scholarly journals Soc-IoT: A Proof-of-Concept for Citizen-Centric Environmental Monitoring

2021 ◽  
Author(s):  
Sachit Mahajan
Keyword(s):  
Data Analysis ◽  
Environmental Monitoring ◽  
Large Scale ◽  
Low Cost ◽  
Large Data ◽  
Sensor Data ◽  
Proof Of Concept ◽  
Environment Monitoring ◽  
Systematic Analysis ◽  
Visualization Application

Cities around the world are struggling with environmental pollution. The conventional monitoring approaches are not effective for undertaking large-scale environmental monitoring due to logistical and cost-related issues. The availability of low-cost and low-power Internet of Things (IoT) devices has proved to be an effective alternative to monitor the ambient environment. Such systems have opened up environment monitoring opportunities to researchers and citizens while simultaneously confronting them with challenges like sensor accuracy, accumulation of large data sets, and data analysis, which itself is a formidable task that requires extensive computational resources and technical expertise. To address this challenge, a social, open-source, and citizen-centric IoT (Soc-IoT) framework is proposed that combines tools for real-time environmental sensing with an intuitive data analysis and visualization application. Soc-IoT has two main components: (1) CoSense Unit – a resource-efficient, portable and modular environment monitoring device intended for citizen sensing and complementing official environment monitoring infrastructure, and (2) exploreR – an intuitive cross-platform data analysis and visualization application that offers a comprehensive set of tools for systematic analysis of sensor data without any coding requirement. Developed as a proof-of-concept framework to monitor the environment at scale, Soc-IoT aims to promote environmental resilience and open innovation by reducing technological barriers.

scholarly journals Soc-IoT: A Proof-of-Concept for Citizen-Centric Environmental Monitoring

2021 ◽  
Author(s):  
Sachit Mahajan
Keyword(s):  
Data Analysis ◽  
Environmental Monitoring ◽  
Large Scale ◽  
Low Cost ◽  
Large Data ◽  
Sensor Data ◽  
Proof Of Concept ◽  
Environment Monitoring ◽  
Systematic Analysis ◽  
Visualization Application

Cities around the world are struggling with environmental pollution. The conventional monitoring approaches are not effective for undertaking large-scale environmental monitoring due to logistical and cost-related issues. The availability of low-cost and low-power Internet of Things (IoT) devices has proved to be an effective alternative to monitor the ambient environment. Such systems have opened up environment monitoring opportunities to researchers and citizens while simultaneously confronting them with challenges like sensor accuracy, accumulation of large data sets, and data analysis, which itself is a formidable task that requires extensive computational resources and technical expertise. To address this challenge, a social, open-source, and citizen-centric IoT (Soc-IoT) framework is proposed that combines tools for real-time environmental sensing with an intuitive data analysis and visualization application. Soc-IoT has two main components: (1) CoSense Unit – a resource-efficient, portable and modular environment monitoring device intended for citizen sensing and complementing official environment monitoring infrastructure, and (2) exploreR – an intuitive cross-platform data analysis and visualization application that offers a comprehensive set of tools for systematic analysis of sensor data without any coding requirement. Developed as a proof-of-concept framework to monitor the environment at scale, Soc-IoT aims to promote environmental resilience and open innovation by reducing technological barriers.

scholarly journals Tree-Aggregated Predictive Modeling of Microbiome Data

2020 ◽  
Author(s):  
Jacob Bien ◽  
Xiaohan Yan ◽  
Léo Simpson ◽  
Christian L. Müller
Keyword(s):  
Data Analysis ◽  
Predictive Modeling ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
Compositional Data ◽  
Low Cost ◽  
Primary Data ◽  
Compositional Data Analysis ◽  
Taxonomic Rank ◽  
Microbiome Data

AbstractModern high-throughput sequencing technologies provide low-cost microbiome survey data across all habitats of life at unprecedented scale. At the most granular level, the primary data consist of sparse counts of amplicon sequence variants or operational taxonomic units that are associated with taxonomic and phylogenetic group information. In this contribution, we leverage the hierarchical structure of amplicon data and propose a data-driven, parameter-free, and scalable tree-guided aggregation framework to associate microbial subcompositions with response variables of interest. The excess number of zero or low count measurements at the read level forces traditional microbiome data analysis workflows to remove rare sequencing variants or group them by a fixed taxonomic rank, such as genus or phylum, or by phylogenetic similarity. By contrast, our framework, which we call trac (tree-aggregation of compositional data), learns data-adaptive taxon aggregation levels for predictive modeling making user-defined aggregation obsolete while simultaneously integrating seamlessly into the compositional data analysis framework. We illustrate the versatility of our framework in the context of large-scale regression problems in human-gut, soil, and marine microbial ecosystems. We posit that the inferred aggregation levels provide highly interpretable taxon groupings that can help microbial ecologists gain insights into the structure and functioning of the underlying ecosystem of interest.

