Journal of Ambient Intelligence and Smart Environments

Building Information Modeling (BIM) is a powerful process for creating and managing data throughout the life cycle of a building. Traditionally, measuring the well-being of building occupants has been addressed solely through objective physical variables such as temperature or relative air humidity. However, recent studies indicate that the built environment influences subjective aspects of human well-being. This article presents a scoping review to find information related to the use of BIM in the assessment of the mental and emotional state of inhabitants. A scoping review has been undertaken following the PRISMA-ScR guidelines by searching in Scopus, ACM, IEEE Xplore and PsycINFO databases. Fourteen articles meeting the inclusion criteria were found after the screening process, all of them published in the last decade, twelve in the last five years. Two ways of using BIM have been identified in relation to the subject matter of this review: (i) for visualization and monitoring of occupant well-being and (ii) for showing building design alternatives to future occupants. The included papers show that BIM has potential for assessing the mental and emotional state of building occupants. However, the results of these studies are still limited and much research in this area remains pending.

Indoor air quality (IAQ) is a critical challenge much less controlled in comparison with outdoor air quality. Bad IAQ is related to significant health complications such as respiratory problems, heart disease, and cancer. Many people spend most of their days inside buildings and don’t have air quality monitoring systems. Therefore, the occupants don’t know when the space has a higher quantity of pollutants than recommended, saturating the environment, and compromising people’s health. This is a problem that can be addressed by using Internet of Things (IoT) technologies to develop monitoring systems that allow a greater number of possibilities regarding the storage and processing of data and access to information by the end user, assisting the decision-making process regarding the indoor air pollution problem. Real-time data can be compared to default values, alerting the user of that situation, and suggesting an action to decrease the air pollutants concentration. There already are multiple solutions involving IoT-based technologies, many of them using low-cost sensors. Those are analyzed in this systematic review. Furthermore, the COVID-19 pandemic pointed out the importance of IAQ monitoring to evaluate the risk of contamination. The microcontrollers, IAQ parameters, sensors, data storage and visualization methods used in monitoring systems have been analyzed. The results show that most of the studies store data in Cloud systems and use Web platforms for data consulting. However, sensor calibration and efficient energy consumption are challenges that still exist.

Detecting anomalies in patterns of sensor data is important in many practical applications, including domestic activity monitoring for Active Assisted Living (AAL). How to represent and analyse these patterns, however, remains a challenging task, especially when data is relatively scarce and an explicit model is required to be fine-tuned for specific scenarios. This paper, therefore, presents a new approach for temporal modelling of long-term human activities with smart-home sensors, which is used to detect anomalous situations in a robot-assisted environment. The model is based on wavelet transforms and used to forecast smart sensor data, providing a temporal prior to detect unexpected events in human environments. To this end, a new extension of Hybrid Markov Logic Networks has been developed that merges different anomaly indicators, including activities detected by binary sensors, expert logic rules, and wavelet-based temporal models. The latter in particular allows the inference system to discover deviations from long-term activity patterns, which cannot be detected by simpler frequency-based models. Two new publicly available datasets were collected using several smart-sensors to evaluate the approach in office and domestic scenarios. The experimental results demonstrate the effectiveness of the proposed solutions and their successful deployment in complex human environments, showing their potential for future smart-home and robot integrated services.

The rapid proliferation of Internet of Things (IoT) devices and applications has resulted in an increasing demand for Low Power and Wide Area Network (LPWAN) solutions. The adoption of IoT networks still faces several challenges, despite the rapid advancement of low-power communication technology. Homogenizing this sector requires allowing interoperability between many technologies, which is now one of the largest obstacles. In this article, we present the design and implementation of the hybrid LPWAN architecture that can accomplish wide-area communication coverage and low-power consumption for IoT applications by leveraging two LPWAN technologies, Wireless Smart Ubiquitous Network (Wi-SUN) and Long Range (LoRa). In particular, LoRa is used for long-range communication, and Wi-SUN for a low-latency mesh network. Additionally, we implemented smart street light controlling system as a real-world deployment at the university campus to showcase the efficiency of the hybrid network. Our results demonstrate that the hybrid LPWAN architecture provides a better coverage and capacity while consuming less power than that of the LoRa or Wi-SUN network. The results of this study demonstrate the effectiveness of the proposed hybrid LPWAN architecture as a viable solution for next-generation IoT applications.

Road crash prediction is a fundamental key in designing efficient intelligent transportation systems. There has been a pronounced progress in the use of machine learning models for crash events assessment by the transportation safety research community in recent years. However, little attention has been paid so far to evaluating reduced-visibility crash occurrences within a heuristic ensemble system. This study presents a proactive multicriteria decision-making system that can predict crash occurrences based on real-time roadway properties, land zones’ characteristics, vehicle telemetry, driver inputs and weather conditions collected using a desktop driving simulator. A key novelty of this work is implementing a genetic algorithm-based feature selection approach along with ensemble modeling strategies using AdaBoost, XGBoost and RF techniques to establish effective crash predictions. Furthermore, since crash events occur in rare instances tending to be underrepresented in the dataset, an imbalance-learning methodology to overcome the issue was adopted on the basis of several data resampling approaches to increase the predictive performance namely SMOTE, Borderline-SMOTE, SMOTE-Tomek Links and ADASYN strategies. To our knowledge, there has been a limited interest at adopting an ensemble-based imbalance-learning strategy examining the impact of real-time features’ combinations on the prediction of road crash events under reduced visibility settings.

Mobile CrowdSensing (MCS) is a computational paradigm designed to gather sensing data by using personal devices of MCS platform users. However, being the mobility of devices tightly correlated with mobility of their owners, the locations from which data are collected might be limited to specific sub-regions. We extend the data coverage capability of a traditional MCS platform by exploiting unmanned aerial vehicles (UAV) as mobile sensors gathering data from low covered locations. We present a probabilistic model designed to measure the coverage of a location. The model analyses the user’s trajectories and the detouring capability of users towards locations of interest. Our model provides a coverage probability for each of the target locations, so that to identify low-covered locations. In turn, these locations are used as targets for the StationPositioning algorithms which optimizes the deployment of k UAV stations. We analyze the performance of StationPositioning by comparing the ratio of the covered locations against Random, DBSCAN and KMeans deployment algorithm. We explore the performance by varying the time period, the deployment regions and the existence of areas where it is not possible to deploy any station. Our experimental results show that StationPositioning is able to optimize the selected target location for a number of UAV stations with a maximum covered ratio up to 60%.