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

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 /

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

Energy Efficient In-network RFID Data Filtering Scheme in Wireless Sensor Networks

RFID (Radio frequency identification) and wireless sensor networks are backbone technologies for pervasive environments. In integration of RFID and WSN, RFID data uses WSN protocols for multi-hop communications. Energy is a critical issue in WSNs; however, RFID data contains a lot of duplication. These duplications can be eliminated at the base station, but unnecessary transmissions of duplicate data within the network still occurs, which consumes nodes’ energy and affects network lifetime. In this paper, we propose an in-network RFID data filtering scheme that efficiently eliminates the duplicate data. For this we use a clustering mechanism where cluster heads eliminate duplicate data and forward filtered data towards the base station. Simulation results prove that our approach saves considerable amounts of energy in terms of communication and computational cost, compared to existing filtering schemes

The Challenges and Issues Facing the Deployment of RFID Technology

Griffith Sciences, School of Information and Communication TechnologyFull Tex

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

RFID data reliability optimizer 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. It provides several features such as to monitor, to identify and to track specific item hidden in a large group of objects in a short range of time. RFID system uses 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 removed to ensure reliability of information produced from the data streams. A small occurrence of false positive can change the whole information, while duplicate readings unnecessarily occupied storage and processing resources. Many approaches have been proposed to remove false positive and duplicate readings, but they are done separately. These readings exist in the same data stream and must be removed using a single mechanism only. In this thesis, an efficient approach based on Bloom filters was proposed to remove both noisy and duplicate data from the RFID data streams. The noise and duplicate filter 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. In order to test the algorithm, synthetic data was generated by using Poisson distribution. The simulation results show that our proposed approach outperformed other existing approaches in terms of data reliability

Batch study on COD and ammonia nitrogen removal using granular activated carbon and cockle shells

Landfills generate leachate that contains elevated concentration of contaminants and is hazardous to human health and the ecosystem. In this study, the mixture of granular activated carbon and cockle shells was investigated for remediation of COD and ammonia from stabilized landfill leachate. All adsorbent media were sieved to a particle size between 2.00 and 3.35 mm. The optimum mixing ratio, shaking speed, shaking time, pH, and dosage were determined. Characterization results show that the leachate had a high concentration of COD (1763 mg/L), ammonia nitrogen (573 mg/L), and BOD5/COD ratio (0.09). The optimum mixing ratio of granular activated carbon and cockle shells was 20:20, shaking speed 150 rpm, pH level 6, shaking time 120 min, and dosage 32 g. The adsorption isotherm analysis reveals that the Langmuir isotherm yielded the best fit to experimental data as compared with the Freundlich isotherm. The media produce encouraging results and can be used as a good and economical adsorbent