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About 4,530 papers for “sensor data mining”

Mobile Sensor Data Mining

Lockhart, Jeff, FCRH '13 · Fordham Research Commons (Fordham University) · 2013

At an ever increasing rate, the smartphones and other devices people carry with them in their everyday lives are packed with sensors and processing power. This provides an unprecedented opportunity to apply data mining techniques to people’s activities as they go about their daily lives, without changing their routine. The goal of the Wireless Sensor Data Mining (WISDM) Project is to explore the possibilities of data mining on these powerful mobile platforms.1 Data mining involves extracting knowledge from data using computer algorithms. A major sensor in these devices is the tri-axial accelerometer originally included for screen rotation and advanced gaming. Our work, so far, has focused on using data mining methods on the accelerometer data to identify the activities users are performing (activity recognition) while carrying the phone. Many useful applications can be built if accelerometers can be used to recognize a person’s activity. We have also demonstrated that accelerometer data can be used to uniquely identify and authenticate users. While some previous work has examined sensor-based gait recognition,2-12 our work in this communication differs in that we identify users based on the way they move during multiple activities (i.e., not just walking) using only commercially available smartphones, which are carried in the user’s pocket.

Cited by 4Open access

Dynamic Data Mining of Sensor Data

Yunfei Yin, Lianjie Long, Xiyu Deng · IEEE Access · 2020

The research of data mining has aroused widespread concern in academia and industry. However, an important mark of the Internet of Things era is that sensor data replaces artificially compiled data. How to extract valuable knowledge and patterns from a large amount of data generated by sensors is a meaningful research topic. This paper proposes a dynamic data mining framework for processing sensor data. A sensor data mining model which can be used in the process of dynamic change is constructed. In this model, different sensor network environments are considered as different physical systems. The physical system and its parameters are trained by collecting and mining historical changes in sensor data; the associations between different sensor network environments are discovered by mining the associations between the parameters of different physical systems. In our limited experimental environment, the physical quantities considered included transmission distance, transmission delay, sensor data, data changes, and so on. Experiments were carried out on the designated experimental platform, and the results showed that the model could mine the dynamic data and find stable patterns. Through the analysis of the experimental results, it was found that the model had reference value for the dynamic mining of sensor data, and was expected to construct new methods for industrial big data analysis.

Cited by 16Open access

Adaptive data mining for sensor network data

Gianluca Bontempi, Yann‐Aël Le Borgne, Mehdi Moussaïd · Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) · 2005

info:eu-repo/semantics/published

Cited by 0

Trajectory Mining Using Uncertain Sensor Data

Muhammad Muzammal, Moneeb Gohar, Arif Ur Rahman et al. · IEEE Access · 2017

Trajectory mining is an interesting data mining problem. Traditionally, it is either assumed that the time-ordered location data recorded as trajectories are either deterministic or that the uncertainty, e.g., due to equipment or technological limitations, is removed by incorporating some pre-processing routines. Thus, the trajectories are processed as deterministic paths of mobile object location data. However, it is important to understand that the transformation from uncertain to deterministic trajectory data may result in the loss of information about the level of confidence in the recorded events. Probabilistic databases offer ways to model uncertainties using possible world semantics. In this paper, we consider uncertain sensor data and transform this to probabilistic trajectory data using pre-processing routines. Next, we model this data as tuple level uncertain data and propose dynamic programming-based algorithms to mine interesting trajectories. A comprehensive empirical study is performed to evaluate the effectiveness of the approach. The results show that the trajectories could be modeled and worked as probabilistic data and that the results could be computed efficiently using dynamic programming.

