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/.

INFO ABOUT THE BOT-IOT DATASET, NOTE: only the csv files stated in the description are used

The BoT-IoT dataset can be downloaded from HERE. You can also use our new datasets: the TON_IoT and UNSW-NB15.

--------------------------------------------------------------------------

The BoT-IoT dataset was created by designing a realistic network environment in the Cyber Range Lab of UNSW Canberra. The network environment incorporated a combination of normal and botnet traffic. The dataset’s source files are provided in different formats, including the original pcap files, the generated argus files and csv files. The files were separated, based on attack category and subcategory, to better assist in labeling process.

The captured pcap files are 69.3 GB in size, with more than 72.000.000 records. The extracted flow traffic, in csv format is 16.7 GB in size. The dataset includes DDoS, DoS, OS and Service Scan, Keylogging and Data exfiltration attacks, with the DDoS and DoS attacks further organized, based on the protocol used.

To ease the handling of the dataset, we extracted 5% of the original dataset via the use of select MySQL queries. The extracted 5%, is comprised of 4 files of approximately 1.07 GB total size, and about 3 million records.

--------------------------------------------------------------------------

Free use of the Bot-IoT dataset for academic research purposes is hereby granted in perpetuity. Use for commercial purposes should be agreed by the authors. The authors have asserted their rights under the Copyright. To whom intent the use of the Bot-IoT dataset, the authors have to cite the following papers that has the dataset’s details: .

Koroniotis, Nickolaos, Nour Moustafa, Elena Sitnikova, and Benjamin Turnbull. "Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset." Future Generation Computer Systems 100 (2019): 779-796. Public Access Here.

Koroniotis, Nickolaos, Nour Moustafa, Elena Sitnikova, and Jill Slay. "Towards developing network forensic mechanism for botnet activities in the iot based on machine learning techniques." In International Conference on Mobile Networks and Management, pp. 30-44. Springer, Cham, 2017.

Koroniotis, Nickolaos, Nour Moustafa, and Elena Sitnikova. "A new network forensic framework based on deep learning for Internet of Things networks: A particle deep framework." Future Generation Computer Systems 110 (2020): 91-106.

Koroniotis, Nickolaos, and Nour Moustafa. "Enhancing network forensics with particle swarm and deep learning: The particle deep framework." arXiv preprint arXiv:2005.00722 (2020).

Koroniotis, Nickolaos, Nour Moustafa, Francesco Schiliro, Praveen Gauravaram, and Helge Janicke. "A Holistic Review of Cybersecurity and Reliability Perspectives in Smart Airports." IEEE Access (2020).

Koroniotis, Nickolaos. "Designing an effective network forensic framework for the investigation of botnets in the Internet of Things." PhD diss., The University of New South Wales Australia, 2020.

--------------------------------------------------------------------------

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

namely

This dataset is an enhanced version of NetFlow-based datasets, incorporating 53 extracted features that provide detailed insights into network flows. The dataset includes binary and multi-class labels, distinguishing between normal traffic and nine different types of attacks. It is structured in CSV format, with each row representing a single network flow, labeled accordingly. One of the key aspects of this dataset is the inclusion of temporal features, which allow for a more detailed analysis of traffic over time. The dataset records precise timestamps for each flow, including start and end times, enabling a more structured understanding of flow duration and activity patterns. Additionally, it captures inter-packet arrival time (IPAT) statistics, including minimum, maximum, average, and standard deviation values for both source-to-destination and destination-to-source packet transmissions.Note, there are minor changes to the dataset description in this data record, which is slightly different from the information in the download files description. The information presented in this data record is the most up-to-date.

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.

Operating systems data and Network data.

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.

IOT PROJECTS

## Overview

IOT PROJECTS is a dataset for object detection tasks - it contains Grass Or Weeds annotations for 500 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.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset was created by a LoRaWAN sniffer and contains packets, which are thoroughly analyzed in the paper Exploring LoRaWAN Traffic: In-Depth Analysis of IoT Network Communications (not yet published). Data from the LoRaWAN sniffer was collected in four cities: Liege (Belgium), Graz (Austria), Vienna (Austria), and Brno (Czechia).

Gateway ID: b827ebafac000001

• Uplink reception (end-device => gateway)
• Only packets containing CRC, inverted IQ
• RX0: 867.1 MHz, 867.3 MHz, 867.5 MHz, 867.7 MHz, 867.9 MHz - BW 125 kHz and all SF
• RX1: 868.1 MHz, 868.3 MHz, 868.5 MHz - BW 125 kHz and all SF

Gateway ID: b827ebafac000002

• Downlink reception (gateway => end-device)
• Includes packets without CRC, non-inverted IQ
• RX0: 867.1 MHz, 867.3 MHz, 867.5 MHz, 867.7 MHz, 867.9 MHz - BW 125 kHz and all SF
• RX1: 868.1 MHz, 868.3 MHz, 868.5 MHz - BW 125 kHz and all SF

Gateway ID: b827ebafac000003

• Downlink reception (gateway => end-device) and Class-B beacon on 869.525 MHz
• Includes packets without CRC, non-inverted IQ
• RX0: 869.525 MHz - BW 125 kHz and all SF, BW 125 kHz and SF9 with implicit header, CR 4/5 and length 17 B

To open the pcap files, you need Wireshark with current support for LoRaTap and LoRaWAN protocols. This support will be available in the official 4.1.0 release. A working version for Windows is accessible in the automated build system.

