The number of smartphone users in the United States was forecast to continuously increase between 2024 and 2029 by in total 17.4 million users (+5.61 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 327.54 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Mexico and Canada.

Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

Description for each of the variables:

1. Brands: The brands of smartphones included in the dataset.
2. Colors: The colors available for the smartphones.
3. Memory: The storage capacity of the smartphones, typically measured in gigabytes (GB) or megabytes (MB).
4. Storage: The internal storage capacity of the smartphones, often measured in gigabytes (GB) or megabytes (MB).
5. Rating: The user ratings or scores assigned to the smartphones, reflecting user satisfaction or performance.
6. Selling Price: The price at which the smartphones are sold to consumers.
7. Original Price: The original or list price of the smartphones before any discounts or promotions.
8. Mobile: Indicates whether the device is a mobile phone.
9. Discount: The discount applied to the original price to calculate the selling price.
10. Discount percentage: The percentage discount applied to the original price to calculate the selling price.

As of January 2025, around 13.7 percent of paid iOS apps admitted collecting data from users engaging with their mobile products. In comparison, approximately 53 percent of free-to-download iOS apps reported they collect private data from users worldwide, while approximately 86 percent of paid apps have not declared whether they collect users' privacy data.

North America registered the highest mobile data consumption per connection in 2023, with the average connection consuming ** gigabytes per month. This figure is set to triple by 2030, driven by the adoption of data intensive activities such as 4K streaming.

App Tracking Transparency Key StatisticsATT Opt-In Rate by App CategoryATT Opt-In Rate by Game CategoryATT Opt-In Rate by CountryiOS Apps User TrackingiOS Apps Background Location AccessiOS Apps...

As of May 2023, product interaction data were the most commonly collected data points, with 94 over the 100 analyzed apps reporting to collect such data. User ID and crash data were collected by by 93 and 92 apps over 100, respectively. Over the 10 leading shopping apps hosted on the Apple App Store, the totality collected precise location, physical address, and payment info.

This dataset encompasses mobile web clickstream behavior on any browser, collected from over 150,000 triple-opt-in first-party US Daily Active Users (DAU). Use it for measurement, attribution or path to purchase and consumer journey understanding. Full URL deliverable available including searches.

Context

This dataset was recorded as part of an investigation into machine learning algorithms for iOS. 20,136 glyphs were drawn by 257 subjects on the touch screen of an iPhone 6.

An iOS app was developed to record the dataset. Firstly, subjects entered their age, sex, nationality and handedness. Each subject was then instructed to draw the digits 0 to 9 on the touchscreen using their index finger and thumb. This was repeated four times for each subject resulting in 80 glyphs drawn per subject, 40 using index finger and 40 using thumb. The sequence of glyph entry was random. Instructions to the user were provided using voice synthesis to avoid suggesting a specific glyph rendering.

The index finger and thumb were both used to account for situations in which the subject may only have one hand free. The aim here was to train a model that could accurately classify the glyph drawn in as many real life scenarios as possible.

Cubic interpolation of touches during gesture input was rendered on the screen to provide visual feedback to the subject and to compute arclengths. The screen was initially blank (white) and the gestures were displayed in black. The subject could use most of screen to draw with small areas at the top and bottom reserved for instructions/interactions/guidance. The subject was permitted to erase and repeat the entry, if desired.

Content

https://raw.githubusercontent.com/PhilipCorr/numeral-gesture-dataset/master/database.png" alt="Database Schema">

The database consists of 4 tables as seen in the schema. The tables are Subject, Glyph, Stroke and Touch. This is a logical structure as each subject draws 80 glyphs, each glyph consists of a number of strokes and each stroke consists of a number of touches. The four tables are presented in csv format and sqlite format.

Note that, in the files below, all columns start with a capital Z. This is automatically prepended to column names by Core Data, apples database framework. Column names which start with Z_ were automatically created by Core Data and hence, do not appear in the schema above.

The tables are connected through the first column in each table (Z_PK). This primary key links to the relevant column name in the next table. For example, the subject that entered any given glyph can be found by taking the value from the ZSUBJECT column in the glyph table and finding the matching Z_PK value in the subject table.

Some questions to get you started...