Cited by 33Open access

Sensor Network & Weather Data Stream Mining

Hakilo Sabit, Adnan Al‐Anbuky · 2011

Interest in environmental studies and awareness reflects sustainable growth. This in effect results in growing volume and speed of environmental data acquired from field sensors and systems. Real world environmental spatial data have the form of being vast, highdimensional, raw, sparse, and lends itself to being dynamically streamed. Data with such characteristics calls for handling approaches and methodologies different from traditional ones in support for valuable environmental information. This paper proposes an efficient machine learning algorithm with less than 10% error bounds to extract bushfire hazard information from high arrival rate weather data streams using the resource-constrained wireless sensor network (WSN). The aim is to provide high spatial and temporal resolution bushfire hazard report. This could be achieved by employing dense multi-point and low cost weather sensors. The bushfire hazard prediction model of the Canadian fire weather index (FWI) system is implemented via in-network distributed computing. Simulation results indicate the potential of this approach to efficiently handle weather data stream mining with significant improvement in both temporal and spatial resolution.

Cited by 2

Mining and visualising wireless sensor network data

Song Ci · International Journal of Sensor Networks · 2007

In this paper, we propose a new network management framework for large-scale randomly-deployed sensor networks, called Energy Map, which explores the inherent relationships between the energy consumption and the sensor operation. Through nonlinear manifold learning algorithms, we are able to: 1) visualise the residual energy level of each sensor in a largescale network 2) infer the sensor locations and the current network topology through mining the collected residual energy data in a randomly-deployed sensor network 3) explore the inherent relation between sensor operation and energy consumption to find the dynamic patterns from a large volume of sensor network data for further network design, such as which set of sensors in a network will be the best candidates to be the future cluster heads, which is usually very important to develop a good sensor network protocol stack such as clustering algorithms and routing protocols.

Cited by 9

Data stream mining in medical sensor-cloud

Le Sun · Victoria University Research Repository (Victoria University) · 2016

Data stream mining has been studied in diverse application domains. In recent years, a population aging is stressing the national and international health care systems. Along with the advent of hundreds and thousands of health monitoring sensors, the traditional wireless sensor networks and anomaly detection techniques cannot handle huge amounts of information. Sensor-cloud makes the processing and storage of big sensor data much easier. Sensor-cloud is an extension of Cloud by connecting the Wireless Sensor Networks (WSNs) and the cloud through sensor and cloud gateways, which consistently collect and process a large amount of data from various sensors located in different areas. In this thesis, I will focus on analysing a large volume of medical sensor data streams collected from Sensor-cloud. To analyse the Medical data streams, I propose a medical data stream mining framework, which is targeted on tackling four main challenges ...

Cited by 0Open access

Data mining on urban sound sensor networks

Dick Botteldooren, Talis Vertriest, Michiel Boes et al. · Ghent University Academic Bibliography (Ghent University) · 2016

ICA 2016, 22nd International Congress on Acoustics, BUENOS AIRES, ARGENTINE, 05-/09/2016 - 09/09/2016

Cited by 0Open access

Mining Sensor Data in Larger Physical Systems

Paul O‘Leary, Matthew Harker, Roland Ritt et al. · IFAC-PapersOnLine · 2016

This paper presents a framework for the collection, management and mining of sensor data in large cyber-physical systems. Particular emphasis has been placed on mathematical methods, data structures and implementations which enable the real-time solution of inverse problems associated with the system in question. That is, given a system model, to obtain an estimate for the phenomenological cause of the sensor observation. This enables the use of causality, rather than mere correlation, when computing measures of significance during machine learning and knowledge discovery in very large data sets. The model is an abstract representation of a real physical system establishing the relationships between cause and effects. The pertinent behaviour of the model is captured in the form of equations, e.g., differential equations. The inverse solution of these model-equations, within certain constraints, permit us to establish the semantic reference between the sensor observation and its cause. Without this semantic reference there can be no physically based knowledge discovery. Embrechts pyramid of knowledge is addressed and shown that it will not suffice for future developments. The issue of information content is addressed more formally than in most data mining literature. Additionally the Epistemology for the emergent-perceptive portion of speech is presented and a prototype implementation with experimental results in data mining are presented. A lexical symbolic analysis of sensor data is implemented.

