Abstract
Energy efficiency is one of the most crucial concerns for WSNs, and almost all researches assume that the process for data transmission consumes more energy than that of data collection. However, a few sophisticated collection processes of sensory data will consume much more energy than traditional transmission processes such as image and video acquisitions. Given this hypothesis, this article proposed an adaptive sampling algorithm based on temporal and spatial correlation of sensory data for clustered WSNs. First, according to spatial correlations between sensor nodes, a distributed clustering mechanism based on data gradient and residual energy level is proposed, and the whole network is divided into several independent clusters. Afterwards, each cluster head maintains an autoregressive prediction model for sensory data, which is derived from historical data in the temporal domain. With that, each cluster head has the ability of self-adjusting temporal sampling intervals within each cluster. Consequently, redundant data transmission is reduced by adjusting temporal sampling frequency while ensuring desired prediction accuracy. Finally, several distinct sampler collection sets are selected within each cluster following intra-cluster correlation matrix, and only one sampler collection needs to be activated at each round time. Sensory data of non-sampler can be substituted by those of sampler due to strong spatial correlation between them. Simulation results demonstrate the performance benefits of proposed algorithm.
Highlights
Due to inherent advantages to monitor region of interest (ROI) in a fully distributed manner, wireless sensor networks (WSNs) have drawn considerable research interests and academic attentions in recent years.[1,2] WSNs consist of a large number of self-organizing sensor nodes which are deployed in interesting or unattainable areas to acquire environmental information
Section ‘‘Adaptive sampling for clustered WSNs’’ illustrates an analytical model and the overall principle of proposed adaptive sampling algorithm for clustered WSNs, and several crucial components of the proposed ASbST algorithm are described in detail from sections ‘‘Clusters construction’’ to ‘‘Autoregressive moving average (ARMA)based predictive model at cluster head (CH).’’ Simulation and performance evaluation are presented in section ‘‘Simulations and analysis.’’ section ‘‘Conclusion’’ concludes this article
We introduced an adaptive sampling algorithm based on temporal and spatial correlations for clustered WSNs
Summary
Due to inherent advantages to monitor region of interest (ROI) in a fully distributed manner, wireless sensor networks (WSNs) have drawn considerable research interests and academic attentions in recent years.[1,2] WSNs consist of a large number of self-organizing sensor nodes which are deployed in interesting or unattainable areas to acquire environmental information. With the advancements in system-on-chip (SOC) miniaturization and integration, it is possible to deploy abundant sensor nodes for practical applications[3,4] Generally, each sensor node in WSNs collects raw sensory data periodically and transmits to the sink node through wireless multi-hop communication for further analysis. This approach will result in excessive communication and energy consumption. Section ‘‘Adaptive sampling for clustered WSNs’’ illustrates an analytical model and the overall principle of proposed adaptive sampling algorithm for clustered WSNs, and several crucial components of the proposed ASbST algorithm are described in detail from sections ‘‘Clusters construction’’ to ‘‘Autoregressive moving average (ARMA)based predictive model at cluster head (CH).’’ Simulation and performance evaluation are presented in section ‘‘Simulations and analysis.’’ section ‘‘Conclusion’’ concludes this article
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