scholarly journals Development of an IoT System for Environmental Monitoring: Software

2021 ◽  
Author(s):  
Benjamin Secker
Keyword(s):  
Environmental Monitoring ◽  
Low Cost ◽  
Proof Of Concept ◽  
Embedded Devices ◽  
Data Loggers ◽  
Environmental Monitoring System ◽  
Use Of The Internet ◽  
High Level ◽  
Monitoring Software ◽  
The Internet Of Things

Use of the Internet of Things (IoT) is poised to be the next big advancement in environmental monitoring. We present the high-level software side of a proof-of-concept that demonstrates an end-to-end environmental monitoring system,<br><div>replacing Greater Wellington Regional Council’s expensive data loggers with low-cost, IoT centric embedded devices, and it’s supporting cloud platform. The proof-of-concept includes a Micropython-based software stack running on an ESP32 microcontroller. The device software includes a built-in webserver that hosts a responsive Web App for configuration of the device. Telemetry data is sent over Vodafone’s NB-IoT network and stored in Azure IoT Central, where it can be visualised and exported.</div><br>While future development is required for a production-ready system, the proof-of-concept justifies the use of modern IoT technologies for environmental monitoring. The open source nature of the project means that the knowledge gained can be re-used and modified to suit the use-cases for other organisations.

Large-Scale Sensor Network Analysis

2013 ◽  
pp. 314-347 ◽  
Author(s):  
Joaquin Vanschoren ◽  
Ugo Vespier ◽  
Shengfa Miao ◽  
Marvin Meeng ◽  
Ricardo Cachucho ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Scale ◽  
Vital Signs ◽  
Sensor Data ◽  
Atmospheric Conditions ◽  
Big Data Applications ◽  
The World ◽  
Sheer Size ◽  
Effective Use

Sensors are increasingly being used to monitor the world around us. They measure movements of structures such as bridges, windmills, and plane wings, human’s vital signs, atmospheric conditions, and fluctuations in power and water networks. In many cases, this results in large networks with different types of sensors, generating impressive amounts of data. As the volume and complexity of data increases, their effective use becomes more challenging, and novel solutions are needed both on a technical as well as a scientific level. Founded on several real-world applications, this chapter discusses the challenges involved in large-scale sensor data analysis and describes practical solutions to address them. Due to the sheer size of the data and the large amount of computation involved, these are clearly “Big Data” applications.

scholarly journals Automated Incident Detection Using Real-Time Floating Car Data

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Maarten Houbraken ◽  
Steven Logghe ◽  
Marco Schreuder ◽  
Pieter Audenaert ◽  
Didier Colle ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Large Scale ◽  
Low Cost ◽  
Field Trials ◽  
Traffic Monitoring ◽  
Sensor Data ◽  
Feasibility Trial ◽  
Incident Detection ◽  
The Road ◽  
Floating Car Data

The aim of this paper is to demonstrate the feasibility of a live Automated Incident Detection (AID) system using only Floating Car Data (FCD) in one of the first large-scale FCD AID field trials. AID systems detect traffic events and alert upcoming drivers to improve traffic safety without human monitoring. These automated systems traditionally rely on traffic monitoring sensors embedded in the road. FCD allows for finer spatial granularity of traffic monitoring. However, low penetration rates of FCD probe vehicles and the data latency have historically hindered FCD AID deployment. We use a live country-wide FCD system monitoring an estimated 5.93% of all vehicles. An FCD AID system is presented and compared to the installed AID system (using loop sensor data) on 2 different highways in Netherlands. Our results show the FCD AID can adequately monitor changing traffic conditions and follow the AID benchmark. The presented FCD AID is integrated with the road operator systems as part of an innovation project, making this, to the best of our knowledge, the first full chain technical feasibility trial of an FCD-only AID system. Additionally, FCD allows for AID on roads without installed sensors, allowing road safety improvements at low cost.

scholarly journals Tree-aggregated predictive modeling of microbiome data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jacob Bien ◽  
Xiaohan Yan ◽  
Léo Simpson ◽  
Christian L. Müller
Keyword(s):  
Data Analysis ◽  
Predictive Modeling ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
Compositional Data ◽  
Low Cost ◽  
Primary Data ◽  
Compositional Data Analysis ◽  
Taxonomic Rank ◽  
Microbiome Data

AbstractModern high-throughput sequencing technologies provide low-cost microbiome survey data across all habitats of life at unprecedented scale. At the most granular level, the primary data consist of sparse counts of amplicon sequence variants or operational taxonomic units that are associated with taxonomic and phylogenetic group information. In this contribution, we leverage the hierarchical structure of amplicon data and propose a data-driven and scalable tree-guided aggregation framework to associate microbial subcompositions with response variables of interest. The excess number of zero or low count measurements at the read level forces traditional microbiome data analysis workflows to remove rare sequencing variants or group them by a fixed taxonomic rank, such as genus or phylum, or by phylogenetic similarity. By contrast, our framework, which we call  (ee-ggregation of ompositional data), learns data-adaptive taxon aggregation levels for predictive modeling, greatly reducing the need for user-defined aggregation in preprocessing while simultaneously integrating seamlessly into the compositional data analysis framework. We illustrate the versatility of our framework in the context of large-scale regression problems in human gut, soil, and marine microbial ecosystems. We posit that the inferred aggregation levels provide highly interpretable taxon groupings that can help microbiome researchers gain insights into the structure and functioning of the underlying ecosystem of interest.

