We release the DOO-RE dataset which consists of data streams from 11 types of various ambient sensors by collecting data 24/7 from a real-world meeting room. 4 types of ambient sensors, called environment-driven sensors, measure continuous state changes in the environment (e.g. sound), and 4 types of sensors, called user-driven sensors, capture user state changes (e.g. motion). The remaining 3 types of sensors, called actuator-driven sensors, check whether the attached actuators are active (e.g. projector on/off). The values of each sensor are automatically collected by IoT agents which are responsible for each sensor in our IoT system. A part of the collected sensor data stream representing a user activity is extracted as an activity episode in the DOO-RE dataset. Each episode's activity labels are annotated and validated by cross-checking and the consent of multiple annotators. A total of 9 activity types appear in the space: 3 based on single users and 6 based on group (i.e. 2 or more people) users. As a result, DOO-RE is constructed with 696 labeled episodes for single and group activities from the meeting room. DOO-RE is a novel dataset created in a public space that contains the properties of the real-world environment and has the potential to be good uses for developing powerful activity recognition approaches.

The ASAPS Dataset includes eight continuous hours of data from across a fictitious ASAPS City representative of a normal day in a small city. The dataset includes multiple data types - video, audio, text, sensor, social media. The video data was created from a series of staged events, and then synchronized and augmented with recorded audio, simulated data for sensor, text, and social media related to the variety of emergency events across the city. The dataset represents a geographic area of approximately 68 square blocks. All of the GPS coordinates represent a fictitious location which includes 390 buildings and parks, each with an identity/name, street address, and latitude/longitude location. The first-of-its-kind dataset combines video recordings of staged emergencies with scripted audio and textual public safety communications and simulated Computer Aided Dispatch and gunshot sensor data. It was constructed to represent a continuous eight-hour snapshot of the emergencies happening during the day in a small city and includes 45 time-synchronized data streams. No actual emergencies occur in the data and it includes only simulated data and audio and video recordings of consenting subjects who participated as actors in the staging of the data. The subjects agreed to their video and audio being recorded and distributed to support open research in automated emergency analysis in a formal data collection consent process. The ASAPS Dataset was created by the Lafayette Group INC under contract award #GS-23F-0134N on behalf of NIST PSCR. The data collection protocol was reviewed and approved in April 2020 by the New England Institutional Review Board (NEIRB) in accordance with 45 CFR 46, the Protection of Human Subjects and by the NIST Research Protections Office in accordance with 15 CFR 27.112, Review by Institution. The data is organized and annotated to be used for R&D in automated emergency event analysis and its use is restricted to the terms and conditions of the data use agreement. The ASAPS Dataset is available to Data Recipients by registering for an account via the ASAPS Dataset website (https://asapsdata.nist.gov).

Iowa Flood Information System (IFIS) sensor data from the Iowa Flood Center Stream Sensors. More information about them can be found at: http://iowafloodcenter.org/projects/stream-stage-sensor/

air pollution

Driving behavior plays a vital role in maintaining safe and sustainable transport, and specifically, in the area of traffic management and control, driving behavior is of great importance since specific driving behaviors are significantly related with traffic congestion levels. Beyond that, it affects fuel consumption, air pollution, public health as well as personal mental health and psychology. Use of Smartphone sensors for data acquisition has emerged as a means to understand and model driving behavior. Our aim is to analyze driving behavior using on Smartphone sensors’ data streams. We present Smartphone sensor (Accelerometer, Gyroscope, Proximity, etc.) data recorded in live traffic while driver was executing the driving events. The datasets folder include .csv files of sensor data like Accelerometer, Gyroscope, etc. This data was recorded in live traffic while driver was executing certain driving events. The travel time for each one way trip was approximately 5kms - 20kms. The smartphone position was fixed horizontally in the vehicles utility box. Vehicle type used for data recording was LMV.

Multi Sensor Data Fusion Module Market Outlook

As per the latest research conducted in 2025, the global Multi Sensor Data Fusion Module market size stood at USD 4.28 billion in 2024. The market is anticipated to grow at a robust CAGR of 13.7% during the forecast period, reaching a projected value of USD 13.14 billion by 2033. The primary growth driver for this market is the rapid advancement in sensor technologies, coupled with the increasing demand for real-time data processing and decision-making capabilities across diverse industries.

One of the most significant factors propelling the growth of the Multi Sensor Data Fusion Module market is the surge in adoption of autonomous systems in automotive, aerospace, and industrial sectors. With the proliferation of autonomous vehicles, drones, and robotics, there is a growing need to integrate data from multiple sensors to enhance situational awareness, safety, and operational efficiency. The deployment of advanced sensor fusion modules enables seamless amalgamation of data from image, radar, LiDAR, and inertial sensors, leading to more reliable and accurate insights. This technological evolution is further accelerated by the integration of artificial intelligence and machine learning algorithms, which significantly enhance the performance and adaptability of data fusion systems in dynamic environments.

Another crucial growth factor is the expanding application of sensor fusion modules in healthcare and consumer electronics. In healthcare, multi-sensor data fusion is revolutionizing patient monitoring, diagnostics, and surgical navigation by providing comprehensive, real-time information from a variety of biosensors and imaging devices. Similarly, the consumer electronics segment is witnessing a surge in demand for smart devices equipped with multiple sensors, such as smartphones, wearables, and home automation systems, all of which rely on sophisticated data fusion algorithms to deliver enhanced user experiences. The continuous miniaturization of sensors and advancements in embedded systems further facilitate the integration of multi-sensor fusion modules into compact devices, broadening their application scope.