Cited by 3Open access

A Big Data Framework for Mining Sensor Data Using Hadoop

Engy El-Shafeiy, Ali I. El-Desouky · Studies in Informatics and Control · 2017

The data gathered from IOTs is considered of high business value.The IOTs devices sense the natural conditions using sensor network comprised of sensor nodes.Mining of big sensor data for useful knowledge extraction is a very challenging task.Frequent itemsets is one of the most effective mining techniques that find important itemsets from big sensor data.In this paper, a MapReduce Frequent Nodesets-based Boundary POC tree (MR-FNBP) framework is proposed for mining Frequent Nodesets for big sensor data.The MapReduce framework is used to implement MR-FNBP to enhance its performance in highly distributed environments.Additionally, the proposed Boundary (FNBP) creates a Boundary as an early stage to exclude the infrequent itemsets, and this may reduce the overall memory and time usage.Moreover, a number of experiments were performed to evaluate the performance of MR-FNBP framework.The results show high scalability rate and a less time consuming process for MR-FNBP framework over different recent systems.

Cited by 6Open access

Controlling in-patient environment by mining sensor data

Azhar Mahmood, Ke Shi, Shaheen Khatoon · 2013

This paper demonstrates the application of data mining on sensor data in order to develop a classification method for determination of inpatient's light and thermal comfort preferences. Hierarchical clustering along with incremental learning is proposed to develop an intelligent approach to control the temperature and light usage for each patient according to their comfort. By using the incremental learning the clustering model is adoptable to new usage patterns. For example, as new usage patterns are observed they are incrementally learned by the model and grouped into appropriate cluster. Application of two clustering techniques named Hierarchical and SimpleK-Means are evaluated on real dataset collected from medical unit to investigate the appropriate method for the classification. The results shows that Hierarchical clustering outperforms SimpleK-Means in term of number of clusters, cluster size, accuracy, update time and change rate. The proposed approach is evaluated on 10 days data which shows that the energy consumption is decreased by 11.32% and patient comfort level is increased by 16.74%.

Cited by 0

Autonomous Sensor Data Cleaning in Stream Mining Setting

Kenda, Klemen, Mladenić, Dunja · Zenodo (CERN European Organization for Nuclear Research) · 2019

Internet of Things (IoT), earth observation and big scientific experiments<br> are sources of extensive amounts of sensor big data today. We are faced with large<br> amounts of data with low measurement costs. A standard approach in such cases is<br> a stream mining approach, implying that we look at a particular measurement only<br> once during the real-time processing. This requires the methods to be completely<br> autonomous. In the past, very little attention was given to the most time-consuming<br> part of the data mining process, i.e. data pre-processing.

Cited by 0Open access

Mining State Dependencies Between Multiple Sensor Data Sources

Vasile-marian Scuturici, Marc Plantevit, Céline Robardet · 2013

Pattern mining over data streams is critical to a variety of applications such as prediction and evolution of weather phenomena or anomaly detection in security applications. Most of the current techniques attempt to discover associations between events appearing on the same data stream but are not able to discover associations over multiple heterogeneous data streams. In this work, we aim to identify temporal dependencies between data streams. We represent event streams by state streams that are induced by the streams ’ events themselves. Each state has a duration, represented as a set of disjoint time intervals with respect to the events that occurred in the stream. Temporal relations between these interval sets infers dependencies between the corresponding datasources. Our interval-based approach is robust to the temporal variability of events that characterizes the time intervals during which the events are related. It links two types of events if the occurrence of one is often followed by the appearance of the other in a certain time interval. The proposed approach determines the most appropriate time intervals of a temporal dependency whose validity is assessed by a χ 2 test. As several intervals may redundantly describe the same dependency, the approach retrieves only the few most specific intervals with respect to a dominance relationship over temporal dependencies, and thus avoids the classical problem of pattern flooding in data mining. TEDDY algorithm, TEmporal Dependency DiscoverY, prunes the search space while certifying the discovery of all valid and significant temporal dependencies. We present empirical results on simulated data to show the scalability and the robustness 1 of our approach. We also report on case studies from smart real-world environments equipped with a number of cameras and motion sensors. These experiments demonstrate the efficiency and the effectiveness of our approach.

Cited by 0