JSSDR: Joint-Sparse Sensory Data Recovery in wireless sensor networks

Data loss is ubiquitous in wireless sensor networks (WSNs) mainly due to the unreliable wireless transmission, which results in incomplete sensory data sets. However, the completeness of a data set directly determines its availability and usefulness. Thus, sensory data recovery is an indispensable operation against the data loss problem. However, existing solutions cannot achieve satisfactory accuracy due to special loss patterns and high loss rates in WSNs. In this work, we propose a novel sensory data recovery algorithm which exploits the spatial and temporal joint-sparse feature. Firstly, by mining two real datasets, namely the Intel Indoor project and the GreenOrbs project, we find that: (1) for one attribute, sensory readings at nearby nodes exhibit inter-node correlation; (2) for two attributes, sensory readings at the same node exhibit inter-attribute correlation; (3) these inter-node and inter-attribute correlations can be modeled as the spatial and temporal joint-sparse features, respectively. Secondly, motivated by these observations, we propose two Joint-Sparse Sensory Data Recovery (JSSDR) algorithms to promote the recovery accuracy. Finally, real data-based simulations show that JSSDR outperforms existing solutions. Typically, when the loss rate is less than 65%, JSSDR can estimate missing values with less than 10% error. And when the loss rate reaches as high as 80%, the missing values can be estimated by JSSDR with less than 20% error.