Description
Abstract
Some of the quality of service parameters(QoS) for wireless sensor networks is network coverage, the optimal number of active nodes, lifetime and the amount of consumed energy. In this thesis, three fundamental problems in wireless sensor networks are addressed and with the aim of promoting the QoS parameters, efficient solutions based on cellular learning automata(CLA) for them are proposed. In the first part of this thesis, the problem of coverage in wireless sensor networks is considered and a solution based on CLA for solving it is proposed. In this method, by deactivating unnecessary nodes it is tried to reduce the energy consumption by the network and as a result, increasing the lifetime of the network. In the second part of this thesis, the problem of clustering is addressed and a solution based on CLA for solving it is proposed. The nodes in the network are clustered in such a way that energy consumption is balanced among the node of the network which results in increasing the lifetime of the network. Finally, in the third part, a method based on CLA for data aggregation with the aim of increasing the lifetime of the network is proposed. All the proposed methods are simulated using J-Sim simulator. Results of the simulations show that the proposed methods perform better than similar existing methods.
Keywords: Wireless sensor network, learning automata, Quality of service, Coverage, Clustering, Data aggregation.
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