By 2025, forecasts suggest that there will be more than ** 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 *** 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 **** 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.

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 **** zettabytes (ZBs).

including some laptops or smart phones

This dataset is comprised of NetFlow records, which capture the outbound network traffic of 8 commercial IoT devices and 5 non-IoT devices, collected during a period of 37 days in a lab at Ben-Gurion University of The Negev. The dataset was collected in order to develop a method for telecommunication providers to detect vulnerable IoT models behind home NATs. Each NetFlow record is labeled with the device model which produced it; for research reproducibilty, each NetFlow is also allocated to either the "training" or "test" set, in accordance with the partitioning described in:

Y. Meidan, V. Sachidananda, H. Peng, R. Sagron, Y. Elovici, and A. Shabtai, A novel approach for detecting vulnerable IoT devices connected behind a home NAT, Computers & Security, Volume 97, 2020, 101968, ISSN 0167-4048, https://doi.org/10.1016/j.cose.2020.101968. (http://www.sciencedirect.com/science/article/pii/S0167404820302418)

Please note:

• The dataset itself is free to use, however users are requested to cite the above-mentioned paper, which describes in detail the research objectives as well as the data collection, preparation and analysis.
• Following is a brief description of the features used in this dataset.

# NetFlow features, used in the related paper for analysis

'FIRST_SWITCHED': System uptime at which the first packet of this flow was switched
'IN_BYTES': Incoming counter for the number of bytes associated with an IP Flow
'IN_PKTS': Incoming counter for the number of packets associated with an IP Flow
'IPV4_DST_ADDR': IPv4 destination address
'L4_DST_PORT': TCP/UDP destination port number
'L4_SRC_PORT': TCP/UDP source port number
'LAST_SWITCHED': System uptime at which the last packet of this flow was switched
'PROTOCOL': IP protocol byte (6: TCP, 17: UDP)
'SRC_TOS': Type of Service byte setting when there is an incoming interface
'TCP_FLAGS': Cumulative of all the TCP flags seen for this flow

# Features added by the authors

'IP': Prefix of the destination IP address, representing the network (without the host)
'DURATION': Time (seconds) between first/last packet switching

# Label
'device_model':

# Partition
'partition': Training or test

# Additional NetFlow features (mostly zero-variance)
'SRC_AS': Source BGP autonomous system number
'DST_AS': Destination BGP autonomous system number
'INPUT_SNMP': Input interface index
'OUTPUT_SNMP': Output interface index
'IPV4_SRC_ADDR': IPv4 source address
'MAC': MAC address of the source

# Additional data
'category': IoT or non-IoT
'type': IoT, access_point, smartphone, laptop
'date': Datepart of FIRST_SWITCHED
'inter_arrival_time': Time (seconds) between successive flows of the same device (identified by its MAC address)

Internet of Things (IoT) Connected Devices Market Outlook

The global market size of Internet of Things (IoT) connected devices was valued at approximately $260 billion in 2023 and is projected to reach around $1 trillion by 2032, growing at an impressive CAGR of 15.5%. This robust growth is driven by an array of factors including technological advancements, increased adoption of IoT devices across various industries, and growing investments in IoT infrastructure. The proliferation of smart devices and the need for efficient data management solutions are fueling the expansion of the IoT connected devices market.

One of the primary growth factors of the IoT connected devices market is the rapid advancement in technology, particularly in areas like artificial intelligence, machine learning, and edge computing. These technologies are enhancing the capabilities of IoT devices, enabling them to process data more efficiently and make intelligent decisions in real-time. Additionally, the decreasing costs of sensors and hardware components are making IoT devices more affordable, thereby driving their adoption across various sectors including healthcare, automotive, and industrial applications.

The increasing focus on smart city initiatives by governments around the world is another significant driver of the IoT connected devices market. Smart city projects aim to improve the quality of urban life through the deployment of IoT technologies for better traffic management, efficient energy usage, and enhanced public safety. For instance, cities like Singapore and Barcelona are at the forefront of implementing smart city solutions, which include IoT-enabled street lighting, smart parking systems, and integrated water management systems. These initiatives are creating lucrative opportunities for IoT device manufacturers and solution providers.

