By 2025, forecasts suggest that there will be more than 75 billion Internet of Things (IoT) connected devices in use. This would be a nearly threefold increase from the IoT installed base in 2019.

What is the Internet of Things?

The IoT refers to a network of devices that are connected to the internet and can “communicate” with each other. Such devices include daily tech gadgets such as the smartphones and the wearables, smart home devices such as smart meters, as well as industrial devices like smart machines. These smart connected devices are able to gather, share, and analyze information and create actions accordingly. By 2023, global spending on IoT will reach 1.1 trillion U.S. dollars.

How does Internet of Things work?

IoT devices make use of sensors and processors to collect and analyze data acquired from their environments. The data collected from the sensors will be shared by being sent to a gateway or to other IoT devices. It will then be either sent to and analyzed in the cloud or analyzed locally. By 2025, the data volume created by IoT connections is projected to reach a massive total of 79.4 zettabytes.

Privacy and security concerns 

Given the amount of data generated by IoT devices, it is no wonder that data privacy and security are among the major concerns with regard to IoT adoption. Once devices are connected to the Internet, they become vulnerable to possible security breaches in the form of hacking, phishing, etc. Frequent data leaks from social media raise earnest concerns about information security standards in today’s world; were the IoT to become the next new reality, serious efforts to create strict security stands need to be prioritized.

This dataset presents the IoT network traffic generated by connected objects. In order to understand and characterise the legitimate behaviour of network traffic, a platform is created to generate IoT traffic under realistic conditions. This platform contains different IoT devices: voice assistants, smart cameras, connected printers, connected light bulbs, motion sensors, etc. Then, a set of interactions with these objects is performed to allow the generation of real traffic. This data is used to identify anomalies and intrusions using machine learning algorithms and to improve existing detection models. Our dataset is available in two formats: pcap and csv and was created as part of the EU CEF VARIoT project https://variot.eu. To download the data in pcap format and for more information, our database is available on this web portal : https://www.variot.telecom-sudparis.eu/.

IoT-23 is a dataset of network traffic from Internet of Things (IoT) devices. It has 20 malware captures executed in IoT devices, and 3 captures for benign IoT devices traffic. It was first published in January 2020, with captures ranging from 2018 to 2019. These IoT network traffic was captured in the Stratosphere Laboratory, AIC group, FEL, CTU University, Czech Republic. Its goal is to offer a large dataset of real and labeled IoT malware infections and IoT benign traffic for researchers to develop machine learning algorithms. This dataset and its research was funded by Avast Software. The malware was allow to connect to the Internet.

Aposemat IoT-23 - a Labeled Dataset with Malcious and Benign Iot Network Traffic

Homepage: https://www.stratosphereips.org/datasets-iot23 This dataset contains a subset of the data from 20 captures of Malcious network traffic and 3 captures from live Benign Traffic on Internet of Things (IoT) devices. Created by Sebastian Garcia, Agustin Parmisano, & Maria Jose Erquiaga at the Avast AIC laboratory with the funding of Avast Software, this dataset is one of the best in the field… See the full description on the dataset page: https://huggingface.co/datasets/19kmunz/iot-23-preprocessed-allcolumns.

The statistic shows the overall data volume of connected devices/IoT connections worldwide in 201 and 2025. By 2025, total data volume of connected IoT devices worldwide is forecast to reach 79.4 zettabytes (ZBs).

This dataset comprises network traffic collected from 24 Internet of Things (IoT) devices over a span of 119 days, capturing a total of over 110 million packets. The devices represent 19 distinct types and were monitored in a controlled environment under normal operating conditions, reflecting a variety of functions and behaviors typical of consumer IoT products (pcapIoT). The packet capture (pcap) files preserve complete packet information across all protocol layers, including ARP, TCP, HTTP, and various application-layer protocols. Raw pcap files (pcapFull) are also provided, which contain traffic from 36 non-IoT devices present in the network. To facilitate device-specific analysis, a CSV file is included that maps each IoT device to its unique MAC address. This mapping simplifies the identification and filtering of packets belonging to each device within the pcap files. 3 extra CSV (CSVs) files provide metadate about the states that the devices were in at different times. Additionally, Python scripts (Scripts) are provided to assist in extracting and processing packets. These scripts include functionalities such as packet filtering based on MAC addresses and protocol-specific data extraction, serving as practical examples for data manipulation and analysis techniques. This dataset is valuable for researchers interested in network behavior analysis, anomaly detection, and the development of IoT-specific network policies. It enables the study and differentiation of network behaviors based on device functions and supports behavior-based profiling to identify irregular activities or potential security threats.