• What is the best model for classifying glyphs?
• What is the best model for classifying sequences of these glyphs?
• What is the best model to predict what number a glyph is before completion?
• How much of the glyph needs to be completed before a prediction can be made?
• What is the best method for interpolating between the touches in the dataset?
• How can a trained model be integrated into iOS apps?

CITATION REQUEST

Please cite the following paper in any publications reporting on use of this dataset:

Philip J. Corr, Guenole C. Silvestre, Chris J. Bleakley Open Source Dataset and Deep Learning Models for Online Digit Gesture Recognition on Touchscreens Irish Machine Vision and Image Processing Conference (IMVIP) 2017 Maynooth, Ireland, 30 August-1 September 2017 http://arxiv.org/abs/1709.06871

Key Apple App Store StatisticsApple App Store App and Game RevenueApple App Store Gaming App RevenueApple App Store App RevenueApple App Store App and Game DownloadsApple App Store Game...

This dataset contains synthetic logs designed to simulate the activity of the Pegasus malware, providing a rich resource for cybersecurity research, anomaly detection, and machine learning applications. The dataset includes 1000 entries with 17 columns, each capturing detailed information about device usage, network traffic, and potential security events

Columns: user_id: Unique identifier for each user device_type: Type of device used (e.g., iPhone, Android) os_version: Operating system version of the device app_usage_pattern: Usage pattern of the applications (Low, Normal, High) timestamp: Timestamp of the recorded activity source_ip: Source IP address of the network traffic destination_ip: Destination IP address of the network traffic protocol: Network protocol used (e.g., HTTPS, FTP, SSH) data_volume: Volume of data transferred in the session log_type: Type of log entry (system, application, security) event: Specific event type (e.g., App Install, System Update, Logout, App Crash) event_description: Description of the event error_code: Error code associated with the event file_accessed: File path accessed during the event process: Process name involved in the event anomaly_detected: Description of any detected anomalies (e.g., Unknown Process Execution, Suspicious File Access) ioc: Indicators of Compromise (e.g., Pegasus Signature, Known Malicious IP)

This dataset is ideal for those looking to develop and test cybersecurity solutions, understand malware behavior, or create educational tools for cybersecurity training. The data captures various scenarios of potential malware activities, making it a versatile resource for a range of cybersecurity applications.

Snapshot img

Smartphone Market Size 2025-2029

The smartphone market size is forecast to increase by USD 99.8 million, at a CAGR of 4.1% between 2024 and 2029.

The market is experiencing significant growth, driven by several key trends. One major factor is the increasing adoption of artificial intelligence (AI) in smartphones, enhancing user experience through features like voice recognition and facial recognition. Sensor fusion technology is another trend, enabling devices to collect and analyze data from various sensors for improved functionality and accuracy. However, ongoing trade wars are posing challenges to market growth, with tariffs and import taxes affecting smartphone sales, particularly in key markets. These trends and challenges are shaping the future of the smartphone industry.

What will be the Size of the Smartphone Market During the Forecast Period?

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The market continues to evolve, driven by advancements in telecom infrastructure and the proliferation of affordable handsets. Mobile phone users increasingly seek devices capable of leveraging 5G network technologies, with chipmakers responding by producing 5G chips for integration into mobile handsets. Android and Windows Phone operating systems dominate the market, while third-party originators challenge the status quo. Improved hardware and software capabilities enable advanced digital functions such as web browsing, music, video, gaming, and camera capability. The integration of artificial intelligence enhances user experience. Governmental assistance and the transition from feature phones to smartphones further fuel market growth. Overall, the market remains dynamic, with a focus on affordable, high-performance devices that cater to the diverse needs of consumers.

How is this Smartphone Industry segmented and which is the largest segment?

The smartphone 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.

Technology

  Android
  IOS
  Others


Price Range

  Between USD 150-USD 800
  Greater than USD 800
  Less than USD150


Screen Size

  Greater than 6 inches
  Between 5-6 inches
  Less than 5 inches


Geography

  APAC

    China
    India
    Japan
    South Korea


  Europe

    Germany
    UK
    France


  North America

    Canada
    US


  Middle East and Africa



  South America

    Brazil

By Technology Insights

The android segment is estimated to witness significant growth during the forecast period.

