in most cases

The dataset has been introduced by the below-mentioned researches: E. C. P. Neto, S. Dadkhah, R. Ferreira, A. Zohourian, R. Lu, A. A. Ghorbani. "CICIoT2023: A real-time dataset and benchmark for large-scale attacks in IoT environment," Sensor (2023) – (submitted to Journal of Sensors). The present data contains different kinds of IoT intrusions. The categories of the IoT intrusions enlisted in the data are as follows: DDoS Brute Force Spoofing DoS Recon Web-based Mirai

There are several subcategories are present in the data for each kind of intrusion types in the IoT. The dataset contains 1191264 instances of network for intrusions and 47 features of each of the intrusions. The dataset can be used to prepare the predictive model through which different kind of intrusive attacks can be detected. The data is also suitable for designing the IDS system.

including some laptops or smart phones

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.

namely

building IoT IDS requires the availability of datasets to process

ABSTRACT In this project, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning. Specifically, the proposed testbed is organized into seven layers, including, Cloud Computing Layer, Network Functions Virtualization Layer, Blockchain Network Layer, Fog Computing Layer, Software-Defined Networking Layer, Edge Computing Layer, and IoT and IIoT Perception Layer. In each layer, we propose new emerging technologies that satisfy the key requirements of IoT and IIoT applications, such as, ThingsBoard IoT platform, OPNFV platform, Hyperledger Sawtooth, Digital twin, ONOS SDN controller, Mosquitto MQTT brokers, Modbus TCP/IP, ...etc. The IoT data are generated from various IoT devices (more than 10 types) such as Low-cost digital sensors for sensing temperature and humidity, Ultrasonic sensor, Water level detection sensor, pH Sensor Meter, Soil Moisture sensor, Heart Rate Sensor, Flame Sensor, ...etc.). However, we identify and analyze fourteen attacks related to IoT and IIoT connectivity protocols, which are categorized into five threats, including, DoS/DDoS attacks, Information gathering, Man in the middle attacks, Injection attacks, and Malware attacks. In addition, we extract features obtained from different sources, including alerts, system resources, logs, network traffic, and propose new 61 features with high correlations from 1176 found features. After processing and analyzing the proposed realistic cyber security dataset, we provide a primary exploratory data analysis and evaluate the performance of machine learning approaches (i.e., traditional machine learning as well as deep learning) in both centralized and federated learning modes.

Instructions:

Great news! The Edge-IIoT dataset has been featured as a "Document in the top 1% of Web of Science." This indicates that it is ranked within the top 1% of all publications indexed by the Web of Science (WoS) in terms of citations and impact.

Please kindly visit kaggle link for the updates: https://www.kaggle.com/datasets/mohamedamineferrag/edgeiiotset-cyber-sec...

Free use of the Edge-IIoTset dataset for academic research purposes is hereby granted in perpetuity. Use for commercial purposes is allowable after asking the leader author, Dr Mohamed Amine Ferrag, who has asserted his right under the Copyright.

The details of the Edge-IIoT dataset were published in following the paper. For the academic/public use of these datasets, the authors have to cities the following paper:

Mohamed Amine Ferrag, Othmane Friha, Djallel Hamouda, Leandros Maglaras, Helge Janicke, "Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning", IEEE Access, April 2022 (IF: 3.37), DOI: 10.1109/ACCESS.2022.3165809

Link to paper : https://ieeexplore.ieee.org/document/9751703

The directories of the Edge-IIoTset dataset include the following:

•File 1 (Normal traffic)

-File 1.1 (Distance): This file includes two documents, namely, Distance.csv and Distance.pcap. The IoT sensor (Ultrasonic sensor) is used to capture the IoT data.

-File 1.2 (Flame_Sensor): This file includes two documents, namely, Flame_Sensor.csv and Flame_Sensor.pcap. The IoT sensor (Flame Sensor) is used to capture the IoT data.

-File 1.3 (Heart_Rate): This file includes two documents, namely, Flame_Sensor.csv and Flame_Sensor.pcap. The IoT sensor (Flame Sensor) is used to capture the IoT data.