The market is also benefiting from increased investments in research and development activities by both public and private sectors. Governments and industry leaders are focusing on developing robust sensor networks and data fusion frameworks to support smart city initiatives, intelligent transportation systems, and defense applications. The emergence of Industry 4.0 and the Internet of Things (IoT) has created a fertile ground for the adoption of multi sensor data fusion modules, as organizations strive to optimize operational efficiencies and gain actionable insights from vast streams of heterogeneous data. However, the complexity of integrating diverse sensor types and ensuring interoperability remains a challenge, necessitating continuous innovation and collaboration among stakeholders.

From a regional perspective, North America currently dominates the Multi Sensor Data Fusion Module market, driven by the presence of leading technology providers, strong R&D infrastructure, and early adoption of advanced sensor fusion technologies in automotive, defense, and industrial automation. Europe and Asia Pacific are also witnessing significant growth, fueled by the rising demand for smart manufacturing solutions, increasing investments in autonomous mobility, and expanding consumer electronics markets. The Asia Pacific region, in particular, is expected to exhibit the highest growth rate during the forecast period, owing to rapid industrialization, urbanization, and government initiatives supporting digital transformation and smart infrastructure development.

Component Analysis

The Multi Sensor Data Fusion Module market by component is segmented into Hardware, Software, and Services. The hardware segment encompasses physical sensor devices, processing units, and embedded systems that form the backbone of sensor fusion architectures. This segment currently ho

# Motion Sensor-Based Mobile Device Fingerprinting (MSMDF)

This database was collected by Carlos **Sulbaran Fandino** under the supervision of **Anne Josiane Kouam** and **Konrad Rieck**.

The database is divided in 2 directories: *raw data*, and *fingerprints*. Along with it the *figures* directory provides different visualizations of the different fingerprinting-datasets.

## Raw data
This repository contains the sensor data collected for our experiments. The repository is divided in 4 sub-repositories:
 1. **Example recordings:** This directory contains 1 recording representing a full data collection session and 2 motivational recordings representing two devices placed side by side on a desk. 
 2. **Original recordings:** This repository contains *340 two-minutes* recordings each named *"Device ID - Recording Instance"*. Each recording instance contains a *Metadata.csv* file (device name, platform, device id) together with 4 csv files corresponding to the different sensors: *Accelerometer.csv* , *Gravity.csv* , *Gyroscope.csv* , *Orientation.csv*. 
 3. **Separated by setting:** This repository contains 6 sub-repositories each corresponding to a different data collection setting (environmental condition). The sensor data in this directory is the result of the pre-processing stage of our MSMDF evaluation, therefore additional data streams have been added to each csv file and the *Orientation.csv* file is not included. 
 4. **Protected data - reduced:** This repository contains a reduced version of *separated by setting*, for each of the parameters used to evaluate the countermeasures. To produce a full version use: [CountermeasureApplier.py](https://github.com/carlossulba/MSMDF-Study/blob/main/Code/CountermeasureApplier.py) 


Each recording corresponds to a data collection session where the user: **1.** Holds its phone in hand for 10 seconds, **2.** Places it on a desk for 10 seconds, **3.** Holds it again in hand for 10 seconds but with inaudible audio stimulation, **4.** Again places the phone on a desk but with inaudible audio stimulation, **5.** Holds the phone on hand with extended arm while taking 10 steps in a straight line, and finally **6.** Repeats step five.


## Fingerprints
This repository contains a fingerprint-dataset for each fingerprint design. For each fingerprint design parameter you can find a respective sub-directory.

The following directories contain the fingerprint-datasets for each design parameter: 
 1. Sensor selection
 2. DC conditions
 3. Data stream set
 4. Feature set
 5. Window length (s)
 6. Sampling rate (Hz)
 7. Default
 8. Min FPs per device 

Inside them you will find 1 pickle file (.pkl) for each fingerprint design.

The following directories contain the fingerprint-dataset for each countermeasure parameter:
 1. Countermeasure strength
 2. Countermeasure resampling frequency

These were extracted after applying an anonimization step to the raw sensor data before extracting the fingerprints with the default fingerprint design.



## Using a fingerprint-dataset
The following data-structure describes the fingerprint-datasets. You can use them for training your own models or evaluating their distribution in the space.
```python
{
  'fingerprints': dict
  'config': FingerprintConfig
}
```

For opening a fingerprint-dataset you can use the following example code: [open fingerprint.py]()

The following data-structure describes the *fingerprints* dictionary.
```python
{
  'Setting 1': {
    'Device-01': [
      Fingerprint_01,
      Fingerprint_02
      ],
    'Device-02': [
      Fingerprint_01,
      Fingerprint_02,
      ],
  },
  'Setting 2': {
    'Device-01': [
      Fingerprint_01,
      Fingerprint_02,
      ],
  }
}
```

The following data-structure describes the *FingerprintConfig*.
```python
{
  "data_location": string,
  "fingerprint_length": int,
  "sampling_rate": int,
  "enabled_settings": list,
  "enabled_sensors": list,
  "enabled_streams": list,
  "enabled_features": list,
  "min_recordings": int,
  "repositioning": bool,
  "spectral_brightness_threshold": int,
  "spectral_rolloff_threshold": float,
  "frame_duration": float
}
```

# License
This database is open-source and available under the [GNU General Public License (GPL)](https://www.gnu.org/licenses/gpl-3.0.en.html). By using this database, you agree to the following conditions:
- Use responsibly and ethically.
- Cite this repository in your work or research.
- Ensure that any derivative works or modifications are open-sourced under the same license.

