Energy Efficient Data Gathering Algorithms in Sensor Networks

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Energy Efficient Data Gathering Algorithms in
Sensor Networks

• Vikramaditya

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What is a Sensor Network?

• Sensor networks mainly constitute of inexpensive
  sensors densely deployed for data collection from
  the field in a variety of scenarios
• A sensor node is an autonomous device with
  integrated sensing, processing, and communication
  capabilities

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Data gathering in Sensor Network

• A typical application in sensor network is
  gathering sensed data at a distant base station.
  
• Each sensor node has power control and the
  ability to transmit data to any other sensor node
  or directly to the BS.
• In each round of this data gathering application,
  all data from all nodes need to be collected and
  transmitted to the BS, where the end user can
  access the data

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The difficulties in collect and transmit data in
Sensor Network

• Each node transmits its data directly to the Base
  Station? Not suggested
• BS is usually far away from sensors
• such communication will be a high cost
• and drain the power quickly
• 2. Sensor's battery is not replaceable, and
  sensors may operate in hostile or remote
  environments
• Energy consumption is considered as the most
  important concern in sensor network

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Approaches for Data Gathering
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Direct Approach

• Each sensor sends its data directly to the base
  station
• quickly drain the battery of the nodes and reduce
  the system lifetime
• the only receptions in this protocol occur at the
  base station
• Only the base station is close to nodes, or the
  energy required to receive data is large, direct
  approach may be an acceptable (and possibly
  optimal) method of communication

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MTE Based Approach

• Nodes route data to the Base Station through
  intermediate nodes
• Intermediate nodes act as routers for other
  nodes data and work as sensing nodes as well
• Intermediate nodes are chosen if and only if the
  transmit amplifier energy is minimized

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Clustering Based Approach

• Nodes are organized into groups, or say clusters
• Each cluster has a cluster head
• Other nodes in the same cluster sends data to its
  cluster head
• Cluster heads transmit the data to the Base
  Station
• Only cluster heads can send data to the Base
  Station

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Clustering Based Approach (Continue)

• Static Dynamic Clustering Approach
• The difference is the way to choose cluster heads
  
• Static Clustering Approach has a fixed cluster
  heads
• Dynamic Clustering Approach has dynamic cluster
  heads

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Clustering Approach An Example (Continue)
All nodes marked with the same symbol belong to
the same Cluster and the cluster-head nodes are
marked with a ?.
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Chain Based Approaches

• Each node receives from and transmits to close
  neighbours and takes turns being the leader for
  transmission to the Base Station
• Assumed that all nodes have global knowledge of
  the network and employ the greedy algorithm
• Starts with the furthest node from the BS to
  ensure that nodes farther from the Base Station
  have close neighbours
• Data gathering is performed in rounds. In each
  round, each node receives data from one
  neighbour, fuses with its own data, and transmits
  to the other neighbour on the chain

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Chain Based Approaches An Example
node c(2) is the leader. Node c(0) will pass its
data to node c(1). Node c(1) fuses node c(0)s
data with its own and then transmits to the
leader. Node c(3) and c(4) do the same thing.
Node c(2) waits to receive data from both
neighbours c(1) and c(3) and then fuses its
data with its neighbours data. Finally, node
c(2) transmits one message to the BS
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Energy-Efficient Data Gathering with Multiple
Paths
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Multiple Path Construction Mechanism
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Data Forwarding Mechanism
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AN EXAMPLE SCENARIO
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Performance evaluation Environment

• NS 2 Simulation
• 200 Nodes
• 1000 m x 1000 m Area
• Each simulation runs for 300 Seconds
• Each Node transmission range 250 m
• CBR 40 Bytes Sized Traffic
• Energy at Each Node 10 J
• Energy Transmitting Data 0.6W
• Energy Receiving Data 0.3 W

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Parameters Compared

• Experiment 1
• Comparison EDGM with AODV
• a Throughput
• b Nodal Life
• Experiment 2
• Energy Saving not considered.
• Random Selection Technique was considered.
• a Nodal Life in Dense Network,
• b Nodal Life in Sparse Network.

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Throughput EDGM vs. AODV
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Nodal Life EDGM vs. AODV
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Nodal Life Dense Network
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Nodal Life Sparse Network
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Conclusion

• Energy consumption is considered as the most
  important concern.
• It is hard to say which approach we point in this
  paper is the best one. We only choose the
  approach fitting for the particular case.
• Should talk more about there NS 2 Simulation
  should include Energy Equations if any included.
• Static and Dynamic nodes evaluation should be
  included.