One of the main elements of location-based services (LBS) is the awareness and knowledge of the user’s location information inside the smart buildings. In this study, a smartphone sensor-based indoor positioning system (IPS) is proposed to track a person’s location in Texting and Pocket carrying modes in a smart building. The gravity, gyroscope, and magnetometer sensors data were combined using a gradient descent algorithm (GDA) to estimate the heading angle. This system was implemented in three straight, complex, and rectangular paths. The mean (M) and standard deviation (SD) of the absolute heading error of each step were obtained as (1.68°, 1.97°) in the Texting mode and (4.39°, 5.22°) in the Pocket mode, respectively. Acceleration and angle-based models were employed to estimate the step length in the Texting and Pocket modes, respectively. The mean relative error (MRE) of the distance in the Texting and Pocket modes were obtained as %4.8 and %4.37, respectively. Experimental results indicated the MRE of the final position along the three paths in the two carrying modes of Texting and Pocket by magnetic and proposed methods reduced from %3.75 to %2.66 and %7.02 to %4.24, respectively.

An important feature of mobile devices relates to positioning, mainly relying on the global navigation satellite system sensor. In optimal conditions, this sensor provides horizontal positioning sufficient for most location-based services. The elevation, on the other hand, still lacks sufficient accuracy and reliability – mostly due to mobile device inadequacies that stem from technological limitations and environmental and physical conditions, which impact the observations quality. We suggest augmenting the elevation measurements of this sensor with measurements from supplementary embedded mobile device sensors, such as barometers and accelerometers, and with data from external mapping and environmental databases, namely topography and weather. We developed an artificial neural network deep-learning model that identifies parameter values for producing the highest predictive accuracy of the elevation value while relying on a comprehensive set of measurements. Our findings indicate very promising results, whereby we enhanced the elevation accuracy of testing data by 428%, while significantly reducing the elevation variance. These results show that using supplementary measurements and data improves elevation values while significantly reducing errors commonly associated with mobile device global navigation satellite system sensors. The proposed method has the capacity to improve outdoor kinematic positioning for location-based services, with a focus on urban and concealed areas.

Internet of vehicles (IoV) is rapidly growing as key enablers of new applications related to Intelligent Transportation System (ITS), including: autonomous driving, teleoperation, cooperative manoeuvre and perception, etc. The design of such new applications relies mainly on the performance of data dissemination techniques, which enable vehicles to exchange data with their surroundings. In fact, these new applications are coming with new requirements such as ultra-low latency, high bandwidth and communication reliability. This limits also the use of data dissemination techniques designed for traditional vehicular network, especially with the increasing number of connected vehicles on roads. In this paper, we review a taxonomy of data dissemination techniques for IoV based on four new classes: networking-based class, intelligent-based class, traditional-based class, and hybrid class. Furthermore, the paper not only reviews some recent contributions addressing data dissemination in IoV, but also emphasis their enabling technologies, services, architectures, their used simulation tools, and open challenges.

The importance of human mobility analyses is growing in both research and practice, especially as applications for urban planning and mobility rely on them. Aggregate statistics and visualizations play an essential role as building blocks of data explorations and summary reports, the latter being increasingly released to third parties such as municipal administrations or in the context of citizen participation. However, such explorations already pose a threat to privacy as they reveal potentially sensitive location information, and thus should not be shared without further privacy measures. There is a substantial gap between state-of-the-art research on privacy methods and their utilization in practice. We thus conceptualize a mobility report with differential privacy guarantees and implement it as open-source software to enable a privacy-preserving exploration of key aspects of mobility data in an easily accessible way. Moreover, we evaluate the benefits of limiting user contributions using three data sets relevant to research and practice. Our results show that even a strong limit on user contribution alters the original geospatial distribution only within a comparatively small range, while significantly reducing the error introduced by adding noise to achieve privacy guarantees.

The positioning performance of the Global Positioning System (GPS) is severely degraded in urban canyons due to buildings blocking or attenuating signals. This paper proposes a weighted GPS positioning method using a dual-polarisation antenna in order to deal with this issue. The GPS signals are first classified and labelled into NLOS signals and LOS/MP signals by considering the right-hand circular polarised (RHCP) and the left-hand circular polarised (LHCP) signal strengths. Then, a Bayesian optimisation-based Gaussian process is used to fit the pseudorange error related to the input of the carrier-to-noise ratio of the RHCP QUOTE $\mathrm{C}/{{\mathrm{N}}_0}^{\mathrm{R}}$( $C/N_0^R$) and the elevation angle of the LOS/MP signals. The pseudorange errors of the LOS/MP signals are therefore predicted based on the rules extracted from the fitting results. The positioning solutions are then obtained from the weighted least-squares algorithm, with the weighting strategies being based on the predicted pseudorange errors. Static positioning results in deep urban areas showed that the horizontal root-mean-square error (RMSE) positioning has been improved by 64.6% and 57.3%, respectively, while 3D RMSEwas improved by 74.3% and 48.1%, respectively, compared with traditional single-point positioning results.

