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Heterogeneity of the sensor nodes with limited energy sources will lead to uneven energy consumption and traffic imbalanced across the densely deployed network. On employment of the energy efficient algorithm could try to achieve energy efficiency among the heterogeneous sensor nodes to prolong the network lifetime using mobile sink. However despite of many advantageous of routing algorithm, it introduces hotspot problem on deployment of the multiple mobile sink for data gathering by set of data collection points as it frequently updates sink location information to all the sensor nodes in multi hop manner. In order to mitigate the hotspot problem, a new source traffic defined multiple mobile sink routing protocol has been employed to mitigate the hotspot issue towards improving the energy efficiency and network lifetime on extraction of multiple parameter including energy, coverage, data collection points, data fusion degree, schedule patterns, data redundancy transmission success ratio in the trace file of the particular topology. Particular network topology achieves good scalability, long network lifetime and low data collection latency. In addition, Source traffic defined Clustering techniques projected in this work will self organize the sensor nodes into effective clusters on generation of multiple cluster head to facilitate the data transmission. Cluster head along with information of data collection points plans the trajectory of the multiple mobile sinks for effective data collection from the sensor nodes. Trajectory of the multiple mobile sinks can be enabled using particle swarm optimization to reduce the energy depletion and moving distance of the sink nodes for data collection. Extensive simulations are conducted using NS2 simulator to evaluate the effectiveness of the proposed scheme. The performance results show that proposed model achieves more energy saving per node and energy saving on cluster heads on large traffic while comparing with data collection through multi-hop relay to the single mobile data sinks using existing state of art approaches.
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