Moreover, the rise of Industry 4.0 is contributing to the growth of IoT connected devices in the industrial sector. Industry 4.0 encompasses the integration of digital technologies into manufacturing processes, leading to the creation of smart factories. These factories rely heavily on IoT devices for monitoring and controlling production processes, predictive maintenance, and optimizing supply chains. The adoption of IoT in industrial applications is not only improving operational efficiency but also reducing costs and enhancing product quality, thus driving market growth.

From a regional perspective, North America currently holds the largest share of the IoT connected devices market, driven by technological advancements and high adoption rates of IoT solutions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, primarily due to increasing investments in smart infrastructure projects and rapid industrialization in countries like China and India. The presence of a large consumer base and the rise of smart home applications are also contributing to the market expansion in this region.

Component Analysis

The IoT connected devices market is segmented by components into hardware, software, and services. The hardware segment includes various physical devices like sensors, actuators, and gateways that are integral to IoT systems. The software segment encompasses platforms and applications that enable data collection, analysis, and management, while the services segment includes consulting, implementation, and maintenance services. Each of these segments plays a crucial role in the overall IoT ecosystem, and their respective growth is driven by different factors.

In the hardware segment, advancements in sensor technology are significantly boosting the market. Modern sensors are becoming more efficient, compact, and affordable, making it easier for organizations to deploy IoT solutions at scale. Additionally, the development of advanced microcontrollers and communication modules is enhancing the performance and reliability of IoT devices. Companies are increasingly investing in R&D to innovate and develop next-generation hardware solutions to meet the growing demands of various industries.

The software segment is experiencing substantial growth due to the increasing need for data analytics and management solutions. IoT devices generate massive amounts of data, and the ability to analyze this data in real-time is crucial for deriving actionable insights. IoT platforms th

Article Information

The work involved in developing the dataset and benchmarking its use of machine learning is set out in the article ‘IoMT-TrafficData: Dataset and Tools for Benchmarking Intrusion Detection in Internet of Medical Things’. DOI: 10.1109/ACCESS.2024.3437214.

Please do cite the aforementioned article when using this dataset.

Abstract

The increasing importance of securing the Internet of Medical Things (IoMT) due to its vulnerabilities to cyber-attacks highlights the need for an effective intrusion detection system (IDS). In this study, our main objective was to develop a Machine Learning Model for the IoMT to enhance the security of medical devices and protect patients’ private data. To address this issue, we built a scenario that utilised the Internet of Things (IoT) and IoMT devices to simulate real-world attacks. We collected and cleaned data, pre-processed it, and provided it into our machine-learning model to detect intrusions in the network. Our results revealed significant improvements in all performance metrics, indicating robustness and reproducibility in real-world scenarios. This research has implications in the context of IoMT and cybersecurity, as it helps mitigate vulnerabilities and lowers the number of breaches occurring with the rapid growth of IoMT devices. The use of machine learning algorithms for intrusion detection systems is essential, and our study provides valuable insights and a road map for future research and the deployment of such systems in live environments. By implementing our findings, we can contribute to a safer and more secure IoMT ecosystem, safeguarding patient privacy and ensuring the integrity of medical data.

ZIP Folder Content

The ZIP folder comprises two main components: Captures and Datasets. Within the captures folder, we have included all the captures used in this project. These captures are organized into separate folders corresponding to the type of network analysis: BLE or IP-Based. Similarly, the datasets folder follows a similar organizational approach. It contains datasets categorized by type: BLE, IP-Based Packet, and IP-Based Flows.

To cater to diverse analytical needs, the datasets are provided in two formats: CSV (Comma-Separated Values) and pickle. The CSV format facilitates seamless integration with various data analysis tools, while the pickle format preserves the intricate structures and relationships within the dataset.

This organization enables researchers to easily locate and utilize the specific captures and datasets they require, based on their preferred network analysis type or dataset type. The availability of different formats further enhances the flexibility and usability of the provided data.

Datasets' Content

Within this dataset, three sub-datasets are available, namely BLE, IP-Based Packet, and IP-Based Flows. Below is a table of the features selected for each dataset and consequently used in the evaluation model within the provided work.

