Dynamic Forwarding over TreeonDAG for Scalable Data Aggregation in Sensor Networks

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Dynamic Forwarding over Tree-on-DAG for Scalable
Data Aggregation in Sensor Networks

• Kai-Wei Fan
• Sha Liu
• Prasun Sinha Arun Sudhir

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Agenda

• Background Related Work
• The proposed protocol
• Performance analysis 7 Evaluation
• Large-scale simulation using ns2
• Conclusion

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Background Related Work
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Data Aggregation

• Active research area in sensor networks. Why?
• Raw data from sensors has an inherent redundancy
• Aggregation reduces this redundancy by forwarding
  only the extracted information.
• Thus, it reduces communication cost and
  energy.

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Data Aggregation Techniques

• Can be structured or unstructured.
• What to use depends on the nature of the
  application data gathering or event-based.

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Structured or Unstructured ?

• Data gathering applications call for structured
  approach. ( why ? )?
• Data gathering low traffic, low maintenance
  overhead.
• Example environment monitoring
• Event-based applications call for unstructured
  approach. (why? )?
• Event-based source nodes change dynamically.
• Example intrusion detection, hazard detection.

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Goal of the proposed protocol

• Focused on event-based applications.
• Design goals are
• Scalability
• Low maintenance overhead
• Semistructured approach
• Design challenge determine a scalable packet
  forwarding strategy for early aggregation.

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Keep these in mind

• Good spatial and temporal convergence of packets
  is key for aggregation.
• Spatial all packets at the same place
• Temporal at the same time too.
• DAA Data Aware Anycast - first structureless
  protocol which improves spatial and temporal
  aggregation.
• Anycast A routing scheme where a packet is
  forwarded to the best or any of a group of
  destinations based on some metrics.
• ToD Tree on DAG (explained later)?
• Skip the next two slides if you know what a
  graph, spanning tree, DAG and stretch is.

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Some (simple) Graph Theory

• Graph (greyed)?
• Spanning Tree (solid black)?
• Shortest Path Spanning Tree
• Dijkstra's Algorithm
• Stretch XY in Tree / XY on the Graph.
• Low stretch gt Spanning Tree is good
• DAG Directed Acyclic Graph
• Edges have directions
• No cycles

Graph with spanning tree
DAG
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Some (simple) Graph Theory

• Shortest path spanning tree provides a path from
  its root to any other node.
• But, it may provide longer paths for other pairs
  of nodes compared to the original graph.
• So how do we know if the spanning tree we have is
  good to follow for going for any X-Y path?
• One answer is stretch
• The maximum or average stretch can serve as a
  metric
• A tree minimising the max stretch is minimum max
  stretch tree (MMST)?
• A tree minimising the avg stretch is minimum max
  stretch tree (MAST)

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Related Work

• Structured approaches focus on effective tree
  construction techniques.
• Like Steiner Minimum Tree or Multiple Shared Tree
• These are useful ONLY IF source is known in
  advance. (not for event-based)?
• Also suffer from the long stretch problem.
• DCTC A structure-based protocol for event-based
  applications
• dynamically forms a tree with the event source as
  root and acheives good aggregation.
• Has heavy message exchanges tree creation and
  maintenance takes up upto 33 of data collection!
• DAA Structureless
• Aggregation without tree overhead with good
  spatial and temporal aggregation.
• Forwards packets to one-hop neighbours and
  aggregates well at source
• No guarantee that all packets are aggregated
• Cost of forwarding non-aggregated packets limits
  scalability

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A closer look at DAA

• Spatial convergence Uses anycast. In wireless
  radio transmission, nodes can tell if they have
  packets to be aggregated with the sender's
  packet.
• Temporal Randomized waiting is employed and a
  node just waits a random amount of time before
  transmitting the packet.
• If a node has no neighbors with packets for
  aggregation, it simply forwards its packet
  towards the sink using geographical routing.
• This can have a higher overhead if there are many
  unaggregated packets and if the distance from the
  source to sink is large.

