Masters of Science Thesis

1
Masters of Science Thesis

• AMPL Active Multiple Power Level Cluster
  Formation
• By
• Jeffrey D Rupp
• 10/30/2020

2
Thesis Work

• Researched existing wireless cluster formation
  algorithms
• Researched existing wireless sensor network
  simulators
• Created and tested a cluster formation algorithm
  for wireless sensors
• Created a Java based simulator focused on power
  consumption for wireless sensors

3
Presentation Outline

• Wireless sensor background
• Wireless sensor cluster formation introduction
• AMPL introduction
• AMPL implementation details
• AMPL performance results
• Simulator background
• Simulator implementation details

4
Wireless Sensor Background

• Small inexpensive wireless sensors are quickly
  finding broad application in todays market place
• As sensors find more use, long network life is
  becoming market driven
• Wireless sensors can be used to remotely monitor
  temperature, sound, motion, light, and many other
  environmental parameters
• To be useful, sensor data must be gathered at a
  central point, a sink node

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Wireless Sensor Cluster Formation

• Cluster formation has been shown to enhance the
  lifespan of wireless sensor networks
• Child nodes transmit their data to their cluster
  head at low power
• Cluster heads then relay the data to the sink
  node at a higher power level
• The Cluster heads have the option of aggregating
  the data before relaying it, thus reducing the
  power consumed

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Example Wireless Network Topology
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Related Work

• There are many cluster forming algorithms in
  existence already
• TEEN (Threshold sensitive Energy Efficient sensor
  Network protocol)1 for reactive networks.
• Fault Tolerant Clustering of Wireless Sensor
  Networks 2 which addresses fail-over in
  networks
• . Scaleable Self-Assembly for Ad Hoc Wireless
  Sensor Networks 2 which describes a link level
  Ad-hoc network scheme.
• A Clustering Scheme for Hierarchical Control in
  Multi-hop Wireless Networks 3 which uses
  geometry, and thus knowledge of the nodes
  location, to for an efficient topology.

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AMPL Active Multiple Power Level Cluster
Formation

• AMPL differs from most other cluster formation
  algorithms by actively establishing the proximity
  relationships of all the nodes at the initial
  network deployment
• Most other cluster formation algorithms rely on a
  rounds-based approach to establish and
  dynamically change cluster heads
• Since AMPL establishes the proximity
  relationships once, it does not lend itself to
  mobile networks, the node locations must be fixed

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AMPL Implementation Hello (1)

• The initial proximity relationships are
  established through a multiple level hello
  protocol
• Every node performs the hello sequence
• The sink node tells the closest node to begin the
  hello sequence
• The node sends out a broadcast message starting
  at its lowest power level, then increments the
  power level up to its maximum power
• Nodes that hear the transmitting node enter it
  into a table along with the lowest level at which
  they heard it

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AMPL Implementation Hello (2)

• The nodes that hear the transmitting node mark
  themselves as needing to perform the hello
  sequence after they hear the transmitting node at
  the highest level
• This provides all the nodes with a table of the
  nodes they could hear and the levels at which
  they heard those nodes

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AMPL Implementation Hello (3)
Initial hello showing range at each of the
levels. Note that the algorithm used a linear
increment of the power in dBm, which is
logarithmic, so the distance increment of the
outer rings is greater.
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AMPL Implementation Cluster Formation (1)

• Once all of the nodes have finished their hello
  sequence clusters are formed
• The sink node tells the closest node to form a
  cluster
• The node selects the nodes it heard below a
  pre-configured level to join its cluster
• The nodes heard above the selected level are told
  to form their own clusters

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AMPL Implementation Cluster Formation (2)

• The number of nodes in a cluster can be limited
  if the node density is high
• This prevents a cluster from taking a long time
  for each data gathering phase.
• Child nodes are given a time slot and a unique
  code for spreading to prevent conflicting
  transmissions

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AMPL Implementation Cluster Formation (3)

• The nodes with the white circles around them
  represent one cluster
• The nodes with the blue squares at the upper left
  are cluster heads
• The pink node in the far upper left is the sink
  node

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AMPL Implementation Cluster Head Transfer (1)

• When the cluster head reaches 40 of the battery
  power it was at when it assumed the cluster head
  duties it transfers the duties to another cluster
  member.
• The tired cluster head sends a message to all its
  children asking them to report their current
  battery power
• When all the children have reported, or a timeout
  period has expired, the cluster head transfers
  the head duties to the strongest child node
• This allows an active transfer without relying on
  timing as with the rounds-based approaches
• This approach takes very little time since the
  cluster members are already established

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AMPL Implementation Cluster Head Transfer (2)

• The left image is the initial cluster, with the
  initial head, the center image is after the
  cluster head duties where transferred the first
  time, the right image is the fourth node to
  become cluster head
• The half circle below the nodes represents the
  battery power level

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AMPL Implementation Normal Communication (1)

