scholarly journals Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3456
Author(s):  
Robin Kraft ◽  
Ferdinand Birk ◽  
Manfred Reichert ◽  
Aniruddha Deshpande ◽  
Winfried Schlee ◽  
...  
Keyword(s):  
Architectural Design ◽  
Environmental Data ◽  
Common Interest ◽  
Sensor Data ◽  
Smart Sensors ◽  
Use Case ◽  
Sound Levels ◽  
Efficient Processing ◽  
Interactive Map ◽  
Technical Solutions

Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case.

A solution for annotating sensor data streams - An industrial use case in building management system

2020 ◽  
Author(s):  
Dumindu Madithiyagasthenna ◽  
Prem Prakash Jayaraman ◽  
Ahsan Morshed ◽  
Abdur Rahim Mohammad Forkan ◽  
Dimitrios Georgakopoulos ◽  
...  
Keyword(s):  
Data Streams ◽  
Management System ◽  
Sensor Data ◽  
Use Case ◽  
Building Management ◽  
Building Management System ◽  
Industrial Use

scholarly journals Exploiting Inter- and Intra-Base Crossing with Multi-Mappings: Application to Environmental Data

2018 ◽  
Vol 2 (3) ◽  
pp. 25 ◽  
Author(s):  
Hicham Hajj-Hassan ◽  
Anne Laurent ◽  
Arnaud Martin
Keyword(s):  
Spatial Location ◽  
Point Of View ◽  
Environmental Data ◽  
Sensor Data ◽  
Global Ocean ◽  
Global Changes ◽  
Geospatial Information ◽  
Multiple Points ◽  
Points Of View ◽  
Single Data

Environmental data are currently gaining more and more interest as they are required to understand global changes. In this context, sensor data are collected and stored in dedicated databases. Frameworks have been developed for this purpose and rely on standards, as for instance the Sensor Observation Service (SOS) provided by the Open GeoSpatial Consortium (OGC), where all measurements are bound to a so-called Feature of Interest (FoI). These databases are used to validate and test scientific hypotheses often formulated as correlations and causality between variables, as for instance the study of the correlations between environmental factors and chlorophyll levels in the global ocean. However, the hypotheses of the correlations to be tested are often difficult to formulate as the number of variables that the user can navigate through can be huge. Moreover, it is often the case that the data are stored in such a manner that they prevent scientists from crossing them in order to retrieve relevant correlations. Indeed, the FoI can be a spatial location (e.g., city), but can also be any other object (e.g., animal species). The same data can thus be represented in several manners, depending on the point of view. The FoI varies from one representation to the other one, while the data remain unchanged. In this article, we propose a novel methodology including a crucial step to define multiple mappings from the data sources to these models that can then be crossed, thus offering multiple possibilities that could be hidden from the end-user if using the initial and single data model. These possibilities are provided through a catalog embedding the multiple points of view and allowing the user to navigate through these points of view through innovative OLAP-like operations. It should be noted that the main contribution of this work lies in the use of multiple points of view, as many other works have been proposed for manipulating, aggregating visualizing and navigating through geospatial information. Our proposal has been tested on data from an existing environmental observatory from Lebanon. It allows scientists to realize how biased the representations of their data are and how crucial it is to consider multiple points of view to study the links between the phenomena.

scholarly journals The Architectural Design Requirements of a Blockchain-Based Port Community System

Logistics ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 30
Author(s):  
Elnaz Irannezhad
Keyword(s):  
Data Storage ◽  
Architectural Design ◽  
Business Processes ◽  
Modular Design ◽  
Use Case ◽  
Value Proposition ◽  
Central Value ◽  
Design Requirements ◽  
The Right ◽  
Technical Assessment

This paper presents the value proposition of blockchain for Port Community Systems (PCS) by dissecting the business processes in port logistics and unfolding functionalities of blockchain in lowering the transaction cost. This paper contributes to the research by a detailed technical assessment of the plethora of currently available blockchain platforms and consensus mechanisms, against the identified requirements in this specific use case. The results of this technical assessment highlight the central value proposition of blockchain for landlord ports, which is independency from a central authority as the controlling agent. Bridging between two research domains of Information Technology and Logistics, this paper proposes the preferred architectural design requirements of a blockchain-based PCS, including provisioning private sidechains, modular design with inter-chain interoperability, and encrypted off-chain data storage. Availability—the readiness for correct service, and reliability—the continuity of correct service, are heavily reliant on the right choice being made for blockchain design for such a complex use case. A preliminary comparative analysis among different decentralisation levels in this paper suggests that a permissioned public blockchain offers the best trade-off in performance measures for this use case. This technical review identifies six research agenda from a design perspective.