-File 1.4 (IR_Receiver): This file includes two documents, namely, IR_Receiver.csv and IR_Receiver.pcap. The IoT sensor (IR (Infrared) Receiver Sensor) is used to capture the IoT data.

-File 1.5 (Modbus): This file includes two documents, namely, Modbus.csv and Modbus.pcap. The IoT sensor (Modbus Sensor) is used to capture the IoT data.

-File 1.6 (phValue): This file includes two documents, namely, phValue.csv and phValue.pcap. The IoT sensor (pH-sensor PH-4502C) is used to capture the IoT data.

-File 1.7 (Soil_Moisture): This file includes two documents, namely, Soil_Moisture.csv and Soil_Moisture.pcap. The IoT sensor (Soil Moisture Sensor v1.2) is used to capture the IoT data.

-File 1.8 (Sound_Sensor): This file includes two documents, namely, Sound_Sensor.csv and Sound_Sensor.pcap. The IoT sensor (LM393 Sound Detection Sensor) is used to capture the IoT data.

-File 1.9 (Temperature_and_Humidity): This file includes two documents, namely, Temperature_and_Humidity.csv and Temperature_and_Humidity.pcap. The IoT sensor (DHT11 Sensor) is used to capture the IoT data.

-File 1.10 (Water_Level): This file includes two documents, namely, Water_Level.csv and Water_Level.pcap. The IoT sensor (Water sensor) is used to capture the IoT data.

•File 2 (Attack traffic):

-File 2.1 (Attack traffic (CSV files)): This file includes 13 documents, namely, Backdoor_attack.csv, DDoS_HTTP_Flood_attack.csv, DDoS_ICMP_Flood_attack.csv, DDoS_TCP_SYN_Flood_attack.csv, DDoS_UDP_Flood_attack.csv, MITM_attack.csv, OS_Fingerprinting_attack.csv, Password_attack.csv, Port_Scanning_attack.csv, Ransomware_attack.csv, SQL_injection_attack.csv, Uploading_attack.csv, Vulnerability_scanner_attack.csv, XSS_attack.csv. Each document is specific for each attack.

-File 2.2 (Attack traffic (PCAP files)): This file includes 13 documents, namely, Backdoor_attack.pcap, DDoS_HTTP_Flood_attack.pcap, DDoS_ICMP_Flood_attack.pcap, DDoS_TCP_SYN_Flood_attack.pcap, DDoS_UDP_Flood_attack.pcap, MITM_attack.pcap, OS_Fingerprinting_attack.pcap, Password_attack.pcap, Port_Scanning_attack.pcap, Ransomware_attack.pcap, SQL_injection_attack.pcap, Uploading_attack.pcap, Vulnerability_scanner_attack.pcap, XSS_attack.pcap. Each document is specific for each attack.

•File 3 (Selected dataset for ML and DL):

-File 3.1 (DNN-EdgeIIoT-dataset): This file contains a selected dataset for the use of evaluating deep learning-based intrusion detection systems.

-File 3.2 (ML-EdgeIIoT-dataset): This file contains a selected dataset for the use of evaluating traditional machine learning-based intrusion detection systems.

Step 1: Downloading The Edge-IIoTset dataset From the Kaggle platform from google.colab import files

!pip install -q kaggle

files.upload()

!mkdir ~/.kaggle

!cp kaggle.json ~/.kaggle/

!chmod 600 ~/.kaggle/kaggle.json

!kaggle datasets download -d mohamedamineferrag/edgeiiotset-cyber-security-dataset-of-iot-iiot -f "Edge-IIoTset dataset/Selected dataset for ML and DL/DNN-EdgeIIoT-dataset.csv"

!unzip DNN-EdgeIIoT-dataset.csv.zip

!rm DNN-EdgeIIoT-dataset.csv.zip

Step 2: Reading the Datasets' CSV file to a Pandas DataFrame: import pandas as pd

import numpy as np

df = pd.read_csv('DNN-EdgeIIoT-dataset.csv', low_memory=False)

Step 3 : Exploring some of the DataFrame's contents: df.head(5)

print(df['Attack_type'].value_counts())

