@article{Klus2021,

title = {Direct Lightweight Temporal Compression for Wearable Sensor Data},

author = {Lucie Klus and Roman Klus and Elena Simona Lohan and Carlos Granell-Canut and Jukka Talvitie and Mikko Valkama and Jari Nurmi},

doi = {https://doi.org/10.1109/LSENS.2021.3051809  },

issn = {2475-1472},

year  = {2021},

date = {2021-01-15},

journal = {IEEE Sensors Letters},

volume = {5},

number = {2},

pages = {7000404},

abstract = {Emerging technologies enable massive deployment of wireless sensor networks across many industries. Internet of Things (IoT) devices are often deployed in critical infrastructure or health monitoring and require fast reaction time, reasonable accuracy, and high energy efficiency. In this letter, we introduce a lossy compression method for time-series data, named direct lightweight temporal compression (DLTC), enabling energy-efficient data transfer for power-restricted devices. Our method is based on the lightweight temporal compression method, targeting further reconstruction error minimization and complexity reduction. This letter highlights the key advantages of the proposed method and evaluates the method's performance on several sensor-based, time-series data types. We prove that DLTC outperforms the considered benchmark methods in compression efficiency at the same reconstruction error level.

},

keywords = {A-wear, Sensors, wearables},

pubstate = {published},

tppubtype = {article}

}

@article{Trilles-Oliver2020f,

title = {Reliability Evaluation of the Data Acquisition Potential of a Low-Cost Climatic Network for Applications in Agriculture},

author = {Sergio Trilles-Oliver and Pablo Juan-Verdoy and Carlos Díaz-Avalos and S. Ribeiro and Marco Painho},

doi = {https://doi.org/10.3390/s20226597 },

issn = {1424-8220},

year  = {2020},

date = {2020-12-02},

journal = {Sensors},

volume = {20},

number = {22},

pages = {6597},

abstract = {Temperature, humidity and precipitation have a strong influence on the generation of diseases in different crops, especially in vine. In recent years, advances in different disciplines have enabled the deployment of sensor nodes on agricultural plots. These sensors are characterised by a low cost and so the reliability of the data obtained from them can be compromised, as they are built from low-confidence components. In this research, two studies were carried out to determine the reliability of the data obtained by different SEnviro nodes installed in vineyards. Two networks of meteorological stations were used to carry out these studies, one official and the other professional. The first study was based on calculating the homogenisation of the data, which was performed using the Climatol tool. The second study proposed a similarity analysis using cross-correlation. The results showed that the low-cost node can be used to monitor climatic conditions in an agricultural area in the central zone of the province of Castelló (Spain) and to obtain reliable observations for use in previously published fungal disease models.},

keywords = {Agriculture, crop monitoring, Sensor networks, Sensors},

pubstate = {published},

tppubtype = {article}

}

@article{Marsh-Huhn2020,

title = {A comparative study in the standardisation of IoT devices using geospatial web standards},

author = {Daniel Marsh-Huhn and Sergio Trilles-Oliver and Alberto González-Pérez and Francisco Ramos-Romero},

doi = {https://doi.org/10.1109/JSEN.2020.3031315},

year  = {2020},

date = {2020-12-01},

journal = {IEEE Sensors},

volume = {21},

number = {4},

pages = {5512-5528},

abstract = {Although billions of devices are embedded in the World Wide Web through the Internet of Things, there is still a lack of a common, interoperable way to connect them and make them interact seamlessly. IoT has also found its way into the spatial web. Environmental monitoring and sensing platforms connected over the web by wireless sensor networks are now a common way to monitor natural phenomena. This study compares two open Web Standards ( OGC’s Sensor Observation Service and SensorThings API ) from the geospatial point of view. An IoT platform, called SEnviro , is used to integrate and evaluate implementations for each standard and contrast their qualitative and quantitative traits. The results of the study show that the SensorThings API proves to be the adequate Web Standard for IoT applications in terms of interoperability. It outperforms the contesting Web Standard in terms of flexibility and scalability, which strongly impacts on developer and user experience.	},

keywords = {API, Internet of things, Mastergeotech, Sensors},

pubstate = {published},

tppubtype = {article}

}

@article{Trilles-Oliver2020c,

title = {An IoT platform based on microservices and serverless paradigms for smart farming purposes},

