In WSN/IoT, node localization is essential to long-running applications for accurate environment monitoring and event detection, often covering a large area in the field. Due to the lower time resolution of typical WSN/IoT platforms (e.g., 1 microsecond on ESP32 platforms) and the jitters in timestamping, packet-level localization techniques cannot provide meter-level resolution. For high-precision localization as well as world-wide interoperability via 2.4-GHz ISM band, a new variant of LoRa, called LoRa 2.4 GHz, was proposed by semtech, which provides a radio frequency (RF) time of flight (ToF) ranging method for meter-level localization. However, the existing datasets reported in the literature are limited in their coverages and do not take into account varying environmental factors such as temperature and humidity. To address these issues, LoRa 2.4 GHz RF ToF ranging data was collected on a sports field at the XJTLU south campus, where three LoRa nodes logged samples of ranging with a LoRa base station, together with temperature and humidity, at reference points arranged as a 3x3 grid covering 400 square meter over three weeks and uploaded all measurement records to the base station equipped with an ESP32-based transceiver for machine and user communications. The results of a preliminary investigation based on a simple deep neural network (DNN) model demonstrate that the environmental factors, including the temperature and humidity, significantly affect the accuracy of ranging, which calls for advanced methods of compensating for the effects of environmental factors on LoRa RF ToF ranging outdoors.

Human activity recognition (HAR) has gained significant attention due to its applications in health monitoring, intelligent environments, and human-computer interaction Hussain, Khan, Khan, Bhatt, Farouk, Bhola, and Baik (2024); Mishra, Gupta, Gupta, and Dutta (2022). Traditional HAR approaches employed wearable inertial sensors, vision-based methods, and environmental sensors for HAR. However, each method has inherent limitations such as discomfort, privacy concerns, or complex deployment requirements Wang, Huang, Zhao, Zhu, Huang, and Wu (2024); Mishra and Gupta (2025). The human body engages with its environment in diverse ways, one of which is the interaction between the lungs and the external environment through the act of breathing via the nose. The breathing pattern encompasses plenty of useful information that can be processed to fetch different behaviours and health information Mongelli, Orani, Cambiaso, Vaccari, Paglialonga, Braido, and Catalano (2020); Zhang, Wang, and Li (2024). Moreover, the breathing patterns are influenced by metabolic and physiological factors, offering a non-invasive and unobtrusive means of HAR.

Meteorological factors (MF) are crucial in day-ahead load forecasting as they significantly influence the electricity consumption behaviors of consumers. Numerous studies have incorporated MF into the load forecasting model to achieve higher accuracy. Selecting MF from one representative location or the averaged MF as the inputs of the forecasting model is a common practice. However, the difference in MF collected in various locations within a region may be significant, which poses a challenge in selecting the appropriate MF from numerous locations. A representation learning framework is proposed to extract geo-distributed MF while considering their spatial relationships. In addition, this paper employs the Shapley value in the graph-based model to reveal connections between MF collected in different locations and loads. To reduce the computational complexity of calculating the Shapley value, an acceleration method is adopted based on Monte Carlo sampling and weighted linear regression. Experiments on two real-world datasets demonstrate that the proposed method improves the day-ahead forecasting accuracy, especially in extreme scenarios such as the "accumulation temperature effect" in summer and "sudden temperature change" in winter. We also find a significant correlation between the importance of MF in different locations and the corresponding area's GDP and mainstay industry.

Mosques are worship places of Allah and must be preserved clean, immaculate, provide all the comforts of the worshippers in them. The prophet's mosque in Medina/ Saudi Arabia is one of the most important mosques for Muslims. It occupies second place after the sacred mosque in Mecca/ Saudi Arabia, which is in constant overcrowding by all Muslims to visit the prophet Mohammad's tomb. This paper aims to propose a smart dome model to preserve the fresh air and allow the sunlight to enter the mosque using artificial intelligence techniques. The proposed model controls domes movements based on the weather conditions and the overcrowding rates in the mosque. The data have been collected from two different resources, the first one from the database of Saudi Arabia weather's history, and the other from Shanghai Technology Database. Congested Scene Recognition Network (CSRNet) and Fuzzy techniques have applied using Python programming language to control the domes to be opened and closed for a specific time to renew the air inside the mosque. Also, this model consists of several parts that are connected for controlling the mechanism of opening/closing domes according to weather data and the situation of crowding in the mosque. Finally, the main goal of this paper has been achieved, and the proposed model has worked efficiently and specifies the exact duration time to keep the domes open automatically for a few minutes for each hour head.