Identified Key Features Within Bluetooth Dataset

Feature	Meaning
btle.advertising_header	BLE Advertising Packet Header
btle.advertising_header.ch_sel	BLE Advertising Channel Selection Algorithm
btle.advertising_header.length	BLE Advertising Length
btle.advertising_header.pdu_type	BLE Advertising PDU Type
btle.advertising_header.randomized_rx	BLE Advertising Rx Address
btle.advertising_header.randomized_tx	BLE Advertising Tx Address
btle.advertising_header.rfu.1	Reserved For Future 1
btle.advertising_header.rfu.2	Reserved For Future 2
btle.advertising_header.rfu.3	Reserved For Future 3
btle.advertising_header.rfu.4	Reserved For Future 4
btle.control.instant	Instant Value Within a BLE Control Packet
btle.crc.incorrect	Incorrect CRC
btle.extended_advertising	Advertiser Data Information
btle.extended_advertising.did	Advertiser Data Identifier
btle.extended_advertising.sid	Advertiser Set Identifier
btle.length	BLE Length
frame.cap_len	Frame Length Stored Into the Capture File
frame.interface_id	Interface ID
frame.len	Frame Length Wire
nordic_ble.board_id	Board ID
nordic_ble.channel	Channel Index
nordic_ble.crcok	Indicates if CRC is Correct
nordic_ble.flags	Flags
nordic_ble.packet_counter	Packet Counter
nordic_ble.packet_time	Packet time (start to end)
nordic_ble.phy	PHY
nordic_ble.protover	Protocol Version

Identified Key Features Within IP-Based Packets Dataset

Feature	Meaning
http.content_length	Length of content in an HTTP response
http.request	HTTP request being made
http.response.code	Sequential number of an HTTP response
http.response_number	Sequential number of an HTTP response
http.time	Time taken for an HTTP transaction
tcp.analysis.initial_rtt	Initial round-trip time for TCP connection
tcp.connection.fin	TCP connection termination with a FIN flag
tcp.connection.syn	TCP connection initiation with SYN flag
tcp.connection.synack	TCP connection establishment with SYN-ACK flags
tcp.flags.cwr	Congestion Window Reduced flag in TCP
tcp.flags.ecn	Explicit Congestion Notification flag in TCP
tcp.flags.fin	FIN flag in TCP
tcp.flags.ns	Nonce Sum flag in TCP
tcp.flags.res	Reserved flags in TCP
tcp.flags.syn	SYN flag in TCP
tcp.flags.urg	Urgent flag in TCP
tcp.urgent_pointer	Pointer to urgent data in TCP
ip.frag_offset	Fragment offset in IP packets
eth.dst.ig	Ethernet destination is in the internal network group
eth.src.ig	Ethernet source is in the internal network group
eth.src.lg	Ethernet source is in the local network group
eth.src_not_group	Ethernet source is not in any network group
arp.isannouncement	Indicates if an ARP message is an announcement

Identified Key Features Within IP-Based Flows Dataset

Feature	Meaning
proto	Transport layer protocol of the connection
service	Identification of an application protocol
orig_bytes	Originator payload bytes
resp_bytes	Responder payload bytes
history	Connection state history
orig_pkts	Originator sent packets
resp_pkts	Responder sent packets
flow_duration	Length of the flow in seconds
fwd_pkts_tot	Forward packets total
bwd_pkts_tot	Backward packets total
fwd_data_pkts_tot	Forward data packets total
bwd_data_pkts_tot	Backward data packets total
fwd_pkts_per_sec	Forward packets per second
bwd_pkts_per_sec	Backward packets per second
flow_pkts_per_sec	Flow packets per second
fwd_header_size	Forward header bytes
bwd_header_size	Backward header bytes
fwd_pkts_payload	Forward payload bytes
bwd_pkts_payload	Backward payload bytes
flow_pkts_payload	Flow payload bytes
fwd_iat	Forward inter-arrival time
bwd_iat	Backward inter-arrival time
flow_iat	Flow inter-arrival time
active	Flow active duration

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.

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.

Snapshot img

Internet Of Things (Iot) Data Management Market Size 2024-2028

The internet of things (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, driven by the increasing adoption of industrial automation and the leveraging of manufacturing data for predictive maintenance. Companies are recognizing the value of IoT initiatives and investments, as they enable real-time monitoring, analysis, and optimization of business processes. However, despite these opportunities, challenges persist. One major obstacle is the lack of awareness and understanding of efficient methods for managing the vast amounts of data generated by IoT devices. Addressing this challenge requires a strategic approach to data management, including the implementation of advanced analytics tools and the development of robust data architectures. Companies seeking to capitalize on the opportunities presented by the IoT Data Management Market must navigate these challenges effectively, ensuring they are well-positioned to harness the power of data to drive operational efficiency and strategic decision-making.

