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In multisink wireless sensor networks, synchronized data collection among multiple sinks is a significant and challenging task. In this paper, we propose an unbalanced threshold based distributed data collection scheme to reconstruct the synchronized sensed data of the whole sensor network in all sinks. The proposed scheme includes the unbalanced threshold based distributed top-K query algorithm and the distributed iterative hard thresholding algorithm. By computing unbalanced thresholds and pruning unnecessary element exchanging, each sink can synchronize the top-K aggregated values efficiently via the unbalanced threshold based distributed top-K query algorithm. After that, the synchronized sensed data of the whole sensor network can be reconstructed through the distributed iterative hard thresholding algorithm in a distributed and cooperative manner. We show through experiments that the proposed scheme can reduce the interaction times and decrease the number of transmitted data and that of computed data compared to the existing schemes while maintaining the similar data reconstruction accuracy. The communication and computational performances of the proposed scheme are also analyzed in detail in the paper. [ABSTRACT FROM AUTHOR]

We propose a three-phase top-k query based distributed data collection scheme which is designed for clustered or multisink wireless sensor networks. The proposed scheme consists of a distributed iterative hard thresholding algorithm and a three-phase top-k query algorithm. In the distributed iterative hard thresholding algorithm, the cluster heads or sink nodes reconstruct the compressed data in a distributed and cooperative manner. Meanwhile, the top-k query operation in the above algorithm is realized by pruning unnecessary elements among cluster heads or sink nodes in the three-phase top-k query algorithm. Simulation results show that there is no obvious difference in the performance of data reconstruction between our proposed scheme and existing compressive sensing theory based data collection schemes. However, both the number of interactions and the amount of transmitted data among cluster heads or sink nodes can be effectively reduced in the proposed scheme. The performance of the proposed scheme is analyzed in detail in this paper to support the claims. [ABSTRACT FROM AUTHOR]

In recent years, energy-efficient data collection has evolved into the core problem in the resource-constrained Wireless Sensor Networks (WSNs). Different from existing data collection models in WSNs, we propose a collaborative data collection scheme based on optimal clustering to collect the sensed data in an energy-efficient and load-balanced manner. After dividing the data collection process into the intra-cluster data collection step and the inter-cluster data collection step, we model the optimal clustering problem as a separable convex optimization problem and solve it to obtain the analytical solutions of the optimal clustering size and the optimal data transmission radius. Then, we design a Cluster Heads (CHs)-linking algorithm based on the pseudo Hilbert curve to build a CH chain with the goal of collecting the compressed sensed data among CHs in an accumulative way. Furthermore, we also design a distributed cluster-constructing algorithm to construct the clusters around the virtual CHs in a distributed manner. The experimental results show that the proposed method not only reduces the total energy consumption and prolongs the network lifetime, but also effectively balances the distribution of energy consumption among CHs. By comparing it o the existing compression-based and non-compression-based data collection schemes, the average reductions of energy consumption are 17.9% and 67.9%, respectively. Furthermore, the average network lifetime extends no less than 20-times under the same comparison. [ABSTRACT FROM AUTHOR]