Energy-Efficient Parameter Adaptation and Prediction Algorithms for the Estimation of Temperature Development Inside a Food Container

Palafox-Albarrán, Javier; Jedermann, Reiner; Lang, Walter

doi:10.1007/978-3-642-19539-6_5

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…The following parameters have to be estimated: β, which is a scaling factor depending on the amount of food and is given in kilograms, b 1 , which is the zero of the first-order linear system and , which is the pole of an equivalent pseudo-linear system. The algorithms to calculate the required parameters of the resulting system are presented in [8]; they have lower order matrix dimensions and do not need any matrix inversion. In total, three parameters for the equivalent system are estimated ( , ) and are updated after each measurement.…”

Section: Data Processingmentioning

confidence: 99%

“…The interconnected sensors are: a temperature sensor integrated in the ADC LTC2487, a three axis accelerometer MMA7455L, and an ADJD-S311-CR999 RGB light sensor [8].…”

Section: The Sunspot As Example Sensor Nodementioning

confidence: 99%

“…To demonstrate how sensor nodes communicate between each other and process sensor data locally, an algorithm was implemented, which foresees the final temperature at the end of transport in a refrigerated container transporting perishable goods. The algorithm applies the Feedback-Hammerstein training and prediction algorithms [8] to estimate the parameters of a model describing the temperature changes. The sensors send the actual and future temperature via radio to a gateway which generates a notification either via SMS or e-mail to inform about the current and future conditions.…”

Section: Introductionmentioning

confidence: 99%

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Combining Machine-to-Machine Communications with Intelligent Objects in Logistics

Palafox-Albarrán

Dannies

Sanjeeva

et al. 2012

Communications in Computer and Information Science

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Abstract. The combination of Machine-to-Machine (M2M) communication and intelligent objects can largely improve the supervision of logistic processes. This requires global mobile communications and short-range wireless sensor nodes. We assembled a demonstrator using off-the-shelf hardware for tests and classroom experiments that shows the feasibility of implementation of the future Internet of Things. It includes the use of embedded devices to perform local intelligent data processing and allows an evaluation of the advantages of ubiquitous M2M communication on a laboratory scale. New software features can be deployed, either to the gateway-device in the form of OSGi-bundles or to the sensor nodes in the form of MIDlet-suites, by using M2M-technology. As an example, we programmed an algorithm for predicting temperature curves in a container using real acquired datasets. A gateway bridges the local and the global network. Sensor messages can be forwarded via email and SMS or be provided by a web server.

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Section: Data Processingmentioning

confidence: 99%

“…The interconnected sensors are: a temperature sensor integrated in the ADC LTC2487, a three axis accelerometer MMA7455L, and an ADJD-S311-CR999 RGB light sensor [8].…”

Section: The Sunspot As Example Sensor Nodementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Combining Machine-to-Machine Communications with Intelligent Objects in Logistics

Palafox-Albarrán

Dannies

Sanjeeva

et al. 2012

Communications in Computer and Information Science

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DYNAMIC JAVA COMPONENTS IN PERVASIVE SYSTEMS - A Review of the Feasibility of Dynamic Data Processing on Wireless Platforms

Dannies¹,

Palafox-Albarrán²,

Lang³

et al. 2012

Proceedings of the 2nd International Conference on Pervasive Embedded Computing and Communication Systems

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The Intelligent Container - A Cognitive Sensor Net for Fruit Logistics

Lang

Janssen²,

Jedermann³

2014

Proceedings of the 3rd International Conference on Sensor Networks

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The Intelligent Container is a wireless sensor network for the control of perishable goods such as vegetables, fruits or meat. Several data interpretation tools are implemented in the sensor nodes. These can estimate temperature related quality losses, supervise sensor deployment and measurement intervals, and detect malfunctioning sensors. In order to retrieve information about the ripening directly from the transported fruits, the ripening indicator ethylene is detected using a newly developed highly sensitive and selective gas measurement system. The intelligent container allows the realisation of the new logistic paradigm of dynamic FEFO (First Expire First Out): the remaining life time-estimated shelf life-of the transported fruits is used to control the logistic process. This paper describes the developments performed on the Intelligent Container by the University of Bremen and its partners.

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Energy-Efficient Parameter Adaptation and Prediction Algorithms for the Estimation of Temperature Development Inside a Food Container

Cited by 3 publications

References 4 publications

Combining Machine-to-Machine Communications with Intelligent Objects in Logistics

Combining Machine-to-Machine Communications with Intelligent Objects in Logistics

DYNAMIC JAVA COMPONENTS IN PERVASIVE SYSTEMS - A Review of the Feasibility of Dynamic Data Processing on Wireless Platforms

The Intelligent Container - A Cognitive Sensor Net for Fruit Logistics

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