Researchers at Central Building Research Institute (CBRI) in Roorkee have developed an Artificial Intelligence (AI) model to predict the "transmission probability" of Covid-19 in a closed space in a building. ROORKEE: Researchers at Central Building Research Institute (CBRI) in Roorkee have developed an Artificial Intelligence (AI) model to predict the "transmission probability" of Covid-19 in a closed space in a building. The model uses an electronic device to detect carbon dioxide concentration, temperature and humidity of a room. These and other input parameters are used to show the probability of the presence of Covid-19 virus in an office, classroom or any other closed space in a building. After computing the parameters, a software determines the transmission probability and displays the results in the form of a text alert on the screen.

Efficient air quality sensing serves as one of the essential services provided in any recent smart city. Mostly facilitated by sparsely deployed Air Quality Monitoring Stations (AQMSs) that are difficult to install and maintain, the overall spatial variation heavily impacts air quality monitoring for locations far enough from these pre-deployed public infrastructures. To mitigate this, we in this paper propose a framework named AQuaMoHo that can annotate data obtained from a low-cost thermo-hygrometer (as the sole physical sensing device) with the AQI labels, with the help of additional publicly crawled Spatio-temporal information of that locality. At its core, AQuaMoHo exploits the temporal patterns from a set of readily available spatial features using an LSTM-based model and further enhances the overall quality of the annotation using temporal attention. From a thorough study of two different cities, we observe that AQuaMoHo can significantly help annotate the air quality data on a personal scale.

Temperature and humidity are two of the rudimentary factors that must be controlled during egg incubation. Improper temperature and humidity levels during the incubation period often result in unwanted conditions. This paper proposes the design of an efficient Mamdani fuzzy interference system instead of the widely used Takagi-Sugeno system in this field for controlling the temperature and humidity levels of an egg incubator. Though the optimum incubation temperature and humidity levels used here are that of chicken egg, the proposed methodology is applicable to other avian species as well. Theinput functions have been used here as per estimated values forsafe hatching using Mamdani whereas defuzzification method, COA, has been applied for output. From the model output,a stabilized heat from temperature level and fan speed to control the humidity level of an egg incubator can be obtained. This maximizes the hatching rate of healthy chicks under any conditions in the field.

We aim to investigate the potential impacts of smart homes on human behavior. To this end, we simulate a series of human models capable of performing various activities inside a reinforcement learning-based smart home. We then investigate the possibility of human behavior being altered as a result of the smart home and the human model adapting to one-another. We design a semi-Markov decision process human task interleaving model based on hierarchical reinforcement learning that learns to make decisions to either pursue or leave an activity. We then integrate our human model in the smart home which is based on Q-learning. We show that a smart home trained on a generic human model is able to anticipate and learn the thermal preferences of human models with intrinsic rewards similar to the generic model. The hierarchical human model learns to complete each activity and set optimal thermal settings for maximum comfort. With the smart home, the number of time steps required to change the thermal settings are reduced for the human models. Interestingly, we observe that small variations in the human model reward structures can lead to the opposite behavior in the form of unexpected switching between activities which signals changes in human behavior due to the presence of the smart home.

We've come a long way from baby monitors that just let you listen in on your child's coos and cries. The Miku Smart Baby Monitor is great example of how cutting-edge video technology and artificial intelligence are being used together to give parents more information about their sleeping bundles of joy. In addition to now-standard capabilities such as sound and movement (and non-movement) detection, the Miku tracks your child's breathing patterns and aggregates all this data in digestible sleep analysis reports. Though some hiccups I encountered during testing suggest there's still some room for improvement, the Miku has the all the makings of a great baby monitor. The Miku is attractive, with a white matte finish and silver trim.

As the snow begins to melt away, you may be thinking about planting a garden in your backyard. While you'll still need all of the outdoor essentials like gardening gloves and string trimmers to prep your outdoor garden space, there are a few tech gadgets that can help make planting a feast of vegetables and herbs in your yard a little easier. With the Nest Outdoor Cam, you can check in on your garden from anywhere. With our favorite outdoor security camera, the Nest Cam Outdoor, you can check in on your garden from anywhere. With a Nest Aware subscription, you can review and share video clips of your hard work, watch for pesky critters who want to eat your garden for lunch, and even create a time-lapse video of your garden taking shape.