Search

Current weather monitoring systems often remain out of reach for small-scale users and local communities due to their high costs and complexity. This paper addresses this significant issue by introducing a cost-effective, easy-to-use local weather station. Utilizing low-cost sensors, this weather station is a pivotal tool in making environmental monitoring more accessible and user-friendly, particularly for those with limited resources. It offers efficient in-site measurements of various environmental parameters, such as temperature, relative humidity, atmospheric pressure, carbon dioxide concentration, and particulate matter, including PM 1, PM 2.5, and PM 10. The findings demonstrate the stations capability to monitor these variables remotely and provide forecasts with a high degree of accuracy, displaying an error margin of just 0.67%. Furthermore, the stations use of the Autoregressive Integrated Moving Average (ARIMA) model enables short-term, reliable forecasts crucial for applications in agriculture, transportation, and air quality monitoring. Furthermore, the weather stations open-source nature significantly enhances environmental monitoring accessibility for smaller users and encourages broader public data sharing. With this approach, crucial in addressing climate change challenges, the station empowers communities to make informed decisions based on real-time data. In designing and developing this low-cost, efficient monitoring system, this work provides a valuable blueprint for future advancements in environmental technologies, emphasizing sustainability. The proposed automatic weather station not only offers an economical solution for environmental monitoring but also features a user-friendly interface for seamless data communication between the sensor platform and end users. This system ensures the transmission of data through various web-based platforms, catering to users with diverse technical backgrounds. Furthermore, by leveraging historical data through the ARIMA model, the station enhances its utility in providing short-term forecasts and supporting critical decision-making processes across different sectors.

Residential buildings can contribute to save energy and to decrement electricity consumption in the world. On the other hand, the Internet of Things has allowed the implementation of smart homes that can profile the users. Nevertheless, end-users are not accepting the smart homes due to behavioral problems and usability problems with the Human–Machine Interface (HMI) or with the household appliances. As a solution, social products promote the interaction between the smart home and the consumer by including gamification features in the interface. Thus, smart homes can interact and compete with other houses to reduce energy consumption. Therefore, this paper proposes a three-step framework that takes advantage of social products to promote interaction between smart homes within a smart community to reduce electrical energy consumption. The first step collects from the literature review, the characteristics of the end-users, the behavioral and usability problems, and the most common gamification elements that teach, engage and motivate the user to reduce energy consumption. The second step proposes the gamification elements required for a tailored HMI in each social product and smart home through a fuzzy logic decision. The third step evaluates the interaction between social products in smart homes and the users to test which smart home is reducing more energy consumption. Finally, a three-level tailored gamified mock-up is depicted: level 1 for a single social product, level 2 for the smart home, and level three for the smart community. This mock-up can be implemented in small communities as residential complexes.

The residential Heating Ventilation and Air-Conditioning (HVAC) system use around 3/5 of the total energy consumption. Connected thermostats optimize the HVAC operation; however, householders have personality traits that lead into behavioral and usability problems toward the thermostat's interface usage. Thus, a serious game applied in the thermostat interface can balance entertainment and education. Therefore, thermostat interfaces must address strategies that reduce energy without losing thermal comfort. This paper proposed an interactive interface type and a predicted interface type based on an HVAC strategy and a Natural Ventilation strategy. These strategies measured the impact of adaptive thermal comfort, energy consumption, and costs. Hence, twelve energy models located in California (Concord, Riverside, Los Angeles, and San Diego) were simulated using EnergyPlus™ through LadybugTools. The first interactive interface included Serious Game elements, so the householder interacted with the date, location, and setpoint. The second interface predicted the energy consumption and thermal comfort during winter and summer in Concord by a two-layer feed-forward Artificial Neural Network structure. The proposed structure decreases the energy consumption by at least 62% without losing thermal comfort.

Thermal comfort is associated with clothing insulation, conveying a level of satisfaction with the thermal surroundings. Besides, clothing insulation is commonly associated with indoor thermal comfort. However, clothing classification in smart homes might save energy when the end-user wears appropriate clothes to save energy and obtain thermal comfort. Furthermore, object detection and classification through Convolutional Neural Networks has increased over the last decade. There are real-time clothing garment classifiers, but these are oriented towards single garment recognition for texture, fabric, shape, or style. Consequently, this paper proposes a CNN model classification for the implementation of these classifiers on cameras. First, the Fashion MNIST was analyzed and compared with the VGG16, Inceptionvv4, TinyYOLOv3, and ResNet18 classification algorithms to determine the best clo classifier. Then, for real-time analysis, a new dataset with 12,000 images was created and analyzed with the YOLOv3 and TinyYOLO. Finally, an Azure Kinect DT was employed to analyze the clo value in real-time. Moreover, real-time analysis can be employed with any other webcam. The model recognizes at least three garments of a clothing ensemble, proving that it identifies more than a single clothing garment. Besides, the model has at least 90% accuracy in the test dataset, ensuring that it can be generalized and is not overfitting.

