The Minimum Number of Sensors – Interpolation of Spatial Temperature Profiles in Chilled Transports

Jedermann, Reiner; Lang, Walter

doi:10.1007/978-3-642-00224-3_15

Cited by 13 publications

(9 citation statements)

References 9 publications

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“…The Kriging method was applied to a set of 40 sensors that were installed inside one compartment of a loaded delivery truck [30]. The estimated Variogram shows that a temperature deviation has an influence on its neighborhood with a radius of 2.8 meter.…”

Section: Adjusting the Focus Of The Sensor Systemmentioning

confidence: 99%

The “Intelligent Container”—A Cognitive Sensor Network for Transport Management

et al. 2011

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Abstract-The "Intelligent Container" is a sensor network used for the management of logistic processes, especially for perishable goods such as fruit and vegetables. The system measures relevant parameters such as temperature and humidity. The concept of "cognitive systems" provides an adequate description of the complex supervision tasks and sensor data handling. The cognitive system can make use of several algorithms in order to estimate temperature related quality losses, detect malfunctioning sensors, and to control the sensor density and measurement intervals. Based on sensor data, knowledge about the goods, their history and the context, decentralized decision making is realized by decision support tools. The amount of communication between the container and the headquarters of the logistic company is reduced, while at the same time the quality of the process control is enhanced. The system is also capable of self-evaluation using plausibility checking of the sensor data.

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Section: Adjusting the Focus Of The Sensor Systemmentioning

confidence: 99%

The “Intelligent Container”—A Cognitive Sensor Network for Transport Management

et al. 2011

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“…This approach not only provides more accurate interpolation but also a means of calculating the expected interpolation error o k . A detailed introduction to the Kriging method can be found in standard text books [2] or in our previous publication [4], The first step to apply the Kriging method comprises of calculating the experimental Variogram from the data set and estimating the parameters of a theoretical model to approximate the experimental function. Typical Variogram models are described by three parameters: range, nugget, and sill.…”

Section: Applied Interpolation Methodsmentioning

confidence: 99%

“…Further data sets were recorded in delivery trucks with 3 separate temperature zones [4] in cooperation with Rungis Express, which is a German food supplier for hotels and restaurants. Two sets (Experiment 9 and 10) were recorded in the deep freezer compartment with a size of 2.9 × 2.5 × 2.35 meters at a set point of -29°C.…”

Section: Typical Temperature Variance In Food Transport and Storagementioning

confidence: 99%

“…The compartment was partly filled with frozen meat in air-permeable boxes. The manual fitting of the Variogram parameters in [4] was replaced by the automated search described in section 2. The average Variogram range of 3.25 meter was just slightly different from the tests in loaded storage rooms.…”

Section: Typical Temperature Variance In Food Transport and Storagementioning

confidence: 99%

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Interpolation of spatial temperature profiles by sensor networks

Jedermann¹,

Palafox-Albarrán²,

Elorza³

et al. 2011

2011 IEEE SENSORS Proceedings

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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.

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“…(5) Spatial interpolation can predict temperatures for positions that are not equipped with a sensor. The sensor measurements are multiplied with a weighting factor according to the Kriging method [64]. The calculation of the weighting factors needs the inversion of a matrix with a size equivalent to the number of sensors.…”

Section: Local and Flexible Pre-processing Of Sensor Datamentioning

confidence: 99%

Communication techniques and challenges for wireless food quality monitoring

Jedermann

Pötsch

Lloyd

2014

Phil. Trans. R. Soc. A.

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Remote measurement of product core temperature is an important prerequisite to improve the cool chain of food products and reduce losses. This paper examines and shows possible solutions to technical challenges that still hinder practical applications of wireless sensor networks in the field of food transport supervision. The high signal attenuation by water-containing products limits the communication range to less than 0.5 m for the commonly used 2.4 GHz radio chips. By theoretical analysis of the dependency of signal attenuation on the operating frequency, we show that the signal attenuation can be largely reduced by the use of 433 MHz or 866 MHz devices, but forwarding of messages over multiple hops inside a sensor network is mostly unavoidable to guarantee full coverage of a packed container. Communication protocols have to provide compatibility with widely accepted standards for integration into the global Internet, which has been achieved by programming an implementation of the constrained application protocol for wireless sensor nodes and integrating into IPv6-based networks. The sensor's battery lifetime can be extended by optimizing communication protocols and by in-network pre-processing of the sensor data. The feasibility of remote freight supervision was demonstrated by our full-scale ‘Intelligent Container’ prototype.

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The Minimum Number of Sensors – Interpolation of Spatial Temperature Profiles in Chilled Transports

Cited by 13 publications

References 9 publications

The “Intelligent Container”—A Cognitive Sensor Network for Transport Management

The “Intelligent Container”—A Cognitive Sensor Network for Transport Management

Interpolation of spatial temperature profiles by sensor networks

Communication techniques and challenges for wireless food quality monitoring

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