Development of Neuro Fuzzy Algorithm for Load Sensor

Abstract -This paper presents the development of load sensor by using neuro-fuzzy algorithm. Neuro-fuzzy system combines the learning capabilities of neural networks and control capabilities of fuzzy logic system. It is two inputs and one output model, inputs and output are taken from the optical fiber sensor. Load and displacement are taken as inputs for load sensor and voltage is taken as output. The model is simulated using ANFIS Edit GUI Toolbox and simulation results are shown in this paper Index Terms-load sensor, neuro-fuzzy, Optical fiber sensor

Smart Query Answering for Marine Sensor Data

We review existing query answering systems for sensor data. We then propose an extended query answering approach termed smart query, specifically for marine sensor data. The smart query answering system integrates pattern queries and continuous queries. The proposed smart query system considers both streaming data and historical data from marine sensor networks. The smart query also uses query relaxation technique and semantics from domain knowledge as a recommender system. The proposed smart query benefits in building data and information systems for marine sensor networks

A Survey of Model-based Sensor Data Acquisition and Management

In recent years, due to the proliferation of sensor networks, there has been a genuine need of researching techniques for sensor data acquisition and management. To this end, a large number of techniques have emerged that advocate model-based sensor data acquisition and management. These techniques use mathematical models for performing various, day-to-day tasks involved in managing sensor data. In this chapter, we survey the state-of-the-art techniques for model-based sensor data acquisition and management. We start by discussing the techniques for acquiring sensor data. We, then, discuss the application of models in sensor data cleaning; followed by a discussion on model-based methods for querying sensor data. Lastly, we survey model-based methods proposed for data compression and synopsis generation

Wireless sensor data processing for on-site emergency response

This thesis is concerned with the problem of processing data from Wireless Sensor Networks (WSNs) to meet the requirements of emergency responders (e.g. Fire and Rescue Services). A WSN typically consists of spatially distributed sensor nodes to cooperatively monitor the physical or environmental conditions. Sensor data about the physical or environmental conditions can then be used as part of the input to predict, detect, and monitor emergencies. Although WSNs have demonstrated their great potential in facilitating Emergency Response, sensor data cannot be interpreted directly due to its large volume, noise, and redundancy. In addition, emergency responders are not interested in raw data, they are interested in the meaning it conveys. This thesis presents research on processing and combining data from multiple types of sensors, and combining sensor data with other relevant data, for the purpose of obtaining data of greater quality and information of greater relevance to emergency responders. The current theory and practice in Emergency Response and the existing technology aids were reviewed to identify the requirements from both application and technology perspectives (Chapter 2). The detailed process of information extraction from sensor data and sensor data fusion techniques were reviewed to identify what constitutes suitable sensor data fusion techniques and challenges presented in sensor data processing (Chapter 3). A study of Incident Commanders’ requirements utilised a goal-driven task analysis method to identify gaps in current means of obtaining relevant information during response to fire emergencies and a list of opportunities for WSN technology to fill those gaps (Chapter 4). A high-level Emergency Information Management System Architecture was proposed, including the main components that are needed, the interaction between components, and system function specification at different incident stages (Chapter 5). A set of state-awareness rules was proposed, and integrated with Kalman Filter to improve the performance of filtering. The proposed data pre-processing approach achieved both improved outlier removal and quick detection of real events (Chapter 6). A data storage mechanism was proposed to support timely response to queries regardless of the increase in volume of data (Chapter 7). What can be considered as “meaning” (e.g. events) for emergency responders were identified and a generic emergency event detection model was proposed to identify patterns presenting in sensor data and associate patterns with events (Chapter 8). In conclusion, the added benefits that the technical work can provide to the current Emergency Response is discussed and specific contributions and future work are highlighted (Chapter 9)