author = {Sergio Trilles-Oliver and Alberto González-Pérez and Joaquín Huerta-Guijarro},

doi = {10.3390/s20082418 },

year  = {2020},

date = {2020-05-23},

journal = {Sensors},

volume = {20},

pages = {2418},

abstract = {Nowadays, the concept of “Everything is connected to Everything” has spread to reach increasingly diverse scenarios, due to the benefits of constantly being able to know, in real-time, the status of your factory, your city, your health or your smallholding. This wide variety of scenarios creates different challenges such as the heterogeneity of IoT devices, support for large numbers of connected devices, reliable and safe systems, energy efficiency and the possibility of using this system by third-parties in other scenarios. A transversal middleware in all IoT solutions is called an IoT platform. the IoT platform is a piece of software that works like a kind of “glue” to combine platforms and orchestrate capabilities that connect devices, users and applications/services in a “cyber-physical” world. In this way, the IoT platform can help solve the challenges listed above. This paper proposes an IoT agnostic architecture, highlighting the role of the IoT platform, within a broader ecosystem of interconnected tools, aiming at increasing scalability, stability, interoperability and reusability. For that purpose, different paradigms of computing will be used, such as microservices architecture and serverless computing. Additionally, a technological proposal of the architecture, called SEnviro Connect, is presented. This proposal is validated in the IoT scenario of smart farming, where five IoT devices (SEnviro nodes) have been deployed to improve wine production. A comprehensive performance evaluation is carried out to guarantee a scalable and stable platform},

keywords = {Internet of things, precision agriculture, Sensors},

pubstate = {published},

tppubtype = {article}

}

@article{Iskandaryan2020,

title = {Air quality prediction in smart cities using machine learning technologies based on sensor data: A Review},

author = {Ditsuhi Iskandaryan and Francisco Ramos-Romero and Sergio Trilles-Oliver},

doi = {https://doi.org/10.3390/app10072401 },

year  = {2020},

date = {2020-04-15},

journal = {Applied sciences},

volume = {10},

number = {7},

pages = {2401},

abstract = {The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features},

keywords = {Air quality sensors, machine learning, Sensors, Smart Cities},

pubstate = {published},

tppubtype = {article}

}

@article{Triller-Oliver2019,

title = {Reliability Validation of a Low-Cost Particulate Matter IoT Sensor in Indoor and Outdoor Environments Using a Reference Sampler},

author = {Sergio Trilles-Oliver and A.B. Vicente and Pablo Juan-Verdoy and Francisco Ramos-Romero and Sergio Meseguer and Laura Serra},

year  = {2019},

date = {2019-00-01},

journal = {Sustainability  },

volume = {11},

pages = {7220},

abstract = {A suitable and quick determination of air quality allows the population to be alerted with respect to high concentrations of pollutants. Recent advances in computer science have led to the development of a high number of low-cost sensors, improving the spatial and temporal resolution of air quality data while increasing the effectiveness of risk assessment. The main objective of this work is to perform a validation of a particulate matter (PM) sensor (HM-3301) in indoor and outdoor environments to study PM2.5 and PM10 concentrations. To date, this sensor has not been evaluated in real-world situations, and its data quality has not been documented. Here, the HM-3301 sensor is integrated into an Internet of things (IoT) platform to establish a permanent Internet connection. The validation is carried out using a reference sampler (LVS3 of Derenda) according to EN12341:2014. It is focused on statistical insight, and environmental conditions are not considered in this study. The ordinary Linear Model, the Generalized Linear Model, Locally Estimated Scatterplot Smoothing, and the Generalized Additive Model have been proposed to compare and contrast the outcomes. The low-cost sensor is highly correlated with the reference measure (R2 greater than 0.70), especially for PM2.5, with a very high accuracy value. In addition, there is a positive relationship between the two measurements, which can be  ppropriately fitted through the Locally Estimated Scatterplot Smoothing model.},

keywords = {Air quality sensors, Internet of things, Sensors},

pubstate = {published},

tppubtype = {article}

}

@article{RamosRomero2014,

title = {A Location Aware Mobile Tool for Direct and Indirect Climate Data Sensors},

author = { Francisco Ramos-Romero and Aida Monfort-Muriach and Joaquín Huerta-Guijarro},

