Multiple Controlled Mobile Elements Data Mules for Data Collection in Sensor Networks

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Multiple Controlled Mobile Elements (Data Mules)
for Data Collection in Sensor Networks

• David Jea
• Arun Somasundara
• Mani Srivastava

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Sensor Networks
Event Tracking
Habitat Monitoring
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Data Collection in Sensor NetworksStatic
Multihop Routing

• More burden on certain nodes

• Need to form a connected network

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Data Collection in Sensor NetworksMobile Base
Station

• Solves both problems of static multihop routing
• No over-burdened nodes (Increased Lifetime)
• Need not form a connected network

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Mobility alternatives for Base Station
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Controlled Mobility

• Control in Time Domain
• Fixed trail
• Adaptively decide the speed with which mobile
  moves to maximize data collection
• MobiSys 2004
• Control in Space Domain
• Decide where the mobile goes
• RTSS 2004

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This paper

• Multiple Controlled Mobile Elements
• Decide the number of mobiles
• Load balancing (given number of mobiles)

Controlled Mobile Element Data Mule
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Recap Single Data Mule

• Given
• Single Data Mule
• Fixed path
• Goal
• Schedule of Data Mule to maximize data collection
• Design
• Network Algorithms
• Adaptive Motion control

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Recap Single Data MuleNetwork Algorithms

• Initialization
• Set up of routing trees

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Recap Single Data MuleNetwork Algorithms

• Initialization
• Set up of routing trees
• Local multihops
• Nodes not on path send data to on path nodes

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Recap Single Data MuleNetwork Algorithms

• Initialization
• Set up of routing trees
• Local multihops
• Nodes not on path send data to on path nodes
• Data Collection by Data Mule

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Recap Single Data MuleMotion Control Algorithms

• Given RTT (Round Trip Time)
• Move at constant speed s (Trail length/RTT)
• Change the speed adaptively to maximize data
  collection
• Stop at all nodes to clear its buffer
• RTT would depend on number of nodes

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Scalability

• More number of nodes
• Given RTT
• Data to be collected by more nodes in same time.
• Stop at all nodes
• Longer time to complete a round
• Buffer overflow at the next visit

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SolutionMultiple Data Mules

• Divide the area into equal parts, having one Data
  Mule in each.
• Each Data Mule and the corresponding nodes run
  the single Data Mule algorithm
• Works if nodes are uniformly randomly distributed
• Each Data Mule services approximately same number
  of nodes.
• 2 issues
• Number of Data Mules
• Handling of nodes shared by Data Mules

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Multiple Data Mules(a) Number of Data Mules

• 2nd form of motion control
• Data Mule stops at each node
• buffer_fill_time Time to fill a node's buffer
• service_time Time for the data mule to empty a
  node's buffer
• RTT Round trip time for the data mule
• (mule_travel_time) (num_nodes x service_time)
• If RTT ? buffer_fill_time,
• 1 data mule
• Otherwise,
• ceil(RTT/buffer_fill_time) data mules

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Multiple Data Mules(b) Common Nodes

• Nodes will be serviced by closer data mule
• N1, N2 by M1
• N4, N5 by M2
• Ties broken randomly
• N3 can be serviced by either

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Necessity ofLoad Balancing

• Multiple Data Mule solution works
• If nodes are uniformly randomly distributed
• In practice
• Nodes will be placed by field experts and lead to
  non-uniform distribution.
• It may not be feasible to have data mule paths
  anywhere we want.
• Problem
• Each Data Mule will serve different number of
  nodes.

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Problem Statement

• Assumption Each node at 1 hop from
• At least 1 data mule
• At most 2 data mules
• non_shareable nodes can only be served by single
  data mule.
• shareable nodes can be attached to either of the
  mules.

• Find data mule assignment for shareable nodes, so
  that each mule services more or less same number
  of nodes.

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Example 1

• 50 nodes, 2 data mules M1 and M2.
• M1 has 25 non_shareable nodes
• M2 has 5 non_shareable nodes
• 20 shareable nodes
• Average Load, 25 nodes per mule

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20
5
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Example 2

• 50 nodes, 2 data mules M1 and M2.
• M1 has 35 non_shareable nodes
• M2 has 5 non_shareable nodes
• 10 shareable nodes
• Average Load, 25 nodes per mule

35
10
5
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Load Balancing Algorithm

• Initially, all data mules in one group.
• Try make the load of each mule equal to the
  average load of that group.
• Split into two groups when any mule must take
  less or more loads.
• Recalculate average loads of the two groups.
• Try to balance the load of each group
  recursively.
• Recursion terminated when reach last mule of the
  group.

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Group Split Condition

• If the minimum load that has to be assigned to
  the mule under consideration is more than group
  average.
• If the maximum load that can be assigned to the
  mule is less than the group average.

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Illustration A
- Condition 1
Group Split!!
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illustration A
Group 1Increasing Average Load should carry
Group 2Decreasing Average Load should carry
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illustration B
- Condition 2
Group Split!!
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illustration B
- Condition 1
Group 2Increasing Average Load should carry
Group 1Decreasing Average Load should carry
Group Split!!
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illustration B
Group 2Increasing Average Load should carry
Group 2Decreasing Average Load should carry
Group 1
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Multiple Data Mules System

• Initialization data mules collect a list of
  nodes on its path.
• Leader election mules select one leader and
  tranmit own information to it.
• Load Balancing decide number of shareable nodes
  each mule should serve.
• Assignment assign data mule for nodes based on
  above result.
• Data Collection mules collect data from the
  designated nodes.

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Simulation Methodology

• Evaluated on TinyOS/TOSSIM
• Tython used to simulate mobility
• 3 schemes for sharing the load
• First Come First Serve
• Equal sharing
• Load balancing algorithm

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Simulation Topology
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Simulation Results
Average of packets per node per round
FCFS
EQUAL
Load Balance
Data Mule Id
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Conclusion Future Work

• Controlled mobile elements to collect data in
  wireless sensor networks.
• A load balancing algorithm to determine number of
  nodes each Data mule should serve.
• Remove the assumption that each node can talk to
  at least one mule and at most two mules.
• Consider costs of multi-hop.
• Mobile element can be added or removed during
  system runtime.

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• Thank you

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