Neuro Wireless Sensor Network architecture: Cool stores dynamic thermal mapping

N. Yamani, A. Al-Anbuky
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引用次数: 2

Abstract

This work focuses on developing cool store's thermal mapping system based on the neuro Wireless Sensor Network (nWSN). The network intelligence is taken care of by the sensor network embedded neural net. The target application of the architecture development is for cool stores with emphasis on meat storage. The meat quality is a significant characteristic within the cold chain management. Temperature is the main parameter that needs to be monitored. nWSN architecture deals with the distributed neural network (NN) that predicts the temperature variations within the space. WSN nodes have categorized into infrastructural sensor nodes and portable sensor nodes. The neural net algorithm is embedded into the fully functional infrastructural nodes while portable nodes provide the surrounding information. The portable nodes that are attached to the meat carcass are dynamically establishing connection with one of the infrastructural nodes. The overall network formulates clusters based on Dijkstra's algorithm. The Nodes Message interaction (NMi) model is developed to organize the communication between the infrastructural nodes and portable nodes. This work disclosed the viability of nWSN architecture to execute further on a real-time test bed. It is found that the Mean Absolute Error (MAE) at the infrastructural nodes has a variation of 1°C. The resulting MAE is considerably good where nWSN can be capable of yielding similar applications of predictions.
神经无线传感器网络架构:Cool存储动态热映射
本研究的重点是开发基于神经无线传感器网络(nWSN)的冷库热成像系统。网络智能由传感器网络嵌入神经网络来实现。该架构开发的目标应用是针对冷藏库,重点是肉类存储。肉品质量是冷链管理的一个重要特征。温度是需要监测的主要参数。nWSN体系结构处理分布式神经网络(NN)来预测空间内的温度变化。WSN节点分为基础传感器节点和便携式传感器节点。神经网络算法嵌入到功能齐全的基础设施节点中,而便携式节点提供周围信息。附着在肉胴体上的便携式节点动态地与其中一个基础设施节点建立连接。整个网络基于Dijkstra算法制定聚类。提出了节点消息交互(NMi)模型,用于组织基础节点与可移植节点之间的通信。这项工作揭示了无线传感器网络架构在实时测试平台上进一步执行的可行性。研究发现,基础节点的平均绝对误差(MAE)变化幅度为1°C。所得的MAE相当好,nWSN可以产生类似的预测应用。
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