Statistical Models for Querying and Managing Time-Series Data

In recent years we are experiencing a dramatic increase in the amount of available time-series data. Primary sources of time-series data are sensor networks, medical monitoring, financial applications, news feeds and social networking applications. Availability of large amount of time-series data calls for scalable data management techniques that enable efficient querying and analysis of such data in real-time and archival settings. Often the time-series data generated from sensors (environmental, RFID, GPS, etc.), are imprecise and uncertain in nature. Thus, it is necessary to characterize this uncertainty for producing clean answers. In this thesis we propose methods that address these important issues pertaining to time-series data. Particularly, this thesis is centered around the following three topics: Computing Statistical Measures on Large Time-Series Datasets. Computing statistical measures for large databases of time series is a fundamental primitive for querying and mining time-series data [31, 81, 97, 111, 132, 137]. This primitive is gaining importance with the increasing number and rapid growth of time-series databases. In Chapter 3, we introduce the Affinity framework for efficient computation of statistical measures by exploiting the concept of affine relationships [113, 114]. Affine relationships can be used to infer a large number of statistical measures for time series, from other related time series, instead of computing them directly; thus, reducing the overall computational cost significantly. Moreover, the Affinity framework proposes an unified approach for computing several statistical measures at once. Creating Probabilistic Databases from Imprecise Data. A large amount of time-series data produced in the real-world has an inherent element of uncertainty, arising due to the various sources of imprecision affecting its sources (like, sensor data, GPS trajectories, environmental monitoring data, etc.). The primary sources of imprecision in such data are: imprecise sensors, limited communication bandwidth, sensor failures, etc. Recently there has been an exponential rise in the number of such imprecise sensors, which has led to an explosion of imprecise data. Standard database techniques cannot be used to provide clean and consistent answers in such scenarios. Therefore, probabilistic databases that factor-in the inherent uncertainty and produce clean answers are required. An important assumption i while using probabilistic databases is that each data point has a probability distribution associated with it. This is not true in practice — the distributions are absent. As a solution to this fundamental limitation, in Chapter 4 we propose methods for inferring such probability distributions and using them for efficiently creating probabilistic databases [116]. Managing Participatory Sensing Data. Community-driven participatory sensing is a rapidly evolving paradigm in mobile geo-sensor networks. Here, sensors of various sorts (e.g., multi-sensor units monitoring air quality, cell phones, thermal watches, thermometers in vehicles, etc.) are carried by the community (public vehicles, private vehicles, or individuals) during their daily activities, collecting various types of data about their surrounding. Data generated by these devices is in large quantity, and geographically and temporally skewed. Therefore, it is important that systems designed for managing such data should be aware of these unique data characteristics. In Chapter 5, we propose the ConDense (Community-driven Sensing of the Environment) framework for managing and querying community-sensed data [5, 19, 115]. ConDense exploits spatial smoothness of environmental parameters (like, ambient pollution [5] or radiation [2]) to construct statistical models of the data. Since the number of constructed models is significantly smaller than the original data, we show that using our approach leads to dramatic increase in query processing efficiency [19, 115] and significantly reduces memory usage

Wireless sensor data processing for on-site emergency response

This thesis is concerned with the problem of processing data from Wireless Sensor Networks (WSNs) to meet the requirements of emergency responders (e.g. Fire and Rescue Services). A WSN typically consists of spatially distributed sensor nodes to cooperatively monitor the physical or environmental conditions. Sensor data about the physical or environmental conditions can then be used as part of the input to predict, detect, and monitor emergencies. Although WSNs have demonstrated their great potential in facilitating Emergency Response, sensor data cannot be interpreted directly due to its large volume, noise, and redundancy. In addition, emergency responders are not interested in raw data, they are interested in the meaning it conveys. This thesis presents research on processing and combining data from multiple types of sensors, and combining sensor data with other relevant data, for the purpose of obtaining data of greater quality and information of greater relevance to emergency responders. The current theory and practice in Emergency Response and the existing technology aids were reviewed to identify the requirements from both application and technology perspectives (Chapter 2). The detailed process of information extraction from sensor data and sensor data fusion techniques were reviewed to identify what constitutes suitable sensor data fusion techniques and challenges presented in sensor data processing (Chapter 3). A study of Incident Commanders' requirements utilised a goal-driven task analysis method to identify gaps in current means of obtaining relevant information during response to fire emergencies and a list of opportunities for WSN technology to fill those gaps (Chapter 4). A high-level Emergency Information Management System Architecture was proposed, including the main components that are needed, the interaction between components, and system function specification at different incident stages (Chapter 5). A set of state-awareness rules was proposed, and integrated with Kalman Filter to improve the performance of filtering. The proposed data pre-processing approach achieved both improved outlier removal and quick detection of real events (Chapter 6). A data storage mechanism was proposed to support timely response to queries regardless of the increase in volume of data (Chapter 7). What can be considered as “meaning” (e.g. events) for emergency responders were identified and a generic emergency event detection model was proposed to identify patterns presenting in sensor data and associate patterns with events (Chapter 8). In conclusion, the added benefits that the technical work can provide to the current Emergency Response is discussed and specific contributions and future work are highlighted (Chapter 9).EThOS - Electronic Theses Online ServiceGBUnited Kingdo