Snapshot img

Internet Of Things Sensors Market Size 2025-2029

The internet of things (IoT) sensors market size is forecast to increase by USD 90.23 billion at a CAGR of 43.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing demand for smart factories and Industrial Internet of Things (IIoT) applications. The need for remote monitoring and real-time data collection is fueling the adoption of IoT sensors in various industries, including manufacturing, healthcare, and energy. However, the market faces challenges in the form of regulatory compliance and the need to adhere to different standards. Machine learning and deep learning algorithms enable advanced data analysis and process automation, driving digital transformation initiatives. Ensuring sensors meet various regulatory requirements and industry-specific standards can be a complex and time-consuming process, posing a significant challenge for market participants.
Despite these obstacles, the potential for IoT sensors is vast, offering companies opportunities to improve operational efficiency, enhance product quality, and create new revenue streams. By addressing regulatory challenges and continuing to innovate, companies can effectively capitalize on the growing demand for IoT sensors and contribute to the market's continued expansion. In transportation, grid technology and edge computing are essential for autonomous vehicle safety and efficient energy management.

What will be the Size of the Internet Of Things (IoT) Sensors Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

The market is experiencing significant growth and impact across various industries, including home security, healthcare, precision farming, and industrial automation systems. IoT sensors offer benefits such as real-time data collection, process optimization, and cost optimization. In healthcare, temperature sensors and biometric authentication are revolutionizing connected healthcare, while in agriculture, precision farming relies on humidity and soil moisture sensors for sustainable growth. Energy management solutions leverage temperature and lighting sensors to optimize building efficiency, while city infrastructure uses location tracking and asset management to improve sustainability. Wearable technology and virtual reality applications in industries like consulting and education are also experiencing a rise in popularity.

IoT sensors play a crucial role in optimizing energy usage, improving asset management, and enhancing security in industrial automation systems. Cloud-based solutions and platform integration are key trends, enabling seamless data sharing and analysis. IoT sensors are transforming industries by providing valuable insights and enabling innovative applications.

How is this Internet of Things (IoT) Sensors Industry segmented?

The Internet of Things (IoT) sensors industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Consumer electronics
  Automotive
  Food and beverages
  Healthcare
  Others


Type

  Temperature sensor
  Pressure sensor
  Humidity sensor
  Flow sensor
  Others


Environment

  Indoor sensor
  Outdoor sensor


Connectivity

  Wired sensor
  Wireless sensor


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By End-user Insights

The Consumer electronics segment is estimated to witness significant growth during the forecast period. The market encompasses various applications, from city infrastructure to healthcare and agriculture. IoT sensors play a pivotal role in enhancing process optimization, energy efficiency, and predictive maintenance across industries. In construction, sensors monitor humidity levels during building processes for improved quality and energy savings. Occupancy and motion sensors in offices and homes enable response time improvement and energy management solutions. Lighting sensors optimize energy consumption based on natural light availability. In urban planning, IoT sensors facilitate real-time analytics for traffic management and predictive analytics for public safety. Grid technology integrates IoT sensors to monitor energy consumption and distribution for improved efficiency.

Healthcare services facilities utilize ECG sensors for remote patient monitoring and biometric authentication for secure access. Energy efficiency is a significant focus, with IoT sensors and communication technology used to optimize operation

Multisensor Data Fusion Module Market Outlook

According to our latest research, the Multisensor Data Fusion Module market size reached USD 2.74 billion in 2024, with a robust compound annual growth rate (CAGR) of 13.8% projected through the forecast period. By 2033, the market is expected to achieve a value of USD 8.03 billion, underpinned by increasing demand for real-time data analytics, advancements in sensor technologies, and the proliferation of applications across automotive, industrial automation, and defense sectors. The market’s dynamic growth is attributed to the convergence of artificial intelligence and machine learning with multisensor data fusion, enabling enhanced decision-making and operational efficiency.

One of the primary growth factors propelling the multisensor data fusion module market is the rapid evolution and deployment of advanced sensor technologies across a diverse range of industries. The integration of image, radar, LiDAR, and acoustic sensors has enabled organizations to collect and process multidimensional data, leading to improved situational awareness, safety, and automation. In the automotive sector, for example, the adoption of multisensor data fusion modules is accelerating the development of autonomous vehicles and advanced driver-assistance systems (ADAS), which rely on the seamless integration of data from multiple sensor sources to navigate complex environments safely. Additionally, the industrial and robotics sectors are leveraging these modules to enhance machine vision, fault detection, and predictive maintenance, thus minimizing downtime and optimizing productivity.

Another significant driver is the increasing adoption of artificial intelligence (AI) and machine learning (ML) algorithms in data fusion processes. The convergence of AI/ML with multisensor data fusion modules allows for the extraction of actionable insights from vast and heterogeneous data streams. This has become particularly critical in applications such as surveillance, healthcare diagnostics, and aerospace & defense, where the accuracy and timeliness of information can have a profound impact on outcomes. Enhanced computational capabilities and the proliferation of edge computing are further enabling real-time data processing, reducing latency, and supporting mission-critical decision-making. The growing trend towards smart cities and connected infrastructure is also creating new opportunities for the deployment of multisensor data fusion modules in urban mobility, traffic management, and public safety.

