Mobile phone data provide high-resolution, near real-time measurements of population mobility and have become an increasingly valuable source for public health research, enabling rapid evaluation of policy impacts on human movement and pandemic control. However, the methodological challenges surrounding the extraction, governance, and validation of mobile phone data for the public health community remain insufficiently explored. Following the PRISMA-ScR framework, we conduct a scoping review to synthesize major research themes, opportunities, and challenges in the use of mobile phone data for public health, particularly pandemic-related studies. Our findings highlight limitations in the empirical use of these datasets, including demographic and population coverage, representativeness, and equity issues, as well as the transparency of data extraction and processing. We also provide guidance for future research, including the development of standardized frameworks for data curation and validation, a clear understanding of algorithms that extract mobility information, and rigorous interpretation of mobility metrics.

Mobile Robot Dataset Versioning Market Outlook

According to our latest research, the global mobile robot dataset versioning market size reached USD 412 million in 2024, and is expected to grow at a robust CAGR of 16.2% during the forecast period, reaching approximately USD 1.15 billion by 2033. This growth is primarily driven by the increasing adoption of mobile robots across diverse industries and the critical need for robust dataset management solutions to ensure accurate training, deployment, and continuous improvement of autonomous systems. The proliferation of AI-powered robots and rapid advancements in machine learning algorithms are further fueling the demand for sophisticated dataset versioning platforms, enabling organizations to manage, track, and audit data changes efficiently.

One of the most significant growth factors for the mobile robot dataset versioning market is the exponential increase in the deployment of autonomous robots in industries such as logistics, manufacturing, and healthcare. As these robots become more sophisticated, the datasets required for their training and operation also become larger and more complex. Accurate dataset versioning ensures that every iteration of training and operational data is meticulously tracked, which is essential for regulatory compliance, quality assurance, and continuous performance improvement. Companies are increasingly recognizing the role of dataset versioning in minimizing errors, reducing operational downtime, and accelerating the development lifecycle of autonomous systems. The ability to roll back to previous dataset versions or audit changes has become a vital requirement, especially in safety-critical applications.

Another key driver is the rise of collaborative robotics and multi-robot systems, which generate vast amounts of heterogeneous data from diverse sources such as sensors, cameras, and LIDAR. Managing these datasets in real time, especially when updates and modifications are frequent, necessitates advanced versioning solutions that can handle distributed environments. The growing emphasis on data quality, integrity, and traceability is pushing organizations to invest in specialized software and services that provide granular control over dataset modifications. Furthermore, the integration of cloud-based platforms with dataset versioning capabilities allows for seamless collaboration among geographically dispersed teams, thus enhancing productivity and innovation in robot development and deployment.

The market is also benefiting from increased research activities in academia and industry, focusing on improving the accuracy and efficiency of autonomous navigation, mapping, and object recognition. These research initiatives generate vast volumes of experimental data that must be versioned and managed efficiently to support reproducibility and peer collaboration. The growing adoption of open-source frameworks and standardized dataset management practices is further catalyzing market growth. At the same time, regulatory requirements for data transparency and auditability in sectors like healthcare and defense are compelling organizations to adopt advanced dataset versioning solutions, ensuring that all data used in robot training and operation is properly documented and traceable.

From a regional perspective, North America and Europe currently dominate the mobile robot dataset versioning market, driven by robust investments in robotics research, a strong presence of technology vendors, and early adoption of advanced data management solutions. However, the Asia Pacific region is emerging as the fastest-growing market, propelled by rapid industrialization, increased automation in manufacturing and logistics, and significant government initiatives supporting AI and robotics innovation. The Middle East & Africa and Latin America are also witnessing steady growth, albeit from a smaller base, as organizations in these regions increasingly recognize the benefits of dataset versioning in optimizing robot performance and ensuring data compliance. The global landscape is thus characterized by a dynamic interplay of technological advancement, regulatory evolution, and industry-specific adoption patterns.

Component Analysis

The component segment of the mobile robot dataset versioning market is divided into software, hardware, and services, each playing a distinct role in the ecosystem. Software solutions form the backb

The City of Perth Scheme Maps provide a comprehensive spatial representation of key planning and zoning frameworks that guide land use and property development within the City of Perth local government area. These map layers incorporate detailed information from multiple statutory schemes and redevelopment plans, enabling planners, developers, residents, and government officials to understand regulatory controls and future urban growth directions. Show full description

Introduction

Android is one of the most used mobile operating systems worldwide. Due to its technological impact, its open-source code and the possibility of installing applications from third parties without any central control, Android has recently become a malware target. Even if it includes security mechanisms, the last news about malicious activities and Android´s vulnerabilities point to the importance of continuing the development of methods and frameworks to improve its security.

To prevent malware attacks, researches and developers have proposed different security solutions, applying static analysis, dynamic analysis, and artificial intelligence. Indeed, data science has become a promising area in cybersecurity, since analytical models based on data allow for the discovery of insights that can help to predict malicious activities.

In this work, we propose to consider some network layer features as the basis for machine learning models that can successfully detect malware applications, using open datasets from the research community.

Content

This dataset is based on another dataset (DroidCollector) where you can get all the network traffic in pcap files, in our research we preprocessed the files in order to get network features that are illustrated in the next article:

López, C. C. U., Villarreal, J. S. D., Belalcazar, A. F. P., Cadavid, A. N., & Cely, J. G. D. (2018, May). Features to Detect Android Malware. In 2018 IEEE Colombian Conference on Communications and Computing (COLCOM) (pp. 1-6). IEEE.

Acknowledgements

Cao, D., Wang, S., Li, Q., Cheny, Z., Yan, Q., Peng, L., & Yang, B. (2016, August). DroidCollector: A High Performance Framework for High Quality Android Traffic Collection. In Trustcom/BigDataSE/I SPA, 2016 IEEE (pp. 1753-1758). IEEE

The City of Perth Scheme Maps provide a comprehensive spatial representation of key planning and zoning frameworks that guide land use and property development within the City of Perth local government area. These map layers incorporate detailed information from multiple statutory schemes and redevelopment plans, enabling planners, developers, residents, and government officials to understand regulatory controls and future urban growth directions.The City Planning Scheme No. 2 (CPS 2) applies to the whole of the city with the exception of parts of Crawley and Nedlands which were transferred from the Cities of Subiaco and Nedlands to the City on the 1 July 2016.The City of Subiaco Town Planning Scheme No. 4 (TPS 4) and City of Nedlands Town Planning Scheme No. 2 (TPS 2) apply to the parts of Crawley and Nedlands which were transferred from the City of Subiaco and City of Nedlands to the City of Perth on the 1 July 2016.

Studies that focused on the application of IoT mobile app for diabetic elderly healthcare services.