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The Proposed ProtocolDynamic Forwarding over ToD
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Proposed Protocol- Dynamic Forwarding over ToD

• Two phases
• DAA
• Dynamic Forwarding
• DAA phase Packets are forwarded, aggregated
  using DAA
• Dynamic Forwarding phase Unaggregated packets in
  DAA phase are now dynamically forwarded using a
  structure ToD (Tree on DAG) and NOT routed to
  sink directly using geographic routing thus
  decreasing overhead compared to DAA.
• Why ToD ?
• Using a fixed tree will have the long stretch
  problem
• Using a dynamic tree (DCTC) has high message
  exchange overhead
• ToD is an implicit structure over which Dynamic
  Forwarding is done.

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ToD in a one-dimensional network

• F-Tree F-cells -gt F-aggregators F-Cluster A
  pair of F-cells
• S-Tree S-cells -gt S-aggregators S-Cluster
  A pair of S-cells
• F-Tree overlapped with S-Tree A DAG christened
  as ToD

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How aggregation works here

• DAA is first used to aggregate as many packets as
  possible.
• DAA
• Dynamic Forwarding
• Nodes then forward their packets to their
  F-aggregators for aggregation.
• If an event only triggers nodes within an
  F-cluster, the packets travel up the F-Tree to
  the sink. eg (A,B) -gt F1
• If nodes of adjacent F-clusters are involved, the
  F-aggregator then forwards packets to the
  S-aggregator. eg (C,D) -gt (F4,F5) -gt S4
• Thus aggregation involves one or at most two
  steps in a single dimension

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ToD in a two-dimensional network

• F-Tree F-cells -gt F-aggregators F-Cluster 4
  F-cells
• S-Tree S-cells -gt S-aggregators S-Cluster
  4 S-cells
• A,B,C,D.. -gt F-clusters
• F-Tree overlapped with S-Tree ToD

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How aggregation works here

• DAA is first used to aggregate as many packets as
  possible.
• DAA
• Dynamic Forwarding
• Nodes then forward their packets to their
  F-aggregators for aggregation.
• Case 1 If an event only triggers nodes within an
  F-cluster, the packets travel up the F-Tree to
  the sink. eg (C1,C2) -gt X
• Case 2 If nodes of adjacent F-clusters are
  involved, the F-aggregator then forwards packets
  to the S-aggregators.
• Thus aggregation involves one or at most three
  steps in a single dimensionCase 2(C1,C2)
  -gtX, C3 -gtYX -gt S1 Y -gt S2S1 -gt S2

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Clustering Aggregator selection

• Principle The size of a cell should be greater
  than or equal to the maximum size of the event.
• Any clustering method would work (hexagonal,
  triangular)?
• Why choose grid ?
• Size of grid can be parametrized as a grid
  parameter easily
• The cell, F-cluster and S-cluster can be
  determined from geographic location easily.
  (assumption nodes have a GPS)?

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Clustering Aggregator selection

• Nodes take turns to play aggregator to evenly
  distribute energy cost as aggregator emans extra
  energy consumption.
• A good metric for aggregator election can be
  residual energy. Nodes elect themselves as
  aggregator and then advertise to all other nodes
  in F-cluster.
• In case of tie, node ID is used.
• Alternative approach is to hash time in days or
  hours (why?)?
• Aggregator change frequqncy is very low
• hash(current time) k , 1 ltk lt n where n
  number of nodes in cluster.
• Then, node k is elected as aggregator (read
  cluster-head)?

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Clustering Aggregator selection

• Choosing F-aggregators and then doing the same
  process for electing S-aggregators involves extra
  overhead.
• The solution is the concept of Aggregating
  Cluster
• The Aggregating Cluster of an S-cluster is that
  F-cluster which is closest to the sink among all
  F-clusters that the S-cluster overlaps
  with.
• IMPORTANT If an F-aggregator needs to forward
  packets to two S-aggregators, it forwards it to
  the F-cluster closer to itself (might be itself!)
  as the aggregating cluster for the first
  S-aggregator.

