Autonome Sensorsysteme in der Transport- und Lebensmittellogistik

A concise supervision of food products during transport is an essential precondition for the improvement of their quality and reduction of losses. However, existing remote or telemetric systems implement only parts of the entire supervision task. Standard systems measure temperature only at one or two points, and the evaluation of sensor data has to be done manually. This thesis presents a system which measures a spatial profile of temperature and other parameters. The idea of remote transport supervision is extended to a self-contained sensor system that locally processes measurement data and detects critical situations autonomously. The algorithms for sensor data evaluation are implemented inside the means of transport; they can either share a common embedded processor unit or run separately on wireless sensors nodes, which are attached to the loaded freight objects. The system automatically adapts the supervision process to different kinds of goods. This intelligent container combines technologies from different fields, such as RFID, wireless sensor networks, and telemetric system, which have so far been applied separately. A shelf life model, based on the dynamic temperature profile, estimates the amount of quality loss during transport. The quality supervision is implemented as a set of software agents. Each freight object is supervised by an individual sensory way bill . A demonstration system for the supervision of food transports shows the feasibility of this new approach

Spatial temperature profiling by semi-passive RFID loggers for perishable food transportation

Perishable food products are at risk of suffering various damages along the cold chain. The parties involved should control and monitor the conditions of goods in order to ensure their quality for consumers and to comply with all legal requirements. Among environmental parameters during transport, temperature is the most important in prolonging the shelf life of the products. Radio Frequency IDentification (RFID) is an emergent technology that has proven its suitability for tracking and tracing in logistics. This paper shows how miniaturized RFID temperature loggers can be adapted to analyze the amount of local deviations, detect temperature gradients, and estimate the minimum number of sensors that are necessary for reliable monitoring inside a truck or container. These devices are useful tools for improving the control during the transport chain and detecting weaknesses by identifying specific problem areas where corrective actions are necessitated. In a first step, the RFID tags were tested by studying the temperature distribution in a pallet. Then, 15 shipments from a wholesale company in Germany in compartmented trucks were monitored, covering different temperature range conditions. During transport, several temperature differences were found in the same compartment. Using a factorial Analysis of Variance (ANOVA) the influence of different factors has been studied, such as: the location of the logger, type of truck, and external temperature. The shelf life, or keeping quality model, was applied to the recorded temperature profiles. Suggestions for future research areas are also discussed

Interpolation of spatial temperature profiles by sensor networks

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 metho

Application of Neurocomputing for Data Approximation and Classification in Wireless Sensor Networks

A new application of neurocomputing for data approximation and classification is introduced to process data in a wireless sensor network. For this purpose, a simplified dynamic sliding backpropagation algorithm is implemented on a wireless sensor network for transportation applications. It is able to approximate temperature and humidity in sensor nodes. In addition, two architectures of “radial basis function” (RBF) classifiers are introduced with probabilistic features for data classification in sensor nodes. The applied approximation and classification algorithms could be used in similar applications for data processing in embedded systems

Autonomous sensor systems for transport and food logistics

A concise supervision of food products during transport is an essential precondition for the improvement of their quality and reduction of losses. However, existing remote or telemetric systems implement only parts of the entire supervision task. Standard systems measure temperature only at one or two points, and the evaluation of sensor data has to be done manually. This thesis presents a system which measures a spatial profile of temperature and other parameters. The idea of remote transport supervision is extended to a self-contained sensor system that locally processes measurement data and detects critical situations autonomously. The algorithms for sensor data evaluation are implemented inside the means of transport; they can either share a common embedded processor unit or run separately on wireless sensors nodes, which are attached to the loaded freight objects. The system automatically adapts the supervision process to different kinds of goods. This intelligent container combines technologies from different fields, such as RFID, wireless sensor networks, and telemetric system, which have so far been applied separately. A shelf life model, based on the dynamic temperature profile, estimates the amount of quality loss during transport. The quality supervision is implemented as a set of software agents. Each freight object is supervised by an individual sensory way bill . A demonstration system for the supervision of food transports shows the feasibility of this new approach

Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method

Wireless sensor systems often fail to provide measurements with uniform time spacing. Measurements can be delayed or even miss completely. Resampling to uniform intervals is necessary to satisfy the requirements of subsequent signal processing. Common resampling algorithms, based on symmetric finite impulse response (FIR) filters, entail a group delay of 10 s of samples, which is not acceptable regarding the typical interval of wireless sensors of seconds or minutes. The purpose of this paper is to verify the feasibility of single-delay resampling, i.e., the algorithm resamples the data without waiting for future samples. A new method to parametrize Kriging interpolation is presented and compared with two variants of Lagrange interpolation in detailed simulations for the resulting prediction error. Kriging provided the most accurate resampling in the group-delay scenario. The single-delay scenario required almost double the OSR to achieve the same signal-to-noise ratio (SNR). An OSR between 1.8 and 3.1 was necessary for single-delay resampling, depending on the required SNR and signal distortions in terms of jitter, missing samples, and noise. Kriging was the least noise-sensitive method. Especially for signals with missing samples, Kriging provided the best accuracy. The simulations showed that single-delay resampling is feasible, but at the expense of higher OSR and limited SNR