Furfari F, Crivello A, Baronti P, Barsocchi P, Girolami M, Palumbo F, Quezadagaibor D, Mendoza Silva Gm, Torressospedra J
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 Base... d 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. [show more]Source: INTERNET OF THINGS, vol. 13 (issue 100334), pp. 1-14
DOI: 10.1016/j.iot.2020.100334
Project(s): A-WEAR
Metrics:
Smart CitationsSee how this article has been cited at scite.ai
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
See at:
Internet of Things 
| Recolector de Ciencia Abierta, RECOLECTA | CNR IRIS | ISTI Repository | www.sciencedirect.com | ZENODO | CNR IRIS 
Barsocchi P., Calabrò A., Crivello A., Daoudagh S., Furfari F., Girolami M., Marchetti E.
The way people access services in indoor environments has dramatically changed in the last year. The countermeasures to the COVID-19 pandemic imposed a disruptive requirement, namely preserving social distance among people in indoor environments. We explore in this work the possibility of adopting t... he indoor localization technologies to measure the distance among users in indoor environments. We discuss how information about people's contacts collected can be exploited during three stages: before, during, and after people access a service. We present a reference architecture for an Indoor Localization System (ILS), and we illustrate three representative use-cases. We derive some architectural requirements, and we discuss some issues that concretely cope with the real installation of an ILS in real-world settings. In particular, we explore the privacy and trust reputation of an ILS, the discovery phase, and the deployment of the ILS in real-world settings. We finally present an evaluation framework for assessing the performance of the architecture proposed. [show more]Source: ARRAY, vol. 9
DOI: 10.1016/j.array.2020.100051
Project(s): CyberSec4Europe
Metrics:
Smart CitationsSee how this article has been cited at scite.ai
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
See at:
Array | CNR IRIS | ISTI Repository | Array | CNR IRIS | CNR IRIS
Denna E, Civiello M, Porcelli S, Crivello A, Mavilia F, Palumbo F
Monitoring system have been customized to collect data and to analyse several aspects of the users' life, the reason of this custom solution came from the needs to join physical activity of the user, life usage, social interaction and mind activities, all these features are not present in standard d... evices all together, so we arrived to a new system architecture where the monitoring system is the first front end versus the user. This chapter describes the general monitoring system architecture and provides insight into the contribution and role of sensors. Such sensing solutions are not only designed to match the needs and requirements of the user but also to reduce intrusiveness and usage complexity. By doing so the system is designed around the life of its users and maximizes the effectiveness of data collection. Example from NESTORE project are taken as reference. [show more]Source: RESEARCH FOR DEVELOPMENT, pp. 55-76
DOI: 10.1007/978-3-030-72663-8_4
Project(s): NESTORE
Metrics:
Smart CitationsSee how this article has been cited at scite.ai
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
See at:
CNR IRIS | link.springer.com | CNR IRIS
Shao W, Zhao F, Luo H, Tian H, Li J, Crivello A
Pedestrian dead reckoning based on particle filter is commonly used for enabling seamless smartphone-based indoor positioning. However, compass directions indoor are heavily distorted due to the presence of ferromagnetic materials. Conventional particle filters convert the raw compass direction to a... distribution adding a constant variance noise and leveraging a particle swarm to simulate the distribution. Finally, the selection of eligible directions is performed applying external constraints mainly imposed from the indoor map. However, the choice of a constant parameter decreases the positioning performances because the variance of nearby context, including topography, ferromagnetic materials, and particle distribution, is not represented. Therefore, we propose the particle filter reinforcement able to adaptively learn and adjust the variance of the direction observing the context in real-time. Experiments in real-world scenarios show that the proposed method improves the positioning accuracy by more than 20% at the 80% probability compared with state-of-the-art methods. [show more]Source: IEEE COMMUNICATIONS LETTERS, vol. 25, pp. 3144-3148
DOI: 10.1109/lcomm.2021.3090300
Metrics:
Smart CitationsSee how this article has been cited at scite.ai
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
See at:
CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS | IEEE Communications Letters