Step 4: Dropping data (Columns, duplicated rows, NAN, Null..): from sklearn.utils import shuffle

drop_columns = ["frame.time", "ip.src_host", "ip.dst_host", "arp.src.proto_ipv4","arp.dst.proto_ipv4",

 "http.file_data","http.request.full_uri","icmp.transmit_timestamp",

 "http.request.uri.query", "tcp.options","tcp.payload","tcp.srcport",

 "tcp.dstport", "udp.port", "mqtt.msg"]

df.drop(drop_columns, axis=1, inplace=True)

df.dropna(axis=0, how='any', inplace=True)

df.drop_duplicates(subset=None, keep="first", inplace=True)

df = shuffle(df)

df.isna().sum()

print(df['Attack_type'].value_counts())

Step 5: Categorical data encoding (Dummy Encoding): import numpy as np

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

from sklearn import preprocessing

def encode_text_dummy(df, name):

dummies = pd.get_dummies(df[name])

for x in dummies.columns:

dummy_name = f"{name}-{x}"

df[dummy_name] = dummies[x]

df.drop(name, axis=1, inplace=True)

encode_text_dummy(df,'http.request.method')

encode_text_dummy(df,'http.referer')

encode_text_dummy(df,"http.request.version")

encode_text_dummy(df,"dns.qry.name.len")

encode_text_dummy(df,"mqtt.conack.flags")

encode_text_dummy(df,"mqtt.protoname")

encode_text_dummy(df,"mqtt.topic")

Step 6: Creation of the preprocessed dataset df.to_csv('preprocessed_DNN.csv', encoding='utf-8')

For more information about the dataset, please contact the lead author of this project, Dr Mohamed Amine Ferrag, on his email: mohamed.amine.ferrag@gmail.com

More information about Dr. Mohamed Amine Ferrag is available at:

https://www.linkedin.com/in/Mohamed-Amine-Ferrag

https://dblp.uni-trier.de/pid/142/9937.html

https://www.researchgate.net/profile/Mohamed_Amine_Ferrag

https://scholar.google.fr/citations?user=IkPeqxMAAAAJ&hl=fr&oi=ao

https://www.scopus.com/authid/detail.uri?authorId=56115001200

https://publons.com/researcher/1322865/mohamed-amine-ferrag/

https://orcid.org/0000-0002-0632-3172

Last Updated: 27 Mar. 2023

Context

This is a dataset of DDoS Botnet attacks from IOT devices.

Content

Contains all features about packets from bots.

Inspiration:

For making DDoS attack preventable.

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.

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 for… See the full description on the dataset page: https://huggingface.co/datasets/19kmunz/iot-23-preprocessed-minimumcolumns.

IOT

## Overview

IOT is a dataset for object detection tasks - it contains Defects IaZX annotations for 1,588 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

Anomaly detection is a well-known topic in cybersecurity. Its application to the Internet of Things can lead to suitable protection techniques against problems such as denial of service attacks.

The CIC IoT Dataset 2023 is a comprehensive benchmark developed by the Canadian Institute for Cybersecurity (CIC) to advance intrusion detection research in real-world Internet of Things (IoT) environments. This dataset was created using a network of 105 actual IoT devices, encompassing smart home gadgets, sensors, and cameras, to simulate authentic IoT traffic and attack scenarios.

Key Features:

• Diverse Attack Scenarios: The dataset includes 33 distinct attacks categorized into seven classes: DDoS, DoS, Reconnaissance, Web-based, Brute Force, Spoofing, and Mirai. These attacks were executed by compromised IoT devices targeting other IoT devices, reflecting realistic threat vectors.(University of New Brunswick)

• Extensive Data Collection: Network traffic was captured in real-time, resulting in over 46 million records. The data is available in various formats, including raw PCAP files and pre-extracted CSV features, facilitating different research needs.

• Realistic IoT Topology: Unlike many datasets that rely on simulations, this dataset was generated using a large-scale IoT testbed with devices from multiple vendors, providing a heterogeneous and realistic network environment.

