Data Dissemination Based on Ant Swarms for Wireless Sensor Networks S. Selvakennedy, S. Sinnappan, and Yi Shang IEEE 2006 CONSUMER COMMUNICATIONS and NETWORKING CONFERENCE

Outline 1. Introduction 2. Related Work 3. The T-ANT Clustering Algorithm 4. Results and Discussions 5. Conclusions

1. Introduction Communication for the conservation of the battery power Clustering with data aggregation is an important technique Minimum number of clusters and transmission distance

LEACH – Probabilistic self-election (2003) cluster head number for this round number of nodes

Time-Controlled Clustering Algorithm (TCCA) – Multi-hop clusters dynamically – Two or three hops is most appropriate size (2005) The desired CH probability Minimum threshold

Based on TCCA clustering algorithm – An ant swarm dynamically controls the CH election process – ANT election scheme is termed as the T-ANT algorithm

2. Related Work A particle swarm model for swarm-based networked sensor systems(2002) – Separation: Avoid collisions with nearby agents – Alignment: Attempt to match velocity with nearby agents – Cohesion: Attempt to stay close to nearby agents

3. The T-ANT Clustering Algorithm Two-phase clustering process – Cluster setup phase – Steady state phase Sink releases a number of ants – Ants and sensor nodes proportion is 0.1 [12] – The next node is randomly chosen – The ant restricted by time-to-live (TTL)

3-1 Cluster setup phase Controlled through a CS timer When timer expires, node checks has ant will becomes CH CH will broadcasting ADV message – Its node id – TTL

Receiving ADV message’s node record: – The CH id – The sender’s id as its parent – The hop distance to this CH – The number of ADV messages received – Total hop distance to all seen CHs If TTL permits rebroadcasts

When its join-timer expires, nodes computes its pheromone level: – Total hop distance (h) to CHs – The number of CHs (n) in its neighborhood – Residual energy

Node chooses the best cluster based on its hop distance to the CH Node’s hop distance selected CH Residual energy Learning rate Number of CHs Total hop distance to CHs

Sending a JOIN message – Its id – Selected CH id – Its pheromone level Highest pheromone attract ant next round to be the future CH

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3-2 Steady state phase It is possible, ants may die: – Environmental uncertainty – Node failure When ants die, sink re-release ants restart

4. Results and Discussions 100 sensor nodes distributed randomly 500 x 500 m² Message sizes 30 bytes at 2-second interval CH node retains its status for 20 seconds Anti-pheromone rate is 0.1

The CH election fitness function S to capture the separation behavior Number of CH The number of ADVs seen by CH i CH i’s hop distance to CH j

CH election fitness at different simulation time for T-ANT, m-LEACH, and TCCA

Clustering fitness function A Number of regular nodes Node i’s hop distance to its CH

Clustering fitness at different simulation time for T-ANT, m-LEACH, and TCCA

Logical topology of T-ANT at round number

Network lifetime against simulation time of T-ANT, TCCA, m-LEACH, and the flat strategy

5. Conclusions Uses a swarm of ants to control the cluster-head election T-ANT also stores less than 10% of state overhead

Mobile sensors and controlled the nodes physical movements Ensure safe separation between the agents to ensure coverage efficiency Maintains a level of connectivity between the mobile agents