2023
González-Pérez, Alberto; Matey-Sanz, Miguel; Granell-Canut, Carlos; Díaz-Sanahuja, Laura; Bretón-López, Juana; Casteleyn, Sven
AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health Journal Article
In: Journal of Biomedical Informatics, vol. 141, pp. 104359, 2023, ISSN: 1532-0464.
Abstract | Links | BibTeX | Tags: context-aware computing, digital phenotyping, location-based services, mHealth, smartphone app, symptoms
@article{Gonzalez-Perez2023a,
title = {AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health},
author = {Alberto González-Pérez and Miguel Matey-Sanz and Carlos Granell-Canut and Laura Díaz-Sanahuja and Juana Bretón-López and Sven Casteleyn},
doi = {10.1016/j.jbi.2023.104359},
issn = {1532-0464},
year = {2023},
date = {2023-04-20},
journal = {Journal of Biomedical Informatics},
volume = {141},
pages = {104359},
abstract = {In recent years, interest and investment in health and mental health smartphone apps have grown significantly. However, this growth has not been followed by an increase in quality and the incorporation of more advanced features in such applications. This can be explained by an expanding fragmentation of existing mobile platforms along with more restrictive privacy and battery consumption policies, with a consequent higher complexity of developing such smartphone applications. To help overcome these barriers, there is a need for robust, well-designed software development frameworks which are designed to be reliable, power-efficient and ethical with respect to data collection practices, and which support the sense-analyse-act paradigm typically employed in reactive mHealth applications. In this article, we present the AwarNS Framework, a context-aware modular software development framework for Android smartphones, which facilitates transparent, reliable, passive and active data sampling running in the background (sense), on-device and server-side data analysis (analyse), and context-aware just-in-time offline and online intervention capabilities (act). It is based on the principles of versatility, reliability, privacy, reusability, and testability. It offers built-in modules for capturing smartphone and associated wearable sensor data (e.g. IMU sensors, geolocation, Wi-Fi and Bluetooth scans, physical activity, battery level, heart rate), analysis modules for data transformation, selection and filtering, performing geofencing analysis and machine learning regression and classification, and act modules for persistence and various notification deliveries. We describe the framework’s design principles and architecture design, explain its capabilities and implementation, and demonstrate its use at the hand of real-life case studies implementing various mobile interventions for different mental disorders used in clinical practice.},
keywords = {context-aware computing, digital phenotyping, location-based services, mHealth, smartphone app, symptoms},
pubstate = {published},
tppubtype = {article}
}
In recent years, interest and investment in health and mental health smartphone apps have grown significantly. However, this growth has not been followed by an increase in quality and the incorporation of more advanced features in such applications. This can be explained by an expanding fragmentation of existing mobile platforms along with more restrictive privacy and battery consumption policies, with a consequent higher complexity of developing such smartphone applications. To help overcome these barriers, there is a need for robust, well-designed software development frameworks which are designed to be reliable, power-efficient and ethical with respect to data collection practices, and which support the sense-analyse-act paradigm typically employed in reactive mHealth applications. In this article, we present the AwarNS Framework, a context-aware modular software development framework for Android smartphones, which facilitates transparent, reliable, passive and active data sampling running in the background (sense), on-device and server-side data analysis (analyse), and context-aware just-in-time offline and online intervention capabilities (act). It is based on the principles of versatility, reliability, privacy, reusability, and testability. It offers built-in modules for capturing smartphone and associated wearable sensor data (e.g. IMU sensors, geolocation, Wi-Fi and Bluetooth scans, physical activity, battery level, heart rate), analysis modules for data transformation, selection and filtering, performing geofencing analysis and machine learning regression and classification, and act modules for persistence and various notification deliveries. We describe the framework’s design principles and architecture design, explain its capabilities and implementation, and demonstrate its use at the hand of real-life case studies implementing various mobile interventions for different mental disorders used in clinical practice.
2022
Weerapanpisit, Ponlawat; Trilles-Oliver, Sergio; Huerta-Guijarro, Joaquín; Painho, Marco
A Decentralized Location-Based Reputation Management System in the IoT Using Blockchain Journal Article
In: IEEE Internet of Things Journal, vol. 9, no. 16, pp. 15100 - 15115, 2022, ISSN: 2327-4662.
