Reducing False Negative Reads in RFID Data Streams Using an Adaptive Sliding-Window Approach

Massawe, Libe Valentine; Kinyua, Johnson; Vermaak, H.J.

doi:10.3390/s120404187

Cited by 10 publications

(9 citation statements)

References 20 publications

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“…Their results showed that SMURF techniques could adjust window size and outperformed all static window schemes. Massawe et al (2012) proposed a window sub-range transition detection (WSTD) adaptive sliding window approach, which used binomial sampling to calculate the appropriate window size [36]. Their results show that the WSTD scheme outperformed SMURF.…”

Section: False Positive Detectionmentioning

confidence: 99%

False Positive RFID Detection Using Classification Models

et al. 2019

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Radio frequency identification (RFID) is an automated identification technology that can be utilized to monitor product movements within a supply chain in real-time. However, one problem that occurs during RFID data capturing is false positives (i.e., tags that are accidentally detected by the reader but not of interest to the business process). This paper investigates using machine learning algorithms to filter false positives. Raw RFID data were collected based on various tagged product movements, and statistical features were extracted from the received signal strength derived from the raw RFID data. Abnormal RFID data or outliers may arise in real cases. Therefore, we utilized outlier detection models to remove outlier data. The experiment results showed that machine learning-based models successfully classified RFID readings with high accuracy, and integrating outlier detection with machine learning models improved classification accuracy. We demonstrated the proposed classification model could be applied to real-time monitoring, ensuring false positives were filtered and hence not stored in the database. The proposed model is expected to improve warehouse management systems by monitoring delivered products to other supply chain partners.

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Section: False Positive Detectionmentioning

confidence: 99%

False Positive RFID Detection Using Classification Models

et al. 2019

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“…In this section, simulation is used to analyse the performance of the proposed duplicate reading detection and removal approach. In the experiment, we used the same RFID data streams as in [9,10,14,17]. The data streams have been generated using Poisson distribution to illustrate the tag arrival in such granule time.…”

Section: Performance Analysismentioning

confidence: 99%

“…These duplicate readings will flood the network bandwidth and may have adverse effect in applications such as stock management [9]. To address this problem, a variety of Bloom Filter-based [9,10,12,16] and non-BF-based [14,17] approaches have been proposed in the literature. The latter approaches include those that used sliding window [14,17] and landmark window in filtering duplicate readings [9].…”

Section: Introductionmentioning

confidence: 99%

“…To address this problem, a variety of Bloom Filter-based [9,10,12,16] and non-BF-based [14,17] approaches have been proposed in the literature. The latter approaches include those that used sliding window [14,17] and landmark window in filtering duplicate readings [9]. The sliding window approach is more suitable to the applications that always have new readings that have not been recorded before.…”

Section: Introductionmentioning

confidence: 99%

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Filtering Redundant Data from RFID Data Streams

2016

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Radio Frequency Identification (RFID) enabled systems are evolving in many applications that need to know the physical location of objects such as supply chain management. Naturally, RFID systems create large volumes of duplicate data. As the duplicate data wastes communication, processing, and storage resources as well as delaying decision-making, filtering duplicate data from RFID data stream is an important and challenging problem. Existing Bloom Filter-based approaches for filtering duplicate RFID data streams are complex and slow as they use multiple hash functions. In this paper, we propose an approach for filtering duplicate data from RFID data streams. The proposed approach is based on modified Bloom Filter and uses only a single hash function. We performed extensive empirical study of the proposed approach and compared it against the Bloom Filter, d-Left Time Bloom Filter, and the Count Bloom Filter approaches. The results show that the proposed approach outperforms the baseline approaches in terms of false positive rate, execution time, and true positive rate.

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“…Massawe et al . [ 30 ] proposed an adaptive sliding window based approach called Window Sub-Range Transition Detection (WSTD), where a binomial sampling concept was used to calculate the appropriate window size and π-estimator to estimate the number of tags as proposed by SMURF. WSTD then used the comparison of the two window sub-range observations or estimated tag counts and some rules to detect when transition occurred within the window and then adjusted the window size appropriately.…”

Section: Literature Reviewmentioning

confidence: 99%

Abnormal Condition Monitoring of Workpieces Based on RFID for Wisdom Manufacturing Workshops

Zhang

Yao

Zhang

2015

Sensors

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Radio Frequency Identification (RFID) technology has been widely used in many fields. However, previous studies have mainly focused on product life cycle tracking, and there are few studies on real-time status monitoring of workpieces in manufacturing workshops. In this paper, a wisdom manufacturing model is introduced, a sensing-aware environment for a wisdom manufacturing workshop is constructed, and RFID event models are defined. A synthetic data cleaning method is applied to clean the raw RFID data. The Complex Event Processing (CEP) technology is adopted to monitor abnormal conditions of workpieces in real time. The RFID data cleaning method and data mining technology are examined by simulation and physical experiments. The results show that the synthetic data cleaning method preprocesses data well. The CEP based on the Rifidi® Edge Server technology completed abnormal condition monitoring of workpieces in real time. This paper reveals the importance of RFID spatial and temporal data analysis in real-time status monitoring of workpieces in wisdom manufacturing workshops.

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Reducing False Negative Reads in RFID Data Streams Using an Adaptive Sliding-Window Approach

Cited by 10 publications

References 20 publications

False Positive RFID Detection Using Classification Models

False Positive RFID Detection Using Classification Models

Filtering Redundant Data from RFID Data Streams

Abnormal Condition Monitoring of Workpieces Based on RFID for Wisdom Manufacturing Workshops

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