scholarly journals A Fully Open-Source Approach to Intelligent Edge Computing: AGILE’s Lesson

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1309
Author(s):  
Massimo Vecchio ◽  
Paolo Azzoni ◽  
Andreas Menychtas ◽  
Ilias Maglogiannis ◽  
Alexander Felfernig
Keyword(s):  
Low Cost ◽  
Cost Effective ◽  
Pollution Monitoring ◽  
Quantified Self ◽  
Air Pollution Monitoring ◽  
Industrial Iot ◽  
Funded Project ◽  
Software And Hardware ◽  
Iot Devices ◽  
The Internet Of Things

In this paper, we describe the main outcomes of AGILE (acronym for “Adaptive Gateways for dIverse muLtiple Environments”), an EU-funded project that recently delivered a modular hardware and software framework conceived to address the fragmented market of embedded, multi-service, adaptive gateways for the Internet of Things (IoT). Its main goal is to provide a low-cost solution capable of supporting proof-of-concept implementations and rapid prototyping methodologies for both consumer and industrial IoT markets. AGILE allows developers to implement and deliver a complete (software and hardware) IoT solution for managing non-IP IoT devices through a multi-service gateway. Moreover, it simplifies the access of startups to the IoT market, not only providing an efficient and cost-effective solution for industries but also allowing end-users to customize and extend it according to their specific requirements. This flexibility is the result of the joint experience of established organizations in the project consortium already promoting the principles of openness, both at the software and hardware levels. We illustrate how the AGILE framework can provide a cost-effective yet solid and highly customizable, technological foundation supporting the configuration, deployment, and assessment of two distinct showcases, namely a quantified self application for individual consumers, and an air pollution monitoring station for industrial settings.

scholarly journals Virtualizing AI at the Distributed Edge Towards Intelligent IoT Applications

2021 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Claudia Campolo ◽  
Giacomo Genovese ◽  
Antonio Iera ◽  
Antonella Molinaro
Keyword(s):  
Resource Constraints ◽  
Smart Cities ◽  
Traditional Approach ◽  
Low Cost ◽  
Iot Applications ◽  
Software And Hardware ◽  
Iot Devices ◽  
Consumer Devices ◽  
Hardware Platforms ◽  
Smart Factories

Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.

scholarly journals H2020 project CAPTOR dataset: raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network.

Data in Brief ◽  
2021 ◽  
pp. 107127
Author(s):  
Jose M. Barcelo-Ordinas ◽  
Pau Ferrer-Cid ◽  
Jorge Garcia-Vidal ◽  
Mar Viana ◽  
Ana Ripoll
Keyword(s):  
Air Pollution ◽  
Low Cost ◽  
Monitoring Network ◽  
Pollution Monitoring ◽  
Raw Data ◽  
Air Pollution Monitoring

scholarly journals Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 256
Author(s):  
Pengfei Han ◽  
Han Mei ◽  
Di Liu ◽  
Ning Zeng ◽  
Xiao Tang ◽  
...  
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Short Term Memory ◽  
Hot Spot ◽  
Low Cost ◽  
Field Evaluation ◽  
Pollution Monitoring ◽  
Coefficient Of Determination ◽  
Air Pollution Monitoring ◽  
National Monitoring

Pollutant gases, such as CO, NO2, O3, and SO2 affect human health, and low-cost sensors are an important complement to regulatory-grade instruments in pollutant monitoring. Previous studies focused on one or several species, while comprehensive assessments of multiple sensors remain limited. We conducted a 12-month field evaluation of four Alphasense sensors in Beijing and used single linear regression (SLR), multiple linear regression (MLR), random forest regressor (RFR), and neural network (long short-term memory (LSTM)) methods to calibrate and validate the measurements with nearby reference measurements from national monitoring stations. For performances, CO > O3 > NO2 > SO2 for the coefficient of determination (R2) and root mean square error (RMSE). The MLR did not increase the R2 after considering the temperature and relative humidity influences compared with the SLR (with R2 remaining at approximately 0.6 for O3 and 0.4 for NO2). However, the RFR and LSTM models significantly increased the O3, NO2, and SO2 performances, with the R2 increasing from 0.3–0.5 to >0.7 for O3 and NO2, and the RMSE decreasing from 20.4 to 13.2 ppb for NO2. For the SLR, there were relatively larger biases, while the LSTMs maintained a close mean relative bias of approximately zero (e.g., <5% for O3 and NO2), indicating that these sensors combined with the LSTMs are suitable for hot spot detection. We highlight that the performance of LSTM is better than that of random forest and linear methods. This study assessed four electrochemical air quality sensors and different calibration models, and the methodology and results can benefit assessments of other low-cost sensors.

scholarly journals INCORPOARATION OF GEOSENSOR NETWORKS INTO INTERNET OF THINGS FOR ENVIRONMENTAL MONITORING

2015 ◽  
Vol XL-1-W5 ◽  
pp. 259-261 ◽  
Author(s):  
R. Habibi ◽  
A. A. Alesheikh
Keyword(s):  
Internet Of Things ◽  
Environmental Monitoring ◽  
Communication Technologies ◽  
Environmental Data ◽  
Pollution Monitoring ◽  
The Internet ◽  
Air Pollution Monitoring ◽  
Sensor Web ◽  
The Internet Of Things ◽  
Resolution Data

Thanks to the recent advances of miniaturization and the falling costs for sensors and also communication technologies, Internet specially, the number of internet-connected things growth tremendously. Moreover, geosensors with capability of generating high spatial and temporal resolution data, measuring a vast diversity of environmental data and automated operations provide powerful abilities to environmental monitoring tasks. Geosensor nodes are intuitively heterogeneous in terms of the hardware capabilities and communication protocols to take part in the Internet of Things scenarios. Therefore, ensuring interoperability is an important step. With this respect, the focus of this paper is particularly on incorporation of geosensor networks into Internet of things through an architecture for monitoring real-time environmental data with use of OGC Sensor Web Enablement standards. This approach and its applicability is discussed in the context of an air pollution monitoring scenario.

