Browsing by Author "Sobron, Iker"
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Item Device-Free People Counting in IoT Environments: New Insights, Results, and Open Challenges(2018-12) Sobron, Iker; Del Ser, Javier; Eizmendi, Inaki; Velez, Manuel; IAIn the last years multiple Internet of Things (IoT) solutions have been developed to detect, track, count, and identify human activity from people that do not carry any device nor participate actively in the detection process. When WiFi radio receivers are employed as sensors for device-free human activity recognition, channel quality measurements are preprocessed in order to extract predictive features toward performing the desired activity recognition via machine learning (ML) models. Despite the variety of predictors in the literature, there is no universally outperforming set of features for all scenarios and applications. However, certain feature combinations could achieve a better average detection performance compared to the use of a thorough feature portfolio. Such predictors are often obtained by feature engineering and selection techniques applied before the learning process. This manuscript elaborates on the feature engineering and selection methodology for counting device-free people by solely resorting to the fluctuation and variation of WiFi signals exchanged by IoT devices. We comprehensively review the feature engineering and ML models employed in the literature from a critical perspective, identifying trends, research niches, and open challenges. Furthermore, we present and provide the community with a new open database with WiFi measurements in several indoor environments (i.e., rooms, corridors, and stairs) where up to five people can be detected. This dataset is used to exhaustively assess the performance of different ML models with and without feature selection, from which insightful conclusions are drawn regarding the predictive potential of different predictors across scenarios of diverse characteristics.Item Ensemble Learning for Seated People Counting using WiFi Signals: Performance Study and Transferability Assessment(Institute of Electrical and Electronics Engineers Inc., 2021) Bernaola, Jose Ramon Merino; Sobron, Iker; Del Ser, Javier; Landa, Iratxe; Eizmendi, Inaki; Velez, Manuel; IAThe detection, location, and behavior recognition of human beings in different environments is not only a subject of a wide range of studies, but has also triggered the development of a large number of applications, including those which enhance sustainability and efficiency of infrastructures. For instance, the estimation of the occupancy could improve the energy management of a building. Due to human presence or movement over a particular area, the analysis of variations in wireless signal properties of already deployed wireless technology such as WiFi systems provides the information needed for Machine Learning models to accomplish the non-intrusive (device-free) detection and classification of different human activities. In this context, this work focuses on detecting seated people in an indoor scenario by using ensemble learning, a particular branch of Machine Learning models for supervised learning that hinges on combining the outputs of individual predictors. Furthermore, we evaluate the transferability of the knowledge modeled by ensemble learners. When trained in a particular frequency or channel, such models are used to classify data captured over another different frequency. Our experimental setup and discussed results reveal that while ensembles attain satisfactory levels of predictive accuracy predictions, their knowledge cannot be transferred among different frequencies. This conclusion opens an exciting future towards new means to perform effective knowledge transfer over the frequency domain.Item Wireless network optimization for massive V2I data collection using multiobjective harmony search heuristics(Springer Verlag, 2017) Sobron, Iker; Alonso, Borja; Vélez, Manuel; Del Ser, Javier; Del Ser, Javier; IAThis paper proposes to improve the efficiency of the deployment of wireless network infrastructure for massive data collection from vehicles over regional areas. The increase in the devices that are carried by vehicles makes it especially interesting being able to gain access to that data. From a decisional point of view, this collection strategy requires defining a wireless Vehicular-to-Infrastructure (V2I) network that jointly optimizes the level of service and overall CAPEX/OPEX costs of its deployment. Unfortunately, it can be intuitively noted that both optimization objectives are connecting with one another: adding more equipment will certainly increase the level of service (i.e. coverage) of the network, but costs of the deployment will rise accordingly. A decision making tool blending together both objectives and inferring therefrom a set of Pareto-optimal deployments would be of utmost utility for stakeholders in their process of provisioning budgetary resources for the deployment. This work will explore the extent to which a multi-objective Harmony Search algorithm can be used to compute the aforementioned Pareto-optimal set of deployment by operating on two different optimization variables: the geographical position on which wireless receivers are to be deployed and their type, which determines not only their coverage range but also their bandwidth and cost. In particular we will utilize a non-dominated sorting strategy criterion to select the harmonies (solution vectors) evolved by Harmony Search heuristics.