Location prediction optimisation in WSNs using Kriging interpolation

Abstract

Many wireless sensor network (WSN) applications rely on precise location or distance information. Despite the potentials of WSNs, efficient location prediction is one of the subsisting challenges. This paper presents novel prediction algorithms based on a Kriging interpolation technique. Given that each sensor is aware of its location only, the aims of this work are to accurately predict the temperature at uncovered areas and estimate positions of heat sources. By taking few measurements within the field of interest and by using Kriging interpolation to iteratively enhance predictions of temperature and location of heat sources in uncovered regions, degree of accuracy is significantly improved. Following a range of independent Monte Carlo runs in different experiments, it is shown through a comparative analysis that proposed algorithm delivers approximately 98% prediction accuracy.

A Networked Sensor System for the Analysis of Plot-Scale Hydrology

“…The Ordinary Kriging (OK) interpolation method is the most widely used geostatistical interpolation technique and is acknowledged as the standard approach for surface interpolation [35,65,70,76,77,78,79,80,81]. OK assumes that the distance or direction between sample points reflects a spatial correlation that can be used to explain variation in the surface.…”

“…The weight, λ i , depends on a fitted model to the measured points, the distance to the prediction location, and the spatial relationships among the measured values around the prediction location. In this study, an OK interpolation method with a spherical semi-variogram model is used to estimate the spatiotemporal trends of SM and WP, since this model has been found to satisfactorily represent the spatial dependence in previous studies [67,70,76,82,83,84]. The root mean square error (RMSE) is used to assess the performance of the selected interpolation method.…”

A Comparative Study of Various Location Prediction and Interpolation Techniques

“…Kriging is a geostatistical interpolation that believes both distance and the variation degrees to estimate the unknown data points [7]. A surface is made based on the points identified using Z-values.…”

Optimal dynamic routing protocols for agro-sensor communication in MANETs

“…Hayes et al [19] proposed a location aware sensor routing (LASeR) protocol. A prediction technique (location challenges) using the Kringing Interpolation technique was proposed with a prediction algorithm in [20]. A model of heterogeneous WSN (consisting both BPSN and EHSN with a cost function oriented routing strategy) was proposed with some better-attained parameters such as end-to-end path reliability, cost and energy consumption for a better QoS [21].…”