Complex Event Processing in EPC Sensor Network Middleware for Both RFID and WSN

In an integration system of RFID and wireless sensor network (WSN), RFID is used to identify objects while WSN can provide context environment information of these objects. Thus, it increases system intelligent in pervasive computing. We propose the EPC Sensor Network (ESN) architecture as an integration system of RFID and WSN. This ESN architecture is based on EPCglobal architecture, the de facto international standard for RFID. The core of ESN is the middleware part which is also implemented in our work. In this paper, complex event processing (CEP) technology is used in our ESN middleware which can handle large volume of events from distributed RFID and sensor readers in real time. Through filtering, grouping, aggregating and constructing complex event, ESN middleware provides a more meaningful report for the clients and increases system automation

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

Using Hybrid Query Tree to Cope with Capture Effect in RFID Tag Identification

Tag collision is one of the important issues in RFID systems. Many algorithms were proposed to address this issue. One of these algorithms is Query Tree (QT) which is an effective method. In addition, RFID suffers from Capture Effect (CE). CE occurs when a reader identifies one tag in the presence of a collision. We consider this as a bad phenomenon for QT, because under CE reader will not identify all of collided tags. Besides, CE is good phenomenon for some algorithms like Dynamic Framed Slotted Aloha (DFSA), because it can identify one tag even in collision slots. So we combine QT and DFSA to improve the QT performance, then we evaluate our proposed algorithm, called Hybrid QT, to show that it outperforms other similar algorithm

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

Capturing Data Uncertainty in High-Volume Stream Processing

We present the design and development of a data stream system that captures data uncertainty from data collection to query processing to final result generation. Our system focuses on data that is naturally modeled as continuous random variables. For such data, our system employs an approach grounded in probability and statistical theory to capture data uncertainty and integrates this approach into high-volume stream processing. The first component of our system captures uncertainty of raw data streams from sensing devices. Since such raw streams can be highly noisy and may not carry sufficient information for query processing, our system employs probabilistic models of the data generation process and stream-speed inference to transform raw data into a desired format with an uncertainty metric. The second component captures uncertainty as data propagates through query operators. To efficiently quantify result uncertainty of a query operator, we explore a variety of techniques based on probability and statistical theory to compute the result distribution at stream speed. We are currently working with a group of scientists to evaluate our system using traces collected from the domains of (and eventually in the real systems for) hazardous weather monitoring and object tracking and monitoring.Comment: CIDR 200

Distributed Inference and Query Processing for RFID Tracking and Monitoring

In this paper, we present the design of a scalable, distributed stream processing system for RFID tracking and monitoring. Since RFID data lacks containment and location information that is key to query processing, we propose to combine location and containment inference with stream query processing in a single architecture, with inference as an enabling mechanism for high-level query processing. We further consider challenges in instantiating such a system in large distributed settings and design techniques for distributed inference and query processing. Our experimental results, using both real-world data and large synthetic traces, demonstrate the accuracy, efficiency, and scalability of our proposed techniques.Comment: VLDB201

Enhancing RFID data quality and reliability

This thesis addressed the problem of data quality, reliability and energy consumption of networked Radio Frequency Identification systems for business intelligence applications decision making processes. The outcome of the research substantially improved the accuracy and reliability of RFID generated data as well as energy depletion thus prolonging RFID system lifetime

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts