Performance of matrix completion approaches for aquaponics data

Abstract

Technological innovations in Internet of Things (IoT) have resulted in smart agricultural solutions such as a remotely monitored Aquaponics system and a wireless sensor network (WSN) of such systems (nodes). IoT enables continuous sensing of temperature and pH data at each node of the WSN, which is periodically transmitted to a remote fusion centre. In this regard, the data matrices acquired at the fusion centre often suffer from data vacancies and missing data problems, owing to typical wireless multipath fading environment, sensor malfunctions and node failures. This paper explores the applicability of different matrix completion approaches for missing data reconstruction. Specifically, the performance of baseline predictor, correlation based approaches such as baseline predictor with temporal model, k-nearest neighbors (kNN) and low rank based approaches such as Sparsity Regularized Singular Value Decomposition (SRSVD) and Augmented Lagrangian Sparsity Regularized Matrix Factorization (ALSRMF) have been explored. Reliable temperature and pH data for 19 independent acquisition hours with 60 samples per hour are acquired at the fusion centre via Ultra High Frequency (UHF) transmission at 470 MHz and suitable pre-processing. Simulating different data integrity scenarios, the reconstruction error plots from each of these matrix completion approaches is extracted. A hybrid of kNN and baseline predictor with temporal model rendered a Mean Absolute Percentage Error (MAPE) of 1.75% for temperature and 0.86% for pH, at 0.5 data integrity. Further, with ALSRMF, which exploits the low rank constraint, the error reduced to 1.25% for temperature and 0.7% for pH, thus substantiating a promising approach for Aquaponics system data reconstruction.