The Android operating system, provided by Alphabet Inc. (Google), is a globally popular choice for smartphones. With over 2.5 million apps available In the Google Play Store, users have access to a vast selection of applications catering to their diverse needs. Notable features of the Android OS include smart reply for messaging apps, focus mode options, Wi-Fi sharing via QR codes, and Google Assistant. Google offers essential web services such as Google Search, Google Maps, and YouTube free of charge. The Android OS's extensive feature set has contributed to its increasing popularity among consumers worldwide.

In addition, high-speed data connectivity and integration with Internet of Things (IoT) applications further enhance its appeal. Application developers create software for various lifestyle, social media, mobile utility, and other categories, ensuring a rich and diverse app ecosystem. The Android OS is written primarily in Java and C++, with support for in-app purchases and in-app course subscriptions.

Get a glance at the Smartphone Industry report of share of various segments Request Free Sample

The android segment was valued at USD 203.60 million in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 48% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

The market in APAC has experienced substantial growth, with China, Japan, India, South Korea, and Indonesia being the primary contributors to revenue generation. The expansion of urban populations and the subsequent increase in disposable income have fueled the demand for smartphones In the region. Key drivers of this market growth include the advancement of telecom infrastructure and the emergence of affordable smartphone options. Major global smartphone manufacturers have established manufacturing facilities in China, Taiwan, South Korea, Japan, and India to cater to the increasing demand.

Additionally, digital information consumption, human-computer interaction advancements, and the integrat

The iPhone docks market has seen significant evolution over the years, becoming a crucial accessory for iPhone users who seek both convenience and functionality. These docking stations not only serve as charging points but also enhance the user experience by allowing users to connect their devices to speakers, telev

The latest Apple iOS version includes a new privacy feature, which means that mobile apps are forced to ask users for permission to allow them to collect tracking data. Among those that have already installed the iOS 14.5 update, the opt-in rate (how many people are choosing to allow app tracking) is around ** percent, as of April 2022. With so many users concerned about their online activities being tracked, a low opt-in rate had been predicted.

Blockchain Smartphone Market Outlook

The global blockchain smartphone market size is estimated to be valued at USD 1.2 billion in 2023 and is forecasted to reach USD 7.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 22.6% during the forecast period. This impressive growth is predominantly driven by increasing consumer demand for enhanced security features, the rising penetration of blockchain technology in various applications, and the proliferating adoption of smartphones globally.

One of the primary growth factors for the blockchain smartphone market is the heightened awareness and demand for data security and privacy. As cyber threats and data breaches become more frequent, individuals and enterprises are increasingly seeking devices that can provide robust security features. Blockchain smartphones, with their decentralized data storage and encryption capabilities, offer a compelling solution to these security concerns, thus driving market growth.

Additionally, the integration of blockchain technology in smartphones ensures secure and transparent transactions, which is appealing to users who engage frequently in digital transactions. The surge in mobile payments, driven by the e-commerce boom and the need for contactless transactions, particularly during the COVID-19 pandemic, has further accelerated the demand for blockchain smartphones. Blockchain's inherent features, such as immutability and transparency, provide an extra layer of trust and security, making these devices attractive to a broad spectrum of users.

Moreover, the increasing popularity of decentralized applications (dApps) and cryptocurrency trading has significantly contributed to the growth of the blockchain smartphone market. These smartphones are tailored to support various blockchain-related activities, providing users with a seamless experience. The rising acceptance and legalization of cryptocurrencies in numerous countries have further bolstered the market, as these smartphones provide a secure and user-friendly platform for managing digital assets.

The regional outlook for the blockchain smartphone market indicates substantial growth across various geographies. North America and Europe are anticipated to lead the market owing to their advanced technological infrastructure and early adoption of blockchain technology. The Asia Pacific region is expected to exhibit the highest growth rate, driven by the large smartphone user base, increasing internet penetration, and growing awareness of blockchain technology. Latin America and the Middle East & Africa regions are also expected to witness significant growth, fueled by rising digital transformation initiatives and increasing investments in blockchain technology.