The market is also witnessing a surge in demand due to the increasing complexity of modern systems and the need for robust, scalable, and interoperable solutions. As industries continue to digitize and automate their operations, the volume, velocity, and variety of data generated from disparate sensor types have increased exponentially. This necessitates sophisticated data fusion modules capable of harmonizing and interpreting data from multiple sources in real time. Furthermore, the rise of Industry 4.0 and the Internet of Things (IoT) is driving investments in multisensor data fusion technologies to support applications such as predictive analytics, quality control, and asset tracking. The ongoing miniaturization of sensors and advancements in communication protocols are also contributing to the widespread adoption of these modules across both established and emerging markets.

From a regional perspective, North America continues to dominate the multisensor data fusion module market, accounting for the largest share in 2024, driven by substantial investments in defense, automotive innovation, and industrial automation. Europe follows closely, with strong growth in the automotive and aerospace sectors. The Asia Pacific region, however, is expected to witness the fastest growth rate during the forecast period, fueled by rapid industrialization, expanding manufacturing capabilities, and increasing adoption of smart technologies in countries such as China, Japan, and South Korea. Latin America and the Middle East & Africa are also emerging as promising markets, supported by government initiatives to modernize infrastructure and enhance public safety.

Smart Pavement Sensor Network Market Outlook

According to our latest research, the global Smart Pavement Sensor Network market size reached USD 1.21 billion in 2024. The market is experiencing robust expansion, registering a compound annual growth rate (CAGR) of 16.5% from 2025 to 2033. By the end of this forecast period, the global market is projected to attain a value of USD 4.24 billion by 2033. This impressive growth is primarily driven by the increasing adoption of smart infrastructure solutions, the rising need for real-time traffic and structural health monitoring, and the global push for smarter, safer, and more sustainable urban environments.

A significant growth factor for the Smart Pavement Sensor Network market is the escalating demand for intelligent transportation systems (ITS) across both developed and developing regions. Governments and municipal authorities are investing heavily in advanced road infrastructure to address urban congestion, reduce accident rates, and enhance public safety. The integration of sensor networks into roadways enables real-time data collection on traffic flow, vehicle loads, and environmental conditions, thereby facilitating more efficient traffic management and maintenance planning. This proactive approach not only extends the lifespan of critical infrastructure but also minimizes the risk of catastrophic failures, positioning smart pavement sensor networks as a cornerstone of future-ready transport ecosystems.

Technological advancements in sensor miniaturization, wireless communication, and data analytics are further fueling the market’s expansion. Modern sensors are now more robust, energy-efficient, and capable of transmitting large volumes of data with minimal latency. The emergence of the Internet of Things (IoT) and 5G connectivity has enabled seamless integration of these sensors into broader smart city frameworks, allowing for predictive maintenance, automated incident detection, and optimized traffic signal control. Such innovations are making smart pavement sensor networks increasingly attractive to both public agencies and private sector stakeholders, driving adoption in new infrastructure projects as well as retrofitting of existing road networks.

Another critical growth driver is the growing emphasis on sustainable urban development and environmental monitoring. Smart pavement sensor networks play a pivotal role in supporting green mobility initiatives by enabling more efficient use of road resources, reducing fuel consumption through better traffic flow, and providing real-time data on weather and surface conditions. These capabilities are particularly valuable for regions facing extreme weather events or rapid urbanization, where maintaining road safety and resilience is paramount. As cities worldwide commit to smart city initiatives and carbon reduction goals, the demand for sophisticated pavement sensor networks is expected to surge.

From a regional perspective, North America currently leads the Smart Pavement Sensor Network market, driven by substantial investments in smart infrastructure and a strong focus on road safety. However, Asia Pacific is rapidly emerging as a high-growth region, propelled by large-scale urbanization, government-led smart city programs, and the modernization of transportation networks in countries such as China, India, and Japan. Europe also remains a significant market, benefiting from stringent regulatory standards and a proactive approach to sustainable infrastructure. As technology costs continue to decline and awareness of the benefits of smart sensor networks grows, adoption rates are expected to increase across all major regions.

Sensor Type Analysis

The sensor type segment forms the technological backbone of the Smart Pavement Sensor Network market, encompassing temperature sensors, pressure sensors, moisture sensors, strain gauges, and other specialized devices. Temperature sensors are critical for monitoring pavement surface and subsurface temperatures, which is essential for predicting conditions such as ice formation or heat-induced material degradation. Their deployment is particularly prevalent in regions with extreme seasonal variations, where real-time temperature data can inform timely maintenance interventions and enhance road safety. The increasing sophistication of temperature sensors, including improved accuracy and durability, is driving their adoption in both new and retrofit projects.

Pre

This dataset is available on Brisbane City Council’s open data website – data.brisbane.qld.gov.au. The site provides additional features for viewing and interacting with the data and for downloading the data in various formats.

Rainfall and Stream Height gauges owned by Brisbane City Council. This is raw, unprocessed data.

Council installs and maintains telemetry gauges at various locations across Brisbane as part of its hydrometric network. These gauges form part of the Bureau of Meteorology Flood Warning Network. This Brisbane City Council gauge information (raw data) is passed onto the Bureau of Meteorology which they display on their public website via various interfaces.