Torressospedra J, Silva I, Klus L, Quezadagaibor D, Crivello A, Barsocchi P, Pendao C, Lohan Es, Nurmi J, Moreira A
The evaluation of Indoor Positioning Systems (IPSs) mostly relies on local deployments in the researchers' or partners' facilities. The complexity of preparing comprehensive experiments, collecting data, and considering multiple scenarios usually limits the evaluation area and, therefore, the assess... ment of the proposed systems. The requirements and features of controlled experiments cannot be generalized since the use of the same sensors or anchors density cannot be guaranteed. The dawn of datasets is pushing IPS evaluation to a similar level as machine-learning models, where new proposals are evaluated over many heterogeneous datasets. This paper proposes a way to evaluate IPSs in multiple scenarios, that is validated with three use cases. The results prove that the proposed aggregation of the evaluation metric values is a useful tool for high-level comparison of IPSs. [show more]DOI: 10.1109/ipin51156.2021.9662560
Metrics:
Smart CitationsSee how this article has been cited at scite.ai
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
See at:
CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS
Shao W, Luo H, Zhao F, Tian H, Huang J, Crivello A
Traditional Wi-Fi-based floor identification methods mainly have been tested in small experimental scenarios, and generally, their accuracies drop significantly when applied in real large and multistorey environments. The main challenge emerges when the complexity of Wi-Fi signals on the same floor ... exceeds the complexity between the floors along the vertical direction, leading to a reduced floor distinguishability. A second challenge regards the complexity of Wi-Fi features in environments with atrium, hollow areas, mezzanines, intermediate floors, and crowded signal channels. In this article, we propose an adaptive Wi-Fi-based floor identification algorithm to achieve accurate floor identification also in these environments. Our algorithm, based on the Wi-Fi received signal strength indicator and spatial similarity, first identifies autonomous blocks parcelling the whole environment. Then, local floor identification is performed through the proposed Wi-Fi models to fully harness the Wi-Fi features. Finally, floors are estimated through the joint optimization of the autonomous blocks and the local floor models. We have conducted extensive experiments in three real large and multistorey buildings greater than 140 000 m 2 using 19 different devices. Finally, we show a comparison between our proposal and other state-of-the-art algorithms. Experimental results confirm that our proposal performs better than other methods, and it exhibits an average accuracy of 97.24%. [show more]Source: IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, vol. 18 (issue 2), pp. 847-858
DOI: 10.1109/tii.2021.3074153
Metrics:
Smart CitationsSee how this article has been cited at scite.ai
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS | CNR IRIS | IEEE Transactions on Industrial Informatics
Potortì F., Torres-Sospedra J., Quezada-Gaibor D., Jiménez A. R., Seco F., Pérez-Navarro A., Ortiz M., Zhu N., Renaudin V., Ichikari R., Shimomura R., Ohta N., Nagae S., Kurata T., Wei D., Ji X., Zhang W., Kram S., Stahlke M., Mutschler C., Crivello A., Barsocchi P., Girolami M., Palumbo F., Chen R., Wu Y., Li W., Yu Y., Xu S., Huang L., Liu T., Kuang J., Niu X., Yoshida T., Nagata Y., Fukushima Y., Fukatani N., Hayashida N., Asai Y., Urano K., Ge W., Lee N. T., Fang S. H., Jie Y. C., Young S. R., Chien Y. R., Yua C. C., Ma C., Wub B., Zhangc W., Wang Y., Fan Y., Poslad S., Selviah D. R., Wangd W., Yuan H., Yonamoto Y., Yamaguchi M., Kaichi T., Zhou B., Liue X., Gu Z., Yang C., Wu Z., Xie D., Huang C., Zheng L., Peng A., Jin G., Wangh Q., Luo H., Xiong H., Bao L., Zhangi P., Zhao F., Yuj C. A., Hung C. H., Antsfeld L., Chidlovskii B., Jiang H., Xia M., Yan D., Li Y., Dong Y., Silva I., Pendão C., Meneses F., Nicolau M. J., Costa A., Moreira A., De Cock C., Plets D., Opiela M., Dzama J., Zhang L., Li H., Chen B., Liu Y., Yean S., Lim B. Z., Teo W. J., Leep B. S., Oh H. L.
Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologi... es, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1m for the Smartphone Track and 0.5m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements. [show more]Source: IEEE SENSORS JOURNAL (ONLINE), vol. 22 (issue 6), pp. 5011-5054
DOI: 10.1109/jsen.2021.3083149
Metrics:
Smart CitationsSee how this article has been cited at scite.ai
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
See at:
CNR IRIS | ieeexplore.ieee.org | ISTI Repository | ISTI Repository | CNR IRIS | CNR IRIS