This archive contains the files submitted to the 4th International Workshop on Data: Acquisition To Analysis (DATA) at SenSys. Files provided in this package are associated with the paper titled "Dataset: Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices"

With the rapid development and usage of Internet-of-Things (IoT) and smart-home devices, researchers continue efforts to improve the ''smartness'' of those devices to address daily needs in people's lives. Such efforts usually begin with understanding evolving user behaviors on how humans utilize the devices and what they expect in terms of their behavior. However, while research efforts abound, there is a very limited number of datasets that researchers can use to both understand how people use IoT devices and to evaluate algorithms or systems for smart spaces. In this paper, we collect and characterize more than 50,000 recipes from the online If-This-Then-That (IFTTT) service to understand a seemingly straightforward but complicated question: ''What kinds of behaviors do humans expect from their IoT devices?'' The dataset we collected contains the basic information of the IFTTT rules, trigger and action event, and how many people are using each rule.

For more detail about this dataset, please refer to the paper listed above.

The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices’ security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established. However, due to the resource constraint property of IoT devices and the distinct behavior of IoT protocols, the existing security mechanisms cannot be deployed directly for securing the IoT devices and network from the cyber-attacks. To enhance the level of security for IoT, researchers need IoT-specific tools, methods, and datasets. To address the mentioned problem, we provide a framework for developing IoT context-aware security solutions to detect malicious traffic in IoT use cases. The proposed framework consists of a newly created, open-source IoT data generator tool named IoT-Flock. The IoT-Flock tool allows researchers to develop an IoT use-case comprised of both normal and malicious IoT devices and generate traffic. Additionally, the proposed framework provides an open-source utility for converting the captured traffic generated by IoT-Flock into an IoT dataset. Using the proposed framework in this research, we first generated an IoT healthcare dataset which comprises both normal and IoT attack traffic. Afterwards, we applied different machine learning techniques to the generated dataset to detect the cyber-attacks and protect the healthcare system from cyber-attacks. The proposed framework will help in developing the context-aware IoT security solutions, especially for a sensitive use case like IoT healthcare environment.

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Internet Of Things Sensors Market Size 2024-2028

The internet of things sensors market size is forecast to increase by USD 63.09 billion at a CAGR of 41.29% between 2023 and 2028.

The market is experiencing significant growth due to several key trends. The increasing demand for smart factories and Industrial IoT (IIoT) is driving the market, as sensors play a crucial role in enabling real-time monitoring and automation of industrial processes. 
Additionally, the need for remote monitoring of various applications, such as healthcare services and agriculture, is leading to a surge in demand for IoT sensors. Furthermore, regulatory compliance is becoming increasingly important, and sensors are essential for ensuring adherence to various standards and regulations. These factors are expected to continue fueling the growth of the IoT sensors market in the coming years.

What will be the Size of the IoT Sensors Market During the Forecast Period?

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The market is experiencing robust growth, driven by the increasing adoption of IoT technologies in various industries. IoT sensors play a crucial role in building security systems, connected healthcare, supply chain optimization, and smart home automation, among others. These sensors enable real-time data analysis, response time improvement, and AI-powered decision making in areas such as temperature and lighting control, edge computing, and error identification.
Market trends include the integration of IoT sensors in industrial automation, workflow optimization, and smart grid technology. In the realm of consumer devices, wearable technology trends, inertial sensors, and proximity-based systems are gaining traction. IoT sensors are also revolutionizing sectors like healthcare with applications in health monitoring, including electrocardiograms and occupancy sensors.
Additionally, IoT sensors are essential for digital transformation strategies in industries like transportation, enabling sustainable transportation solutions and autonomous vehicle development. In the realm of smart cities, IoT sensors are instrumental in optimizing energy management, air quality monitoring, and smart city infrastructure. Furthermore, IoT sensors are transforming industries like agriculture with precision farming and process optimization. In the realm of security, IoT sensors are being used for advanced robotics and occupancy detection, providing enhanced security measures. Overall, the IoT sensors market is a dynamic and evolving landscape, offering numerous opportunities for businesses seeking to leverage real-time data and improve operational efficiency.

How is this Internet Of Things Sensors Industry segmented and which is the largest segment?