What will be the Size of the Internet Of Things (Iot) Data Management Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
Request Free SampleThe market is characterized by continuous evolution and dynamic market activities. IoT sensors generate vast amounts of data, necessitating robust data governance and management solutions. Machine learning algorithms and cloud computing facilitate data analysis, enabling real-time insights and predictive analytics. Data lineage and modeling are crucial for understanding data origins and relationships, while big data and data warehousing provide scalable storage solutions. Data sovereignty and privacy concerns are paramount, with data security, access control, masking, anonymization, and encryption essential for safeguarding sensitive information. Data quality, data lakes, and data catalogs ensure data accuracy and accessibility. Industrial IoT, smart cities, smart homes, wearable technology, connected vehicles, and edge computing are among the sectors experiencing significant growth in IoT data management applications. Data integration, data monitoring, and data backup are essential components of IoT data management, ensuring seamless data flow and disaster recovery. Predictive analytics and business intelligence provide actionable insights, driving operational efficiency and strategic decision-making. The ongoing unfolding of market activities and evolving patterns underscore the importance of staying informed and adaptable in this rapidly evolving landscape.

How is this Internet Of Things (Iot) Data Management Industry segmented?

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. ComponentSolutionsServicesDeploymentPrivate/hybridPublicGeographyNorth AmericaUSCanadaEuropeGermanyUKAPACChinaRest of World (ROW).

By Component Insights

The solutions segment is estimated to witness significant growth during the forecast period.In the dynamic landscape of the IoT data management market in 2023, software and hardware solutions in the solutions segment hold a significant share. The global expansion of IT and retail industries, driving the generation of vast amounts of data, fuels this market growth. In emerging economies like China, India, Brazil, Indonesia, and Mexico, the number of SMEs is increasing, leading to a rising demand for software-based IoT data management solutions to derive valuable business insights. companies in this market offer software solutions to various industries, enabling them to collect and analyze data in real-time for informed decision-making. Artificial intelligence, machine learning, and predictive analytics play crucial roles in extracting valuable insights from the massive data streams. Data pipelines and data streaming ensure seamless data transfer and processing, while data visualization tools help organizations gain a comprehensive understanding of their data. Data governance, data privacy, and data security are essential aspects of IoT data management, with cloud computing and edge computing offering flexible and secure solutions. Data lineage, data modeling, and big data analytics enable organizations to gain deeper insights and make data-driven decisions. The integration of IoT sensors, wearable technology, and smart devices in various applications, from industrial IoT to smart cities and homes, further expands the market's reach. Data access control,

Internet of Things (IoT) devices are growing constantly in numbers, being forecasted to reach 27 billions in 2025. With such a large number of connected devices, the energy consumption concerns are a major priority for the upcoming years. Cloud / edge / fog computing are critically associated with IoT devices as enablers for data communication and coordination among devices. In this paper, we look at the distribution of semantic reasoning between different IoT devices and define a new class of reasoning, multi-step reasoning that can be associated at the level of the edge or fog node in the context of an IoT cloud / edge / fog computing topology. We conduct an experiment based on synthetic datasets to evaluate the performance of multi-step reasoning in terms of power consumption and other metrics. Overall we found that multi-step reasoning can help in reducing computation time and energy consumption on IoT devices in presence of larger datasets.

The number of Internet of Things (IoT) devices worldwide is forecast to more than double from 19.8 billion in 2025 to more than 40.6 billion IoT devices by 2034. In 2034, the highest number of IoT devices will be found in China, with around 7.51 billion consumer devices. IoT devices are used in all types of industry verticals and consumer markets, with the consumer segment accounting for around 60 percent of all IoT or connected devices in 2025. This share is projected to stay at this level over the next ten years. Major verticals and use cases Major industry verticals with currently more than 100 million connected IoT devices are electricity, gas, steam & A/C, water supply & waste management, retail & wholesale, transportation & storage, and government. Overall, the number of IoT devices across all industry verticals is forecast to grow to more than eight billion by 2033. Major use-cases The most important use case for IoT devices in the consumer segment are consumer internet & media devices such as smartphones, where the number of IoT devices is forecast to grow to more than 17 billion by 2033. Other use cases with more than one billion IoT devices by 2033 are connected (autonomous) vehicles, IT infrastructure, asset tracking & monitoring, and smart grid.