The hydrometric gauges:

• collect real\-time information on water levels and rainfall
• provide information to trigger flood alerts and warnings
• provide information to trigger flooded road signage/flashing lights
• provide historic flood information for use in hydrology and hydraulic modelling.

Automatically collected telemetry data for rainfall and stream height gauges owned by Brisbane City Council.

The dataset includes raw gauge readings in 5\-minute increments covering a 24\-hour rolling period. The data is updated every 10 minutes. Dataset includes gauge readings, descriptions and location details.

The Data and resources section of this dataset contains further information for this dataset.

The global market for Transportation Vehicle Automated Passenger Counting (APC) Systems is experiencing robust growth, driven by increasing demand for efficient public transportation management and enhanced passenger safety. The market size reached $748.1 million in 2025. While the CAGR is not explicitly provided, considering the rapid technological advancements in sensor technology and the growing adoption of smart city initiatives, a conservative estimate of the Compound Annual Growth Rate (CAGR) for the forecast period (2025-2033) would be around 12%. This growth is fueled by several key drivers. Firstly, the increasing need for real-time data on passenger numbers for optimized service scheduling and resource allocation is a significant factor. Secondly, the growing concerns about passenger safety and security are prompting the adoption of APC systems to monitor passenger flow and identify potential security risks. Finally, the ongoing development and deployment of smart city initiatives are creating a favorable environment for the widespread adoption of automated passenger counting solutions. Market segmentation reveals significant growth potential across various transportation modes, including roadways, railways, and airways, with infrared, time-of-flight, and stereoscopic vision technologies competing to offer the most accurate and efficient solutions. The North American market is expected to hold a substantial share due to early adoption of advanced technologies and significant investments in public transportation infrastructure. The competitive landscape is characterized by a mix of established players and emerging technology providers, each focusing on specific technologies and market segments. Companies are constantly innovating to improve the accuracy, reliability, and cost-effectiveness of APC systems. Future growth will likely be driven by the integration of APC systems with other smart transportation technologies such as intelligent transportation systems (ITS) and big data analytics platforms, leading to a more comprehensive and insightful view of passenger transport dynamics. The advancements in artificial intelligence (AI) and machine learning (ML) are also expected to improve the accuracy and efficiency of APC systems further, opening new avenues for data-driven optimization and enhanced decision-making in the transportation sector. Regional variations will exist, with developed economies leading in adoption rates but developing countries also showing considerable potential for future growth as their public transit infrastructure improves.

Background: Digital data sources have become ubiquitous in modern culture in the era of digital technology but often tend to be under-researched because of restricted access to data sources due to fragmentation, privacy issues, or industry ownership, and the methodological complexity of demonstrating their measurable impact on human health. Even though new big data sources have shown unprecedented potential for disease diagnosis and outbreak detection, we need to investigate results in the existing literature to gain a comprehensive understanding of their impact on and benefits to human health.Objective: A systematic review of systematic reviews on identifying digital data sources and their impact area on people's health, including challenges, opportunities, and good practices.Methods: A multidatabase search was performed. Peer-reviewed papers published between January 2010 and November 2020 relevant to digital data sources on health were extracted, assessed, and reviewed.Results: The 64 reviews are covered by three domains, that is, universal health coverage (UHC), public health emergencies, and healthier populations, defined in WHO's General Programme of Work, 2019–2023, and the European Programme of Work, 2020–2025. In all three categories, social media platforms are the most popular digital data source, accounting for 47% (N = 8), 84% (N = 11), and 76% (N = 26) of studies, respectively. The second most utilized data source are electronic health records (EHRs) (N = 13), followed by websites (N = 7) and mass media (N = 5). In all three categories, the most studied impact of digital data sources is on prevention, management, and intervention of diseases (N = 40), and as a tool, there are also many studies (N = 10) on early warning systems for infectious diseases. However, they could also pose health hazards (N = 13), for instance, by exacerbating mental health issues and promoting smoking and drinking behavior among young people.Conclusions: The digital data sources presented are essential for collecting and mining information about human health. The key impact of social media, electronic health records, and websites is in the area of infectious diseases and early warning systems, and in the area of personal health, that is, on mental health and smoking and drinking prevention. However, further research is required to address privacy, trust, transparency, and interoperability to leverage the potential of data held in multiple datastores and systems. This study also identified the apparent gap in systematic reviews investigating the novel big data streams, Internet of Things (IoT) data streams, and sensor, mobile, and GPS data researched using artificial intelligence, complex network, and other computer science methods, as in this domain systematic reviews are not common.

The global market for social distancing devices is experiencing robust growth, driven by the ongoing need for public health safety and workplace productivity enhancement in the post-pandemic world. While precise figures for market size and CAGR are not provided, considering similar technology sectors and the current market demand, we can reasonably estimate the 2025 market size at approximately $2.5 billion. A conservative Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033 is projected, reflecting continued adoption across various sectors, including healthcare, education, and manufacturing. This growth is fueled by several key drivers: increasing awareness of the importance of infection control, stringent government regulations promoting workplace safety, and technological advancements leading to more sophisticated and user-friendly solutions. The market encompasses a diverse range of devices, from simple proximity sensors and wearable trackers to sophisticated contact tracing systems and thermal cameras. The market segments are witnessing considerable competition, with a wide array of established technology companies and specialized startups vying for market share. Challenges remain, however. The high initial investment cost associated with deploying some social distancing technologies, particularly in smaller businesses, may hinder widespread adoption. Data privacy concerns surrounding contact tracing systems also pose a significant hurdle to overcome. Furthermore, maintaining user engagement and ensuring long-term effectiveness of these devices require careful consideration of user experience and robust data management strategies. Despite these challenges, the market's future remains promising, particularly with ongoing innovation in sensor technology, artificial intelligence, and data analytics. The continued focus on public health and workplace safety will likely sustain the demand for social distancing devices for years to come, ensuring substantial market growth in the forecast period.

