scholarly journals Harmonization and Visualization of Data from a Transnational Multi-Sensor Personal Exposure Campaign

2021 ◽  
Vol 18 (21) ◽  
pp. 11614
Author(s):  
Rok Novak ◽  
Ioannis Petridis ◽  
David Kocman ◽  
Johanna Robinson ◽  
Tjaša Kanduč ◽  
...  
Keyword(s):  
Data Fusion ◽  
Data Streams ◽  
Research Participant ◽  
Data Retrieval ◽  
Personal Exposure ◽  
Sensor Data ◽  
Data Logging ◽  
Activity Data ◽  
Speed Up ◽  
Visualization Of Data

Use of a multi-sensor approach can provide citizens with holistic insights into the air quality of their immediate surroundings and their personal exposure to urban stressors. Our work, as part of the ICARUS H2020 project, which included over 600 participants from seven European cities, discusses the data fusion and harmonization of a diverse set of multi-sensor data streams to provide a comprehensive and understandable report for participants. Harmonizing the data streams identified issues with the sensor devices and protocols, such as non-uniform timestamps, data gaps, difficult data retrieval from commercial devices, and coarse activity data logging. Our process of data fusion and harmonization allowed us to automate visualizations and reports, and consequently provide each participant with a detailed individualized report. Results showed that a key solution was to streamline the code and speed up the process, which necessitated certain compromises in visualizing the data. A thought-out process of data fusion and harmonization of a diverse set of multi-sensor data streams considerably improved the quality and quantity of distilled data that a research participant received. Though automation considerably accelerated the production of the reports, manual and structured double checks are strongly recommended.

scholarly journals Data Fusion and Visualization of a Multi-Sensor Personal Exposure Campaign

2021 ◽  
Author(s):  
Rok Novak ◽  
Ioannis Petridis ◽  
David Kocman ◽  
Johanna Amalia Robinson ◽  
Tjaša Kanduč ◽  
...  
Keyword(s):  
Data Fusion ◽  
Data Streams ◽  
Research Participant ◽  
Data Retrieval ◽  
Personal Exposure ◽  
Sensor Data ◽  
Data Logging ◽  
Activity Data ◽  
Data Gaps ◽  
Speed Up

Use of a multi-sensor approach can provide citizens a holistic insight in the air quality in their immediate surroundings and assessment of personal exposure to urban stressors. Our work, as part of the ICARUS H2020 project, which included over 600 participants from 7 European cities, discusses data fusion and harmonization on a diverse set of multi-sensor data streams to provide a comprehensive and understandable report for participants, and offers possible solutions and improvements. Harmonizing the data streams identified issues with the used devices and protocols, such as non-uniform timestamps, data gaps, difficult data retrieval from commercial devices, and coarse activity data logging. Our process of data fusion and harmonization allowed us to automate the process of generating visualizations and reports and consequently provide each participant with a detailed individualized report. Results showed that a key solution was to streamline the code and speed up the process, which necessitated certain compromises in visualizing the data. A thought-out process of data fusion and harmonization on a diverse set of multi-sensor data streams considerably improved the quality and quantity of data that a research participant receives. Though automatization accelerated the production of the reports considerably, manual structured double checks are strongly recommended.

scholarly journals Time-selective data fusion for in-network processing in ad hoc wireless sensor networks

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881130 ◽  
Author(s):  
Jaanus Kaugerand ◽  
Johannes Ehala ◽  
Leo Mõtus ◽  
Jürgo-Sören Preden
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Data Streams ◽  
Ad Hoc ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Temporal Consistency ◽  
Variable Delays

This article introduces a time-selective strategy for enhancing temporal consistency of input data for multi-sensor data fusion for in-network data processing in ad hoc wireless sensor networks. Detecting and handling complex time-variable (real-time) situations require methodical consideration of temporal aspects, especially in ad hoc wireless sensor network with distributed asynchronous and autonomous nodes. For example, assigning processing intervals of network nodes, defining validity and simultaneity requirements for data items, determining the size of memory required for buffering the data streams produced by ad hoc nodes and other relevant aspects. The data streams produced periodically and sometimes intermittently by sensor nodes arrive to the fusion nodes with variable delays, which results in sporadic temporal order of inputs. Using data from individual nodes in the order of arrival (i.e. freshest data first) does not, in all cases, yield the optimal results in terms of data temporal consistency and fusion accuracy. We propose time-selective data fusion strategy, which combines temporal alignment, temporal constraints and a method for computing delay of sensor readings, to allow fusion node to select the temporally compatible data from received streams. A real-world experiment (moving vehicles in urban environment) for validation of the strategy demonstrates significant improvement of the accuracy of fusion results.

