Processing Uncertain RFID Data in Traceability Supply Chains

Xie, De; Xiao, Jie; Guo, Guangjun; Jiang, Tong

doi:10.1155/2014/535690

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…Fan Hua and Wu Quanyuan et al [8] analyzed the RFID data storage problems. The treatment of uncertain RFID tag data in supply chain monitoring was researched by Xie Dong and Xiao Jie et al [9]. Chong Alain Yee Loong and Liu Martin J et al [10] predicted and analyzed the adoption of RFID technology into medical supply chain from the a user perspective.…”

Section: Literature Reviewmentioning

confidence: 99%

Study on Internet of Things-driven Real-time Status Monitoring of Supply Chain Logistics

Fu¹,

Ma²,

Hu³

et al. 2017

dtetr

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Abstract. Real-time status monitoring of supply chain logistics is the basis of intelligent supply chain implementation. Supply chain logistics is essentially a process of objects move in space over time. It could also be abstracted as a process of materials transferred through nodes. First, therefore, this paper establishes a element node model of supply chain logistics to analyze the information collection required by supply chain logistics. It then uses internet of things to study the method of environment configuration of supply chain logistics real-time status monitoring, and classify the information collection required in supply chain logistics into two categories. A data processing model of supply chain logistics status monitoring is also developed based on Auto-ID calculation, with regard to the second information cannot be obtained directly through the data collection. Finally, software platform is built to verify theories and methods drawn.

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Section: Literature Reviewmentioning

confidence: 99%

Study on Internet of Things-driven Real-time Status Monitoring of Supply Chain Logistics

Fu¹,

Ma²,

Hu³

et al. 2017

dtetr

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“…In this paper, we introduced data function characteristics of the food chain, and the perspective of data methods is analyzed. First we analysis functional data, from the literature [24], and [25] give a description of a system, then that is literature [26] functional data.…”

Section: Analysis Of Time Series Data Function Cluster Of Foodmentioning

confidence: 99%

Mutton Traceability Method Based on Internet of Things

Min-ning

Zhang

et al. 2014

Journal of Sensors

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In order to improve the mutton traceability efficiency for Internet of Things and solve the problem of data transmission, analyzed existing tracking algorithm, proposed the food traceability application model, Petri network model of food traceability and food traceability of time series data of improved K-means algorithm based on the Internet of things. The food traceability application model to convert, integrate and mine the heterogeneous information, implementation of the food safety traceability information management, Petri network model for food traceability in the process of the state transition were analyzed and simulated and provides a theoretical basis to study the behavior described in the food traceability system and structural design. The experiments on simulation data show that the proposed traceability method based on Internet of Things is more effective for mutton traceability data than the traditional K-means methods.

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“…CCA is a method to remove frequent features from multivariate data. However, it failed to apply wide application fields.A new framework was designed in [7] for efficient uncertain RFID data processing and maintaining a number of queries to trackand trace RFID objects. However, the query processing efficiency does not improved at a required level.…”

Section: Introductionmentioning

confidence: 99%

Fusion Layer Topological Space Query Indexing For Uncertain Data Mining

Kalavathi¹,

Suresh²

2016

IJECS

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-Data uncertainty is an intrinsic property in different applications such as sensor network monitoring, object recognition, location-based services (LBS), and moving object tracking. The data mining methods are applied to the above mentionedapplications their uncertainty has to be handled to achieve the accurate query results. The several probabilistic algorithm estimates the location and control for each object but not effective in handling query processing in distributed environments. Probabilistic inverted indexing computes the lower bound and upper bound for a threshold keyword but fails to extend the technique on tackle the correlation. In order to overcome the issues in uncertain data mining, Fusion Layer Topological Space Query Indexing technique (FLTS)is introduced. Initially, the queries are articulated on any random subset of attributes in the uncertain data.The FLTS index technique answers the top-k queries competently. FLTS index correctlyshows in their dominant relationships andsignificantly reduces the number of tuple accessed through query processing by pruning redundant tuple based on two criterions such as layer point sort method and the record point sort method. Initially, layer point sort method is used in FLTS indexto sort out tuple totally on the combination of all attribute values of the tuple. Subsequently, each attributes values particularly used for rating the tuple using record point sort method. Therefore,the correlation is removed significantly. Through an analysis of the interaction of the two sorting methods, derive a fixed bound that reduces the number of tuple retrieved during query processing and obtaining the correct query results in distributed environments. An experimental result shows that the FLTS indexing technique improves the query retrieving efficiency, response time, memory consumption and scalability.

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Processing Uncertain RFID Data in Traceability Supply Chains

Cited by 5 publications

References 28 publications

Study on Internet of Things-driven Real-time Status Monitoring of Supply Chain Logistics

Study on Internet of Things-driven Real-time Status Monitoring of Supply Chain Logistics

Mutton Traceability Method Based on Internet of Things

Fusion Layer Topological Space Query Indexing For Uncertain Data Mining

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