Interpolation of spatial temperature profiles by sensor networks

Abstract

Abstract-The monitoring of spatial profiles of a physical property such as temperature becomes feasible with the decreasing cost of wireless sensor nodes. But to obtain a temperature value for each point in space, it is necessary to interpolate between the existing sensor positions. Accurate spatial temperature supervision is a crucial precondition for maintaining high quality standards in the transportation of food products. The Kriging method was programmed for the ARM processor of the iMote2 sensor nodes and tested with 14 experimental data sets that were recorded in cold storage rooms and transports in trucks and containers. The error of the interpolation by Kriging was 20% lower than the simpler inverse-distance-weighting used as a reference method.

Improvement in fresh fruit and vegetable logistics quality: berry logistics field studies

“…The input to the capacitor method consists of a single ambient temperature vector where the outputs are defined by different rise/fall constants for each location in the pallet. As the second algorithm, the Kriging method has previously been used in environmental estimation problems defined within a continuous feature space (such as temperature inside a homogeneous container) [48]. Finally, as the third algorithm, we assume that the inherently nonlinear relationship between the product temperature inside a pallet and the air temperature can be modelled by an artificial neural network (ANN) as shown in figure 10 [50].…”

“…Note that the goal of this study is not to find the optimal placing or number of sensors which is a different research problem [48]. Three International Safe Transit Association temperature profiles were used, a regular summer profile (30 • C to 25 • C), wide range summer profile (50 • C to 20 • C) and winter profile (14 • C to 4 • C), to develop and compare three different temperature estimation algorithms.…”

New techniques and methods for cold chain monitoring and tracking in perishable products

“…In these cases there must be some alternatives to maintain the cold chain. Precooling and storage are very important part of the cold chain (Kumar et al, 2008;Norton et al, 2013), nonetheless, not too much attention is paid in the transport (James et al, 2006), if it is taken into account that the fresh produce suffer transport distances above several thousand kilometers (Jedermann et al, 2011a;Jedermann et al, 2011b). There is room for improvement and optimization in new protocols during the transportation (Defraeye et al, 2015b).…”

“…A key question is the number of loggers (sensors) required to accurately predict the product area, be it a container, cold room or even a pallet. Extensive literature has been published to address the issue Jedermann et al, 2011b;. These talk about the real possibility of temperature estimation, it means to estimate the temperature in critical places where there is no possibility of placing a sensor or there is no convenience for such a thing.…”

“…It has been reported that the Kriging method works more efficiently when there is a homogeneous and continuous test scenario Jedermann et al, 2011b;). This, however does not apply in this case since there were two different materials in the pallets plus the air gaps between the pallets.…”