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Clustering Aggregator selection

• Benefits of using aggregating clusters for
  S-aggregators
• No leader election for S-clusters (additional
  overhead)?
• Scalable since nodes only need to know
  F-aggregators
• Change in F-aggregator need not be propagated to
  other F-clusters
• Hashing function for leader selction easier to
  use
• No overhead for computing aggregating clusters
  (static)?

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Avoiding Voids

• In a real-world scenario, not all regions will
  have sensors
• Uncovered regions are called voids
• Case 1 Only one aggregating cluster.
• Dark Grey void F-cluster
• Light grey cell containing data

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Avoiding Voids

• Case 2 Two aggregating clusters nearer one is
  a void.

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Avoiding Voids

• Case 3 Two aggregating clusters farther one in
  void.

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Avoiding Voids

• Solution to Case 1 3 If the first S-aggregator
  is in a void, forward to the top-right F-cluster
  from that void. (figure a )
• What if that also is a void ? (figure b)?
• Try forwarding to other near F-clusters
• Or forward directly to sink (F-tree) (Solution
  for case2 too! )

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Performance Analysis Evaluation
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Analysis of the worst case

• Worst case distance
  (How?)

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Performance Evaluation

• Involved comparative evaluation of
• Dynamic Frwarding over ToD
• DAA (Structureless)?
• SPT (opportunistic, structural)?
• SPT-D (SPT with a fixed wait time before
  forwarding)?
• The test bed was
• Comprised of Kansei Sensors
• 105 Mica2 based motes each hooked to a Stargate
• Stargate is 32-bit CrossBow device running Linux
• All stargates connected via wired ethernet
• Transmission power was such that each node could
  have maximum 12 neighbours
• Anycast MAC Protocol on top of Mica MAC layer
• Had only two F-clusters in ToD, a cell had 9 nodes

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Normalized number of transmissions

• ToD has minimum number of transmissions even when
  event size gt cell size

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Large-scale simulation using ns2
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Evaluation using Simulation

• Involved comparative evaluation of
• Dynamic Forwarding over ToD
• DAA (Structureless)?
• SPT (opportunistic)?
• OPT (Optimal Aggregation tree)?
• The simulation was run on
• A 2000 X 1200 grid network with 35 m node
  separation
• 1,938 nodes in the network
• Data Rate of 38.4 Kbps
• Transmission range of a node slightly gt 50 m
• Event moves at 10 m/s for 400 seconds using the
  random waypoint mobility model
• Event size is 400m diameter and sink was a t
  (0,0)?
• Perfect aggregation was the aggregation function
  under evaluation

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Event Size

• ToD better than DAA and SPT but OPT performs
  best in fig 1
• But its overhead was ignored
• In 2 and 3, DAA and ToD have better
  aggregation and hence better performance.

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Scalability

• In 1 and 2, ToD and OPT are steady but SPT and
  DAA dont scale well
• In 3, the number of packets ar not equal to
  1, maybe due to protocol-imposed delay.
• Also, ToD has more packets if the event is nearer
  to sink because then sink is used a
  F-aggregator.

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Aggregation Ratio

• As aggregation ratio decreases, packet size
  increases and soon reaches payload limit.
• OPT had high drop rate. So DAA and TOD are
  better than OPT.

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Cell Size

• ToD downgrades to DAA for extremely small and
  large cell sizes
• ToD peroformance clearly has an optimum cell
  size.

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Conclusion

• ToD
• Semistructured structurelss with Dynnamic
  Forwarding over a ToD
• Scalable to a very higher extent than DAA
• Avoids the long stretch problem with structured
  approaches
• Suited for extended life sensor networks

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Thank You!