Controlling in-plant logistics by deploying RFID system in the item-level manufacturing : a case study

RFID systems are being used in the industries to follow the manufacturing environment by tagging objects where keeping track has a value. The recent research about RFID systems mainly focused on monitoring assets to control shop floor operations in real time. This case study has focused on deployment and improvement of an RFID system with a purpose of choose optimum system installation. The investigation of the production area, collecting item flow data and developing the relational database. The data that has been attained from the system can be used to control internal plant logistics and provide support in order to improve shop floor operations. This kind of RFID system used in industry can be very useful in order to increase the speed of production and regulate the work flow

Online Risk Prediction for Indoor Moving Objects

Technologies such as RFID and Bluetooth have received considerable attention for tracking indoor moving objects. In a time-critical indoor tracking scenario such as airport baggage handling, a bag has to move through a sequence of locations until it is loaded into the aircraft. Inefficiency or inaccuracy at any step can make the bag risky, i.e. the bag may be delayed at the airport or sent to a wrong airport. In this paper, we propose a novel probabilistic approach for predicting the risk of an indoor moving object in real-time. We propose a probabilistic flow graph (PFG) and an aggregated probabilistic flow graph (APFG) that capture the historical object transitions and the durations of the transitions. In the graphs, the probabilistic information is stored in a set of histograms. Then we use the flow graphs for obtaining a risk score of an online object and use it for predicting its riskiness. The paper reports a comprehensive experimental study with multiple synthetic data sets and a real baggage tracking data set. The experimental results show that the proposed method can identify the risky objects very accurately when they approach the bottleneck locations on their paths and can significantly reduce the operation cost.SCOPUS: cp.pinfo:eu-repo/semantics/publishe

DATA MINING FOR INTERNET OF THINGS

The Internet of Things is an emerging subject in right now. It has a huge load of importance in development, business, social and planning fields. This advancement gives an easier technique for correspondence of contraptions with the inconsequential relationship of human. It seems like mission hard to interface everything on the earth together through web; anyway Internet of Things will radically change us inside a sensible time period, by making many "unfathomable" possible. To many, the tremendous data made or got by IoT are seen as having significantly accommodating and critical information. Data mining will no vulnerability accept an essential part in making such a system adequately splendid to offer more worthwhile sorts of help and conditions. This paper begins with a discussion of the IoT. IoT misuses progress in association interconnections and handling ability to propose new techniques. Data mining procedures are used to manage the huge data made by the Internet of Things. Distinctive data mining models have been proposed for Internet of Things. We are presenting a novel data burrowing model for Internet of Thing which considers ordinary IoT challenges

Privacy Protection on RFID Data Publishing

Radio Frequency IDentification (RFID) is a technology of automatic object identification. Retailers and manufacturers have created compelling business cases for deploying RFID in their supply chains. Yet, the uniquely identifiable objects pose a privacy threat to individuals. In this paper, we study the privacy threats caused by publishing RFID data. Even if the explicit identifying information, such as name and social security number, has been removed from the published RFID data, an adversary may identify a target victim's record or infer her sensitive value by matching a priori known visited locations and time. RFID data by its nature is high-dimensional and sparse, so applying traditional k -anonymity to RFID data suffers from the curse of high-dimensionality, and results in poor information usefulness. We define a new privacy model and develop an anonymization algorithm to accommodate special challenges on RFID data. Then, we evaluate its effectiveness on synthetic data sets

Preserving Privacy and Utility in RFID Data Publishing

Radio Frequency IDentification (RFID) is a technology that helps machines identify objects remotely. The RFID technology has been extensively used in many domains, such as mass transportation and healthcare management systems. The collected RFID data capture the detailed movement information of the tagged objects, offering tremendous opportunities for mining useful knowledge. Yet, publishing the raw RFID data for data mining would reveal the specific locations, time, and some other potentially sensitive information of the tagged objects or individuals. In this paper, we study the privacy threats in RFID data publishing and show that traditional anonymization methods are not applicable for RFID data due to its challenging properties: high-dimensional, sparse, and sequential. Our primary contributions are (1) to adopt a new privacy model called LKC-privacy that overcomes these challenges, and (2) to develop an efficient anonymization algorithm to achieve LKC-privacy while preserving the information utility for data mining

Preserving Data Privacy and Information Usefulness for RFID Data Publishing

Radio-Frequency IDentification (RFID) is an emerging technology that employs radio waves to identify, locate, and track objects. RFID technology has wide applications in many areas including manufacturing, healthcare, and transportation. However, the manipulation of uniquely identifiable objects gives rise to privacy concerns for the individuals carrying these objects. Most previous works on privacy-preserving RFID technology, such as EPC re-encryption and killing tags, have focused on the threats caused by the physical RFID tags in the data collection phase, but these techniques cannot address privacy threats in the data publishing phase, when a large volume of RFID data is released to a third party. We explore the privacy threats in RFID data publishing. We illustrate that even though explicit identifying information, such as phone numbers and SSNs, is removed from the published RFID data, an attacker may still be able to perform privacy attacks by utilizing background knowledge about a target victim's visited locations and timestamps. Privacy attacks include identifying a target victim's record and/or inferring their sensitive information. High-dimensionality is an inherent characteristic in RFID data; therefore, applying traditional anonymity models, such as K -anonymity, to RFID data would significantly reduce data utility. We propose a new privacy model, devise an anonymization algorithm to address the special challenges of RFID data, and experimentally evaluate the performance of our method. Experiments suggest that applying our model significantly improves the data utility when compared to applying the traditional K -anonymity model