A Cluster Formation Algorithm with Self-Adaptive Population for Wireless Sensor Networks

1. A Cluster Formation Algorithm with Self-Adaptive Population for Wireless Sensor Networks Luis J. Gonzalez UCCS – CS526

2. Subjects for Discussion • The correlation between the population size and performance of artificial social insect colonies. • The use of self-adaptive techniques in cluster formation algorithms for wireless sensor networks (WSN).

3. Eusociality • Eusociality, the division of labor without any known centralized leadership, and the effectiveness to find the shortest path between the nest and a food source are characteristics of many insect societies.

4. Artificial Insect Colonies Population Constraints • Castes have specialized reproductive and non-reproductive functions. • The level of specialization and population size growth is a natural response to the stimulus created by the ecological context. • The population size parameter is manually predetermined in artificial insect colonies.

5. Castes, Pheromone, and Encounter Rates • Eusocial insects are morphologically different and divided into castes depending on their functions within the colony. • Ant, honey bees, and termite colonies are integrated by reproductive and worker individuals.

6. Queens, Drones and Workers • Queens and drones are the starting point for the endurance of the colony. • All the workers are female and traditionally perform non-reproductive functions, they can be patrollers, foragers, breeders, or responsible for the nest maintenance.

7. Selfish Behavior • Workers may reproduce selfishly rather than performing their traditional non-reproductive duties, which may impacts negatively the performance of the colony.

8. Alteration of the Colony • The survival of a colony depends on the cooperative natural intended work of their members. • The selfish behavior of workers alters the population size and the natural operation of the colony. • The population size may be increased when the performance of the colony is altered by the selfish behavior of workers. • An optimal population size is required to balance opposing selection pressures.

9. Artificial Insect Colonies and Self-adaptation • The population size and level of specialization of workers are fundamental for the efficiency of artificial insect colonies. • The parameters that control those variables should be the response to “ecological stimulus”.

10. A Cluster Formation Algorithm with Self-Adaptive Population • Wireless sensor networks (WSN) are a set of small spatially distributed autonomous battery powered devices or sensors. • The efficiency of WSN depends on the minimization of package collisions, control packet overhead, and overhearing of unnecessary traffic and idle listening to avoid energy wastage, which is the scarcest resource in WSN.

11. Minimize Energy Consumption • The formation of clusters with greater affinity to the cluster leader helps to optimize package transmission and reception, and minimize energy consumption.

12. Hypothesis • The level of specialization or cluster's efficiency depends on the cluster size previously predetermined; however, the cluster sizes are not necessarily optimal when they are calculated manually. • The use of biologically-inspired self-adaptive techniques to set the cluster size can maximize the formation of a uniform population of several clusters with greater affinity to the cluster leader, which will reduce the energy wastage.

13. Architecture and Operation of a wirelesssensor network • The operation of WSN encompasses the cluster and sink tree formation phases.

14. Optimal Cluster Size Calculation • Having a few clusters, which can be counted by the number of leaders or cluster heads, will overload the cluster processing capacity. • Too many leaders with few sensors will cause idleness or under use of the node. • An optimal cluster size is essential for load distribution in WSN.

15. Standard Deviation • The standard deviation of cluster sizes can be used as an indicator to determine the optimal cluster size because "the average cluster size is inversely proportional to the average number of clusters"

16. Standard Deviation • The calculation of the optimal cluster size will have to be oriented to obtain the smallest standard deviation in the WSN. • The smallest standard deviation suggests that the load is distributed uniformly among the leaders or cluster heads. • The optimal cluster size will also help to minimize the load inequality and extend the overall system lifetime.

17. Standard Deviation Calculation • Consider a wireless sensor network with 40 sensors distributed in eight clusters with the following population: 2,4,4,4,5,5,7,9 • There are eight data points with a mean of 5: (2 + 4 + 4 + 4 + 5 + 5 + 7 + 9) / 8 = 5

18. Standard Deviation Calculation • The difference of each data point from the mean is squared as a pre-requisite to calculate the standard deviation:

19. Conclusion • Self-adapting the population size in the cluster formation may contribute to the creation of energy efficiency wireless sensor networks.

20. Questions