• Benchmarking and Evaluation: The dataset has been utilized to evaluate the performance of machine learning and deep learning algorithms in classifying and detecting malicious versus benign IoT network traffic.(University of New Brunswick)

This dataset serves as a valuable resource for researchers and practitioners aiming to develop and test security analytics applications, intrusion detection systems, and other cybersecurity solutions tailored for IoT ecosystems.(University of New Brunswick)

Introduction

The Global IoT Sensors in Healthcare Market is projected to grow from USD 81.4 billion in 2023 to USD 349.9 billion by 2033, at a CAGR of 15.7%. This substantial growth is driven by the increased adoption of remote monitoring and telehealth services. IoT sensors are pivotal in managing chronic diseases outside traditional clinical settings, providing continuous monitoring that improves patient outcomes and reduces healthcare costs.

Advancements in precision medicine are fueled by IoT sensors, which collect detailed data to tailor medical treatments to individual patient characteristics. This approach allows for more personalized and effective healthcare solutions. Additionally, IoT devices enhance patient engagement by providing instant access to health data, fostering active participation in health management and improving patient satisfaction.

IoT technology also increases operational efficiency within healthcare facilities by optimizing patient flow and inventory management. This not only enhances service delivery but also cuts costs, boosting the overall efficiency of healthcare services. Furthermore, the integration of AI and machine learning with IoT devices sets new standards in predictive healthcare, transforming service delivery through advanced diagnostics and optimized treatment plans.

Recent developments in the market underscore the commitment of leading industry players to innovate and enhance technology integration. For instance, Koninklijke Philips N.V. announced collaborations in April 2023 to develop AI-powered healthcare applications using IoT data. GE Healthcare Technology continues to expand remote patient monitoring capabilities, integrating IoT sensors to enhance patient care management.

Moreover, Abbott Laboratories and Medtronic Plc have made strategic moves to strengthen IoT sensors in healthcare market positions. Abbott's acquisitions in February 2023 aim to enhance its diabetes care technology, especially in continuous glucose monitoring. Medtronic's partnership with NVIDIA in January 2023 focuses on advancing medical technology through AI and IoT, highlighting an industry trend towards more integrated, patient-centered care facilitated by these technologies. These efforts demonstrate a significant shift towards enhancing patient care and operational efficiency through sophisticated IoT applications in healthcare.

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':

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.

The global market for Internet of things (IoT) end-user solutions is expected to grow to *** billion U.S. dollars in size by the end of 2019. The technology reached *** billion dollars in market revenue for the first time in 2017, and forecasts suggest that this figure will grow to around *** trillion by 2025. The Internet of Things The Internet of Things (IoT) is a term used to describe the continually growing network of internet connected electronic devices that are in operation around the world today. These devices often share data and information in order to provide added convenience and control to consumers and, in some cases, even allow users automate simple processes such as ordering supplies. Tens of billions of these IoT connected devices already exist around the world and this number will only grow as internet connectivity begins to become a standard feature for a great number of electronics devices. Although heavily integrated into the consumer electronics market, IoT extends far beyond handheld devices and home appliances; IoT subsystems such as industrial internet and connected cities aim at automating factories and urban areas rather than just households. Digital virtual assistants such as Amazon’s Alexa and Google Assistant serve as the bridge between this network of interconnected devices and their human users.

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.

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,

Short-range technologies, including Wi-Fi, Bluetooth, and Zigbee, will dominate the number of Internet of Things (IoT) connections in 2030 compared with other communications technologies, with over 21 billion connections.

Massive machine-type communication tech

These short-range technologies are projected to account for the most sizable portion of communications technologies from 2021 to 2030. Massive machine-type communication (mMTC) using 5G technology - which is needed for the growing deployment of autonomous vehicles - is projected to grow strongly over the next few years. At the same time, the non-mMTC connected devices would depend on low power wide area (LPWA) network.

Future of IoT

In the following decade, IoT will track individuals, cities, crops, and everything else you can imagine. The possibilities for IoT in the future are endless. Increased network agility, integrated artificial intelligence (AI), has increased the ability to deploy, automate, manage, and secure various use cases at hyper-scale. This development will expedite IoT advancements in the coming years.