An Energy Efficient Framework for Densely Distributed WSNs IoT Devices Based on Tree Based Robust Cluster Head
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There is a developing effect of WSNs (wireless Sensor Networks) on genuine applications. Various plans have been proposed for gathering information on multipath routing, tree, clustering and cluster trees. Existing schemes can’t give an ensured dependable system to versatility, movement, and end-to-end association, separately. Such kind of problems to be moderate, the proposed scheme considers a densely distributed WSN system model related to Internet-of-Things (IoT) and tree based cluster formation depending upon sensor node deployment density. For each tree based cluster having one cluster head node to attain energy efficient data gathering, a reinforcement learning based fuzzy inference system (RL-FIS) will applied to determine the data gathering node for every cluster present in the densely distributed WSNs based on three metrics: neighbourhood overlap, bipartivity index and algebraic connectivity. We compare our proposed scheme with the other schemes. Simulation results indicate that our proposed scheme outperform the other schemes in overall energy consumption saving and prolong the lifetime of the network.
KeywordsWireless sensor networks Internet of things Energy efficient data gathering Fuzzy inference system Reinforcement learning
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