In Flanders (Belgium), one in three journeys between home and school by adolescents (12–18 years) is made by bike. Sadly, most cycling accidents also occur in this age group. Although research and cycling policies advocate for safer school environments, their focus is often limited to a restricted area near the school gate. Not only school environments but also trajectories to school and adolescents’ experience along it should be considered when designing cycling policies. Therefore, we investigated perceived cycling safety and its relation with the environment along home-to-school routes of adolescents in Flanders. Data were collected using a location based service (LBS) developed for secondary schools. Relations between perceived cycling safety and a wide range of both subjectively scored and objectively measured environmental data were analysed using multilevel linear regressions, at the level of routes, road segments, and intersections. The models show that traffic volume and cycling infrastructure have the strongest associations with perceived safety, and that accident data have a significant relation as well. Therefore, we believe that decreasing traffic volume and enhancing cycling infrastructure will increase adolescents’ cycling safety perception. Furthermore, the results of this study underline the promising value of LBS to develop proactive and stimulating cycling policies.

While map apps on smartphones are abundant, their everyday usage is still an open empirical research question. With tappigraphy – the quantification of smartphone touchscreen interactions – we aimed to capture continuous data stream of behavioural human-map app usage patterns. The current study introduces a first tappigraphy analysis of the distribution of touchscreen interactions on map apps in 211 remotely observed smartphone users, accumulating a total of 42 days of tap data. We detail the requirements, setup, and data collection to understand how much, when, for how long, and how people use mobile map apps in their daily lives. Supporting prior research, we find that on average map apps are only sparsely used, compared to other apps. The longitudinal fluctuations in map use are not random and are partly governed by general daily and weekly human behaviour cycles. Smartphone session duration including map app use can be clearly distinguished from sessions without any map apps used, indicating a distinct temporal behavioural footprint surrounding map use. With the transfer of the tappigraphy approach to a mobile map app use context, we see a promising avenue to provide research communities interested in the underlying behavioural mechanisms of map use a continuous, in-situ momentary assessment method.

About 200 million people with disabilities live with difficulties to carry out some daily activity. A significant portion of these people have visual impairment, in which such assistive technologies (AT) do not minimise the difficulties faced for safe access in everyday environments. To enable the AT to these people it is necessary to embark on artificial intelligence techniques, promoting the smart assistive technologies (SAT). The SAT have a processing that dynamically adapts to the person’s conditions and to the characteristics of the environment. The objective of this research was to develop an algorithm based on the informed search technique to optimise the routes for people with visual impairment. The algorithm was validated on 1600 combinations and optimised 52.8% of the simulations. This research presents relevant social potential, as it generates a SAT capable of assisting the decision-making of people with visual impairment.

With the continuous development of science and technology, especially computer technology, people need a more convenient and natural way to communicate with the machine. In this paper, based on human-computer speech recognition interaction system, using big data Internet of things as technical support, the contribution of intelligent social service robot to urban fire protection is studied. In order to meet people’s need, a large number of high-rise, super high-rise and underground buildings continue to increase, which not only provides us with convenience, but also makes fire safety a hot concern of the whole society. Fire fighting plays an increasingly important role in the life of urban residents. In order to greatly reduce the lack of fire safety monitoring ability, this paper uses speech recognition technology to design a city fire safety management service platform based on big data Internet of things.

Ongoing urbanisation processes invoke immense construction activities, for which citizens often participate in planning. Yet, imagining planned buildings based on visual representations is a highly demanding task. While traditional methods, such as construction spans, 2D, or 3D visualisation often fail to offer a complete picture, we propose Augmented Reality (AR) as a more adequate tool. We first present an evaluation of the suitability of AR compared to construction spans for a future building and assess which degree of abstraction of AR is most effective, as well as difficulty of interpreting them correctly. In a between-subjects field study we compare construction spans and a prototype AR application including three levels of detail (LOD) of the same building project. Participants solve two estimation tasks using the construction spans and six estimation tasks using the AR application, before answering a questionnaire on the different visualisation methods. We find participants are confident about the potential of AR, but no significant differences between the different LOD groups in subjective assessment. Results suggest that previous knowledge (e.g. in GIS) may have a positive impact on dimension estimation performance. Also, details, such as façade elements or windows, could facilitate estimation tasks because they allow inferences about a building’s size.