Knowledge Discovery for Sensor Network Comprehension

2010 ◽  
pp. 118-135 ◽  
Author(s):  
Pedro Pereira Rodrigues ◽  
João Gama ◽  
Luís Lopes
Keyword(s):  
Sensor Networks ◽  
Knowledge Discovery ◽  
Sensor Network ◽  
Data Streams ◽  
High Rate ◽  
Sensor Data ◽  
Processing Abilities ◽  
Comprehension Ability ◽  
Visualization Of Data ◽  
Different Characteristics

In this chapter we explore different characteristics of sensor networks which define new requirements for knowledge discovery, with the common goal of extracting some kind of comprehension about sensor data and sensor networks, focusing on clustering techniques which provide useful information about sensor networks as it represents the interactions between sensors. This network comprehension ability is related with sensor data clustering and clustering of the data streams produced by the sensors. A wide range of techniques already exists to assess these interactions in centralized scenarios, but the seizable processing abilities of sensors in distributed algorithms present several benefits that shall be considered in future designs. Also, sensors produce data at high rate. Often, human experts need to inspect these data streams visually in order to decide on some corrective or proactive operations (Rodrigues & Gama, 2008). Visualization of data streams, and of data mining results, is therefore extremely relevant to sensor data management, and can enhance sensor network comprehension, and should be addressed in future works.

scholarly journals Robust Fusion of LiDAR and Wide-Angle Camera Data for Autonomous Mobile Robots

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2730 ◽  
Author(s):  
Varuna De Silva ◽  
Jamie Roche ◽  
Ahmet Kondoz
Keyword(s):  
Data Fusion ◽  
Mobile Robots ◽  
Data Streams ◽  
Free Space ◽  
Image Sensor ◽  
Autonomous Mobile Robots ◽  
Sensor Data ◽  
Wide Angle ◽  
Sensor Data Fusion ◽  
Surrounding Environment

Autonomous robots that assist humans in day to day living tasks are becoming increasingly popular. Autonomous mobile robots operate by sensing and perceiving their surrounding environment to make accurate driving decisions. A combination of several different sensors such as LiDAR, radar, ultrasound sensors and cameras are utilized to sense the surrounding environment of autonomous vehicles. These heterogeneous sensors simultaneously capture various physical attributes of the environment. Such multimodality and redundancy of sensing need to be positively utilized for reliable and consistent perception of the environment through sensor data fusion. However, these multimodal sensor data streams are different from each other in many ways, such as temporal and spatial resolution, data format, and geometric alignment. For the subsequent perception algorithms to utilize the diversity offered by multimodal sensing, the data streams need to be spatially, geometrically and temporally aligned with each other. In this paper, we address the problem of fusing the outputs of a Light Detection and Ranging (LiDAR) scanner and a wide-angle monocular image sensor for free space detection. The outputs of LiDAR scanner and the image sensor are of different spatial resolutions and need to be aligned with each other. A geometrical model is used to spatially align the two sensor outputs, followed by a Gaussian Process (GP) regression-based resolution matching algorithm to interpolate the missing data with quantifiable uncertainty. The results indicate that the proposed sensor data fusion framework significantly aids the subsequent perception steps, as illustrated by the performance improvement of a uncertainty aware free space detection algorithm.

scholarly journals COMPRESSION AND PROGRESSIVE RETRIEVAL OF MULTI-DIMENSIONAL SENSOR DATA

