Using RFID for anti-theft in a Chinese electrical supply company: A cost-benefit analysis

Khoo, Benjamin; Cheng, Ya

doi:10.1109/wts.2011.5960892

Cited by 48 publications

(28 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1. If condition given by (10) holds true then the corresponding magnitude of theft, theft location, and duration is computed. These attributes are then used to identify priority order for clusters to be examined.…”

Section: Rule-base Formationmentioning

confidence: 99%

Rule‐based classification of energy theft and anomalies in consumers load demand profile

2019

View full text Add to dashboard Cite

The invent of advanced metering infrastructure (AMI) opens the door for a comprehensive analysis of consumers consumption patterns including energy theft studies, which were not possible beforehand. This study proposes a fraud detection methodology using data mining techniques such as hierarchical clustering and decision tree classification to identify abnormalities in consumer consumption patterns and further classify the abnormality type into the anomaly, fraud, high or low power consumption based on rule-based learning. The proposed algorithm uses real-time dataset of Nana Kajaliyala village, Gujarat, India. The focus has been on generalizing the algorithm for varied practical cases to make it adaptive towards nonmalicious changes in consumer profile. Simultaneously, this study proposes a novel validation technique used for validation, which utilizes predicted profiles to ensure accurate bifurcation between anomaly and theft targets. The result exhibits high detection ratio and low false-positive ratio due to the application of appropriate validation block. The proposed methodology is also investigated from point of view of privacy preservation and is found to be relatively secure owing to low-sampling rates, minimal usage of metadata and communication layer. The proposed algorithm has an edge over state-of-the-art theft detection algorithms in detection accuracy and robustness towards outliers.

show abstract

Section: Rule-base Formationmentioning

confidence: 99%

Rule‐based classification of energy theft and anomalies in consumers load demand profile

2019

View full text Add to dashboard Cite

show abstract

“…A simple shoplifting protection system cannot discriminate each product as having a specific ID. However, tags for anti-theft [5]- [7] that can not only distinguish individual products but also transmit the unique data stored in themselves are available at very economical cost. Such tags, which have individual IDs, are called "basic RFID tags".…”

Section: Passive Rfid Tagsmentioning

confidence: 99%

Survey of RFID and Its Application to International Ocean/Air Container Tracking

Katayama

Hayashi

Shimizu

2012

IEICE Trans. Commun.

View full text Add to dashboard Cite

SUMMARY"Internet of Things" (IoT) requires information to be collected from "anything", "anytime", and "anywhere". In order to achieve this, wireless devices are required that have (1) automatic data acquisition capability, (2) small size, (3) long life, and (4) long range communication capability. One way to meet these requirements is to adopt active Radio Frequency Identification (RFID) systems. Active RFID is more advantageous than passive RFID and enables higher data reading performance over longer distances. This paper surveys active RFID systems, the services they currently promise to provide, technical problems common to these services, and the direction in which research should head in the future. It also reports the results of EPCglobal (EPC: Electronic Product Code) pilot tests conducted on global logistics for tracking ocean/air container transportation using active RFID systems for which we developed several new types of active RFID tags. The test results confirm that our active RFID tags have sufficient capability and low power consumption to well support ocean/air transportation and logistics service.

show abstract

“…In recent years, there has been a global trend to optimize the use of electrical networks by automating and transforming all parts of the traditional electrical power grid: from the user's household electrical appliances to the power-generating stations into an interactive and intelligent grid. The need to improve reliability, increase efficiency, enhance (2011)], and RFID [Khoo and Cheng (2011)]. State-based models are characterized by a high DR and low FPR.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Anomalies Detection in AMI System Combining K-means Clustering and Deep Neural Network

Maamar¹,

Benahmed.²

2019

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Recently, the radical digital transformation has deeply affected the traditional electricity grid and transformed it into an intelligent network (smart grid). This mutation is based on the progressive development of advanced technologies: advanced metering infrastructure (AMI) and smart meter which play a crucial role in the development of smart grid. AMI technologies have a promising potential in terms of improvement in energy efficiency, better demand management, and reduction in electricity costs. However the possibility of hacking smart meters and electricity theft is still among the most significant challenges facing electricity companies. In this regard, we propose a hybrid approach to detect anomalies associated with electricity theft in the AMI system, based on a combination of two robust machine learning algorithms; K-means and Deep Neural Network (DNN). K-means unsupervised machine learning algorithm is used to identify groups of customers with similar electricity consumption patterns to understand different types of normal behavior. DNN algorithm is used to build an accurate anomaly detection model capable of detecting changes or anomalies in usage behavior and deciding whether the customer has a normal or malicious consumption behavior. The proposed model is constructed and evaluated based on a real dataset from the Irish Smart Energy Trials. The results show a high performance of the proposed model compared to the models mentioned in the literature.

show abstract

Using RFID for anti-theft in a Chinese electrical supply company: A cost-benefit analysis

Cited by 48 publications

References 7 publications

Rule‐based classification of energy theft and anomalies in consumers load demand profile

Rule‐based classification of energy theft and anomalies in consumers load demand profile

Survey of RFID and Its Application to International Ocean/Air Container Tracking

A Hybrid Model for Anomalies Detection in AMI System Combining K-means Clustering and Deep Neural Network

Contact Info

Product

Resources

About