Operating System Analysis

When analyzing the blockchain smartphone market by operating system, Android, iOS, and Others are the key segments. Android is anticipated to dominate the market due to its open-source nature, which allows for greater flexibility in integrating blockchain features. Manufacturers can easily customize the Android platform to incorporate robust security protocols and blockchain functionalities, making it a preferred choice for blockchain smartphone developers. The widespread adoption of Android smartphones globally also contributes to its dominant market share.

iOS, while more restrictive in terms of customization compared to Android, still holds a significant market share in the blockchain smartphone market. Apple's focus on privacy and security aligns well with the principles of blockchain technology, making iOS devices attractive to users who prioritize data protection. Apple's ecosystem, known for its seamless integration and user-friendly interface, ensures that blockchain functionalities can be effectively utilized without compromising on the user experience.

The "Others" segment includes alternative operating systems that are either developed in-house by smartphone manufacturers or based on less common platforms. This segment, while smaller, is important for niche markets where specialized blockchain functionalities or higher degrees of customization are required. These operating systems often cater to specific enterprise needs or regional requirements, offering tailored solutions that mainstream platforms may not provide.

The competition between Android and iOS in the blockchain smartphone market is expected to drive innovation, leading to the development of more advanced and secure features. This competitive dynamic will

According to a January 2022 analysis of the leading dating apps downloaded from the Apple App Store, the Badoo mobile app was indexed as collecting the largest number of data types from its users' activity. Bumble and HER ranked second, with an index value of close to **, respectively. Grindr had a reported index value of **, while market leader Tinder was indexed with a value of ****.

Home Inventory Apps Market Outlook

The global home inventory apps market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 4.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 16.2% during the forecast period. The growing awareness about the importance of asset management, coupled with the increasing adoption of digital solutions, is driving the market growth. Furthermore, the rise in technological advancements and the integration of AI and IoT in home inventory apps are significant factors propelling the market forward.

One of the primary growth factors for the home inventory apps market is the increasing awareness among homeowners about the benefits of maintaining a digital record of their assets. This awareness has been significantly boosted by the rising instances of natural disasters and thefts, which have emphasized the need for efficient and accessible inventory management solutions. Home inventory apps offer a systematic way to catalog personal belongings, making it easier for individuals to claim insurance and manage their assets more effectively. Additionally, these apps often come with features like barcode scanning and receipt storage, which further enhances their utility and user experience.

Another crucial driver for the market is the integration of advanced technologies such as artificial intelligence (AI) and the Internet of Things (IoT). AI-driven features in home inventory apps can automate the process of item recognition and categorization, thereby saving time and reducing human errors. IoT devices, on the other hand, can provide real-time updates about the status of various household items, adding an extra layer of security and convenience. The synergy of these technologies is expected to enhance the functionality of home inventory apps significantly, making them more appealing to a broader audience.

The growing penetration of smartphones and improved internet connectivity are also contributing to the market's expansion. With the widespread availability of mobile devices, users can easily download and use home inventory apps at their convenience. The increasing affordability of smartphones has made it feasible for a larger section of the population to access these digital solutions. Moreover, improved internet connectivity ensures that users can synchronize their data across multiple devices in real-time, offering a seamless and integrated experience.

The rise of Insurance Mobile Apps has also played a pivotal role in the growing popularity of home inventory apps. As more insurance companies develop mobile applications to streamline their services, the integration with home inventory apps becomes increasingly seamless. These apps allow users to easily document and update their personal belongings, which can be directly linked to their insurance policies. This integration not only simplifies the claims process but also enhances the accuracy of asset documentation, providing users with peace of mind. The synergy between insurance mobile apps and home inventory solutions is expected to drive further adoption, as policyholders seek efficient and reliable ways to manage their assets and insurance coverage.

Regionally, North America held the largest market share in 2023, driven by the high adoption rate of digital solutions among homeowners and the presence of major market players. The region's robust technological infrastructure and high awareness levels contribute significantly to its market dominance. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the increasing adoption of smartphones, rising disposable incomes, and growing awareness about the benefits of home inventory management solutions.

Platform Analysis

The platform segment of the home inventory apps market is categorized into iOS, Android, and web-based platforms. The iOS platform dominates a significant portion of the market, primarily due to the widespread use of iPhones among homeowners in developed regions. iOS-based apps are known for their seamless integration with other Apple devices, offering a cohesive user experience that appeals to tech-savvy consumers. Moreover, the stringent security protocols of iOS make these apps a preferred choice for users concerned about data privacy and security.

