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A common research focus in deep learning is human activity recognition (HAR), which involves detecting human activities using sensor data from magnetometers, accelerometers, and gyroscopes. For real-time HAR applications, it’s crucial to develop a ...

In recent years, there have been relevant advances in precision beekeeping. These advances are mainly focused on proposing sensor systems that collect crucial information for bee welfare, creating integrated architectures that allow beekeepers to ...

• Multivariate statistical models for predicting highly correlated variables of hives.
• Flexible models allowing the inclusion of external variables affecting the hive.
• Reliable predictions on relative humidity, temperatures, and ...

The exponential growth in data generated by satellites, radars, sensors, and analysis and reanalysis from model outputs for the hydrological domain requires efficient real-time data management and distribution mechanisms. This paper introduces ...

• HydroRTC accelerates large-scale data sharing with next-gen web technologies.
• Three primary scenarios: server-to-peer, peer-to-peer, peer-to-server data exchange.
• Promising solution for collaborative infrastructures in hydrological ...

Degradation trend prognostics plays an important role in industrial prognostics and health management (PHM), requiring data-driven models with higher predictive capability for accurate long time series prediction. Nevertheless, typical ...

The Brunelleschi’s Dome is one of the most iconic symbols of the Renaissance and is among the largest masonry domes ever constructed. Since the late 17th century, first masonry cracks appeared on the Dome, giving the start to a monitoring ...

Smart grids commonly rely on analyzing sensor data to monitor power equipment. However, these sensor data can suffer varying levels of loss or corruption due to complex interferences, leading to a pressing need for precise missing value imputation ...

In recent years, IoT sensors have enabled smart agriculture to grow rapidly with many compelling real-world applications. One such application is the case of smart irrigation. A particular interest exists in forecasting soil water potential to ...

• Temporal Fusion Transformers effectively integrate diverse data sources.
• Global methods outperform local ones in agricultural sensor fusion.
• Findings are backed by a real-world and multi-source agricultural dataset.

Sensor data, such as traffic flow monitoring data, constitutes a type of multimedia data. Forecasting sensor data holds significant potential for decision-making. And we can explore its patterns using time series forecasting methods. In the past ...

With the advent of 5G and the promise of 6G, edge computing is becoming increasingly important. Edge computing involves the proximity of computational resources to the devices and sensors responsible for data generation, reducing delay and ...

Recently, air quality has become a major concern for the protection of the environment and the well-being of people. Air pollution is a key proxy of the quality of life in any city and is crucial in the fight against climate change. Therefore, it ...

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• This AI framework can be deployed for anticipating high air pollution episodes.
• ST-AQF can work with a variable set of sensors and recover from sensor failures.
• Careful combination of variables can improve the forecasting results.

This paper focuses on automating the classification of in-house cattle behavior using collar tags equipped with tri-axial accelerometers to collect data on feeding and ruminating behaviors. The accelerometer data is divided into time intervals (10,...

Environmental sensor networks produce continuously increasing volumes of raw data that need to be transformed into usable data for monitoring ongoing environmental changes and decision-support. The crucial challenge is providing data in real-time,...

• We present the Python package System for automated Quality Control (SaQC).
• SaQC facilitates the implementation of workflows for automated quality control
• It is designed for domain scientists that manage environmental sensor ...

Modern industrial facilities generate large volumes of raw sensor data during the production process. This data is used to monitor and control the processes and can be analyzed to detect and predict process abnormalities. Typically, the data has ...

The Internet of Things (IoT) has empowered enterprises to optimize process efficiency and productivity by analyzing sensor data. This can be achieved with process mining, a technology that enables organizations to extract valuable insights from ...

The imputation of missing values in multivariate time series (MTS) data is critical in ensuring data quality and producing reliable data-driven predictive models. Apart from many statistical approaches, a few recent studies have proposed state-of-...

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In today’s era, one of the important applications of Artificial Intelligence (AI) is Human Activity Recognition (HAR). It has a wide range of applicability in health monitoring for patients with chronic diseases, gaming consoles for gesture ...

To personalize their services (e.g., advertisement, navigation, healthcare), mobile apps collect sensor data. Typically, they upload the collected sensor data to the cloud, which returns the inferred user profiles required to ...

Green infrastructure (GI) is cost-effective for managing urban runoff. However, inspection and maintenance of GI are an increasingly common burden for stormwater managers. For instance, bioretention cells, a popular type of GI, may ...

• Green infrastructure (GI) can mitigate urban runoff.
• GI is prone to clogging, ...

• Review recent landmark-based indoor positioning technologies.
• Categorize them ...

Owing to the increase in the time people spend indoors, coupled with the pervasiveness of high-performance smart devices, the importance of indoor positioning techniques has grown. Researchers have extensively studied indoor ...

We present a personalized approach for frequent fitness monitoring in road cycling solely relying on sensor data collected during bike rides and without the need for maximal effort tests. We use competition and training data of three world-class ...