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

Manufacturing Process Optimization Using Edge Analytics

Most manufacturing plants contain some amount of time series sensor data – streams of values and time stamps. This data, however, isn’t useful with most types of analytics or machine learning for the purpose of process optimization. This thesis presents a novel and innovative solution to the problem using a software stack leveraging the Predix Complex Event Processing Engine (Edge Analytics) to condition the data, combined with RFID for serialization. Each step in the formation of the solution is documented, from connecting equipment to analyzing and ingesting data produced by the edge analytic. This solution was developed and piloted at the GE Grid Solutions plant in Clearwater, FL

Determination of Rule Patterns in Complex Event Processing Using Machine Learning Techniques

AbstractComplex Event Processing (CEP) is a novel and promising methodology that enables the real-time analysis of stream event data. The main purpose of CEP is detection of the complex event patterns from the atomic and semantically low-level events such as sensor, log, or RFID data. Determination of the rule patterns for matching these simple events based on the temporal, semantic, or spatial correlations is the central task of CEP systems. In the current design of the CEP systems, experts provide event rule patterns. Having reached maturity, the Big Data Systems and Internet of Things (IoT) technology require the implementation of advanced machine learning approaches for automation in the CEP domain. The goal of this research is proposing a machine learning model to replace the manual identification of rule patterns. After a pre-processing stage (dealing with missing values, data outliers, etc.), various rule-based machine learning approaches were applied to detect complex events. Promising results with high preciseness were obtained. A comparative analysis of the performance of classifiers is discussed

Controlling in-plant logistics by deploying RFID system in the item-level manufacturing : a case study

RFID systems are being used in the industries to follow the manufacturing environment by tagging objects where keeping track has a value. The recent research about RFID systems mainly focused on monitoring assets to control shop floor operations in real time. This case study has focused on deployment and improvement of an RFID system with a purpose of choose optimum system installation. The investigation of the production area, collecting item flow data and developing the relational database. The data that has been attained from the system can be used to control internal plant logistics and provide support in order to improve shop floor operations. This kind of RFID system used in industry can be very useful in order to increase the speed of production and regulate the work flow

A user profile for information filtering using RFID-SIM card in pervasive network

The appearance of new technologies allows new data processing techniques. Thus, many new data processing techniques make difficult to user to find pertinent information in suitable time, unless knowing what accurately is in search of, where and how getting it. This paper proposes a pervasive network based information filtering system that integrates user profile such as identity, preference and other important data. User profile is embarked in a RFID-SIM card in order to guarantee its privacy, flexibility, mobility and confidentiality. The overall system objectives are privacy, security and providing pertinent information to the user according to his profile at anytime, anywhere, and in any form. The design and implementation of the system is also presented