Android-based home inventory apps also hold a substantial market share, driven by the vast

We provide cross-lingual linked data lexica called xLiD-Lexica. The data set contains the cross-lingual groundings of linked data resources from the Linked Open Data cloud as RDF data, which can be easily integrated into the LOD data sources. In addition, we created a SPARQL endpoint over ourxLiD-Lexica to allow users to easily access them using SPARQL query language. Multilingual and cross-lingual information access can be facilitated by the availability of such lexica, e.g., allowing for an easy mapping of natural language expressions in different languages to linked data resources from LOD. Many tasks in natural language processing, such as natural language generation, cross-lingual entity linking, text annotation and question answering, can benefit from our xLiD-Lexica.

More information can be found in the LREC'14 paper xLiD-Lexica: Cross-lingual Linked Data Lexica and on our website https://km.aifb.kit.edu/sites/xlid-lexica/.

Please cite this data set as follows (see also DBLP):

Lei Zhang, Michael Färber, Achim Rettinger. "xLiD-Lexica: Cross-lingual Linked Data Lexica". In: Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014). Reykjavik, Iceland, 2014, pp. 2101–2105.

Example queries:

1. Retrieve all entities with surface form which contain "iPhone":

Select ?resource, ?label, ?probability from <http://www.xlid-lexica.org> where {
?resource <http://www.xlid-lexica.org/block> ?b1 .
?b1 <http://www.xlid-lexica.org/res#sf> ?sf .
?b1 <http://www.xlid-lexica.org/res#priorProbability> ?probability .
?sf <http://www.xlid-lexica.org/block> ?b2.
?b2 <http://www.xlid-lexica.org/sf#label> ?label .
?label bif:contains "iPhone" . }
order by DESC(?probability) limit 100

2. Retrieve the top 100 resources for a given surface form ("iphone"):

Select ?resource, ?probability from <http://www.xlid-lexica.org> where {
?resource <http://www.xlid-lexica.org/block> ?b1 .
?b1 <http://www.xlid-lexica.org/res#sf> ?sf .
?b1 <http://www.xlid-lexica.org/res#priorProbability> ?probability .
?sf <http://www.xlid-lexica.org/block> ?b2.
?b2 <http://www.xlid-lexica.org/sf#label> "iphone"@en . }
 order by DESC(?probability) limit 100

3. Retrieve the top 100 resources for a given surface form ("iphone", case-insensitive):

Select ?resource, ?probability from <http://www.xlid-lexica.org> where {
?resource <http://www.xlid-lexica.org/block> ?b1 .
?b1 <http://www.xlid-lexica.org/res#sf> ?sf .
?b1 <http://www.xlid-lexica.org/res#priorProbability> ?probability .
?sf <http://www.xlid-lexica.org/block> ?b2.
?b2 <http://www.xlid-lexica.org/sf#label> ?surfaceform .
filter(regex(?surfaceform, "^iphone$", "i")) }
limit 100

4. Retrieve the top 100 surface forms per entity:

Select ?label ?probability from <http://www.xlid-lexica.org>
where {
<http://dbpedia.org/resource/IPhone_5> <http://www.xlid-lexica.org/block> ?b1.
?b1 <http://www.xlid-lexica.org/res#sf> ?sf.
?b1 <http://www.xlid-lexica.org/res#priorProbability> ?probability.
?sf <http://www.xlid-lexica.org/block> ?b2.
?b2 <http://www.xlid-lexica.org/sf#label> ?label.
?b2 <http://www.xlid-lexica.org/block#lang> "en".
}
order by DESC(?probability) limit 100

As of May 2022, mobile period apps Eve, Clover, and My Calendar were collecting the largest number of data on user identifiers among all the commercial female health apps examined. Almost all the examined apps collected at least two data points on users' sensitive information, while none of the examined apps appeared to collect data on users' contacts. euros.

According to a global survey of smartphone users, ** percent of Android users reported they were likely to switch to Apple's mobile operating system iOS for better functionalities. By comparison, ** percent of iOS users reported being likely to switch to Android for the same reason. Data protection was also an important factor for Android users to consider switching to the competition, while only ** percent of iOS users indicated better data protection as a factor likely to draw them to Android.