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.

Snapshot img

IoT Platform Market Size 2024-2028

The IoT platform market size is forecast to increase by USD 20.72 billion, at a CAGR of 26.06% between 2023 and 2028. The market is experiencing significant growth, driven by the increasing adoption of IoT devices in various industries. Large enterprises and Small & Medium Enterprises (SMEs) are leveraging IoT platforms for enterprise applications, consumer products, and industrial automation. IoT integration platforms enable seamless connectivity between different devices and systems, while IoT analytics platforms provide valuable insights from the vast amount of data generated by these devices. Cloud-based and on-premises IoT platforms cater to diverse business needs. The benefits of IoT, such as real-time monitoring, predictive maintenance, and automation, are driving demand for these platforms. However, challenges like privacy and security concerns, interoperability issues, and complex integrations persist.

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The market is witnessing significant growth, driven by the integration of advanced technologies and the increasing demand for efficient data management and analysis. In this context, several components play a crucial role in the IoT ecosystem, including middleware, processing units, memory units, connectivity modules, commercial sensors, actuators, and IoT devices. IoT platforms serve as the backbone of the IoT ecosystem, enabling seamless communication between various components. Middleware acts as a bridge between different applications and devices, ensuring interoperability and data flow. Processing units and memory units facilitate data processing and storage at the edge, reducing latency and improving response times.

Connectivity modules enable IoT devices to communicate with each other and the cloud, ensuring reliable and secure data transfer. Commercial sensors and actuators are essential components of IoT systems, providing real-time data and enabling remote control of devices. IoT architecture can be broadly classified into IoT connectivity platforms and IoT analytics platforms. IoT connectivity platforms focus on managing and securing device connectivity, while IoT analytics platforms process and analyze data generated by IoT devices. Advancements in technologies such as 5G networks, edge computing, big data, AI, and Industry 4.0 are transforming the IoT landscape. 5G networks offer faster data transfer rates and lower latency, enabling real-time data processing and analysis.

Market Segmentation

The market 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

  Manufacturing
  Retail
  Healthcare
  ICT
  Others


Deployment

  Public cloud
  Private cloud
  Hybrid


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  Middle East and Africa



  South America

By End-user Insights

The manufacturing segment is estimated to witness significant growth during the forecast period. In the realm of modern business operations, the Internet of Things (IoT) is revolutionizing various industries, including Manufacturing, Smart Infrastructure, Connected Healthcare, Smart Retail, and Smart Transportation. IoT technology enables machines and devices to connect to the Internet, generating valuable data that fuels insights and drives business growth. In the Manufacturing sector, location-based sensors are increasingly used to optimize inventory management. IoT solutions provide real-time data, allowing manufacturers to enhance productivity, reduce downtime, and gain a competitive edge. However, challenges persist, such as substantial investment requirements, the need for business model transformations, security concerns, and interoperability issues. Government policies and collaborations among key players in the IoT ecosystem can help address these challenges. For instance, in the US, initiatives like the National Institute of Standards and Technology (NIST) IoT Innovation Lab aim to promote IoT research, standardization, and security.

Furthermore, partnerships between tech giants like Microsoft and Amazon Web Services (AWS) are driving innovation and expertise in the IoT space. As 5G networks become more prevalent, the potential for IoT applications will expand significantly. The telecommunications industry is expected to play a crucial role in enabling seamless connectivity for IoT devices. By harnessing the power of IoT technology, businesses can unlock new opportunities and create value in an increasingly interconnected world.

Get a glance at the market share of various segments Request Free Sample

The manufacturing segment was valued at USD 598.40 million in 2018 and showed a gradual increase during the forecast period.

R

Wearables or infrastructure sensors have been widely proposed for automated tracking and analysis of individual-level exercise activities. This dataset is collected as part of building a pervasive, low-cost digital personal trainer system, that supports fine-grained tracking of an individual’s free-weights exercises via a combination of (a) sensors on personal wireless ear-worn devices (‘earables’) and (b) inexpensive IoT sensors attached to exercise equipment (e.g., dumbbells). The dataset is comprised of sensor signals acquired from two 6-axis IMUs and contains a total of 324 samples for 3 different free-weight exercises performed by 27 individuals.

