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

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

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 /

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

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-Based Indoor Spatial Query Evaluation with Bayesian Filtering Techniques

People spend a significant amount of time in indoor spaces (e.g., office buildings, subway systems, etc.) in their daily lives. Therefore, it is important to develop efficient indoor spatial query algorithms for supporting various location-based applications. However, indoor spaces differ from outdoor spaces because users have to follow the indoor floor plan for their movements. In addition, positioning in indoor environments is mainly based on sensing devices (e.g., RFID readers) rather than GPS devices. Consequently, we cannot apply existing spatial query evaluation techniques devised for outdoor environments for this new challenge. Because Bayesian filtering techniques can be employed to estimate the state of a system that changes over time using a sequence of noisy measurements made on the system, in this research, we propose the Bayesian filtering-based location inference methods as the basis for evaluating indoor spatial queries with noisy RFID raw data. Furthermore, two novel models, indoor walking graph model and anchor point indexing model, are created for tracking object locations in indoor environments. Based on the inference method and tracking models, we develop innovative indoor range and k nearest neighbor (kNN) query algorithms. We validate our solution through use of both synthetic data and real-world data. Our experimental results show that the proposed algorithms can evaluate indoor spatial queries effectively and efficiently. We open-source the code, data, and floor plan at https://github.com/DataScienceLab18/IndoorToolKit

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

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