Nowadays, the growth in the consumption of energy and the need to face pollution resulting from its generation are causing concern for consumers and providers. Energy consumption in residential buildings and houses is about 22% of total energy production. Cutting-edge energy managers aim to optimize electrical devices in homes, taking into account users’ patterns, goals, and needs, by creating energy consumption awareness and helping current change habits. In this way, energy manager systems (EMSs) monitor and manage electrical appliances, automate and schedule actions, and make suggestions regarding electrical consumption. Furthermore, gamification strategies may change energy consumption patterns through energy managers, which are seen as an option to save energy and money. Therefore, this paper proposes a personalized gamification strategy for an EMS through an adaptive neuro-fuzzy inference system (ANFIS) decision-making engine to classify the level of electrical consumption and persuade the end-user to reduce and modify consumption patterns, saving energy and money with gamified motivations. These strategies have proven to be effective in changing consumer behavior with intrinsic and extrinsic motivations. The interfaces consider three cases for summer and winter periods to calculate the saving-energy potentials: (1) for a type of user that is interested in home-improvement efforts while helping to save energy; (2) for a type of user that is advocating to save energy; (3) for a type of user that is not interested in saving energy. Hence, each interface considers the end-user’s current consumption and the possibility to modify their consumption habits using their current electrical devices. Finally, an interface displaying the electrical consumption for each case exemplifies its linkage with EMSs.

Depression is a common mental illness characterized by sadness, lack of interest, or pleasure. According to the DSM-5, there are nine symptoms, from which an individual must present 4 or 5 in the last two weeks to fulfill the diagnosis criteria of depression. Nevertheless, the common methods that health care professionals use to assess and monitor depression symptoms are face-to-face questionnaires leading to time-consuming or expensive methods. On the other hand, smart homes can monitor householders' health through smart devices such as smartphones, wearables, cameras, or voice assistants connected to the home. Although the depression disorders at smart homes are commonly oriented to the senior sector, depression affects all of us. Therefore, even though an expert needs to diagnose the depression disorder, questionnaires as the PHQ-9 help spot any depressive symptomatology as a pre-diagnosis. Thus, this paper proposes a three-step framework; the first step assesses the nine questions to the end-user through ALEXA or a gamified HMI. Then, a fuzzy logic decision system considers three actions based on the nine responses. Finally, the last step considers these three actions: continue monitoring through Alexa and the HMI, suggest specialist referral, and mandatory specialist referral.

Changing demography in the Southwest USA has shifted the racial compositions of many neighborhoods that were once predominantly African American to majority Latinx immigrant communities (Orfield and Frankenberg 2014). Today, it's common for these two youth communities to learn side-by-side in urban and suburban classrooms where they are often positioned to share each other's cultural practices (Martinez 2017). However, language and literacy research often positions these two minoritized communities as mutually exclusive (Paris 2010). Moreover, while researchers have urged the analysis of these two youth communities comparatively few studies document classroom units that cultivate spaces for 'linguistic solidarity' (Martinez 2017). Drawing from an ethnographic study of an 11th-grade ethnic studies class, the authors explore how Arturo (third author) took up a critical translingual approach in his classroom where students--both Latinx and African American--collaborated through the cowriting of corridos (Mexican ballads) about their lived experiences with in/justice. The findings demonstrate the ways that this unit invited students to write across racial, ethnic, and linguistic borders and fostered language and cultural sharing, political consciousness, and solidarity.

Connected thermostats (CTs) often save less energy than predicted because consumers may not know how to use them and may not be engaged in saving energy. Additionally, several models perform contrary to consumers’ expectations and are thus not used the way they are intended to. As a result, CTs save less energy and are underused in households. This paper reviews aspects of gamification and serious games focused on engaging consumers. A gamification and serious games framework is proposed for saving energy that is tailored by a fuzzy logic system to motivate connected thermostat consumers. This intelligent gamification framework can be used to customize the gamification and serious game strategy to each consumer so that fuzzy logic systems can be adapted according to the requirements of each consumer. The framework is designed to teach, engage, and motivate consumers while helping them save electrical energy when using their thermostats. It is described the proposed framework as well as a mockup that can be run on a cellphone. Although this framework is designed to be implemented in CTs, it can be translated to their energy devices in smart homes.

The users' behavior in a house impacts the amount of electrical energy consumption in the electrical products of the household; therefore, energy consumption can be optimized by using sense, smart and sustainable products (S3) as Social Products (SPs) for saving energy in the housing. The SPs communicate between devices and consumers through game design elements known as gamification that can yield behavioral changes in users. Furthermore, behavioral models can shape human behavior to motivate the individual to achieve a specific target, like saving energy. For the energy consumption reduction in households, it is proposed a structure with SPs that uses a gamification Human Machine Interface in each device to communicate between products and consumer in a Smart Home (SH). In this descriptive paper, it is proposed a three-steps structure that applies to every social product. First, it requires the classification of the type of end-user, the behavior, and usability problems when the product is deployed. Then, through the analysis of fuzzy logic, either type 1 or type 2, it is proposed which housing gamification elements best tailors the interface for each appliance. Finally, the application sends stimulus to individuals to test their performance and motivation to use each social product. After every social product has its proper gamification, it is possible to have interconnected SPs to get a Gamified SH. Besides, this Gamified SH could communicate with other Gamified Smart Homes to obtain a Gamified community.