Robustness Differences between BioInspired Control and Centralized Control

1
Robustness Differences between Bio-Inspired
Control and Centralized Control

• Osaka University, Japan
• Yuichi Kiri
• Masashi Sugano
• Masayuki Murata

2
Attention to self-organization

• Paradigm shift of control process
• Self-organization
• Is emergence of system-wide adaptive structure
  and functionality from simple local interactions
  between individual entities Prehofer and
  Bettstetter, 2005
• Attracts considerable attention

Centralized Control
Distributed Control
Self-organizing control
Simplicity Effectiveness Determinism
Adaptability Scalability Robustness
3
Self-organization in Nature

• Ants or Bees
• Each agent is simple and unintelligent
• Interacts with neighboring agents
• Obeys the local rules it has as a species
• Their collective action creates biological order
• Allocating tasks
• Finding shortest path to their food
• Grouping eggs by their size

Many researchers have tried to derive
advantageous properties from biological
system Bio-inspired Control
4
Motivations

• Robustness
• Is a property that allows a system to maintain
  its functions despite external and internal
  perturbations ?
• Is extremely important especially for networks in
  variable environment
• Good robustness of bio-inspired control is widely
  reported but
• This is certainly nontrivial
• Why bio-inspired control is robust?
• What factors yield robustness?
• Compare robustness of bio-inspired control and
  centralized control
• Using sensor network scenario
• Quantitatively demonstrate the advantage of
  robustness
• Yield insight why and how bio-inspired control
  achieve good robustness

5
What is WSN

• Wireless Sensor Network (WSN)
• Composed of a number of sensor nodes
• Have miniature sensing devices
• Ex., Moisture, temperature, acceleration
• Communicate with neighboring nodes via wireless
  channel
• Sense their ambient surroundings
• Send the data to a sink
• Collect data over a large area
• Perturbations WSN faces
• Transmission error
• Sensor node failures

Sensor nodes
Data server
Sinks
Monitoring region
6
Centralized control in our comparison

• Cluster-based approach
• Sensor nodes are divided into as many clusters
  as sinks
• Routing is performed in each cluster
• Control station manages network
• Gather and integrate control information from
  each node
• Positions, residual power, etc. of nodes.
• Draw a whole picture of the network
• Manage clusters and routes based on the picture
• Countermeasures against node failures
• Each node periodically transmits hello message
• A node detects failure if a node cannot receive
  the message from another node for a predefined
  time
• Failure indication is sent to the control station

cluster
sink
nodes

7
Bio-inspired control in our comparison

• Cluster-based approach
• Same as the centralized control
• Sensor nodes are divided into some clusters
• Routing is performed in each cluster
• Combination of two swarm intelligence of ants
• Ant-based clustering for clustering
• Ant colony optimization (ACO) for routing
• Fault management
• Failure detection is same as that of centralized
  control
• There is no explicit failure indication

These are mediated by pheromone
8
Ant colony optimization (ACO)

• Probabilistic approach inspired by ants in their
  foraging activity
• Ants find their food, selecting which path to
  take
• Attracted into higher concentration of pheromone
• Leave pheromone on the path depending on the
  quality of the path
• Ants become to follow efficient route
• Packet is a counterpart of ant in communication
  network
• Packet stochastically selects next-hop node

Sensor nodes
food
Sink
9
Ant-based clustering

• Larvae or eggs are sorted according to their size
• Ants repeatedly and stochastically pick up and
  drop their eggs
• based on the similarity of size with neighboring
  eggs
• Clusters emerge through the iterations of picking
  up and dropping eggs
• Does not require global information
• Nodes belongs to a cluster according to their
  cluster pheromone (goodness of a cluster)
• Nodes stochastically pick and drop their cluster
  membership
• Based on the cluster pheromone
• Clusters emerge through the iteration of picking
  and dropping the membership

Applying to WSN
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Simulation settings

• 100x100 m2 monitoring region
• 300 nodes
• 4 sinks

• 10 m communication range
• Data packets is transmitted every 10 seconds to a
  sink
• Using network simulator ns-2

(25, 75)
(75, 75)
Metric of robustness
100 m

• Data-collection rate r/swhere
• r Number of packets received by sinks
• s Number of packets transmitted

(75, 25)
(25, 25)
100 m
Sinks
Nodes
11
Simulation results

• Against Node failures
• Bio-inspired control suffers less influence
• Centralized control is vulnerable to the loss or
  misguiding failure-indication from a node

• Against Bit Error Rate
• Bio-inspired control keeps rate 3 times longer
• Centralized control is vulnerable to the loss of
  command from a control station

1
0.8
-0.4993log(x) -2.8891
0.6
Bio-inspired
Average data collection rate
0.4
-0.7629log(x)-5.6905
0.2
centralized
0
1.0e-05
1.0e-04
1.0e-03
1.0e-02
BER
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Insight from the results

• Strength of dependency on control information
• Control information
• Information exchanged between entities of a given
  network to coordinate their joint operation
• Ex., failure-indication, command from a control
  station, etc.
• Centralized control Strong dependence
• Possibly unreliable control information plays an
  important role
• Control information from a control station is
  lost
• Nodes cannot adapt well
• Control information from a node is lost
• Control station cannot do right management
• Bio-inspired control Weak dependence
• Influence of unreliable information is only
  limited
• Nodes do not depends on control information from
  other nodes

13
Conclusions

• Quantitatively demonstrated the advantage in
  robustness of bio-inspired control
• Against Transmission error
• Against Node failures
• Dependence on control information probably
  differentiates robustness
• Future work
• Quantification of dependency strength
• Generalization of results