The Connected IoT Devices Market is likely to grow to reach USD 11.96 billion by 2033. It is expected to accrue at a massive CAGR of 16.17% during 2023-2033. In this regard, demand is most likely to expand on account of the major adoption of IoT technology in different sectors. Some key drivers of the same include an improved efficiency for operations, customer experience, and data-driven decision-making. The growing trend of smart cities and increasing rollout of 5G are additional factors driving growth for this market. The main demand is being created by smart homes, health care, automotive, industrial automation, and retail sectors where IoT-enabled devices used in these industries are enhancing connectivity and automation. Additionally, smart cities are proliferating with 5G networks that are again propelling the market growth due to real-time data transfer, low-latency communication, etc. However, it would face issues of data security and interoperability issues, among others. Still, developments in AI, edge computing, and cloud-based IoT solutions will open huge opportunities, becoming an important pillar for future digital transformation within several sectors. Recent developments include: April 2024: An industry expert in identity management, fraud prevention, and connected asset services, Somos, Inc., is happy to disclose its collaboration with NetRise, a premier supplier of IoT Software Bill of Materials security analysis. The partnership implies a substantial achievement for SomosID, the company's flagship IoT device registry, as it spearheads efforts to advance transparency and improved security in the IoT device realm. NetRise offers granular visibility into the world's Extended Internet of Things (XIoT) security problem — comprising the modern firmware and software component security challenges of IT, IoT, OT, and other connected cyber-physical systems. The SomosID solution offers enterprises a thorough view of their deployed IoT assets, comprising all the device, application identity, and network attributes, as well as verification of certification of those devices. Somos also maintains comprehensive hardware and software bill of materials information to offer customers a current state of all of their assets. The capability effortlessly incorporates NetRise's IoT device software analysis, including software bills of materials and vulnerability monitoring. It provides a thorough, constant view of an enterprise's IoT security posture and risks across all of its assets. SVP & Chief Technology Officer at Somos, Sri Ramachandran, said that Somos is happy to be able to use NetRise's best-in-class firmware analysis capabilities to fulfill the striking rise in cyber-attacks on IoT devices and the latest compliance requirements posed by the global IoT cybersecurity regulations, including the regulations by the Federal Drug Administration (FDA), the Cyber Resilience Act in Europe and Federal Communications Commission (FCC) in the U.S., June 2023: The Vietnamese IoT market is projected to acquire worth nearly USD 8.5 billion. To attain that target, the nation should apply IoT to measurement devices and means of transport, as per the experts. One report discovered that there are nearly 13 billion IoT devices across the globe, and the compound annual growth rate (CAGR) is 19 percent per annum. Among all these, 2.7 billion IoT connection devices use SIM with a CAGR of around 12 percent. The majority of the world's IoT device market belongs to China. The nation owns more than 10 billion IoT connections, among which 1.84 billion devices use SIM. According to the research, in Vietnam, the IoT market was worth nearly USD 2.5 billion in 2021 and grew by 22.6 percent per year., June 2022: OEMs and skid makers may now remotely evaluate the health and condition of their installed base thanks to Connected OEM, a Honeywell Internet of Things (IoT) service. This technology allows for remote monitoring of compressors, furnaces, pumping stations, analyzer houses, and skids at end-user locations., April 2022: Samsung established a partnership with IoT service provider ABB in order to increase support for home and commercial devices on SmartThings, the company's smart device hub. By collaborating with other companies, Samsung's SmartThings is evolving into a one-stop shop for controlling connected devices.. Key drivers for this market are: Rising demand for connectivity and real-time data Growing awareness of the benefits of IoT solutions Technological advancements in connectivity and data analytics. Potential restraints include: Security concerns and vulnerabilities Interoperability and compatibility issues Cost and complexity of IoT deployments Shortage of skilled professionals. Notable trends are: The integration of IoT devices into home environments is driving market growth. Smart home devices, such as smart speakers, thermostats, and lighting systems, offer convenience, energy efficiency, and enhanced home security. Industrial IoT (IIoT) applications are rapidly expanding, particularly in manufacturing, healthcare, and transportation. IIoT solutions enable real-time monitoring, predictive maintenance, and process optimization, leading to increased efficiency and reduced downtime..

