PCI-MDR: Missing Data Recovery in Wireless Sensor Networks Using Partial Canonical Identity Matrix
Year: 2018
Type: article
Abstract: Data loss in wireless sensor networks (WSNs) is quite prevalent. Since sensor nodes are employed for various critical applications, accurate recovery of missing data is important. Researchers have exploited different characteristics of WSN data, such as low rank, spatial, and temporal correlation for missing data recovery. However, the performance of existing methods is dependent on various factors. For instance, correct rank estimation is required for exploiting the low-rank behavior of WSNs, whereas correlation information among the nodes should be known for exploiting spatial correlation. Further, the amount of missing data should not be massive for exploiting temporal correlation. To overcome the above-mentioned drawbacks, a novel partial canonical identity (PCI) matrix based missing data recovery method has been proposed in this letter. It utilizes compressive sensing with PCI matrix for the recovery of missing data in WSNs. To validate the proposed method, the results have been obtained on the real dataset of temperature sensors from the Intel Lab. The proposed method is observed to perform superior to the existing methods, yielding significant improvement.
Authors: Neha Jain, Anubha Gupta, Vivek Ashok Bohara
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2895159174