Real Time Traffic Data Market Outlook

The global real-time traffic data market size is anticipated to reach USD 15.3 billion by 2032 from an estimated USD 6.5 billion in 2023, exhibiting a robust CAGR of 10.1% over the forecast period. This substantial growth is driven by the increasing need for efficient traffic management systems and the rising adoption of smart city initiatives worldwide. Governments and commercial entities are investing heavily in advanced technologies to optimize traffic flow and enhance urban mobility, thus fostering market expansion.

The surge in urbanization and the consequent rise in vehicle ownership have led to severe traffic congestion issues in many metropolitan areas. This has necessitated the implementation of real-time traffic data systems that can provide accurate and timely information to manage traffic effectively. With the integration of sophisticated technologies such as IoT, AI, and big data analytics, these systems are becoming more efficient, thereby driving market growth. Furthermore, the growing emphasis on reducing carbon emissions and enhancing road safety is also propelling the adoption of real-time traffic data solutions.

Technological advancements are playing a pivotal role in shaping the real-time traffic data market. Innovations in sensor technology, the proliferation of GPS devices, and the widespread use of mobile data are providing rich sources of real-time traffic information. The ability to integrate data from multiple sources and deliver actionable insights is significantly enhancing traffic management capabilities. Additionally, the development of cloud-based solutions is enabling scalable and cost-effective deployment of traffic data systems, further contributing to market growth.

Another critical growth factor is the increasing investment in smart city projects. Governments across the globe are prioritizing the development of smart transportation infrastructure to improve urban mobility and reduce traffic-related issues. Real-time traffic data systems are integral to these initiatives, providing essential data for optimizing traffic flow, enabling route optimization, and enhancing public transport efficiency. The involvement of private sector players in these projects is also fueling market growth by introducing innovative solutions and fostering public-private partnerships.

The exponential rise in Mobile Data Traffic is another significant factor influencing the real-time traffic data market. As more people rely on smartphones and mobile applications for navigation and traffic updates, the demand for real-time data has surged. Mobile data provides a wealth of information about traffic patterns and congestion levels, enabling more accurate and timely traffic management. The integration of mobile data with other data sources, such as GPS and sensor data, enhances the overall effectiveness of traffic data systems. This trend is particularly evident in urban areas where mobile devices are ubiquitous, and the need for efficient traffic management is critical. The ability to harness mobile data for traffic insights is driving innovation and growth in the market, as companies develop new solutions to leverage this valuable resource.

Regionally, North America and Europe are leading the market due to their early adoption of advanced traffic management technologies and significant investments in smart city projects. However, the Asia Pacific region is expected to witness the highest growth rate over the forecast period, driven by rapid urbanization, increasing vehicle ownership, and growing government initiatives to develop smart transportation infrastructure. Emerging economies in Latin America and the Middle East & Africa are also showing promising growth potential, fueled by ongoing infrastructure development and increasing awareness of the benefits of real-time traffic data solutions.

Component Analysis

The real-time traffic data market by component is segmented into software, hardware, and services. Each component plays a crucial role in the overall functionality and effectiveness of traffic data systems. The software segment includes traffic management software, route optimization software, and other analytical tools that help process and analyze traffic data. The hardware segment comprises sensors, GPS devices, and other data collection tools. The services segment includes installation, maintenance, and consulting services that support the deployment and operation of traffic data systems

The global passenger tracking system market is experiencing robust growth, driven by increasing demand for enhanced security, improved operational efficiency, and a better understanding of passenger behavior in various settings like airports, stadiums, and transit hubs. The market, estimated at $1.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $5 billion by 2033. This growth is fueled by several factors. Firstly, the rising adoption of advanced technologies like video analytics, AI-powered passenger flow management, and IoT-enabled sensors provides more accurate and real-time data, leading to better resource allocation and optimized infrastructure planning. Secondly, stringent government regulations regarding passenger safety and security are driving the demand for reliable and comprehensive tracking systems. Finally, the increasing focus on data-driven decision-making in the transportation and hospitality sectors is creating a significant market opportunity for passenger tracking system providers. Companies like Xovis, Xybase Aero, CrowdVision, FootfallCam, Lyngsoe Systems, and TrackIT Solutions are key players, constantly innovating to meet evolving market needs. The market segmentation reveals a significant share held by airports, followed by public transportation and stadiums. However, the market is witnessing diversification, with growing demand from retail spaces and entertainment venues seeking to optimize customer experience and improve operational efficiency. Geographical expansion is also a significant factor, with North America and Europe currently dominating the market share, but rapid growth is anticipated in the Asia-Pacific region due to increasing infrastructure development and rising disposable incomes. Despite the positive outlook, challenges such as high initial investment costs, data privacy concerns, and the need for robust cybersecurity measures could potentially restrain market growth. However, ongoing technological advancements and the development of user-friendly, cost-effective solutions are expected to mitigate these challenges and sustain the market's upward trajectory.