The IoT sensors industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Consumer electronics
  Automotive
  Food and beverages
  Healthcare
  Others


Type

  Temperature sensor
  Pressure sensor
  Humidity sensor
  Flow sensor
  Others


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  South America



  Middle East and Africa

By End-user Insights

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

IoT sensors play a pivotal role in the consumer electronics industry, fueling the growth of markets such as wearable technology and smart homes. The integration of IoT sensors in devices enables enhanced functionality and responsiveness. Notable consumer electronics incorporating these sensors include smartphones, smartwatches, and fitness trackers, which monitor environmental changes, track user movement, and measure vital signs. The advent of IoT sensors has facilitated the development of smart homes, where devices can be remotely controlled via mobile applications. Additionally, IoT sensors are employed in industries like manufacturing, healthcare, transportation, and energy to optimize processes, improve response times, and enable remote monitoring.

Innovations such as connected cars, autonomous driving technologies, smart cities, aerospace, and industrial automation further expand the application scope of IoT sensors. These sensors contribute to energy efficiency, asset tracking, temperature and humidity control, and building automation, among other applications. IoT sensors enable data-driven strategies and facilitate the integration of machine learning and artificial intelligence, enhancing overall system performance.

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The Consumer electronics segment was valued at USD 1.18 billion in 2018 and showed a grad

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Internet Of Things Data Management Market Size 2024-2028

The IoT data management market size is forecast to increase by USD 90.3 billion at a CAGR of 15.72% between 2023 and 2028.

The market is experiencing significant growth due to several key trends. The increasing adoption of industrial automation is driving the demand for efficient data management solutions. Manufacturing industries are leveraging IoT data to implement predictive maintenance strategies, reducing downtime and enhancing productivity.
However, there is a lack of awareness regarding the importance of effective IoT data management and the potential returns on investments. Addressing this challenge will be crucial for businesses looking to maximize the value of their IoT initiatives. Overall, the market is poised for substantial growth as more organizations recognize the benefits of IoT in areas such as operational efficiency, cost savings, and improved customer experiences.

What will be the Size of the IoT Data Management Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing deployment of intelligent devices and the subsequent generation of vast amounts of data. According to recent estimates, IoT is projected to generate over zettabytes of data annually, necessitating robust data management solutions. Data integration is a critical aspect of IoT data management, ensuring seamless data flow between various devices and systems.
Security is another major concern, with IoT botnets and hackers posing significant threats to sensitive data. Cloud services provide scalable storage solutions, while data warehouse architecture offers efficient data processing and analysis. Wireless technologies facilitate real-time data transfer, enabling applications in various sectors, including automotive, fleet management, and intelligent transportation systems.
Stanford University and Avast are among the institutions and companies contributing to IoT research and innovation. Data breaches and shared assessments programs are essential for ensuring data security and privacy. Smart gadgets, wearables, and homes are also driving the demand for advanced IoT data management solutions.

How is this Internet Of Things Data Management Industry segmented and which is the largest segment?

The internet of things (iot) data management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Component

  Solutions
  Services


Deployment

  Private/hybrid
  Public


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  South America



  Middle East and Africa

By Component Insights

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

The IoT data management market is experiencing significant growth due to the increasing generation of data from intelligent devices and wireless technologies. In 2023, the solutions segment, including data integration, security, storage, and data warehouse architecture, dominated the market, driven by the globalization of IT and retail companies and the rise of SMEs in emerging economies. Companies offer software solutions to help organizations collect and analyze data from various end-user industries, enabling meaningful business insights. Data security is a major concern, with IoT botnets and data breaches posing threats. Cloud services provide a cost-effective and scalable solution for storing and managing IoT data.

The automotive market, including self-driving ecosystems, fleet management, and intelligent transportation systems, is a significant end-user industry. IoT initiatives in large enterprises and SMEs continue to expand, with the integration of sensors, scanners, digital gauges, and RFID technology. Key players in the market offer hybrid data management solutions, cloud data warehouses, and data integration technology. IoT data management is essential for gaining valuable insights from the zettabytes of data generated by IoT devices.

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The solutions segment was valued at USD 34.60 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 35% 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.