• Once the initial clusters are established the
  nodes go into the normal communication phase
• During this phase the child nodes each transmit
  their data to the cluster head to be relayed to
  the sink node
• The cluster head can also aggregate the data,
  which is what the implementation tested assumed

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AMPL Implementation Normal Communication (2)

• This picture shows the communication taking place
  in two clusters and between the cluster heads
• This algorithm makes no assumptions for an
  established path to the sink node, so each
  cluster head must relay every message it hears
  destined for the sink node
• This is a weakness of this algorithm as it
  stands, since this consumes power very rapidly.
  However this algorithm is not a sink path
  algorithm but a cluster formation algorithm.
• The dark blue lines are intra-cluster, the light
  blue are inter-cluster

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AMPL Implementation Optimizations Tested

• An optimization that was tested is to have a
  cluster head check if it has no children
• If the cluster head has no children it checks if
  any other node offered to take it as a cluster
  member
• The lonely cluster head joins the cluster that
  offered membership at the lowest power level
• This prevents a single node from running out of
  battery power far earlier than other nodes

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AMPL Performance (1)

• The performance of AMPL was tested via a
  simulator I developed that focuses on wireless
  network power consumption (more on this later)
• Assumptions
• No path to sink node, all cluster heads must
  relay data destined for the sink node at maximum
  power
• Power is only consumed by transmission
  (acceptable for comparison)
• WattHours ((10.0 (powerLeveldBm / 10)) /
  1000) ((TransmitedBitCount / dataRateBps) /
  s_SECONDS_PER_HOUR)
• Free space for signal propagation
• pathloss in dB 20log10((1pidist)/wavelength)
• Where wavelength is 300/(frequency in MHz)
• The dist and wavelength are in the same units

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AMPL Performance (2)

• The following graphs compare AMPL, LEACH, HEED,
  and no clustering
• The X axis is number of nodes, from 10 to 150
• The performance metrics shown are
• Total bits transmitted by the network
• Simulation tics the network was alive for
• Power per packet
• Three cluster forming constraints were tested
  with AMPL
• Form new clusters at 30 of the maximum transmit
  level (AMPL)
• Form new clusters at 10 of the maximum transmit
  level (more nodes clustered) (AMPL_LargeClusters)
• Form clusters from 90 of the nodes heard
  regardless of level (many more nodes clustered)
  (AMPL_Opt)

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AMPL Performance (3)

• Total bits transmitted versus number of nodes

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AMPL Performance (4)

• AMPL achieved more total bits transmitted for all
  constraints tested
• All of the clustering algorithms performed better
  than the non-clustering algorithm

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AMPL Performance (5)

• Total simulation tics before most nodes were out
  of power versus number of nodes

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AMPL Performance (6)

• The non-clustering algorithm had the longest
  duration, which I explain by the time it took
  every node to relay every message, which resulted
  in nodes missing their data gathering interval
  since they were busy relaying messages
• This was compounded by the simple protocol the
  simulator was set for which used carrier detect
  to avoid collisions, since all the nodes were
  always transmitting at max power the protocol had
  to back off quite often
• Of the clustering algorithms AMPL_Opt lasted for
  the most simulation tics
• This is due to the very large clusters that
  AMPL_Opt formed, resulting behavior similar to
  the no-algorithm case

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AMPL Performance (7)

• Average power per packet
• Represents the power level each packet was
  transmitted at
• The no-algorithm case is omitted as it
  over-shadows the others with values around 3e-4
  versus the other algorithms with at 2e-6

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AMPL Performance (8)

• The average power per packet shows the average
  transmit level the packets were sent at
• The non-clustering algorithm transmitted at much
  higher power
• All packets were transmitted at maximum power
• AMPL and HEED formed clusters of a much smaller
  size and hence over smaller distance requiring
  the child nodes to transmit at lower levels

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AMPL Performance (9)

• This graph shows the total data bits transmitted
  versus increasing the simulated area while
  keeping the number of nodes constant at 115 nodes

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AMPL Performance (10)

• This comparison of algorithms across area shows
  that clustering should not include the farthest
  nodes
• The performance of AMPL suffered at medium
  density, but improved once more at the more
  sparse node densities

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AMPL Performance (11)

• This result is really more of a representation of
  the simulator results than specifically for this
  algorithm

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AMPL Performance Summary

• AMPL was tested without any of the optimizations
  that will be mentioned later in Future Work slide
• AMPL out performed existing algorithms
• The hello sequence provides data that could be
  used to implement two significant optimizations
• Sink path
• Optimal clusters

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Simulator Background

• There are already several simulators that work to
  represent wireless networks
• http//www.isi.edu/nsnam/ns/
• http//www.j-sim.org/whitepapers/ns.html
• http//nab.epfl.ch/viz.html
• However these simulators all concentrate on
  network topology, not on power consumption for
  data transmission