The Wi-Fi IoT market is experiencing robust growth, driven by the increasing adoption of smart devices across diverse sectors. The convergence of readily available Wi-Fi infrastructure and the need for seamless connectivity in applications like smart home automation, industrial IoT, and connected healthcare is fueling this expansion. While precise market sizing requires specific figures, observing a general trend of rapid growth in the IoT sector suggests a 2025 market value exceeding $15 billion, with a Compound Annual Growth Rate (CAGR) likely between 15-20% for the forecast period (2025-2033). Key drivers include the declining cost of Wi-Fi-enabled devices, improved network security protocols, and the rising demand for real-time data analytics across various industries. The expansion into new sectors such as smart agriculture and intelligent transportation systems further contributes to market growth. However, potential restraints include concerns about data privacy and security, the interoperability challenges between different Wi-Fi IoT devices, and the need for robust and reliable infrastructure, especially in remote areas. Segmentation reveals a significant demand for solutions and services, reflecting the need for professional implementation and management of Wi-Fi IoT networks, surpassing the hardware market share. The education, retail, and healthcare sectors are witnessing particularly strong adoption. North America and Asia Pacific currently hold leading market shares, but emerging economies are expected to show rapid growth in the coming years. The competitive landscape is dynamic, with established players like Cisco and Huawei competing alongside specialized chip manufacturers like Nordic Semiconductor and Qorvo. The market's success relies on addressing interoperability concerns, developing more robust security measures, and fostering broader industry collaboration to create standardized protocols and seamless integration across various applications. The ongoing development of low-power, long-range Wi-Fi technologies, like Wi-Fi HaLow, will likely further expand market reach and drive adoption in previously underserved sectors. Companies are focusing on developing innovative solutions catering to specific industry needs, which is a significant factor driving market segmentation and enhancing overall growth. The long-term outlook for the Wi-Fi IoT market remains positive, propelled by technological advancements and the continuous growth of the Internet of Things.

Big Data in Internet of Things (IoT) Market Outlook

The global Big Data in Internet of Things (IoT) market size is projected to grow from USD 50 billion in 2023 to USD 220 billion by 2032, exhibiting a CAGR of 18% during the forecast period. This robust growth can be attributed to the increasing adoption of IoT devices and the subsequent data generation, necessitating advanced analytics to drive business insights and operational efficiencies.

One of the primary growth factors driving the Big Data in IoT market is the exponential increase in connected devices. As the number of IoT devices continues to surge globally, the volume of data generated is also growing at a staggering rate. This data is invaluable for businesses seeking to enhance their operations, customer experiences, and product innovations. Additionally, advancements in machine learning and AI technologies have significantly improved the ability to analyze and derive actionable insights from large datasets, further fueling market growth. Moreover, the reduction in IoT sensor costs has made it economically viable for more industries to integrate IoT into their operations, thereby expanding the market's scope.

Another significant growth factor is the increasing emphasis on real-time analytics. In various sectors such as healthcare, transportation, and retail, the ability to analyze data in real-time can lead to immediate operational improvements and decision-making efficiencies. For instance, in healthcare, real-time data analytics can enhance patient monitoring and predictive maintenance of medical equipment. Similarly, in transportation, it can optimize route planning and fleet management. The push towards real-time analytics is driving the demand for advanced Big Data solutions capable of processing and analyzing data instantaneously.

Furthermore, regulatory support and government initiatives promoting smart cities and digital transformation are propelling market growth. Governments worldwide are investing heavily in smart city projects, which rely on extensive IoT networks to manage urban infrastructure efficiently. These initiatives not only generate vast amounts of data but also require sophisticated Big Data analytics to ensure operational efficacy, safety, and sustainability. Additionally, the adoption of 5G technology is expected to accelerate IoT deployments, leading to even more data generation and the need for robust analytics solutions.

Regionally, North America is anticipated to dominate the Big Data in IoT market due to the presence of major technology companies, a highly developed IoT ecosystem, and significant investments in R&D. Europe is also expected to witness substantial growth, driven by smart city initiatives and stringent data protection regulations. Meanwhile, the Asia Pacific region is projected to experience the highest CAGR, fueled by rapid industrialization, urbanization, and increasing adoption of IoT technologies in countries like China and India.

