RFID data reliability optimiser based on two dimensions bloom filter

Radio frequency identification (RFID) is a flexible deployment technology that has been adopted in many applications especially in supply chain management. RFID system used radio waves to perform wireless interaction to detect and read data from the tagged object. However, RFID data streams contain a lot of false positive and duplicate readings. Both types of readings need to be removes to ensure reliability of information produced from the data streams. In this paper, a single approach, which based on Bloom filter was proposed to remove both dirty data from the RFID data streams. The noise and duplicate data filtering algorithm was constructed based on bloom filter. There are two bloom filters in one algorithm where each filter holds function either to remove noise data and to recognize data as correct reading from duplicate data reading. Experimental results show that our proposed approach outperformed other existing approaches in terms of data reliability

RFID Data Reliability Optimiser Based on Two Dimensions Bloom Filter

Radio frequency identification (RFID) is a flexible deployment technology that has been adopted in many applications especially in supply chain management. RFID system used radio waves to perform wireless interaction to detect and read data from the tagged object. However, RFID data streams contain a lot of false positive and duplicate readings. Both types of readings need to be removes to ensure reliability of information produced from the data streams. In this paper, a single approach, which based on Bloom filter was proposed to remove both dirty data from the RFID data streams. The noise and duplicate data filtering algorithm was constructed based on bloom filter. There are two bloom filters in one algorithm where each filter holds function either to remove noise data and to recognize data as correct reading from duplicate data reading. Experimental results show that our proposed approach outperformed other existing approaches in terms of data reliability

Evaluation of Anonymized ONS Queries

Electronic Product Code (EPC) is the basis of a pervasive infrastructure for the automatic identification of objects on supply chain applications (e.g., pharmaceutical or military applications). This infrastructure relies on the use of the (1) Radio Frequency Identification (RFID) technology to tag objects in motion and (2) distributed services providing information about objects via the Internet. A lookup service, called the Object Name Service (ONS) and based on the use of the Domain Name System (DNS), can be publicly accessed by EPC applications looking for information associated with tagged objects. Privacy issues may affect corporate infrastructures based on EPC technologies if their lookup service is not properly protected. A possible solution to mitigate these issues is the use of online anonymity. We present an evaluation experiment that compares the of use of Tor (The second generation Onion Router) on a global ONS/DNS setup, with respect to benefits, limitations, and latency.Comment: 14 page

A framework for distributed managing uncertain data in RFID traceability networks

The ability to track and trace individual items, especially through large-scale and distributed networks, is the key to realizing many important business applications such as supply chain management, asset tracking, and counterfeit detection. Networked RFID (radio frequency identification), which uses the Internet to connect otherwise isolated RFID systems and software, is an emerging technology to support traceability applications. Despite its promising benefits, there remains many challenges to be overcome before these benefits can be realized. One significant challenge centers around dealing with uncertainty of raw RFID data. In this paper, we propose a novel framework to effectively manage the uncertainty of RFID data in large scale traceability networks. The framework consists of a global object tracking model and a local RFID data cleaning model. In particular, we propose a Markov-based model for tracking objects globally and a particle filter based approach for processing noisy, low-level RFID data locally. Our implementation validates the proposed approach and the experimental results show its effectiveness.Jiangang Ma, Quan Z. Sheng, Damith Ranasinghe, Jen Min Chuah and Yanbo W

Pembelajaran berasaskan aplikasi iedutech berdasarkan gaya pembelajaran visual dalam kalangan pelajar Pendidikan Teknikal Dan Vokasional (PTV)

Pada asas setiap individu memiliki pelbagai gaya pembelajran semasa belajar. Jika pelajar dapat mengetahui gaya pembelajaran yang sesuai maka pelajar memperoleh keselesaan pembelajaran dan mengurangkan konflik yang timbul akibat pembelajaran. Gaya pembelajaran fleming VARK merupakan salah satu gaya pembelajaran yang meliputi secara Visual, Auditori, Baca atau Tulis dan Kinestetik dalam proses Pengajaran dan Pembelajaran (PdP). Kajian ini bertujuan untuk mengkaji tahap kebergunaan pembelajaran pelajar berasaskan aplikasi Iedutech berdasarkan gaya pembelajaran visual. Sampel kajian yang dijalankan adalah melibatkan seramai 32 orang pelajar yang mengambil subjek Teknologi Maklumat dalam Pendidikan di Fakulti Pendidikan Teknikal dan Vokasional (FPTV), Universiti Tun Hussein Onn Malaysia (UTHM). Kajian ini menggunakan kajian berbentuk Reka Bentuk Penyelidikan dan Pembangunan. Terdapat dua (2) jenis instrumen yang digunakan dalam kajian ini iaitu set soal selidik dan ujian pencapaian pra-pos. Perisian SPSS (Statistics Package for Social Science Version 22.0 for Windows) telah digunakan dalam menganalisis data yang diperolehi. Analisis data yang dijalankan adalah menggunakan skor min, kekerapan dan peratusan melalui tiga (3) aspek iaitu aspek isi kandungan, aspek interaksi dan aspek persembahan. Manakala Ujian-T (Paired T Test) digunakan bagi menilai pencapaian setiap pelajar. Dapatan kajian bagi tahap kebergunaan aplikasi Iedutech adalah nilai signifikan 0.000 (<0.5). Hal ini menunjukkan bahawa wujudnya perbezaan min yang signifikan di antara markah ujian pelajar dimana markah ujian pra lebih rendah daripada markah ujian pos pelajar. Secara kesimpulannya, aplikasi Iedutech dapat memberi manfaat dan kebaikan kepada pelajar yang dominan terhadap gaya pembelajaran secara visual dan secara tidak langsung dapat menigkatkan pencapaian pelajar dalam pembelajaran. Pengkaji juga berharap agar aplikasi Iedutech ini boleh dikembangkan lagi oleh pengkaji-pengkaji akan datang mengikut kesesuaian semasa

An approach to filtering RFID data streams

RFID is gaining significant thrust as the preferred choice of automatic identification and data collection system. However, there are various data processing and management problems such as missed readings and duplicate readings which hinder wide scale adoption of RFID systems. To this end we propose an approach that filters the captured data including both noise removal and duplicate elimination. Experimental results demonstrate that the proposed approach improves missed data restoration process when compared with the existing method.<br /

An Approach for Removing Redundant Data from RFID Data Streams

Radio frequency identification (RFID) systems are emerging as the primary object identification mechanism, especially in supply chain management. However, RFID naturally generates a large amount of duplicate readings. Removing these duplicates from the RFID data stream is paramount as it does not contribute new information to the system and wastes system resources. Existing approaches to deal with this problem cannot fulfill the real time demands to process the massive RFID data stream. We propose a data filtering approach that efficiently detects and removes duplicate readings from RFID data streams. Experimental results show that the proposed approach offers a significant improvement as compared to the existing approaches

Effective Aggregation and Querying of Probabilistic RFID Data in a Location Tracking Context

RFID applications usually rely on RFID deployments to manage high-level events such as tracking the location that products visit for supply-chain management, localizing intruders for alerting services, and so on. However, transforming low-level streams into high-level events poses a number of challenges. In this paper, we deal with the well known issues of data redundancy and data-information mismatch: we propose an on-line summarization mechanism that is able to provide small space representation for massive RFID probabilistic data streams while preserving the meaningfulness of the information. We also show that common information needs, i.e. detecting complex events meaningful to applications, can be effectively answered by executing temporal probabilistic SQL queries directly on the summarized data. All the techniques presented in this paper are implemented in a complete framework and successfully evaluated in real-world location tracking scenarios