Energy Design by Evolution: Applying Evolutionary Computing to Energy Efficient Architectural Design

2014 ◽  
Vol 899 ◽  
pp. 120-125
Author(s):  
Bernhard Sommer ◽  
Ulrich Pont
Keyword(s):  
Energy Efficiency ◽  
Energy Efficient ◽  
Architectural Design ◽  
Evolutionary Computing ◽  
Environmental Data ◽  
Design Strategies ◽  
Energy Efficient Buildings ◽  
Optimum Solution

In this paper, the authors want to show a method that allows customizing energy efficient buildings to the very task and to the very site by linking environmental data and design strategies through algorithmic processes. An optimum solution for the energy efficiency of a building can then be found by running an evolutionary solver.

Comparison of Data-Driven Models for Cleaning eHealth Sensor Data: Use Case on ECG Signal

2020 ◽  
Vol 114 (2) ◽  
pp. 1501-1517
Author(s):  
Ana Koren ◽  
Marko Jurčević ◽  
Ramjee Prasad
Keyword(s):  
Data Use ◽  
Data Driven ◽  
Sensor Data ◽  
Ecg Signal ◽  
Use Case

scholarly journals Using Sensor-Based Quality Data in Automotive Supply Chains

Machines ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 53 ◽  
Author(s):  
Michael Teucke ◽  
Eike Broda ◽  
Axel Börold ◽  
Michael Freitag
Keyword(s):  
Supply Chains ◽  
Discrete Event ◽  
Transport Processes ◽  
Environmental Data ◽  
Sensor Data ◽  
Quality Data ◽  
Event Management ◽  
Event Simulation ◽  
Mutual Relations ◽  
Automotive Supply

In many current supply chains, transport processes are not yet being monitored concerning how they influence product quality. Sensor technologies combined with telematics and digital services allow for collecting environmental data to supervise these processes in near real-time. This article outlines an approach for integrating sensor-based quality data into supply chain event management (SCEM). The article describes relationships between environmental conditions and quality defects of automotive products and their mutual relations to sensor data. A discrete-event simulation shows that the use of sensor data in an event-driven control of material flows can keep inventory levels more stable. In conclusion, sensor data can improve quality monitoring in transport processes within automotive supply chains.

scholarly journals Scalable Polyhedral Verification of Recurrent Neural Networks

2021 ◽  
pp. 225-248
Author(s):  
Wonryong Ryou ◽  
Jiayu Chen ◽  
Mislav Balunovic ◽  
Gagandeep Singh ◽  
Andrei Dan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Recurrent Neural Networks ◽  
Sensor Data ◽  
Motion Sensor ◽  
Use Case ◽  
Prior Work ◽  
Non Linear ◽  
Speech Classification ◽  
Sampling Optimization

AbstractWe present a scalable and precise verifier for recurrent neural networks, called Prover based on two novel ideas: (i) a method to compute a set of polyhedral abstractions for the non-convex and non-linear recurrent update functions by combining sampling, optimization, and Fermat’s theorem, and (ii) a gradient descent based algorithm for abstraction refinement guided by the certification problem that combines multiple abstractions for each neuron. Using Prover, we present the first study of certifying a non-trivial use case of recurrent neural networks, namely speech classification. To achieve this, we additionally develop custom abstractions for the non-linear speech preprocessing pipeline. Our evaluation shows that Prover successfully verifies several challenging recurrent models in computer vision, speech, and motion sensor data classification beyond the reach of prior work.

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