Component Analysis

The Big Data in IoT market is segmented into software, hardware, and services. The software segment, encompassing data analytics platforms, advanced analytics tools, and AI-driven solutions, is expected to hold the largest market share. This dominance is due to the critical role that software solutions play in analyzing and deriving actionable insights from the massive volumes of data generated by IoT devices. Data analytics platforms enable businesses to process, analyze, and visualize data, thereby facilitating informed decision-making and strategic planning.

The hardware segment, which includes IoT sensors, gateways, and other connected devices, is also anticipated to witness significant growth. IoT sensors are essential for data collection, while gateways facilitate the communication between devices and data processing units. The continuous advancements in sensor technology and the reduction in costs are making hardware components more affordable and efficient, further driving their adoption across various industries. Additionally, the integration of edge computing capabilities in hardware devices is enhancing their ability to process data locally, reducing latency, and improving real-time analytics.

Services, including consulting, deployment, integration, and maintenance services, form another crucial segment of the Big Data in IoT market. As organizations increasingly adopt IoT solutions, they require expert guidance to implement and optimize these technologies effectively. Consulting services help businesses develop IoT strategies and identify suitable s

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) Data Management market is experiencing robust growth, projected to reach $66.02 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 16.7% from 2025 to 2033. This expansion is fueled by the exponential increase in connected devices generating massive volumes of data, necessitating sophisticated management solutions. Key drivers include the rising adoption of cloud-based platforms for data storage and processing, the growing demand for real-time analytics and insights from IoT data, and the increasing need for enhanced security and data governance in connected ecosystems. The market is witnessing a shift towards advanced analytics techniques, including artificial intelligence (AI) and machine learning (ML), to extract valuable insights from complex IoT datasets, enabling predictive maintenance, improved operational efficiency, and innovative business models. Furthermore, the integration of edge computing is gaining traction, facilitating localized data processing and reducing latency, especially crucial for applications requiring immediate responses. Major players like IBM, PTC, Teradata, and others are continuously innovating and expanding their offerings to address the evolving needs of this dynamic market. The competitive landscape is characterized by a mix of established technology vendors and emerging players specializing in IoT-specific data management solutions. The market segmentation includes solutions tailored for diverse industries like manufacturing, healthcare, transportation, and smart cities. The geographic distribution is expected to be fairly widespread, with North America and Europe holding significant market share initially, followed by a rapid expansion in Asia-Pacific and other regions due to increasing digitalization and IoT adoption. Challenges include ensuring data interoperability across different devices and platforms, addressing the complexity of managing vast and varied data streams, and mitigating potential security risks associated with large-scale data collection and analysis. Despite these challenges, the long-term outlook for the IoT Data Management market remains positive, driven by the continued growth of IoT applications and the increasing reliance on data-driven decision-making across various sectors.

The Human Internet of Things (HIoT) market is experiencing robust growth, driven by increasing adoption of wearable technology, advancements in sensor technology, and the rising demand for personalized healthcare solutions. The market is projected to reach a substantial size, exhibiting a Compound Annual Growth Rate (CAGR) that reflects strong market momentum. While precise figures for market size and CAGR are absent from the provided information, considering the involvement of major players like ARM, Intel, and Samsung, and the rapid expansion of connected devices, a reasonable estimate would place the 2025 market size at approximately $15 billion, growing at a CAGR of 15% from 2025-2033. This growth trajectory is fueled by several key drivers, including the increasing affordability of smart devices, improved data analytics capabilities enabling better insights from HIoT data, and a growing awareness of the potential of HIoT for enhancing safety and efficiency in various sectors. The integration of AI and machine learning further enhances the value proposition of HIoT, fostering the development of more sophisticated and personalized applications. However, potential restraints include concerns about data privacy and security, the need for robust infrastructure to support large-scale HIoT deployments, and the challenge of establishing interoperability standards across different devices and platforms. The market segmentation, encompassing various applications and geographic regions, presents significant opportunities for growth. The key players mentioned—ARM, Atmel, Intel, Melexis, Cisco, GE, ABB, LG, Samsung, and Electrolux—highlight the diverse industry involvement and the potential for strategic partnerships and collaborations. Market penetration in regions with developing digital infrastructure is likely to show substantial growth, while mature markets will see continued expansion through the adoption of sophisticated HIoT applications and services. Overall, the HIoT market demonstrates a promising outlook, poised for significant expansion as technology advances and consumer demand continues to rise. The market is dynamically evolving, with continuous innovation driving further growth and new application opportunities.