ASCENT: Adaptive Selfconfiguring sEnsor Networks Topologies

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ASCENT Adaptive Self-configuring sEnsor
Networks Topologies

• January, 2002
• Alberto Cerpa

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Problem Description

• Given an area A, a total number of nodes N
  distributed with a certain probability
  distribution D in A and an average number of
  nodes n per area ?R2, we would like to find a
  subset of nodes that cover area A.
• Different applications may require the node
  subset to have different characteristics. For
  example, we may like the subset to be
• Minimal.
• Homogeneous with a certain degree of
  connectivity.
• Heterogeneous with different degrees of
  connectivity in different regions. Examples of
  these different regions may be
• Along a data flow path.
• Avoiding a data flow path.
• In the border of an event of interest.

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Design Space

• Goal exploit the redundancy provided by high
  density to extend the system lifetime while
  providing communication coverage (build a basic
  topology with active nodes).
• Problem
• If we have few active nodes the distance between
  neighboring nodes may be too great and the
  message loss increases due to lack of
  connectivity, or the energy required to transmit
  the data over the longer distances will be
  prohibitive.
• If we use all deployed nodes simultaneously, the
  system will be expending unnecessary energy, at
  best, and at worst the nodes may interfere with
  one another by congesting the channel.
• Solution
• Not trying to use a distributed localized
  algorithm to identify a single optimal solution.
• This form of adaptive self-configuration using
  localized algorithms is well suited to problems
  spaces that have a large number of possible
  solutions.

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ASCENT Basics

• The nodes can be in active or passive state.
• Active nodes are part of the topology and forward
  data packets (using an orthogonal routing
  mechanism that runs on the topology).
• Nodes in passive state can be sleeping or
  collecting network measurements. They do not
  forward any packets.
• Each node measures the number of neighbors and
  packet loss locally.
• Each node then makes an informed decision to join
  the network topology or to perform some form of
  adaptation (e.g. reducing its duty cycle to save
  energy).

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Preliminary Results
Packet loss percentage as a function of density.
ASCENT reduces collisions by limiting the maximum
number of active nodes transmitting packets while
still providing communication and sensing
coverage.
Energy Savings (normalized to the Active case,
all nodes turn on). as a function of density.
ASCENT provides significant amount of energy
savings, up to a factor of 5 for high density
scenarios.
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Future Work Plan

• The plan for the remaining of the thesis follows
  into three main high level tasks
• Improving ASCENT to interact more closely with
  routing and allow a load balancing policy.
• Global-Local information trade-off
• Design, analysis, simulation and implementation
  of multi-level ASCENT.
• use two different capacity radios long range
  high energy consumption and short range low
  energy consumption.
• Analysis and simulation of ASCENT with multi-hop
  state information.
• use some global information to improve the
  decision process.
• quantify the tradeoff between the improvement vs.
  the additional energy cost.
• Adaptive Fidelity design and evaluation.
• Different regions with different degrees of
  connectivity. What are the problems?
• Examples Alternate path, Path reinforcements,
  Event triggering.