Wireless Sensor Networks: Minimum-energy communication

1
Wireless Sensor Networks Minimum-energy
communication
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Wireless Sensor Networks

• Large number of heterogeneous sensor devices
• Ad Hoc Network
• Sophisticated sensor devices
• communication, processing, memory capabilities

3
Project Goals

• Devise a set communication mechanisms s.t. they
• Minimize energy consumption
• Maximize network nodes lifetimes
• Distribute energy load evenly throughout a
  network
• Are scalable (distributed)

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Minimum-energy unicast
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Unicast communication model

• Link-based model
• each link weighed
• how to chose a weight?
• Power-Aware Metric Chang00
• Maximize nodes lifetimes
• include remaining battery energy (Ei)

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Unicast problem description

• Definitions
• undirected graph G (N, L)
• links are weighed by costs
• the path A-B-C-D is a minimum cost path from node
  A to node D, which is the one-hop neighbour of
  the sink node
• minimum costs at node A are total costs
  aggregated along minimum cost paths
• Minimum cost topology
• Minimum Energy Networks Rodoplu99
• optimal spanning tree rooted at one-hop neighbors
  of the sink node
• each node considers only its closest neighbors -
  minimum neighborhood

D
C
B
A
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Building minimum cost topology

• Minimum neighborhood
• notation - minimum neighborhood of node
• P1 minimum number of nodes enough to ensure
  connectivity
• P2 no node falls into the relay space
  of any other node
• Finding a minimum neighborhood
• nodes maintain a matrix of mutual link costs
  among neighboring nodes (cost matrix)
• the cost matrix defines a subgraph H on the
  network graph G

C
A
B
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Finding minimum neighborhood

• We apply shortest path algorithm to find optimal
  spanning tree rooted at the given node
• Theorem 1 The nodes that immediately follow the
  root node constitute the minimum neighborhood of
  the root node
• Theorem 2 The minimum cost routes are contained
  in the minimum neighborhood
• Each node considers just its min. neighborhood

subgraph H
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Distributed algorithm

• Each node maintains forwarding table
• E.g. originator next hop cost distance
• Phase 1
• find minimum neighborhood
• Phase 2
• each node sends its minimum cost to it neighbors
• upon receiving min. cost update forwarding table
• Eventually the minimum cost topology is built

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An example of data routing

• Properties
• energy efficiency
• scalability
• increased fault-tolerance

• Different routing policies
• different packet priorities
• nuglets Butt01
• packets flow toward nodes with
• lower costs

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Minimum-energy broadcast
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Broadcast communication model

• Omnidirectional antennas
• By transmitting at the power level maxEab,Eac
  node a can reach both node b and node c by a
  single transmission
• Wireless Multicast Advantage (WMA) Wieselthier
  et al.

b
Eab
Ebc
Eac
a
c

• Trade-off between the spent energy and the number
  of newly reached nodes

• Power-aware metric
• include remaining battery energy (Ei)
• embed WMA (ej/Nj)

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Broadcast cover problem (BCP)

• Set cover problem

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Distributed algorithm for BCP

• Phase 1
• learn neighborhoods (overlapping sets)
• Phase 2 (upon receiving a bcast msg)
• 1 if neighbors covered HALT
• 2 recalculate the broadcast cost
• 3 wait for a random time before re-broadcast
• 4 if receive duplicate msg in the mean time goto
  1
• Random time calculation
• random number distributed uniformly between 0 and

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Simulations

• GloMoSim UCLA
• scalable simulation environment for wireless and
  wired networks

average node degree 6
average node degree 12
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Simulation results (1/2)
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Simulation results (2/2)
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Conclusion and future work

• Power-Aware Metrics
• trade-off between residual battery capacity and
  transmission power are necessary
• Scalability
• each node executes a simple localized algorithm
• Unicast communication
• link based model
• Broadcast communication
• node based model
• Can we do better by exploiting WMA properly?

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Minimum-energy broadcast

• Propagation model
• Omnidirectional antennas
• Wireless Multicast Advantage (WMA) Wieselthier
  et al.

b
Pab
Pbc
Pac
a
c

• Challenges
• As the number of destination increases the
  complexity of this formulation increases rapidly.
• Requirement for distributed algorithm.

• What are good criteria for selecting forwarding
  nodes?
• Broadcast Incremental Power (BIP) Wieselthier et
  al.
• Add a node at minimum additional cost
• Centralized
• Cost (BIP) lt Cost (MST)

• Improvements?
• Take MST as a reference
• Branch exchange heuristic
• to embed WMA in MST