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The North American market for IoT data management experiences significant g

With the growing interest in Internet of Things (IoT) devices, a number of communication protocols have been developed to support a variety of IoT use cases. One promising communication paradigm that has been widely adopted in the IoT is the publish-subscribe pattern, which is supported by a number of messaging protocols such as MQTT, AMQP, and XMPP. Due to the diversity of IoT device types, an IoT application may communicate with IoT devices using a variety of messaging protocols, software frameworks, and strategies. To this extent, it becomes critical to determine the robustness of components responsible for message delivery (i.e., message brokers). We conduct a comparative study of the MQTT protocol's performance in this paper, comparing performance variables across a range of payload sizes and security levels. Preliminary results indicate that when the payload size remains small, using higher security levels does not result in significant latency overheads. Additionally, we discovered that implementing mutual authentication via Transport Layer Security (TLS) has no effect on MQTT response times in persistent connections when compared to using the default security level, which authenticates only the server.

Message Queuing Telemetry Transport (MQTT) protocol is one of the most used standards used in Internet of Things (IoT) machine to machine communication. The increase in the number of available IoT devices and used protocols reinforce the need for new and robust Intrusion Detection Systems (IDS). However, building IoT IDS requires the availability of datasets to process, train and evaluate these models.

MQTT-IoT-IDS2020 is the first dataset to simulate an MQTT-based network. The dataset is generated using a simulated MQTT network architecture. The network comprises twelve sensors, a broker, a simulated camera, and an attacker. Five scenarios are recorded: (1) normal operation, (2) aggressive scan, (3) UDP scan, (4) Sparta SSH brute-force, and (5) MQTT brute-force attack. The raw pcap files are saved, then features are extracted. Three abstraction levels of features are extracted from the raw pcap files: (a) packet features, (b) Unidirectional flow features and (c) Bidirectional flow features. The csv feature files in the dataset are suited for Machine Learning (ML) usage. Also, the raw pcap files are suitable for the deeper analysis of MQTT IoT networks communication and the associated attacks.

The network attacks are increasing both in frequency and intensity with the rapid growth of internet of things (IoT) devices. Recently, denial of service (DoS) and distributed denial of service (DDoS) attacks are reported as the most frequent attacks in IoT networks. The traditional security solutions like firewalls, intrusion detection systems, etc., are unable to detect the complex DoS and DDoS attacks since most of them filter the normal and attack traffic based upon the static predefined rules. However, these solutions can become reliable and effective when integrated with artificial intelligence (AI) based techniques. During the last few years, deep learning models especially convolutional neural networks achieved high significance due to their outstanding performance in the image processing field. The potential of these convolutional neural network (CNN) models can be used to efficiently detect the complex DoS and DDoS by converting the network traffic dataset into images. Therefore, in this work, we proposed a methodology to convert the network traffic data into image form and trained a state-of-the-art CNN model, i.e., ResNet over the converted data. The proposed methodology accomplished 99.99% accuracy for detecting the DoS and DDoS in case of binary classification. Furthermore, the proposed methodology achieved 87% average precision for recognizing eleven types of DoS and DDoS attack patterns which is 9% higher as compared to the state-of-the-art.

Globally, 33 percent of respondents have internet of things (IoT) security concerns regarding attacks on devices in 2019. Generally, 99 percent of respondents have internet of things (IoT) data security concerns that also refer to a lack of skilled personnel and sensitive data protection as their top worries. Internet of things broadly refers to a system of internet-connected devices that collect and transfer data over a network without human-to-computer interaction. As an increasing amount of internet of things devices are deployed, security and key management grow in importance to effectively implement data encryption and identity security on devices used.

The Internet of Things (IoT) refers to the network of everyday web-enabled objects that can connect and exchange information. These “smart” objects include more than your computer, smartphone, or tablet. They include items like personal fitness trackers, TVs, thermostats, or cars. This list of IoT devices is continuing to grow. IoT Analytics1 projects that there will be a 39% increase by 2025 in the global market of IoT devices [1]. Understanding how to securely use IoT devices in your organization is increasingly important.

The Internet of Things (IoT) is omnipresent, exposing a large number of devices that often lack security controls to the public Internet. In the modern world, many everyday processes depend on these devices, and their service outage could lead to catastrophic consequences. There are many Deep Packet Inspection (DPI) based intrusion detection systems (IDS). However, their linear computational complexity induced by the event-driven nature poses a power-demanding obstacle in resource-constrained IoT environments. In this paper, we shift away from the traditional IDS as we introduce a novel and lightweight framework, relying on a time-driven algorithm to detect Distributed Denial of Service (DDoS) attacks by employing Machine Learning (ML) algorithms leveraging the newly engineered features containing system and network utilization information. These features are periodically generated, and there are only ten of them, resulting in a low and constant algorithmic complexity. Moreover, we leverage IoT-specific patterns to detect malicious traffic as we argue that each Denial of Service (DoS) attack leaves a unique fingerprint in the proposed set of features. We construct a dataset by launching some of the most prevalent DoS attacks against an IoT device, and we demonstrate the effectiveness of our approach with high accuracy. The results show that standalone IoT devices can detect and classify DoS and, therefore, arguably, DDoS attacks against them at a low computational cost with a deterministic delay.

