Applying CS and WSN methods for improving efficiency of frozen and chilled aquatic products monitoring system in cold chain logistics
|Authors||Xiao, Xinqing, He, Q., Fu, Zetian, Xu, Mark and Zhang, Xiaoshuan|
Wireless Sensor Network (WSN) is applied widely in food cold chain logistics. However, traditional monitoring systems require significant real-time sensor data transmission which will result in heavy data traffic and communication systems overloading, and thus reduce the data collection and transmission efficiency. This research aims to develop a temperature Monitoring System for Frozen and Chilled Aquatic Products (MS-FCAP) based on WSN integrated with Compressed Sending (CS) to improve the efficiency of MS-FCAP. Through understanding the temperature and related information requirements of frozen and chilled aquatic products cold chain logistics, this paper illustrates the design of the CS model which consists of sparse sampling and data reconstruction, and shelf-life prediction. The system was implemented and evaluated in cold chain logistics between Hainan and Beijing in China. The evaluation result suggests that MS-FCAP has a high accuracy in reconstructing temperature data under variable temperature condition as well as under constant temperature condition. The result shows that MS-FCAP is capable of recovering the sampled sensor data accurately and efficiently, reflecting the real-time temperature change in the refrigerated truck during cold chain logistics, and providing effective decision support traceability for quality and safety assurance of frozen and chilled aquatic products.
|Keywords||Food safety and traceability; Cold chain logistics; Monitoring system; Wireless sensor network; Compressed sensing|
|Journal citation||60, pp. 656-666|
|Digital Object Identifier (DOI)||https://doi.org/10.1016/j.foodcont.2015.09.012|
|Web address (URL)||http://hdl.handle.net/10545/624471|
|Publication dates||14 Sep 2015|
|Publication process dates|
|Deposited||13 Feb 2020, 10:41|
|Accepted||11 Sep 2015|
Copyright © 2015 Elsevier Ltd. All rights reserved.
|Contributors||China Agricultural University, Beijing, China, Coventry University and University of Portsmouth|
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