Abstract | Links | BibTeX | Tags: blockchain, Internet of things, location-based services, spatial indexing
@article{Weerapanpisit2022a,
title = {A Decentralized Location-Based Reputation Management System in the IoT Using Blockchain},
author = {Ponlawat Weerapanpisit and Sergio Trilles-Oliver and Joaquín Huerta-Guijarro and Marco Painho},
doi = {https://doi.org/10.1109/JIOT.2022.3147478},
issn = {2327-4662},
year = {2022},
date = {2022-08-15},
journal = {IEEE Internet of Things Journal},
volume = {9},
number = {16},
pages = {15100 - 15115},
abstract = {The Internet of Things (IoT) allows an object to connect to the Internet and observe or interact with a physical phenomenon. The communication technologies allow one IoT device to discover and communicate with another in order to exchange services, in a similar way to what humans do in their social networks. Knowing the reputation of another device is important to consider whether it is trustworthy before establishing a new connection and thus, avoid possible unexpected behaviors as a consequence. Trustworthiness, as a property of a device, can be affected by different factors including its geographical location. Hence, this research work proposes an architecture to manage reputation values of end devices in an IoT system based on the area where they are located. A cloud–fog–edge architecture is proposed, where the fog layer uses the Blockchain technology to keep the reputation management system consistent and fault tolerant across different nodes. The location-based part of the system was done by storing geographical areas in smart contracts (coined as geospatial smart contracts) and making the reputation values subject to different regions depending on the geographical location of the device. To reduce the complexity of the spatial computation, the geographical data are geocoded by either one of two different spatial indexing techniques. This work also introduced two different structures for storing geocoded areas based on either cell list or tree structure. Finally, three experiments to test the proposed architecture are presented, to deploy the architecture in IoT devices, and to compare the two geocoding techniques in smart contracts.},
keywords = {blockchain, Internet of things, location-based services, spatial indexing},
pubstate = {published},
tppubtype = {article}
}
The Internet of Things (IoT) allows an object to connect to the Internet and observe or interact with a physical phenomenon. The communication technologies allow one IoT device to discover and communicate with another in order to exchange services, in a similar way to what humans do in their social networks. Knowing the reputation of another device is important to consider whether it is trustworthy before establishing a new connection and thus, avoid possible unexpected behaviors as a consequence. Trustworthiness, as a property of a device, can be affected by different factors including its geographical location. Hence, this research work proposes an architecture to manage reputation values of end devices in an IoT system based on the area where they are located. A cloud–fog–edge architecture is proposed, where the fog layer uses the Blockchain technology to keep the reputation management system consistent and fault tolerant across different nodes. The location-based part of the system was done by storing geographical areas in smart contracts (coined as geospatial smart contracts) and making the reputation values subject to different regions depending on the geographical location of the device. To reduce the complexity of the spatial computation, the geographical data are geocoded by either one of two different spatial indexing techniques. This work also introduced two different structures for storing geocoded areas based on either cell list or tree structure. Finally, three experiments to test the proposed architecture are presented, to deploy the architecture in IoT devices, and to compare the two geocoding techniques in smart contracts.
2021
Rodríguez-Pupo, Luis Enrique
An Analytics Platform for Integrating and Computing Spatio-Temporal Metrics in Location-aware Games PhD Thesis
Universitat Jaume I. INIT, 2021.
