Artificial Immune
System-Based Mobile
Node Movement
Peter Matthews
Motivation

Mobile Nodes
 Desirable
for many
applications
 Allows dynamic node topology
configuration
 Topology
reconfigures in response
to perceived conditions, internal
and external
 Goal: Adaptively cover a large
area with a small # of nodes, high
sensing fidelity, and high reliability
 Difficult balance of area coverage
and local specialization
Two Desirable Traits

Decentralized Operation
 More
scalable in terms of communication, and thus
requires much less power
 Allows more immediate response to changing
conditions

Both external perceptions and node status changes
 More

robust to node / link failures
Simplicity
 Must
be designed in a fashion mindful of limited sensor
node computational abilities, memory, and power

One way of accomplishing this is via a form of selforganization or “distributed intelligence”
 Biological
inspiration: Swarm Behavior, Ant Behavior,
Artificial Immune Systems, and the like
Relevant Adaptive Immune System
Basics


Natural defense mechanism. Able to discriminate between self and
non-self and respond accordingly to foreign invaders.
Ability to learn about pathogens and respond to them by producing
antibodies that attack antigens associated with the pathogen





Pathogen: Foreign substance
Antigen: Molecule (protein) associated with pathogen
Antibody: Protein that allows B-cell to bind to antigen and destroy it
Antibodies have differing affinity to specific antigens
B-cell surface has many antibodies and when one of these
antibodies binds to an antigen the B-cell becomes stimulated

Level of stimulation depends on





How well it matches the antigen
How well it matches other B-cells in the immune networks
Suppression factor from other B-cells with small affinity
B-Cell: Lymphocyte (white blood cell)
Stimulation leads to antibody production / cloning
Artificial Immune Systems



The immune system is a self-organizing system
that has the ability to process information, to
learn and memorize, to create a diverse
population of well adapted individuals, to
discriminate between self and non-self, and
respond to changing conditions in a
decentralized fashion
AIS attempt to apply the principles and
mechanisms of immune system operation to a
variety of problems
Examples: They have been found well suited to
anomaly detection, pattern recognition, data
clustering, multi-modal optimization, etc.
Artificial Immune System Basis of
The Presented System







When immune system encounters a pathogen, some Bcells are stimulated and secrete antibodies in order to
destroy the antigens.
Likewise, when an event occurs in sensor range some
sensor nodes are stimulated and move in order to
minimize distance and more accurately monitor the
event
B-Cells : Sensor Nodes
Pathogens : Events of Interest
Antigen : Distance to Event
Antibody : Movement
Antibody Density : Speed
Node State

2 Indexes of Node Stimulation
 X-Stimulation,
 [-Max, +Max]

Y-Stimulation
Short-lived buffer of received event messages
 For
discriminating whether the event report has
already been processed by this node

Timer for last beacon message received from
sink
 Used

to avoid node disconnection from sink
Count of number of neighbors at last time
quantum
 Updated
via regular probing / taking note when
overhearing other nodes’ transmissions
If Node Sensor Detects an Event

Node estimates XDistance, YDistance, TotalDistance.


X-Stimulation, Y-Stimulation are suppressed by
Observation_Suppression_Factor ( < 1)




XDistance, YDistance may be negative
Discounts previous stimulation state
X-Stimulation += xDistance / SensorRange *
Max_Detection_Stimulation
Y-Stimulation updated likewise
Transmit message to any nodes in range containing
 Estimated location of event
 Timestamp
 # Hops (= 0)
 # En route neighbors = # Neighbors of this node
 If (# neighbors > Cluster_Size AND totalDistance / SensorRange
< restrictionRange)
 Stimulation = Max_Event_Stimulation / (# neighbors Cluster_Size )
 Else
 Stimulation = Max_Event_Stimulation
If Node Receives Event Message



If already processed copy of same message or if already processed
a report of event with same timestamp and within a small estimated
distance of each other, ignore
Calculate distance to event location
If distance < Max_Distance
and #EnRouteNeighbors > Max_Neighbors

totalStimulation = stimulationIntensity * (
.5 * (1 – distance / Max_Distance) +
.5 * (1 - #EnRouteNeighbors/Max_Neighbors))
 X-Stimulation += xDistance / totalDistance * totalStimulation
 Y-Stimulation likewise

If Estimated # Neighbors < Cluster_Size
and distance < Max_Distance

Retransmit message with

Original location and timestamp
Stimulation = original stimulationIntensity
#Hops += 1

