scholarly journals Semantic Description of Quality of Data in Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6462
Author(s):  
Anupam Prasad Vedurmudi ◽  
Julia Neumann ◽  
Maximilian Gruber ◽  
Sascha Eichstädt
Keyword(s):  
Sensor Networks ◽  
Sensor Data ◽  
Sensor Calibration ◽  
Calibration Data ◽  
Semantic Description ◽  
Quality Of Data ◽  
General Applicability ◽  
Heterogeneous Sensor Networks ◽  
Calibration Models

The annotation of sensor data with semantic metadata is essential to the goals of automation and interoperability in the context of Industry 4.0. In this contribution, we outline a semantic description of quality of data in sensor networks in terms of indicators, metrics and interpretations. The concepts thus defined are consolidated into an ontology that describes quality of data metainformation in heterogeneous sensor networks and methods for the determination of corresponding quality of data dimensions are outlined. By incorporating support for sensor calibration models and measurement uncertainty via a previously derived ontology, a conformity with metrological requirements for sensor data is ensured. A quality description for a calibrated sensor generated using the resulting ontology is presented in the JSON-LD format using the battery level and calibration data as quality indicators. Finally, the general applicability of the model is demonstrated using a series of competency questions.

Measuring patient engagement among cancer patients in eHealth and mHealth interventions on symptom management: an integrated review (Preprint)

2020 ◽  
Author(s):  
Juqing Zhao ◽  
Pei Chen ◽  
Guangming Wan
Keyword(s):  
Cancer Patients ◽  
Patient Engagement ◽  
Symptom Management ◽  
Subjective Experience ◽  
Participation Rate ◽  
Sensor Data ◽  
Quality Of Data ◽  
Mhealth Intervention

BACKGROUND There has been an increase number of eHealth and mHealth interventions aimed to support symptoms among cancer survivors. However, patient engagement has not been guaranteed and standardized in these interventions. OBJECTIVE The objective of this review was to address how patient engagement has been defined and measured in eHealth and mHealth interventions designed to improve symptoms and quality of life for cancer patients. METHODS Searches were performed in MEDLINE, PsychINFO, Web of Science, and Google Scholar to identify eHealth and mHealth interventions designed specifically to improve symptom management for cancer patients. Definition and measurement of engagement and engagement related outcomes of each intervention were synthesized. This integrated review was conducted using Critical Interpretive Synthesis to ensure the quality of data synthesis. RESULTS A total of 792 intervention studies were identified through the searches; 10 research papers met the inclusion criteria. Most of them (6/10) were randomized trial, 2 were one group trail, 1 was qualitative design, and 1 paper used mixed method. Majority of identified papers defined patient engagement as the usage of an eHealth and mHealth intervention by using different variables (e.g., usage time, log in times, participation rate). Engagement has also been described as subjective experience about the interaction with the intervention. The measurement of engagement is in accordance with the definition of engagement and can be categorized as objective and subjective measures. Among identified papers, 5 used system usage data, 2 used self-reported questionnaire, 1 used sensor data and 3 used qualitative method. Almost all studies reported engagement at a moment to moment level, but there is a lack of measurement of engagement for the long term. CONCLUSIONS There have been calls to develop standard definition and measurement of patient engagement in eHealth and mHealth interventions. Besides, it is important to provide cancer patients with more tailored and engaging eHealth and mHealth interventions for long term engagement.

scholarly journals Performance metrics in wireless sensor networks :a survey and outlook

2018 ◽  
Vol 7 (2.26) ◽  
pp. 25
Author(s):  
E Ramya ◽  
R Gobinath
Keyword(s):  
Wireless Sensor Networks ◽  
Quality Of Service ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Performance Metrics ◽  
Resource Constraints ◽  
Sensor Data ◽  
Wireless Sensor

Data mining plays an important role in analysis of data in modern sensor networks. A sensor network is greatly constrained by the various challenges facing a modern Wireless Sensor Network. This survey paper focuses on basic idea about the algorithms and measurements taken by the Researchers in the area of Wireless Sensor Network with Health Care. This survey also catego-ries various constraints in Wireless Body Area Sensor Networks data and finds the best suitable techniques for analysing the Sensor Data. Due to resource constraints and dynamic topology, the quality of service is facing a challenging issue in Wireless Sensor Networks. In this paper, we review the quality of service parameters with respect to protocols, algorithms and Simulations. 