Abstract | Links | BibTeX | Tags: context-aware computing, geogames, geolocation, location-based services
@phdthesis{Rodriguez2021b,
title = {An Analytics Platform for Integrating and Computing Spatio-Temporal Metrics in Location-aware Games},
author = {Luis Enrique Rodríguez-Pupo},
url = {http://hdl.handle.net/10803/671588},
doi = {http://dx.doi.org/10.6035/14101.2021.357562},
year = {2021},
date = {2021-04-28},
school = {Universitat Jaume I. INIT},
abstract = {This thesis presents an analytics platform for calculating spatio-temporal metrics in the context of geogames and context-based applications. It is based on an underlying conceptual model for spatio-temporal metrics, which consists of dimensions and variables to describe spatial and temporal phenomena, metrics functions to calculate application-relevant information and conditions using these data models, and actions to be triggered when certain conditions are met. The analytics platform is implemented as a cloud-based, distributed application that allows developers to define data requirements, collect required (client-generated) data, and define and execute spatio-temporal metrics. It is designed to handle large amounts of (streaming) data and to scale well under increasing amounts of data and metrics computations. The platform is validated in two experiments: a location-aware game for collecting noise data in a city and a mobile application for location-based mental health treatments, which shows its usability, versatility and feasibility in real-world scenario},
keywords = {context-aware computing, geogames, geolocation, location-based services},
pubstate = {published},
tppubtype = {phdthesis}
}
This thesis presents an analytics platform for calculating spatio-temporal metrics in the context of geogames and context-based applications. It is based on an underlying conceptual model for spatio-temporal metrics, which consists of dimensions and variables to describe spatial and temporal phenomena, metrics functions to calculate application-relevant information and conditions using these data models, and actions to be triggered when certain conditions are met. The analytics platform is implemented as a cloud-based, distributed application that allows developers to define data requirements, collect required (client-generated) data, and define and execute spatio-temporal metrics. It is designed to handle large amounts of (streaming) data and to scale well under increasing amounts of data and metrics computations. The platform is validated in two experiments: a location-aware game for collecting noise data in a city and a mobile application for location-based mental health treatments, which shows its usability, versatility and feasibility in real-world scenario
Furfari, Francesco; Crivello, Antonino; Baronti, Paolo; Barsocchi, Paolo; Girolami, Michele; Palumbo, Filippo; Quezada-Gaibor, Darwin; Mendoza-Silva, Germán Martin; Torres-Sospedra, Joaquín
Discovering location based services: A unified approach for heterogeneous indoor localization systems Journal Article
In: Internet of Things, vol. 13, no. 1001511, 2021, ISSN: 2542-6605.
Abstract | Links | BibTeX | Tags: Indoor localization, Indoor positioning, location-based services
@article{Furfari2021,
title = {Discovering location based services: A unified approach for heterogeneous indoor localization systems},
author = {Francesco Furfari and Antonino Crivello and Paolo Baronti and Paolo Barsocchi and Michele Girolami and Filippo Palumbo and Darwin Quezada-Gaibor and Germán Martin Mendoza-Silva and Joaquín Torres-Sospedra},
doi = {https://doi.org/10.1016/j.iot.2020.100334},
issn = {2542-6605},
year = {2021},
date = {2021-03-01},
journal = {Internet of Things},
volume = {13},
number = {1001511},
abstract = {The technological solutions and communication capabilities offered by the Internet of Things paradigm, in terms of raising availability of wearable devices, the ubiquitous internet connection, and the presence on the market of service-oriented solutions, have allowed a wide proposal of Location Based Services (LBS). In a close future, we foresee that companies and service providers will have developed reliable solutions to address indoor positioning, as basis for useful location based services. These solutions will be different from each other and they will adopt different hardware and processing techniques. This paper describes the proposal of a unified approach for Indoor Localization Systems that enables the cooperation between heterogeneous solutions and their functional modules. To this end, we designed an integrated architecture that, abstracting its main components, allows a seamless interaction among them. Finally, we present a working prototype of such architecture, which is based on the popular Telegram application for Android, as an integration demonstrator. The integration of the three main phases –namely the discovery phase, the User Agent self-configuration, and the indoor map retrieval/rendering– demonstrates the feasibility of the proposed integrated architecture.},
keywords = {Indoor localization, Indoor positioning, location-based services},
pubstate = {published},
tppubtype = {article}
}
The technological solutions and communication capabilities offered by the Internet of Things paradigm, in terms of raising availability of wearable devices, the ubiquitous internet connection, and the presence on the market of service-oriented solutions, have allowed a wide proposal of Location Based Services (LBS). In a close future, we foresee that companies and service providers will have developed reliable solutions to address indoor positioning, as basis for useful location based services. These solutions will be different from each other and they will adopt different hardware and processing techniques. This paper describes the proposal of a unified approach for Indoor Localization Systems that enables the cooperation between heterogeneous solutions and their functional modules. To this end, we designed an integrated architecture that, abstracting its main components, allows a seamless interaction among them. Finally, we present a working prototype of such architecture, which is based on the popular Telegram application for Android, as an integration demonstrator. The integration of the three main phases –namely the discovery phase, the User Agent self-configuration, and the indoor map retrieval/rendering– demonstrates the feasibility of the proposed integrated architecture.