# En route neighbors += # Neighbors of this node


Result of Node-Event Interaction


Node is stimulated to move towards the location
of an event of interest
Stimulation falls off as a factor of distance and #
neighbor nodes along route
#
neighbor nodes some indication of how crowded is
route from event to node

If area becomes too crowded, node does not
transmit
 This
avoids node “implosion” effects. Tunable to
specify how large of a cluster is appropriate for a
given application.
Node-Node Stimulation Effects
Via regular probing and overhearing of
transmissions, each node keeps a list of
neighbors within transmission range and
estimated distance
 For each neighboring node

 X-Stimulus
+= (-xDistance / Distance)
* (1 – (Distance / TransmissionRange - α)^2)
* Neighbor_Stimulation_Rate
 Y-Stimulus likewise
Result of Node-Node Interaction



If a node is within transmission range - α of another
sensor node, where α is very small, then this node
receives a repulsive stimulation proportional to the
distance to the neighbor, squared
This leads to an regular, grid-like arrangement of sensor
nodes in the absence of event messages. Complete
coverage of an area results wherever possible.
The tension between the node-event interaction and the
node-node interaction is the key to balancing the need
for preservation of comprehensive area coverage and
the need for localized clustering in response to events.
Beacon



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Beacon broadcast at regular intervals from sink node.
If a node has not received a beacon message in a set
time period, it knows it is disconnected from the sink.
In this case the node is stimulated such that it will move
towards the center of the area until it encounters another
node which believes itself to be connected.
This avoids prolonged node-sink disconnection with
minimal disruption and without the associated overhead
of establishing a routing tree or other such schemes.
Node Movement

At completion of each time unit

If the absolute value of the sum of the stimulation values is
above a Minimum_Threshold




Normalize the absolute sum with respect to a
Maximum_Stimulation_Level.
The total movement is then the normalized sum *
Maximum_Sensor_Movement.
Move in the X and Y directions proportionally to the share of the
total stimulation that each provide, multiplied by the total movement.
Multiply X-Stimulation and Y-Stimulation by some Aging_Factor
( < 1)
Simulation Parameters

800 x 800 Area


Sensor node




Sink in center
Sensor range: 30
Transmission range: 120
Max speed: .2 / time unit
Events of Interest



1 Per time unit
Created at random location in Interest Area, the 35 x 35 “green
box” visible in simulation
Two Interest Area mobility models


Blocked – Randomly jumps to a new location every 1000 time units
Random Waypoint – Chooses a random waypoint and moves
towards it at speed .25 / time unit. Once reached, randomly picks a
new waypoint.
Demonstration

Show videos of system in practice
Percentage of Time Event Detected
Results: Random Node Placement,
Waypoint Event Mobility Model
120
100
80
Static Nodes
60
AIS-Based
Nodes
40
20
0
16
48
80
112
# Sensor Nodes
Percentage of Time Event Detected
Results: Random Node Placement,
Blocked Event Mobility Model
90
80
70
60
Static Nodes
50
AIS-Based
Nodes
40
30
20
10
0
16
48
80
112
# Sensor Nodes
Percentage of Time Event Detected
Results: Grid Node Placement,
Waypoint Event Mobility Model
120
100
80
Static Nodes
60
AIS-Based
Nodes
40
20
0
16
48
80
112
# Sensor Nodes
Percentage of Time Event Detected
Results: Grid Node Placement,
Blocked Event Mobility Model
100
90
80
70
Static Nodes
60
50
AIS-Based
Nodes
40
30
20
10
0
16
48
80
112
# Sensor Nodes
Conclusions



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In early simulations, the Artificial Immune
System-Based mobile node movement system
consistently outperforms the static scheme with
regards to proportion of time an event is sensed.
Primitive data on sensing data utility suggests
that it is not merely quantity, but quality of the
sensor data that improves when the proposed
scheme is utilized.
The proposed scheme incurs a relatively low
level of computational and communication
overhead.
However, further results are needed.
Future Work

Comparison to other comparable algorithms



Simulation to show robustness of scheme in the face of
node failures / geographic obstacles.
Very slow scale operation as way of adapting to
stochastic event locality


Difficult to find an appropriate competitor / canonical test bed
Would allow comparison to schemes that attempt to converge to
this event distribution
Incorporate T-cell role as loosely coupled global
overseer