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.
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.
Recent developments in wireless sensing technology lead to implement advanced algorithms for distributed data processing in various applications; intelligent transportation system is one of the main applications of the advanced networked sensing technology to monitor the environmental conditions for controlling the quality of the products. To ensure the desired performance of a wireless sensor network, the reliability of the network records needs to be evaluated using an efficient data processing algorithm. In this paper, a new application of a bio-inspired technique is introduced for autonomous plausibility checking in a wireless sensor network; at first, an optimized Neuro-immune system is introduced and developed to predict the sensor records; then, performance of the proposed Neuro-immune system is compared with a neural network approximation mechanism. A secondary algorithm evaluates the sensor records to check the plausibility of the records in the wireless sensor network. The proposed data processing algorithm could serve in various applications of wireless sensor networks.Keywords-wireless sensor network; distributed data processing; artificial immune system; artificial neural network.
In measurement systems, environmental conditions are measured based on predefined scenarios. Measured data are then processed in either a decentralized or centralized manner. In advanced systems (especially for distributed data processing), taking artificial intelligence features into consideration could improve measurement performance and reliability. It is assumed as autonomy in measurement system which leads to distributed "intelligent data measurement and processing". In this paper, two different methodologies for "temperature prediction" are compared. A discussion concerning the classification of recorded data is then presented. Both a mathematical approach, the so-called "least squares" approach, and a model-free approach, called back-propagation, are applied and compared for temperature approximation. After approximation, the predicted temperature values are compared with real temperature records for classification purposes. The "classification mechanism" includes signal processing features for improving performance.
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