Improved Data Granularity Management Through a Generalized Model for Sensor Data and Data Mining Outputs in Telemonitoring Applications

2015 ◽  
pp. 166-177
Author(s):  
Pierre Maret ◽  
Shin’ishi Warisawa ◽  
Fabrice Muhlenbach ◽  
Guillaume Lopez ◽  
Ichiro Yamada
Keyword(s):  
Data Mining ◽  
Sensor Data ◽  
Generalized Model ◽  
Data Granularity

Towards Behaviour Recognition with Unlabelled Sensor Data

2013 ◽  
pp. 86-110
Author(s):  
Sook-Ling Chua ◽  
Stephen Marsland ◽  
Hans W. Guesgen
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Inverse Problem ◽  
Sensor Data ◽  
Training Set ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data ◽  
Symbolic Approach ◽  
Behaviour Recognition

The problem of behaviour recognition based on data from sensors is essentially an inverse problem: given a set of sensor observations, identify the sequence of behaviours that gave rise to them. In a smart home, the behaviours are likely to be the standard human behaviours of living, and the observations will depend upon the sensors that the house is equipped with. There are two main approaches to identifying behaviours from the sensor stream. One is to use a symbolic approach, which explicitly models the recognition process. Another is to use a sub-symbolic approach to behaviour recognition, which is the focus in this chapter, using data mining and machine learning methods. While there have been many machine learning methods of identifying behaviours from the sensor stream, they have generally relied upon a labelled dataset, where a person has manually identified their behaviour at each time. This is particularly tedious to do, resulting in relatively small datasets, and is also prone to significant errors as people do not pinpoint the end of one behaviour and commencement of the next correctly. In this chapter, the authors consider methods to deal with unlabelled sensor data for behaviour recognition, and investigate their use. They then consider whether they are best used in isolation, or should be used as preprocessing to provide a training set for a supervised method.

scholarly journals A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4536 ◽  
Author(s):  
Yan Zhong ◽  
Simon Fong ◽  
Shimin Hu ◽  
Raymond Wong ◽  
Weiwei Lin
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Sensor Data ◽  
Detection Methods ◽  
The Internet ◽  
Quality Of Data ◽  
Data Mining Algorithms ◽  
Sensing Applications ◽  
The Internet Of Things

The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such sensor data are massive, most of the data contents are identical and repetitive; for example, human traffic in a park at night. Most of the traditional classification algorithms were originally formulated decades ago, and they were not designed to handle such sensor data effectively. Hence, the performance of the learned model is often poor because of the small granularity in classification and the sporadic patterns in the data. To improve the quality of data mining from the IoT data, a new pre-processing methodology based on subspace similarity detection is proposed. Our method can be well integrated with traditional data mining algorithms and anomaly detection methods. The pre-processing method is flexible for handling similar kinds of sensor data that are sporadic in nature that exist in many ambient sensing applications. The proposed methodology is evaluated by extensive experiment with a collection of classical data mining models. An improvement over the precision rate is shown by using the proposed method.

scholarly journals Ship Spatiotemporal Key Feature Point Online Extraction Based on AIS Multi-Sensor Data Using an Improved Sliding Window Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2706 ◽  
Author(s):  
Miao Gao ◽  
Guo-You Shi
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Behavior Pattern ◽  
Sliding Window ◽  
Feature Point ◽  
Sensor Data ◽  
Identification System ◽  
Trajectory Data ◽  
Ship Navigation ◽  
Online Extraction

Large volumes of automatic identification system (AIS) data provide new ideas and methods for ship data mining and navigation behavior pattern analysis. However, large volumes of big data have low unit values, resulting in the need for large-scale computing, storage, and display. Learning efficiency is low and learning direction is blind and untargeted. Therefore, key feature point (KFP) extraction from the ship trajectory plays an important role in fields such as ship navigation behavior analysis and big data mining. In this paper, we propose a ship spatiotemporal KFP online extraction algorithm that is applied to AIS trajectory data. The sliding window algorithm is modified for application to ship navigation angle deviation, position deviation, and the spatiotemporal characteristics of AIS data. Next, in order to facilitate the subsequent use of the algorithm, a recommended threshold range for the corresponding two parameters is discussed. Finally, the performance of the proposed method is compared with that of the Douglas–Peucker (DP) algorithm to assess its feature extraction accuracy and operational efficiency. The results show that the proposed improved sliding window algorithm can be applied to rapidly and easily extract the KFPs from AIS trajectory data. This ability provides significant benefits for ship traffic flow and navigational behavior learning.

scholarly journals Mining Wireless Sensor Network Data: an adaptive approach based on artificial neuralnetworks algorithm

2011 ◽  
pp. 129-135
Author(s):  
Lambodar Jena ◽  
Ramakrushna Swain ◽  
N.K. kamila
Keyword(s):  
Data Mining ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Dimensionality Reduction ◽  
Sensor Network ◽  
Power Supply ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Limited Power ◽  
Limited Power Supply

This paper proposes a layered modular architecture to adaptively perform data mining tasks in large sensor networks. The architecture consists in a lower layer which performs data aggregation in a modular fashion and in an upper layer which employs an adaptive local learning technique to extract a prediction model from the aggregated information. The rationale of the approach is that a modular aggregation of sensor data can serve jointly two purposes: first, the organization of sensors in clusters, then reducing the communication effort, second, the dimensionality reduction of the data mining task, then improving the accuracy of the sensing task . Here we show that some of the algorithms developed within the artificial neuralnetworks tradition can be easily adopted to wireless sensor-network platforms and will meet several aspects of the constraints for data mining in sensor networks like: limited communication bandwidth, limited computing resources, limited power supply, and the need for fault-tolerance. The analysis of the dimensionality reduction obtained from the outputs of the neural-networks clustering algorithms shows that the communication costs of the proposed approach are significantly smaller, which is an important consideration in sensor-networks due to limited power supply. In this paper we will present two possible implementations of the ART and FuzzyART neuralnetworks algorithms, which are unsupervised learning methods for categorization of the sensory inputs. They are tested on a data obtained from a set of several nodes, equipped with several sensors each.

A Semantic Sensor Web Architecture in the Internet of Things

2019 ◽  
pp. 75-97
Author(s):  
Eliot Bytyçi ◽  
Besmir Sejdiu ◽  
Arten Avdiu ◽  
Lule Ahmedi
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Sensor Data ◽  
Wireless Sensor ◽  
The Internet ◽  
Web Architecture ◽  
Hidden Knowledge ◽  
Semantic Sensor Web ◽  
The Internet Of Things ◽  
The Web

The Internet of Things (IoT) vision is connecting uniquely identifiable devices to the internet, best described through ontologies. Furthermore, new emerging technologies such as wireless sensor networks (WSN) are recognized as essential enabling component of the IoT today. Hence, the interest is to provide linked sensor data through the web either following the semantic web enablement (SWE) standard or the linked data approach. Likewise, a need exists to explore those data for potential hidden knowledge through data mining techniques utilized by a domain ontology. Following that rationale, a new lightweight IoT architecture has been developed. It supports linking sensors, other devices and people via a single web by mean of a device-person-activity (DPA) ontology. The architecture is validated by mean of three rich-in-semantic services: contextual data mining over WSN, semantic WSN web enablement, and linked WSN data. The architecture could be easily extensible to capture semantics of input sensor data from other domains as well.

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