An Energy-aware Iterative Sampling Framework for Data Gathering in Wireless Sensor Networks

Large numbers of nodes are often densely deployed to deliver the desired environmental attributes to the sink in Wireless Sensor Networks (WSNs), so there is a high spatial correlation among the readings of close sensor nodes. Given a certain requirement for accuracy, only part of the sensor nodes should be required to transport the data to sink. We proposed an Energy-aware Iterative Sampling Framework (EISF) for data gathering to reduce the total number of transmissions by exploiting the correlation. In our method, all nodes in a WSNs compete for reporting nodes with energy-related probability and each nonreporting node autonomously determines whether its own readings are redundant or not by utilizing the overheard packets transmitted by the nearby reporting nodes for each epoch. The redundant nodes will be put into sleep mode. After a limited number of iterations, our algorithm can select a set of sampling nodes to transport data with accuracy guarantees. The results of simulation experiments using the real data demonstrate that our proposed approach is effective in prolonging the network life.