This dataset presents network traffic traces data of the 14 D-Link IoT devices from different types including camera, network camera, smart-plug, door-window sensor, and home-hub. It consists of:

• Network packet traces (inbound and outbound traffic) and
• IEEE 802.11 MAC frame traces.

The experimental testbed was set-up in the Network Systems and Signal Processing (NSSP) laboratory at Universiti Brunei Darussalam (UBD) to collect all the network traffic traces from 9th September 2020 to 10th January 2021 including an access point on a laptop. The network traffic traces were captured passively observing the Ethernet interface and the WiFi interface at the access point.

In packet traces, typical communication protocols, such as TCP, UDP, IP, ICMP, ARP, DNS, SSDP, TLS/SSL etc, data are captured which IoT devices use for communication on the Internet. In the probe request frame (a subtype of management frames) traces, data are recorded which IoT devices use to connect access point on the local area network.

The authors would like to thank the Faculty of Integrated Technologies, Universiti Brunei Darussalam, for the support to conduct this research experiment in the Network Systems and Signal Processing laboratory.

Smart Society Charter IoT Architecture principles & guidelines City of Eindhoven In a Smart Society, digital online technologies become seamlessly integrated in the physical offline world, to improve people’s lives and contribute to the development of the society. The most important thing in a Smart Society is that people experience the benefits of what the intensive co-evolution of digital and analogue, virtual and physical, online and offline will bring them. With more and more technologies on the Internet of Things, and increasing volumes of data being collected, it is inevitable that IoT and data-driven services will have a serious impact on our lives. As a pioneer of the Smart Society, the City of Eindhoven is already facing up to imminent changes, and confronting the dilemmas that the new technologies bring with them. In order to safeguard public interest, stimulate innovation, foster a sustainable ecosystem of partners and encourage socially responsible business models, we have put together a few simple common principles to apply to an architecture of all current and emerging IoT initiatives across the city. These principles are being developed in cooperation with commercial partners, start-ups and small enterprises, independent IoT developers, academic and research institutes, citizen-driven initiatives and other public organizations. We believe that these principles reflect our common values, contribute to the development of the city and improve the quality of life of its residents. We call on all IoT parties in Eindhoven, as well as our Dutch and international partners, to adopt, extend and reflect on these principles when building new or improving existing IoT and data infrastructures, platforms, services and applications. In a Smart Society, all participants should benefit from technology's achievements. 1 Privacy first First and foremost, the privacy of the users and citizens should be guaranteed. People should be given insight into the data that is collected and control over the way it is and will be used. Ethical aspects should be taken into account when extending practices into areas not addressed by current legislation. 2 Open data and interfaces We facilitate innovation by making data publicly available and enabling access to IoT & data systems through open interfaces. We stimulate new business models and emerging services that rely on generating added value, rather than exploiting licenses on data or exclusive rights on the infrastructure. We recommend making the infrastructure open on the lowest level and making raw data publicly available whenever this can be done without compromising the privacy and security of the citizens. 3 Embrace open standards Wherever available, the IoT infrastructure, connectivity, platforms, devices and services should be built on open or broadly agreed de-facto standards. Using established standards will facilitate evolution of infrastructure and services, sustain a competitive market and prevent vendor lock-in. Where standards are not yet available, maintaining openness and sharing best practices will help to lay a foundation for the future. 4 Share where possible We expect all IoT and Data developments to provide well-defined, easily accessible stable interfaces for sharing and reusing existing assets. Shared use of grids, sensor networks, connectivity and software components will lower the barriers for their adoption, increase connectivity and stimulate interoperability. The IoT & Data infrastructure should be available for re-use, as well as open to innovation and expansion. 5 Support modularity We recommend adopting a modular architecture with well-defined open interfaces as the core of any IoT or data-driven development. Modularity helps to ensure interoperability between platforms, services and applications and facilitates re-use and cooperation between partners. 6 Maintain security The reliability of components, platforms, solutions and services must be constantly safeguarded. Ensuring confidentiality, integrity and availability is vital to essential services and core parts of the infrastructure, which need to be safeguarded to the highest possible degree. In addition, all digital assets must be well-protected from attack, damage or unauthorized access. 7 Accept social responsibility Providing new technologies and services, and collecting and combining data may result in unforeseen effects on society or individuals. We cannot predict the future. We encourage experimentation, provided responsibility is taken for the consequences.