url = {http://hdl.handle.net/10234/125826},

doi = {10.1111/tgis.12097},

issn = {1361-1682},

year  = {2014},

date = {2014-01-01},

journal = {Transactions in GIS},

volume = {18},

number = {3},

pages = {385-400},

abstract = {Access to GIS data from mobile platforms continues to be a challenge and there is a wide range of fields where it is extremely useful. In this work, we combined three key aspects: climate data sensors, mobile platforms and spatial proximity operations. We published and made use of a web 2.0 network of climate data, where content is user-collected, by means of their meteorological stations, and exposed as available information for the virtual community. Moreover, we enriched this data by giving the users the opportunity to directly inform the system with different climate measures. In general, management of this type of information from a mobile application could result in an important decision tool, as it enables us to provide climate-related data according to a context and a geographical location. Therefore, we implemented a native mobile application for iPhone and iPad platforms by using ArcGIS SDK for iOS and by integrating a series of ArcGIS webmaps, which allows us to perform geospatial queries based on the user's location, offering, at the same time, access to all the data provided by the climate data sensor network and from direct users.},

note = {IF: 1.398, Q2},

keywords = {CIUDADES-SALUDABLES, meteorological sensors, Sensors},

pubstate = {published},

tppubtype = {article}

}

@article{DiazSanchez2013,

title = {Publishing sensor observations into Geospatial Information Infrastructures: A use case in fire danger assessment},

author = { Laura Díaz-Sánchez and Arne Bröring and Daniel McInerney and Giorgio Libertá and Theodor Foerster},

url = {http://hdl.handle.net/10234/159894},

doi = {10.1016/j.envsoft.2013.06.002},

issn = {13648152},

year  = {2013},

date = {2013-01-01},

journal = {Environmental Modelling and Software},

volume = {48},

pages = {65-80},

publisher = {Elsevier Ltd},

abstract = {To improve environmental monitoring, the availability of large coverage, interoperable spatio-temporal data is crucial for its integration into environmental models, for example, to compute fire danger models. In order to produce up-to-date and accurate results, these models require data with high temporal and spatial resolution. Thus, it is promising to consider the increasing number of in-situ sensors providing observations of our environment in real-time. Today, interoperable access to such spatio-temporal data is achieved by Geospatial Information Infrastructures (GIIs). From a technical point of view, GIIs provide these data through standards-based Web service interfaces. While those Web service interfaces already enable the interoperable discovery and retrieval of sensor observations, the functionality to publish sensor observations is still an arduous task. Hence, in this paper, we present an approach to improve the registration of sensors and the publication of their observations via standards-based Web service interfaces. We evaluate our approach by extending a standards-based GII and by applying the developed approach as a proof of concept to integrate in-situ weather observations into the European Forest Fire Information System for assessing fire danger in Spain.},

note = {IF: 4.538, q1},

keywords = {Environmental models, EUROGEOSS, forest fire, Geospatial Information Infrastructures, OGC, Sensors, Spatial data infrastructures (SDI)},

pubstate = {published},

tppubtype = {article}

}

@article{TamayoFong2012a,

title = {Using SWE Standards for Ubiquitous Environmental Sensing: A Performance Analysis},

author = { Alain Tamayo-Fong and Carlos Granell-Canut and Joaquín Huerta-Guijarro},

url = {http://hdl.handle.net/10234/62253},

doi = {10.3390/s120912026},

issn = {1424-8220},

year  = {2012},

date = {2012-08-01},

journal = {Sensors},

volume = {12},

pages = {12026--12051},

abstract = {Although smartphone applications represent the most typical data consumer tool from the citizen perspective in environmental applications, they can also be used for in-situ data collection and production in varied scenarios, such as geological sciences and biodiversity. The use of standard protocols, such as SWE, to exchange information between smartphones and sensor infrastructures brings benefits such as interoperability and scalability, but their reliance on XML is a potential problem when large volumes of data are transferred, due to limited bandwidth and processing capabilities on mobile phones. In this article we present a performance analysis about the use of SWE standards in smartphone applications to consume and produce environmental sensor data, analysing to what extent the performance problems related to XML can be alleviated by using alternative uncompressed and compressed formats.},

note = {IF: 1.939, Q1},

keywords = {environmental monitoring, GEOCLOUD, OSMOSIS, Sensors, Standards},

pubstate = {published},

tppubtype = {article}

}