2016 ◽  
Vol XLI-B2 ◽  
pp. 27-33
Author(s):  
P. Lorkowski ◽  
T. Brinkhoff
Keyword(s):  
Data Streams ◽  
Data Transfer ◽  
Lossless Compression ◽  
Data Retrieval ◽  
Sensor Data ◽  
Data Segment ◽  
The World ◽  
Efficient Data ◽  
Spatio Temporal ◽  
Drifting Buoys

Since the emergence of sensor data streams, increasing amounts of observations have to be transmitted, stored and retrieved. Performing these tasks at the granularity of single points would mean an inappropriate waste of resources. Thus, we propose a concept that performs a partitioning of observations by spatial, temporal or other criteria (or a combination of them) into data segments. We exploit the resulting proximity (according to the partitioning dimension(s)) within each data segment for compression and efficient data retrieval. While in principle allowing lossless compression, it can also be used for progressive transmission with increasing accuracy wherever incremental data transfer is reasonable. In a first feasibility study, we apply the proposed method to a dataset of ARGO drifting buoys covering large spatio-temporal regions of the world´s oceans and compare the achieved compression ratio to other formats.

scholarly journals COMPRESSION AND PROGRESSIVE RETRIEVAL OF MULTI-DIMENSIONAL SENSOR DATA

2016 ◽  
Vol XLI-B2 ◽  
pp. 27-33
Author(s):  
P. Lorkowski ◽  
T. Brinkhoff
Keyword(s):  
Data Streams ◽  
Data Transfer ◽  
Lossless Compression ◽  
Data Retrieval ◽  
Sensor Data ◽  
Data Segment ◽  
The World ◽  
Efficient Data ◽  
Spatio Temporal ◽  
Drifting Buoys

Since the emergence of sensor data streams, increasing amounts of observations have to be transmitted, stored and retrieved. Performing these tasks at the granularity of single points would mean an inappropriate waste of resources. Thus, we propose a concept that performs a partitioning of observations by spatial, temporal or other criteria (or a combination of them) into data segments. We exploit the resulting proximity (according to the partitioning dimension(s)) within each data segment for compression and efficient data retrieval. While in principle allowing lossless compression, it can also be used for progressive transmission with increasing accuracy wherever incremental data transfer is reasonable. In a first feasibility study, we apply the proposed method to a dataset of ARGO drifting buoys covering large spatio-temporal regions of the world´s oceans and compare the achieved compression ratio to other formats.

scholarly journals Deep Convolutional Neural Network with RNNs for Complex Activity Recognition Using Wrist-Worn Wearable Sensor Data

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1685
Author(s):  
Sakorn Mekruksavanich ◽  
Anuchit Jitpattanakul
Keyword(s):  
Neural Networks ◽  
Activity Recognition ◽  
Human Activities ◽  
Recognition Performance ◽  
Confusion Matrix ◽  
Experimental Studies ◽  
Industrial Applications ◽  
Sensor Data ◽  
Complex Activity ◽  
Activity Data

Sensor-based human activity recognition (S-HAR) has become an important and high-impact topic of research within human-centered computing. In the last decade, successful applications of S-HAR have been presented through fruitful academic research and industrial applications, including for healthcare monitoring, smart home controlling, and daily sport tracking. However, the growing requirements of many current applications for recognizing complex human activities (CHA) have begun to attract the attention of the HAR research field when compared with simple human activities (SHA). S-HAR has shown that deep learning (DL), a type of machine learning based on complicated artificial neural networks, has a significant degree of recognition efficiency. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two different types of DL methods that have been successfully applied to the S-HAR challenge in recent years. In this paper, we focused on four RNN-based DL models (LSTMs, BiLSTMs, GRUs, and BiGRUs) that performed complex activity recognition tasks. The efficiency of four hybrid DL models that combine convolutional layers with the efficient RNN-based models was also studied. Experimental studies on the UTwente dataset demonstrated that the suggested hybrid RNN-based models achieved a high level of recognition performance along with a variety of performance indicators, including accuracy, F1-score, and confusion matrix. The experimental results show that the hybrid DL model called CNN-BiGRU outperformed the other DL models with a high accuracy of 98.89% when using only complex activity data. Moreover, the CNN-BiGRU model also achieved the highest recognition performance in other scenarios (99.44% by using only simple activity data and 98.78% with a combination of simple and complex activities).

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