This dataset documents the comparison between sewer flow measurements collected with the DischargeKeeper of Photrack AG via Surface Structure Image Velocimetry (SSIV) and Nivus ultrasonic flow sensors. Data covers timestamped flow rates, depth, and velocity measurements from an experimental combined sewer site in Dübendorf, Switzerland (Eawag HALL laboratory). Analysis scripts and structured FAIR metadata are included to support reproducibility and reuse. All data is openly available under the Creative Commons CC0 1.0 Public Domain Dedication. ## File Structure inside the ZIP Archive The uploaded ZIP file contains: ├── license.txt # License file (CC0 1.0 Public Domain Dedication) │ ├── code/ │ └── DK_vs_NIVUS.py # Python script for comparing DischargeKeeper and NIVUS data │ ├── data/ │ ├── 000Credible_Nivus_Data_with_Timestamp.csv # Quality-checked Nivus flow data │ ├── 000DischargeKeeper_Data_Timestamp_.csv # DischargeKeeper flow data │ ├── discharge_eawag.png # Site reference image │ ├── nivus_data_2019-01-01.csv # Raw Nivus dataset (Jan 2019) │ └── nivus_data_2019-04-01_2019-05-30.csv # Raw Nivus dataset (Apr-May 2019) │ ├── docs/ │ ├── PenaHaro_2021_ABFLUSSMESSUNGEN MIT VIDEOS_Aqua_Et_Gas_Aqua_Urbanica.pdf # Scientific background paper │ ├── Readme_DK.md # README for DischargeKeeper dataset │ └── Readme_NIVUS.md # README for NIVUS dataset │ └── metadata/ ├── metadata_DischargeKeeper.json # FAIR metadata for DischargeKeeper └── metadata_NIVUS.json # FAIR metadata for NIVUS

Public dictionary The dataset locates on Bordeaux Métropole the different recurrent counting sites of cycling flows. It distinguishes between: * bike sensors (counters/automatic loops) that count the number of cyclists in “real time” at the 5-minute time step * punctual survey sites that are the subject of a periodic census within the framework of the Bicycle Observatory of Bordeaux Métropole. The automatic sensors are magnetic loops located on a part of the roadway to identify the flow of bicycles by discriminating it vis-à-vis other vehicle flows (cars, heavy goods vehicles, motorcycles...). On sites with sustained cycling traffic with simultaneous crossings of several cyclists on the sensor, the data may slightly underestimate the actual traffic of cyclists. These data do not therefore correspond to an exhaustive census of cyclists taking the lane where the sensor is located. For automatic sensors, it is possible via the Bordeaux Métropole WebServices: * access current values * to access the values at a moment T past * to do data aggregation per day, month, etc. Historicisation began at the end of 2020. A dataset showing the automatic sensor time history is also available. * * * * This dataset is refreshed all: 3 Minute(s). Attention, for performance reasons, this dataset (Table, Map, Analysis and Export tabs) can be updated less frequently than the source and a discrepancy may exist. Also we invite you to use our Webservices (see BM Webservices tab) to retrieve the freshest data.

A multitude of data streams, such as gridded climate data and biophysical parameters of land and water bodies, are utilized for monitoring various subsystems of the Earth in space and time. As sensor technology advances, the spatial and temporal resolution of these data sets continue to increase, posing a challenge in obtaining global and local insights from the data. In order to explore these large socioeconomic and multivariate remote sensing data cubes effectively, we explored different visualization approaches and extended our existing client-server software architecture Lexcube for interactive exploration and visualization of data cubes. As part of this NFDI4Earth pilot project, Lexcube has been successfully released as open-access at lexcube.org. Furthermore, new features such as animations and axis labels have been developed and a new data set from ECMWF has been added since the public release. Lexcube.org has seen over 2800 users and 163,000 API requests since its public release in May 2022, offering interactive visualizations for open-access to a widely interested audience. In the future, Lexcube will be extended with additional functionality, including integration into data science workflows like Jupyter notebooks, and the ability to visualize multiple cubes simultaneously. Additionally, we are aiming to open-source Lexcube in 2023, which will enable a broader audience to benefit from Lexcube's capabilities and potentially contribute to its future development.

This layer is a snapshot of stream gages from the fall of 2020. It is the product of an attempt to compile a comprehensive, geospatial list of long-term stream gages whose data is publicly available. Initially, the layer will consist of USGS and CDEC gages. Over time, local (county, municipal, etc.) gages will be added. This layer is not claimed to be authoritative. In cases where this layer and the data maintained by the source entity differ, this layer always defers to the source entity. For analysis purposes, the gage point locations have been altered by SWRCB to coincide with the corresponding line features in the National Hydrography Dataset (NHD) Medium Resolution. The original point locations can be found "x" and "y" fields of the layer's attribute table.For questions, contact the SWRCB Division of Water Rights: DWR@waterboards.ca.gov.Data dictionary: Field Name Description Data Type

SiteID Site ID Text

SiteName Site Name Text

Operator Agency or entity which operates the gage Text

DataSource The agency or entity which publishes the data online (source not exclusive) Text

SiteStatus Is the site, in general, active or inactive? Text - Active or Inactive

Stage_YN Did the gage report stage at any time? Text - Y or N or U

Stage_POR Stage period of record in days (if a site had multiple stage sensors or duration codes, then the max POR was used) Integer

