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Article

IoT Sensing for Reality-Enhanced Serious Games, a Fuel-Efficient Drive Use Case

1
Department of Electrical, Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, 16145 Genova, Italy
2
IoT Laboratory, Queen Mary University of London, London E1 4NS, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Jose Manuel Molina López
Sensors 2021, 21(10), 3559; https://doi.org/10.3390/s21103559
Received: 6 April 2021 / Revised: 11 May 2021 / Accepted: 15 May 2021 / Published: 20 May 2021
(This article belongs to the Section Internet of Things)
Internet of Things technologies are spurring new types of instructional games, namely reality-enhanced serious games (RESGs), that support training directly in the field. This paper investigates a key feature of RESGs, i.e., user performance evaluation using real data, and studies an application of RESGs for promoting fuel-efficient driving, using fuel consumption as an indicator of driver performance. In particular, we propose a reference model for supporting a novel smart sensing dataflow involving the combination of two modules, based on machine learning, to be employed in RESGs in parallel and in real-time. The first module concerns quantitative performance assessment, while the second one targets verbal recommendation. For the assessment module, we compared the performance of three well-established machine learning algorithms: support vector regression, random forest and artificial neural networks. The experiments show that random forest achieves a slightly better performance assessment correlation than the others but requires a higher inference time. The instant recommendation module, implemented using fuzzy logic, triggers advice when inefficient driving patterns are detected. The dataflow has been tested with data from the enviroCar public dataset, exploiting on board diagnostic II (OBD II) standard vehicular interface information. The data covers various driving environments and vehicle models, which makes the system robust for real-world conditions. The results show the feasibility and effectiveness of the proposed approach, attaining a high estimation correlation (R2 = 0.99, with random forest) and punctual verbal feedback to the driver. An important word of caution concerns users’ privacy, as the modules rely on sensitive personal data, and provide information that by no means should be misused. View Full-Text
Keywords: Internet of Things (IoT); serious game (SG); reality-enhanced serious games (RESGs); eco-driving; fuel consumption; on-board diagnostic-II (OBD-II); machine learning (ML) Internet of Things (IoT); serious game (SG); reality-enhanced serious games (RESGs); eco-driving; fuel consumption; on-board diagnostic-II (OBD-II); machine learning (ML)
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MDPI and ACS Style

Massoud, R.; Berta, R.; Poslad, S.; De Gloria, A.; Bellotti, F. IoT Sensing for Reality-Enhanced Serious Games, a Fuel-Efficient Drive Use Case. Sensors 2021, 21, 3559. https://doi.org/10.3390/s21103559

AMA Style

Massoud R, Berta R, Poslad S, De Gloria A, Bellotti F. IoT Sensing for Reality-Enhanced Serious Games, a Fuel-Efficient Drive Use Case. Sensors. 2021; 21(10):3559. https://doi.org/10.3390/s21103559

Chicago/Turabian Style

Massoud, Rana, Riccardo Berta, Stefan Poslad, Alessandro De Gloria, and Francesco Bellotti. 2021. "IoT Sensing for Reality-Enhanced Serious Games, a Fuel-Efficient Drive Use Case" Sensors 21, no. 10: 3559. https://doi.org/10.3390/s21103559

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