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Internet Of Things Enabled Industrial Wearables Market Size 2025-2029

The Internet of Things (IoT) enabled industrial wearables market size is forecast to increase by USD 42.82 billion at a CAGR of 69% between 2024 and 2029.

The market is experiencing significant growth due to the rise in digitalization and automation within industries. This trend is driving the demand for wearable devices that can collect and transmit real-time data to enhance operational efficiency and productivity. Another key factor fueling market growth is the rising focus on increasing battery life to ensure uninterrupted usage. However, the high cost of wearable devices and technology remains a challenge for market growth. Companies are investing in research and development to create cost-effective solutions while maintaining the necessary features and functionality. In the manufacturing sector, IoT wearables facilitate Industry 4.0 initiatives, enabling predictive maintenance solutions and real-time data analytics. The IoT wearables market is expected to continue its growth trajectory as industries seek to optimize their operations and improve worker safety and productivity.

What will be the Size of the Internet Of Things (IoT) Enabled Industrial Wearables Market During the Forecast Period?

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The market encompasses a range of smart devices, including head-mounted wearables, hand-worn wearables, and portable wearables. These devices, which include smart eyewear and cases with integrated connectivity components, touchpads, sensors, cameras, and more, are revolutionizing industries such as manufacturing, energy, healthcare, construction, and logistics.
In healthcare, wearables are used for remote patient monitoring and telemedicine. The energy industry benefits from wearables that monitor equipment performance and optimize energy usage. The market for IoT industrial wearables is experiencing significant growth due to the increasing demand for enhanced productivity, improved safety, and cost savings. The integration of advanced technology, such as sensors, connectivity, and data analytics, is driving innovation and expanding the scope of applications for these devices.

How is this Internet Of Things (IoT) Enabled Industrial Wearables Industry segmented and which is the largest segment?

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

Product

  Hand-worn wearables
  Head-mounted wearables
  Smart eyewear


End-user

  Automotive
  Manufacturing
  Aerospace
  Others


Connectivity

  Bluetooth
  Wi-Fi
  Cellular networks
  NFC


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France
    Italy


  APAC

    China
    India
    Japan


  South America



  Middle East and Africa

By Product Insights

The hand-worn wearables segment is estimated to witness significant growth during the forecast period.

Hand-worn wearables, including smart gloves and wrist-worn devices, represent a niche segment of the IoT-enabled industrial wearables market. These devices are predominantly utilized in logistics for administrative tasks and in industries for labor-intensive processes. The proliferation of Industry 4.0 and advancements in sensors and scanning technologies are fueling their adoption. Industry 4.0, also known as the fourth industrial revolution, is revolutionizing traditional manufacturing industries by integrating IoT and automation. Hand-worn devices are integral to this transformation, enabling performance optimization, data analytics, and predictive maintenance solutions. Key components include connectivity components, touchpads and sensors, cameras, Bluetooth 5, and battery life or power harvesting technologies.

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The hand-worn wearables segment was valued at USD 752.70 million in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 43% 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.

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The North American region dominates the IoT enabled industrial wearables market, driven by the presence of major tech companies like Alphabet, Microsoft, and Vuzix, based In the US and Canada. These firms bring substantial financial resources and technological expertise to the table, significantly contributing to market growth. End-user industries, such as GE Electric and Ford Motor

This dataset comprises of multimodal data collected from Internet of Things (IoT) sensors in an office-like environment in which a total of 54 volunteers performed various office tasks. The tasks included typing, gesture-based, and movement-based tasks, where each task was modulated with various levels of difficulty. The assortment of sensors used for the data collection includes multiple inertial measurement units, multiple force sensors, a short milimetre-wave radar, and an 8-channel EEG device. These data are primarily envisioned as a basis for exploratory research in the field of user authentication, however the dataset could be applied to a plethora of different research domains, including human activity recognition, and cognitive load inference. More details on the dataset can be found at: [link to the paper when published].