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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:
Fault-Tolerant Data Collection in Fault-Tolerant Data Collection in - - PowerPoint PPT Presentation
Fault-Tolerant Data Collection in Fault-Tolerant Data Collection in - - PowerPoint PPT Presentation
Fault-Tolerant Data Collection in Fault-Tolerant Data Collection in Heterogeneous Intelligent Monitoring Networks Networks Heterogeneous Intelligent Monitoring Jing Deng Department of Computer Science University of North Carolina at
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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 of
nodes with the file (even partially).
- Tracker provides
swarm information
- Client downloads
pieces from nodes in swarm.
- At the same time,
uploading pieces to
- ther nodes
- Finished clients serve
as seeds (upload only) Even when some of the nodes in the swarm fail (or left), file sharing 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 to
download 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 for
download
– 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 with
P2P file sharing
Peers may go offline without warning Nodes may fail at any time. Redundancy of file pieces among peers Monitoring data redundancy among different nodes A client tries to download a full set of pieces from a swarm of nodes Observer (data sink) tries to collect data from monitoring nodes, which generate the data
P2P File Sharing IMN
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IMN - Connectivity Overview
- Lines connect nodes
who can hear each
- ther (N=100).
- Darker squares mark
more powerful nodes (M=10).
- Result of random
node 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 data
from nodes on their own tree
– In order to provide fault tolerance, they will choose α of other
- verheard transmissions
– α is termed support ratio
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Illustration of Data Collection
- Multi-level data
collection
- We show the [avg,
max] record on the powerful nodes
- α=0.4
- Some nodes fail
(marked with red x)
- Big red dot represents
a fire burning
- None of the powerful
nodes sees any temperature anomaly.
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Illustration of Data Collection (Cont’d)
- The same topology
and data collection trees.
- α=0.4
- With the same failed
nodes, two powerful nodes receive the temperature anomaly (Nmax=2).
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Performance Results - Reading Abnormal Temp.
- Similar simulations
were run and average Nmax computed
- pe is node failure
probability
- Nmax lowers as pe
increases.
- With larger α, Nmax
increases.
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Data Loss due to Failed Sensors
- Failed nodes lead to
data loss
- Support ratio α can
dramatically reduce data loss.
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Conclusions
- Wireless monitoring networks can provide robust
environment monitoring.
- We have proposed a fault-tolerance data collection
technique 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 data
processing algorithms
– Average – Maximum – Minimum – Counting
- Analyze data loss for different support ratios