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Simulator Implementation

• Implemented in Java
• Cross platform portability
• Easy graphics integration
• Pluggable architecture for expansion
• Simulator architecture concentrates on
  transferring data between nodes

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Simulator Implementation Scheduler (1)

• As with any simulator the heart is the scheduler
• All simulated tasks must schedule a call-back via
  the scheduler
• Multi-threaded scheduling to force implementation
  to deal with potential out of order event
  execution
• Since all of the nodes are independent processors
  this simulates this asynchronous behavior much
  better than a single thread of execution

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Simulator Implementation Scheduler (2)

• The threading is done via a thread pool, so as
  the number of simulated nodes grows large there
  isnt as much time spent swapping contexts
• This is a configurable parameter
• An interesting problem encountered as the number
  of nodes was increased was that the simple sorted
  Vector of events quickly became very slow
• Every event insertion required a binary search to
  determine where to insert it

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Simulator Implementation Scheduler (3)

• To reduce this overhead I switched to a hash map
  of bins, mapping from event time to a vector of
  events
• This resulted in each event insert either being
  appended to a Vector, or a new Vector was created
  and the event appended to that
• The speed increase was dramatic
• Reduced the time to iterate from 10 to 150 nodes
  in 10 node steps on the non-clustering case from
  several hours to less than 15 minutes
• The scheduling allowed for parallel events by
  requiring each event inserted to include a time,
  not simply appending to the queue
• This allows nodes to schedule callbacks a
  specific number of tics from what their view of
  the current time is

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Simulator Implementation Plugins (1)

• The simulator has a plugin architecture, allowing
  the interesting pieces to be dynamically changed
• Node type
• Algorithms AMPL, LEACH, HEED
• Protocol type
• Transmission protocols 802.11, carrier detect
  only
• Propagation type
• Free space
• Packet type
• The only data the nodes can pass to each other

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Simulator Implementation Plugins (2)

• To expand the simulator a user is required only
  to implement the appropriate interface
• The simulator then traverses the classpath to
  find all classes implementing the four plugable
  interfaces and adds them to the Setup GUI

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Simulator Implementation

• The simulation only consumes power on transmit
• This could be easily modified by changing the
  various Node classes to consume power for other
  reasons such as reception of data, mundane OS
  chores, general processing, sensor sampling, etc.
• The simulator begins to starve nodes in the
  scheduler above about 700
• The largest number of nodes I simulated without
  evidence of starvation was 500

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Lessons Learned

• Threading makes the scheduler more flexible, but
  it requires more attention in the node classes to
  deal with the asynchronous events
• A map of bins makes for a much faster scheduler
• It is difficult to tell when a stage of the
  simulation is finished, e.g. hello sequence,
  cluster formation
• I did this by monitoring the scheduling queue,
  when it was empty for more than ½ second of real
  time a stage was finished
• Preventing interference between the nodes
• I made the same assumption that LEACH did, that
  the nodes could use signal spreading to prevent
  inter-cluster interference

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Future Work

• The proximity relationships mapped out by the
  hello sequence would allow for reasonably
  accurate localization, which in turn would allow
  the sink node to create optimal clusters and a
  sink path for the network
• Scaling to large networks (greater than 500
  nodes) will require distributing the simulation
  across many machines
• The simulator would allow this expansion via the
  plugin architecture
• One could implement a new propagation class that
  sends messages destined for nodes that arent in
  the local network to other machines via a simple
  socket interface

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Conclusion

• AMPL was shown to be superior to the algorithms
  it was compared to
• AMPL has the potential of superior performance if
  the items mentioned in future work are executed
• The simulator provides a fast and easy way to
  compare algorithms, with the potential for
  convenient expansion

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References

• 1 Arati Manjeshwar, Dharma P. Argawal TEEN A
  Routing Protocol for Enhanced Efficiency in
  Wireless Sensor Networks Center for Distributed
  and Mobile Computing ECECS Department University
  of Cincinnati 2001 http//axp1.csie.ncu.edu.tw/loc
  al/research/conf/ipps/2001/DATA/PDC_12.PDF
• 2 Gaurav Gupta, Mohamed Younis Fault-Tolerant
  Clustering of Wirelses Sensor Networks Dept. of
  Computer Science and Electrical Engineering 2001
  http//axp1.csie.ncu.edu.tw/local/research/conf/WC
  NC/WCNC2003/DATA/59_01.PDF
• 3Katayoun Shorabi, William Merrill, Jeremy
  Elson, Lewis Girod, Fredric Newberg, William
  Kaiser Scaleable Self-Assembly for Ad Hoc
  Wireless Sensor Networks Sensoria Corporation
  http//www.sensoria.com/pdf/ad-hoc-networks.pdf
• 4 Suman Banerjee, Samir Khuller A Clustering
  Scheme for Hierarchical Control in Multi-hop
  Wireless Networks University of Wisconson 2001
  http//www.cs.wisc.edu/suman/pubs/infocom01.pdf