scholarly journals A systematic MEMS sensor calibration framework

2015 ◽  
Vol 4 (1) ◽  
pp. 97-102 ◽  
Author(s):  
A. Dickow ◽  
G. Feiertag
Keyword(s):  
Pressure Sensors ◽  
Calibration Method ◽  
Least Square ◽  
Sensor Data ◽  
Sensor Calibration ◽  
Calibration Point ◽  
Point Sets ◽  
Mems Sensor ◽  
Least Square Fit

Abstract. In this paper we present a systematic method to determine sets of close to optimal sensor calibration points for a polynomial approximation. For each set of calibration points a polynomial is used to fit the nonlinear sensor response to the calibration reference. The polynomial parameters are calculated using ordinary least square fit. To determine the quality of each calibration, reference sensor data is measured at discrete test conditions. As an error indicator for the quality of a calibration the root mean square deviation between the calibration polynomial and the reference measurement is calculated. The calibration polynomials and the error indicators are calculated for all possible calibration point sets. To find close to optimal calibration point sets, the worst 99% of the calibration options are dismissed. This results in a multi-dimensional probability distribution of the probably best calibration point sets. In an experiment, barometric MEMS (micro-electromechanical systems) pressure sensors are calibrated using the proposed calibration method at several temperatures and pressures. The framework is applied to a batch of six of each of the following sensor types: Bosch BMP085, Bosch BMP180, and EPCOS T5400. Results indicate which set of calibration points should be chosen to achieve good calibration results.

A Spatial Correlation Based Data Aggregation Algorithm for Wireless Sensor Networks

2013 ◽  
Vol 655-657 ◽  
pp. 655-659 ◽  
Author(s):  
Lei Chun Wang ◽  
Guo Yu Zhou
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Spatial Correlation ◽  
Data Aggregation ◽  
Wireless Sensor ◽  
Data Detection ◽  
Data Traffic ◽  
Quality Of Data ◽  
Energy Consumption Rate

Data aggregation is the important method to reduce data traffic and lower energy expenditure in wireless sensor networks (WSN). This paper analyzes the characteristics of data sampled by nodes, and gives the method to decide spatial correlation between neighboring nodes and the criteria to classify and decide data in WSN. On the basis of this, this paper proposes a spatial correlation based data aggregation algorithm for WSN, SCBD. SCBD classifies and decides data according to data criteria and spatial correlation among nodes in normal nodes and cluster heads at the same time, and then aggregates different types of data. The results show that SCBD outperforms RAA in terms of energy consumption, rate of data detection and quality of data aggregation.

Learnability of Interestingness with Semantic Similarity and Reasoning in the Knowledge Base of Decision Support Systems

2020 ◽  
Vol 12 (1) ◽  
pp. 26-44
Author(s):  
Sukumar Rajendran ◽  
Prabhu J.
Keyword(s):  
Artificial Intelligence ◽  
Semantic Similarity ◽  
Geographic Location ◽  
Mobile Technologies ◽  
Sensor Data ◽  
Massive Data ◽  
Quality Of Data ◽  
Prime Concern ◽  
Rapid Pace

The evolution of deep learning blended with GPU/TPU has elicited faster computation and assimilation of Big Data at a rapid pace with the exponential learning rate of models. Mobile technologies and cloud-based services are yielding massive data irrespective of geographic location at a rapid pace. Integrating the available plethora of data to find a semantic similarity while providing a rapid response without compromising on the quantity and quality of data is a prime concern. Learning from semantic similarity, utility algorithms turn this data into machine perceivable information, through learnability and utilization of Senticnet. The retainability of knowledge still has its own set of specific needs in terms of different machine learning and artificial intelligence algorithms. Utilization of the semantic similarity for ontology-based learning with interoperability helps preserve privacy for decoding the control attributes. The aspect of learning may further extend for rapidly generated sensor data through things and mobile devices.

