ELACCA: Efficient Learning Automata Based Cell Clustering Algorithm for Wireless Sensor Networks
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Wireless Sensor Networks (WSNs) are a special type of networks deployed in different geographical regions for capturing the important information. WSNs consist of low energy devices called Sensor Nodes (SNs) which are capable of sensing and transferring the gathered information to remote controller called as Base Stations (BSs). Because these devices are generally deployed in unattended environment and are limited in communication and computing power, so it is not always possible to recharge or replace the batteries for these devices. The SNs are supposed to have self healing and built in intelligence to operate independently. Keeping view of the above, in this paper, we propose a new Efficient Learning Automata based Cell Clustering Algorithm (ELACCA) for WSNs. Compared to the earlier approaches, we have taken size of the cell of the area under investigation in rhombus shape rather than the square. The selection of cluster head (CH) is performed by different levels using the participation ratio of the nodes in respective CH. Using the defined participation ratio, a cut off on number of nodes in a particular CH is also computed. Moreover, by varying the angle from base of the cell to its sides, the numbers of CHs formed are also calculated. Using these values, the communication among different CHs is maintained. The performance of the proposed scheme is validated using the extensive simulation with respect to various parameters such as connectivity, coverage and packet delivery ratio. The results obtained show that the proposed scheme is better than the existing schemes with respect to these metrics.
KeywordsWSNs Clustering Learning automaton
This work was supported by the new faculty research program 2013 of Kookmin University in Korea.
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