scholarly journals A sensor data fusion-based locating method for large-scale metrology

ACTA IMEKO ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 136
Author(s):  
Andrea Rega ◽  
Ferdinando Vitolo ◽  
Stanislao Patalano ◽  
Salvatore Gerbino
Keyword(s):  
Data Fusion ◽  
Reverse Engineering ◽  
Large Scale ◽  
Low Cost ◽  
Point Clouds ◽  
Sensor Data ◽  
Sensor Data Fusion ◽  
3D Cad ◽  
Handling Device ◽  
Scanning Systems

<p class="Abstract">The measurement of geometric and dimensional variations in the context of large-sized products is a complex operation. One of the most efficient ways to identify deviations is by comparing the nominal object with a digitalisation of the real object through a reverse engineering process. The accurate digitalisation of large geometric models usually requires multiple acquisitions from different acquiring locations; the acquired point clouds must then be correctly aligned in the 3D digital environment. The identification of the exact scanning location is crucial to correctly realign point clouds and generate an accurate 3D CAD model.</p>To achieve this, an acquisition method based on the use of a handling device is proposed that enhances reverse engineering scanning systems and is able to self-locate. The present paper tackles the device’s locating problem by using sensor data fusion based on a Kalman filter. The method was first simulated in a MatLAB environment; a prototype was then designed and developed using low-cost hardware. Tests on the sensor data fusion have shown a locating accuracy better than that of each individual sensor. Despite the low-cost hardware, the results are encouraging and open to future improvements

scholarly journals Smart Environmental Monitoring and Assessment Technologies (SEMAT)—A New Paradigm for Low-Cost, Remote Aquatic Environmental Monitoring

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2248 ◽  
Author(s):  
Jarrod Trevathan ◽  
Ron Johnstone
Keyword(s):  
Environmental Monitoring ◽  
Real Time ◽  
Large Scale ◽  
Low Cost ◽  
Waste Recycling ◽  
Data Management System ◽  
New Paradigm ◽  
Analysis And Design ◽  
Systems Analysis And Design ◽  
Open Standard

Expense and the logistical difficulties with deploying scientific monitoring equipment are the biggest limitations to undertaking large scale monitoring of aquatic environments. The Smart Environmental Monitoring and Assessment Technologies (SEMAT) project is aimed at addressing this problem by creating an open standard for low-cost, near real-time, remote aquatic environmental monitoring systems. This paper presents the latest refinement of the SEMAT system in-line with the evolution of existing technologies, inexpensive sensors and environmental monitoring expectations. We provide a systems analysis and design of the SEMAT remote monitoring units and the back-end data management system. The system’s value is augmented through a unique e-waste recycling and repurposing model which engages/educates the community in the production of the SEMAT units using social enterprise. SEMAT serves as an open standard for the community to innovate around to further the state of play with low-cost environmental monitoring. The latest SEMAT units have been trialled in a peri-urban lake setting and the results demonstrate the system’s capabilities to provide ongoing data in near real-time to validate an environmental model of the study site.

scholarly journals Security analysis of mobile crowd sensing applications

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nsikak P. Owoh ◽  
M. Mahinderjit Singh
Keyword(s):  
Information Disclosure ◽  
Large Scale ◽  
Low Cost ◽  
Security Analysis ◽  
Sensor Data ◽  
Sensitive Information ◽  
Security Scheme ◽  
Sensing Applications ◽  
Crowd Sensing ◽  
Encryption And Authentication

The proliferation of mobile phones with integrated sensors makes large scale sensing possible at low cost. During mobile sensing, data mostly contain sensitive information of users such as their real-time location. When such information are not effectively secured, users’ privacy can be violated due to eavesdropping and information disclosure. In this paper, we demonstrated the possibility of unauthorized access to location information of a user during sensing due to the ineffective security mechanisms in most sensing applications. We analyzed 40 apps downloaded from Google Play Store and results showed a 100% success rate in traffic interception and disclosure of sensitive information of users. As a countermeasure, a security scheme which ensures encryption and authentication of sensed data using Advanced Encryption Standard 256-Galois Counter Mode was proposed. End-to-end security of location and motion data from smartphone sensors are ensured using the proposed security scheme. Security analysis of the proposed scheme showed it to be effective in protecting Android based sensor data against eavesdropping, information disclosure and data modification.

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