Research on Data Release and Location Monitoring Technology of Sensor Network Based on Internet of Things

2021 ◽  
Author(s):  
Bin Lin
Keyword(s):  
Wireless Sensor Network ◽  
Internet Of Things ◽  
Sensor Network ◽  
Low Cost ◽  
Wireless Sensor ◽  
The Internet ◽  
Network Data ◽  
Monitoring Technology ◽  
Software And Hardware ◽  
The Internet Of Things

The Internet of Things is another information technology revolution and industrial wave after computer, Internet and mobile communication. It is becoming a key foundation and an important engine for the green, intelligent and sustainable development of economic society. The new networked intelligent production mode characterized by the integration innovation of the Internet of Things is shaping the core competitiveness of the future manufacturing industry. The application of sensor network data positioning and monitoring technology based on the Internet of Things in industry, power and other industries is a hot field for the development of the Internet of Things. Sensor network processing and industrial applications are becoming increasingly complex, and new features have appeared in the sensor network scale and infrastructure in these fields. Therefore, the Internet of Things perception data processing has become a research hotspot in the deep integration process between industry and the Internet of Things. This paper deeply analyzes and summarizes the characteristics of sensor network perception data under the new trend of the Internet of Things as well as the research on location monitoring technology, and makes in-depth exploration from the release and location monitoring of sensor network perception data of the Internet of Things. Sensor network technology integrated sensor technology, micro-electromechanical system technology, wireless communication technology, embedded computing technology and distributed information processing technology in one, with easy layout, easy control, low power consumption, flexible communication, low cost and other characteristics. Therefore, based on the release and location monitoring technologies of sensor network data based on the Internet of Things in different applications, this paper studies the corresponding networking technologies, energy management, data management and fusion methods. Standardization system in wireless sensor network low cost, and convenient data management needs, design the iot oriented middleware, and develops the software and hardware system, the application demonstration, the results show that the design of wireless sensor network based on iot data monitoring and positioning technology is better meet the application requirements, fine convenient integration of software and hardware, and standardized requirements and suitable for promotion.

scholarly journals IoT Based Automatic Air Pollution Monitoring and Purification System

2021 ◽  
Vol 9 (9) ◽  
pp. 1378-1383
Author(s):  
Manoj Gurung
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Indoor Air Pollution ◽  
Premature Mortality ◽  
Cost Effective ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Air Purifier

Abstract: Degradation of air quality, like climate change and global warming, has become an all-encompassing existential hazard to humanity and natural life. Exposure to severely polluted air on a regular basis causes pulmonary disorders and contributes to severe allergies and asthma. According to studies, more than 10 million people die each year as a result of irregularities produced directly or indirectly by air pollution. The work of Lelieveld et al. [1] sheds light on the gravity of the problem. It is estimated that by 2050, the worldwide premature mortality from air pollution will exceed 6.6 million fatalities per year (358000 from ozone, the rest from PM 2.5) [1]. As a result, we decided to focus our study on improving indoor air quality. Despite the fact that there are numerous indoor air purifiers on the market, their cost belies their effectiveness, and the effective ones are far too expensive for working-class people to afford [2]. In order to address this issue, we created an automated Internet of Things (IoT) based air filtration system that uses an automated air purifier which is triggered when air quality falls below WHO criteria. Our initiative intends to improve indoor air quality by utilizing the most cost-effective and efficient modules available. Keywords: Indoor Air Pollution, Air Purifier, IAQ, Sharp Dust Sensor GP2Y1010AU0F, IoT, Particulate Matter (PM), HEPA Filter

scholarly journals Model-driven development of asynchronous message-driven architectures with AsyncAPI

2021 ◽  
Author(s):  
Abel Gómez ◽  
Markel Iglesias-Urkia ◽  
Lorea Belategi ◽  
Xabier Mendialdua ◽  
Jordi Cabot
Keyword(s):  
Cyber Physical Systems ◽  
Complex Data ◽  
Previous Knowledge ◽  
Physical Systems ◽  
Everyday Objects ◽  
Model Driven ◽  
Design And Implementation ◽  
Industrial Iot ◽  
Iot Devices ◽  
The Internet Of Things

AbstractIn the Internet-of-Things (IoT) vision, everyday objects evolve into cyber-physical systems. The massive use and deployment of these systems has given place to the Industry 4.0 or Industrial IoT (IIoT). Due to its scalability requirements, IIoT architectures are typically distributed and asynchronous. In this scenario, one of the most widely used paradigms is publish/subscribe, where messages are sent and received based on a set of categories or topics. However, these architectures face interoperability challenges. Consistency in message categories and structure is the key to avoid potential losses of information. Ensuring this consistency requires complex data processing logic both on the publisher and the subscriber sides. In this paper, we present our proposal relying on AsyncAPI to automate the design and implementation of these asynchronous architectures using model-driven techniques for the generation of (part of) message-driven infrastructures. Our proposal offers two different ways of designing the architectures: either graphically, by modeling and annotating the messages that are sent among the different IoT devices, or textually, by implementing an editor compliant with the AsyncAPI specification. We have evaluated our proposal by conducting a set of experiments with 25 subjects with different expertise and background. The experiments show that one-third of the subjects were able to design and implement a working architecture in less than an hour without previous knowledge of our proposal, and an additional one-third estimated that they would only need less than two hours in total.

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