COMPUTING AGGREGATES FOR MONITORING WIRELESS SENSOR NETWORKS

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COMPUTING AGGREGATES FOR MONITORING WIRELESS
SENSOR NETWORKS

• Jerry Zhao, Ramesh Govindan, Deborah Estrin
• Presented by
• Hiren Shah

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Outline

• Motivation and Goals
• Contributions
• Architecture
• Computing Network Digests
• Impact of Packet Loss
• Experiments
• Conclusion

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Motivation and Goals

• Every Network requires monitoring.
• Sensor Network is no exception !!!
• Monitoring in traditional networks SNMP.
• Need for a new architecture.

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Architecture
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Architecture (contd.)

• Three software components of the architecture.
• Dumps
• Detailed node states. E.g. logs with raw data
  reading, packet level details, energy details for
  a node.
• Dumps are costly to collect.
• Scans
• Abstract views of resource consumption in the
  network without referring to individual node.
• Derived using in-network aggregation.

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Architecture (contd.)

• Network Digests
• Energy cost of computing scans over entire
  network is very high.
• Scans should be invoked only when necessary.
• A digest is an aggregate of some network
  property.
• E.g. size of the network, average energy left at
  a node etc.

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Architecture (contd.)

• Digests indicate WHEN should scans be invoked.
• Scans indicate WHERE should dumps be collected
  from.

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Computing Digests

• Naive centralized approach does not scale well
  and has a single point of failure.
• Some Requirements of Digest Computation
• Should be energy efficient.
• Digests should be available all the time and
  everywhere in the network.
• Typical Digest functions are Node with max
  energy, count of no. of nodes, Sum of node
  energies, Avg. residual energy.

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Computing Digests (contd.)

• These functions are decomposable.
• Hence partial result can be added to get overall
  result.
• Hence one can use in-network aggregation.

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Computing Digests (contd.)

• One can use hierarchical approach for computing
  the digests. E.g. clusters like LEACH.
• However this involves overhead in computing
  leaders and maintaining the hierarchy.
• Authors propose Digest Diffusion.

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Digest Diffusion

• Nodes periodically send tuple (Mi, Si, Hi).
• Initially node sets Mi to its perceived maximum
  value e.g. its own residual energy.
• Si is source of maximum. Initialized to i.
• Hi is hop distance of the source of maximum.

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Digest Diffusion

• Upon receiving a tuple from neighbor a node
  performs following processing.
• If Mj gtMi then set MiMj SiSj HiHj1
• Also set parent Pij
• Algorithm converges in steps proportional to
  network diameter.
• Approach is scalable. Periodic messages can be
  PIGGYBACKED.

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Computing other digests
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Computing other digests

• Hence a tree is needed without any overlaps.
• Digest diffusion establishes a tree rooted at the
  node with the maximum value.
• Every node i aggregates values from its children
  and passes the partial result to its parent along
  the tree.
• The root will have the final aggregate value.

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Computing other digests

• Metric for establishing the tree should be
  carefully chosen such that the tree is relatively
  stable. E.g. link degree of a node is a bad
  choice since it fluctuates a lot with node
  failures.

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Digest Tree Maintenance

• With node failures the tree should be suitably
  changed.
• Packet loss needs to be taken into account since
  absence of heartbeat does not mean the neighbor
  has failed.
• Let T0 be the interval between broadcasts.
• If heartbeat is not received from a parent or
  child in Tp 4To seconds change the tree.

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Impact of Packet Loss

• Packet losses are frequent in wireless
  environments.
• Packet losses make aggregation tree unstable and
  hence affect the quality of aggregate digest.

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Impact of packet loss
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Impact of packet loss
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Impact of packet loss
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Solution

• Link Quality Profiling and Rejection
• blacklist links with poor quality and
  asymmetry.
• A node chooses as parent a node with which it has
  good and symmetric communication.
• Use packet sequence number to estimate how many
  of the packets sent by the neighbor are getting
  lost.
• Exchange I CAN HEAR YOU lists to identify
  asymmetric links.

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Experiments

• Used REAL Berkley Motes !!!
• Studied performance of various schemes against
  packet loss.
• Used root mean square error to quantify
  performance.

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Experiments
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Conclusion

• Proposed an architecture for monitoring sensor
  network.
• Suggested design for computing network digests.
• Carefully handling lossy and asymmetric links
  reduces error in digest computation.
• Future work includes experiments on larger
  testbed and designing a full-fledged monitoring
  suite.

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Questions ?