Stage_Status Status of stage reporting (active/inactive) Text

Stage_RealTime Is/was stage reported hourly or more frequently? Text - Y or N

Flow_YN Did the gage report flow at any time? Text - Y or N or U

Flow_POR Flow period of record in days (if a site had multiple flow sensors or duration codes, then the max POR was used) Integer

Flow_Status Status of flow reporting (active/inactive) Text

Flow_RealTime Is/was flow reported hourly or more frequently? Text - Y or N

WatQual_YN Did the gage report one or more water quality parameters at any time? Includes parameters such as water chemistry, dissolved oxygen, and turbidity, but not temperature Text - Y or N or U

WatQual_POR Water quality period of record in days (if a site had multiple water quality sensors or duration codes, then the max POR was used) Integer

WatQual_Status Status of water quality reporting (active/inactive) Text

WatQual_RealTime Is/was water quality reported hourly or more frequently? Text - Y or N

Temp_YN Did the gage report water temperature at any time? Text - Y or N or U

Temp_POR Temperature period of record in days (if a site had multiple temperature sensors or duration codes, then the max POR was used) Integer

Temp_Status Status of temperature reporting (active/inactive) Text

Temp_RealTime Is/was temperature reported hourly or more frequently? Text - Y or N

EcosysMgmt Primary purpose or benefit of gage is ecosystem management (flow and water quality) Y - water manager survey, B - prioritization analysis high raw score

PubSafety Primary purpose or benefit of gage is flood or public safety Y - water manager survey, F - flood water manager survey, B - prioritization analysis high raw score

WtrSupply Primary purpose of gage or benefit is water supply (municipal or agricultural) Y - water manager survey, G - groundwater water manager survey, B - prioritization analysis high raw score

WtrQuality Primary purpose or benefit of gage is water quality B - prioritization analysis high raw score

refpotential Reference gage or potential reference gage with Action Y or N

FloodMgmt Primary purpose or benefit of gage is flood management Y - water manager survey, F - flood survey answer, B - prioritization analysis high raw score

GrdwtrMgmt Primary purpose of gage is groundwater management Text - Y or N

Ref_GagesII Is the gage site considered a reference site in Gages II dataset? Text - Y or N

StrmOrder Strahler stream order Integer

UCDStrmClass UCD eFlows stream classification Text

StreamType Type of water conveyance the gage is measuring (e.g. Stream/River, Canal/Ditch, Artificial Path, etc.) Text

TotDASqKM Total drainage area in square kilometers Double

TotDASqMi Total drainage area in square miles Double

GNISID_MedRes GNIS (Geographic Names Information System) identification number of the NHD line segment the gage is on (from the NHD Medium Resolution dataset) Text

RchCd_MedRes Reach Code identification number of the NHD line segment the gage is on (from the NHD Medium Resolution dataset) Text

COMID_MedRes COM ID (common identifier) of the NHD line segment the gage is on (from the NHD Medium Resolution dataset) Text

Assessment Assessment categories indicating use cautions (generated by SWRCB staff) Text

WtrRtNotes Notes concerning water rights that may impact gage measurements (generated by SWRCB staff) Text

SWRCB_Note Notes to inform use of gage data (generated by SWRCB staff) Text

WebLink Web address to access each gage's data Text

x_orig X coordinate as provided by source entity (NAD83 CA Teale Albers meters) Double

y_orig Y coordinate as provided by source entity (NAD83 CA Teale Albers meters) Double

WtrshdNm_HUC8 Name of containing HUC8 watershed Text

HUC8 Containing HUC 8 (Hydrologic Unit Code 8) identifier Text

WtrshdNm_HUC10 Name of containing HUC10 watershed Text

HUC10 Containing HUC 10 (Hydrologic Unit Code 10) identifier Text

WtrshdNm_HUC12 Name of containing HUC12 watershed Text

HUC12 Containing HUC 12 (Hydrologic Unit Code 12) identifier Text

GageGap_Status Status of Gage for Gage Gap Analysis (e.g. Well-Gaged, AWG = Almost Well-Gaged, or Exclude) Text

Infrastructure Gage is suspected of being located on infrastructure Text - Y or N or YC (yes but connected)

ReactivateSF Gage is a candidate for reactivation Text - Y or N

Gage_History Reactivation gage history priority based on gage metadata alone (e.g. period_of_record, parameter status, end-date and other factors, but not including based on gage gap or management criteria). 1 is the top score. Long

AddFlow_2Stage Upgrade candidate: gage is actively reporting stage, potential upgrade to flow and stage Text - Y or N

AddFlow_2WQ Upgrade candidate: gage is actively reporting water quality or temperature data, but not flow and/or stage. Text - Y or N

AddTelemetry Upgrade candidate: gage is actively reporting stage and/or flow, but not in real-time Text - Y or N

AddTemp_2Flow Upgrade candidate: gage is actively reporting stage and/or flow, but not water temperature Text - Y or N

Gage Status Indicated whether gage is Active - High Quality, Active - Limited Use, Inactive, Underwater, or Not a stream Gage Text

waterbody Name of waterbody that may cover gage Text

reference gage Gage is considered an active reference quality gage Text - Y or N

Tier Indicates priority level for upgrades and reactivation, with 1 the highest Numeric

Primary Benefit Primary benefit of gage for existing gages, reactivation, and upgrade gages Text

SB19 Action Recommended Recommendation for gage improvement, if any Text

CNRFC Gage is a California Nevada River Forecast Center Gage Text = Forecast or Model