peers should the client choose?

• Using all of them is impractical.• Choosing the top N peers w.r.t. download speed (N=5)• However, there might be new peers offering higher speed.• -> dropping one of the current N peers and randomly testing

another peer (un-choke one of the unselected peers)

– Benefits• Utilizing most of the peers with highest D/L speeds.

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BitTorrent Strategies (Cont’d)

• Rarest-first strategy governs how to choose pieces fordownload– Suppose a peer has M of the pieces– Which of these pieces should the client choose?

• Random selection or sequential selections?• -> Always choose the rarest piece among all peers (requiring piece

information from other peers).• So that this piece can be offered to other peers.

– Benefits• Increases piece redundancy• Maintaining torrent health• Improves chance of successful download

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IMN and BitTorrent?

• Data collection in IMNs shares striking similarities withP2P file sharing

Peers may go offline withoutwarningNodes may fail at any time.

Redundancy of file pieces amongpeers

Monitoring data redundancyamong different nodes

A client tries to download a fullset of pieces from a swarm of

nodes

Observer (data sink) tries tocollect data from monitoring

nodes, which generate the data

P2P File SharingIMN

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IMN - Connectivity Overview

• Lines connect nodeswho can hear eachother (N=100).

• Darker squares markmore powerful nodes(M=10).

• Result of randomnode placement

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Fault Tolerant Data Collection

• Powerful nodes collect data from regular nodes– Announcements are made from the powerful nodes.– Multiple trees are formed with data forwarding nodes.

• Usually data forwarding nodes only need to forward datafrom nodes on their own tree– In order to provide fault tolerance, they will choose α of other

overheard transmissions– α is termed support ratio

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Illustration of Data Collection

• Multi-level datacollection

• We show the [avg,max] record on thepowerful nodes

• α=0.4

• Some nodes fail(marked with red x)

• Big red dot representsa fire burning

• None of the powerfulnodes sees anytemperature anomaly.

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Illustration of Data Collection (Cont’d)

• The same topologyand data collectiontrees.

• α=0.4

• With the same failednodes, two powerfulnodes receive thetemperature anomaly(Nmax=2).

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Performance Results - Reading Abnormal Temp.

• Similar simulationswere run and averageNmax computed

• pe is node failureprobability

• Nmax lowers as peincreases.

• With larger α, Nmaxincreases.

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Data Loss due to Failed Sensors

• Failed nodes lead todata loss

• Support ratio α candramatically reducedata loss.

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Conclusions

• Wireless monitoring networks can provide robustenvironment monitoring.

• We have proposed a fault-tolerance data collectiontechnique for IMNs:– Multiple multi-level data collection trees (forest)– Data forwarding nodes process overheard data.– Support ratio α

• Benefits of our scheme have been demonstrated– Low cost– Fault tolerant toward node failures

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Future Work

• Byzantine failure model– Instead of failed delivery, failed nodes may send wrong data!

• Investigating our scheme under different dataprocessing algorithms– Average– Maximum– Minimum– Counting

• Analyze data loss for different support ratios