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As the capabilities of smart sensing and mobile technologies continue to evolve and expand, storing diverse sensor data on smartphones and cloud servers becomes increasingly challenging. Effective data compression is crucial to alleviate these storage ...

A steganographic network storage channel that uses a carrier with a stream of numeric data must consider the possibility that the carrier data is processed before the covert receiver can extract the secret data. A sensor data stream, which we take as an ...

Missing sensor data in human activity recognition is an active field of research that is being targeted with generative models for synthetic data generation. In contrast to most previous approaches, we aim to generate data of a sensor exclusively from ...

This work presents FamilyScope, a sensor-based family informatics system that enables reflection upon family data collected from family activity scenarios (e.g., game playing and movie watching) that include affective aspects of a family's social ...

In the last two decades, smart buildings have profited from AI-based approaches to create smart applications that provide several advantages such as energy efficiency and security. In particular, occupancy estimation (OE) and activity recognition (AR) ...

In this study, we present a novel framework for detecting anomalies in everyday activities within a smart-home environment. Our method utilizes the growing neural gas (GNG) concept to dynamically adapt to the changing behaviors of monitored individuals, ...

The prevailing developments in internet of things (IoT) and other sensor technologies such as cyber physical systems (CPS) and wireless sensor networks (WSNs), the huge amount of sensor data has been generating from various IoT devices and protocols. ...

Entering a cognitive state of flow is a natural response of the mind that allows people to fully concentrate and cope with tedious, and often repetitive tasks. Understanding how to trigger or sustain flow remains limited by retrospective surveys, ...

Groups of sensors collecting time-series data in a variety of modalities are widely used for monitoring humans, environments, and equipment. Datasets with multimodal sensor data pose several challenges not present in many image or language datasets; ...

Sensor-based human activity recognition aims to recognize the activities performed by people with the sensor readings. Most of existing works in this area rely on supervised classification algorithms, and can only recognize activities covered by the ...

Cyber-physical systems (CPS) play a pivotal role in various critical applications, ranging from industrial automation to healthcare monitoring. Ensuring the reliability and accuracy of sensor data within these systems is of paramount importance. This ...

Smart containers equipped with ultrasonic sensors at waste and recycle facilities allow waste and recycling companies to build a more efficient and data-driven approach for the collection of municipal solid waste (MSW). In this paper, we propose ...

Dynamic data (including environmental, traffic, and sensor generated data) were, recently, recognised as an important part of the Open Government Data (OGD) movement. These data are of vital importance in the development of data intelligence ...

Time-series data has an increasingly growing usage in Industrial Internet of Things (IIoT) and large-scale scientific experiments. Managing time-series data needs a storage engine that can keep up with their constantly growing volumes while providing ...

The application of sensors enables mobile devices to generate amounts of content-aware data, such as trajectory, gyro, and video data. Moving video is an emerging new type of moving object that can provide a potential source for geo-referenced ...

With the increasing number of moving videos, anomaly detection of moving videos has become a popular data mining task in the field of intelligent transportation. Traditional road anomaly detection algorithms are hard to detect road bump outliers while ...

Healthcare systems based on the Internet of Things have an increasing demand for health sensing technology. To manage the data collected and sampled by medical devices, traditional centralized data management will lead to attacks such as single point of ...

Mobile malware are malicious programs that target mobile devices. They are an increasing problem, as seen with the rise of detected mobile malware samples per year. The number of active smartphone users is expected to grow, stressing the importance of ...

Internet of things provides ways to obtain information about farm production; however, the more data there is, the harder it is to determine the correct way to process these data. The solutions on the market are aimed specifically at gathering ...

Leveraging sensor data of mobile devices and wearables, activity detection is a critical task in various intelligent systems. Most recent work train deep models to improve the accuracy of recognizing specific human activities, which, however, rely on ...