A Sensor Grouping Method for Industrial Sensor Health Management

2014 ◽  
Vol 621 ◽  
pp. 271-276 ◽  
Author(s):  
Chang Jie Zhang ◽  
Yu Liu
Keyword(s):  
Sensor Networks ◽  
Health Management ◽  
Real Data ◽  
Sensor Data ◽  
Cluster Method ◽  
Grouping Strategy ◽  
The Status ◽  
Grouping Method ◽  
Grouping Algorithm

As many sensor networks have been deployed in industry monitoring area, the focus on sensor data quality has also increased. Sensor networks provide us with process details which we can utilize to help making decisions on process monitoring.In order to make meaningful decisions, the quality of the data produced by sensors must be validated. As we evaluate the status of a specific sensor, we may also regard the status of the related sensors. If a sensor’s data show some abnormal, but the sensors related to it didn’t, we may have much more confidence to believe that the sensor is malfunction. In our early study, the sensors grouping strategy is manual. In this paper, we proposed a sensor grouping algorithm, which combines both PCA decouple method and the K-means cluster method. Finally, a test has been made with real data from an oilfield.

scholarly journals Edge Computing-Enabled Wireless Sensor Networks for Multiple Data Collection Tasks in Smart Agriculture

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaomin Li ◽  
Lixue Zhu ◽  
Xuan Chu ◽  
Han Fu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Collection ◽  
Edge Computing ◽  
Wireless Sensor ◽  
Quality Of Data ◽  
Traditional Methods ◽  
Multiple Data ◽  
Smart Agriculture

At present, precision agriculture and smart agriculture are the hot topics, which are based on the efficient data collection by using wireless sensor networks (WSNs). However, agricultural WSNs are still facing many challenges such as multitasks, data quality, and latency. In this paper, we propose an efficient solution for multiple data collection tasks exploiting edge computing-enabled wireless sensor networks in smart agriculture. First, a novel data collection framework is presented by merging WSN and edge computing. Second, the data collection process is modeled, including a plurality of sensors and tasks. Next, according to each specific task and correlation between task and sensors, on the edge computing server, a double selecting strategy is established to determine the best node and sensor network that fulfills quality of data and data collection time constraints of tasks. Furthermore, a data collection algorithm is designed, based on set values for quality of data. Finally, a simulation environment is constructed where the proposed strategy is applied, and results are analyzed and compared to the traditional methods. According to the comparison results, the proposal outperforms the traditional methods in metrics.

scholarly journals IoT-Based Small Scale Anomaly Detection Using Dixon’s Q Test for e-Health Data

2021 ◽  
Vol 4 (4) ◽  
pp. 100
Author(s):  
Partha Pratim Ray ◽  
Dinesh Dash
Keyword(s):  
Anomaly Detection ◽  
Data Packet ◽  
Sensor Data ◽  
Small Scale ◽  
Quality Of Data ◽  
Specific Data ◽  
Data Packets ◽  
Kolmogorov Smirnov ◽  
Scale Point

Anomaly detection in the smart application domain can significantly improve the quality of data processing, especially when the size of a dataset is too small. Internet of Things (IoT) enables the development of numerous applications where sensor-data-aware anomalies can affect the decision making of the underlying system. In this paper, we propose a scheme: IoTDixon, which works on the Dixon’s Q test to identify point anomalies from a simulated normally distributed dataset. The proposed technique involves Q statistics, Kolmogorov–Smirnov test, and partitioning of a given dataset into a specific data packet. The proposed techniques use Q-test to detect point anomalies. We find that value 76.37 is statistically significant where P=0.012<α=0.05, thus rejecting the null hypothesis for a test data packet. In other data packets, no such significance is observed; thus, no outlier is statistically detected. The proposed approach of IoTDixon can help to improve small-scale point anomaly detection for a small-size dataset as shown in the conducted experiments.

scholarly journals Delay metric in wireless body area sensor net-works

2018 ◽  
Vol 7 (3.3) ◽  
pp. 448
Author(s):  
Mrs. E. Ramya ◽  
Dr R. Gobinath
Keyword(s):  
Sensor Networks ◽  
Wireless Body Area Network ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Area Network ◽  
Body Area ◽  
Fundamental Idea ◽  
Body Area Sensor Networks ◽  
Performance Issues

Wireless Sensor Networks have the potential to greatly impact many aspects of medical care. This paper focuses on fundamental idea about the Protocols, standards, Technologies and measurements taken by the Researchers in the area of Wireless Body Area Sensor. This paper also listed various constraints in Wireless Body Area Sensor Networks and noticed the best suitable techniques for analyzing the Sensor Data. The quality of service is the most fundamental characteristics of any applications like Wireless Network, Wireless Sensor Network and Wireless Body Area Network. The performance factor in WBAN still remains trivial whereas performance issues are also a great concern. This paper given the effort to analyze and present some of the protocols and technologies developed toward performance issues in WBAN.  

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