The source data is available in the log.zip file, which contains the complete dataset obtained by the sniffer. A set of conversion tools for log processing is available on Github. The converted logs, available in Wireshark format, are stored in pcap.zip. For the LoRaWAN decoder, you can use the attached root and session keys. The processed outputs are stored in csv.zip, and graphical statistics are available in png.zip.

This data represents a unique, geographically identifiable selection from the full log, cleaned of any errors. The records from Brno include communication between the gateway and a node with known keys.

Test file :: 00_Test

• short test file for parser verification
• comparison of LoRaTap version 0 and version 1 formats

Brno, Czech Republic :: 01_Brno

• 49.22685N, 16.57536E, ASL 306m
• lines 150873 to 529796
• time 1.8.2022 15:04:28 to 17.8.2022 13:05:32
• preliminary experiment
• experimental device
  • Device EUI: 70b3d5cee0000042
  • Application key: d494d49a7b4053302bdcf96f1defa65a
  • Device address: 00d85395
  • Network session key: c417540b8b2afad8930c82fcf7ea54bb
  • Application session key: 421fea9bedd2cc497f63303edf5adf8e

Liege, Belgium :: 02_Liege :: evaluated in the paper

• 50.66445N, 5.59276E, ASL 151m
• lines 636205 to 886868
• time 25.8.2022 10:12:24 to 12.9.2022 06:20:48

Brno, Czech Republic :: 03_Brno_join

• 49.22685N, 16.57536E, ASL 306m
• lines 947787 to 979382
• time 30.9.2022 15:21:27 to 4.10.2022 10:46:31
• record contains OTAA activation (Join Request / Join Accept)
• experimental device:
  • Device EUI: 70b3d5cee0000042
  • Application key: d494d49a7b4053302bdcf96f1defa65a
  • Device address: 01e65ddc
  • Network session key: e2898779a03de59e2317b149abf00238
  • Application session key: 59ca1ac91922887093bc7b236bd1b07f

Graz, Austria :: 04_Graz :: evaluated in the paper

• 47.07049N, 15.44506E, ASL 364m
• lines 1015139 to 1178855
• time 26.10.2022 06:21:07 to 29.11.2022 10:03:00

Vienna, Austria :: 05_Wien :: evaluated in the paper

• 48.19666N, 16.37101E, ASL 204m
• lines 1179308 to 3657105
• time 1.12.2022 10:42:19 to 4.1.2023 14:00:05
• contains a total of 14 short restarts (under 90 seconds)

Brno, Czech Republic :: 07_Brno :: evaluated in the paper

• 49.22685N, 16.57536E, ASL 306m
• lines 4969648 to 6919392
• time 16.2.2023 8:53:43 to 30.3.2023 9:00:11

Source Data with Authors

In the master's thesis research conducted by student Mohammed Ismail Lifta (2023-2024) at the Department of Computer Science, College of Computer Science and Mathematics- Tikrit University,Iraq.Data were collected from the Agriculture Lab on plants that grow in a IoT greenhouse and Traditional greenhouse .The study was supervised by Professor (Assistant) Wisam Dawood Abdullah, administrator of Cisco Networking Academy / Tikrit University.

Dataset Description

The dataset "Advanced_IoT_Dataset.csv" consists of 30,000 entries and 14 columns. Below are the detailed descriptions of each column:

Random: An identifier for each record, likely indicating a random sample or batch (object type). Average of chlorophyll in the plant (ACHP): The average chlorophyll content in the plant (float type). Plant height rate (PHR): The rate of plant height growth (float type). Average wet weight of the growth vegetative (AWWGV): The average wet weight of vegetative growth (float type). Average leaf area of the plant (ALAP): The average leaf area of the plant (float type). Average number of plant leaves (ANPL): The average number of leaves per plant (float type). Average root diameter (ARD): The average diameter of the plant's roots (float type). Average dry weight of the root (ADWR): The average dry weight of the plant's roots (float type). Percentage of dry matter for vegetative growth (PDMVG): The percentage of dry matter in vegetative growth (float type). Average root length (ARL): The average length of the plant's roots (float type). Average wet weight of the root (AWWR): The average wet weight of the plant's roots (float type). Average dry weight of vegetative plants (ADWV): The average dry weight of vegetative parts of the plant (float type). Percentage of dry matter for root growth (PDMRG): The percentage of dry matter in root growth (float type). Class: The class or category to which the plant record belongs (object type).

More detailed description of the columns in the dataset:

Random: A categorical identifier for each record. This column appears to have values like R1, R2, and R3, which could represent different random samples.

Average of chlorophyll in the plant (ACHP): This column contains float values representing the average chlorophyll content in the plant. Chlorophyll is vital for photosynthesis, and its measurement can indicate the health and efficiency of the plant in converting light energy into chemical energy.

Plant height rate (PHR): This column contains float values representing the rate of growth in the height of the plant. This metric is essential for understanding the vertical growth dynamics of the plant over time.

Average wet weight of the growth vegetative (AWWGV): This column contains float values representing the average wet weight of the vegetative parts of the plant. Wet weight can be an indicator of the water content and overall biomass of the plant's vegetative growth.

Average leaf area of the plant (ALAP): This column contains float values representing the average leaf area of the plant. Leaf area is a critical factor in photosynthesis, as it determines the surface area available for light absorption.

Average number of plant leaves (ANPL): This column contains float values representing the average number of leaves per plant. The number of leaves can correlate with the plant's ability to perform photosynthesis and its overall health.

Average root diameter (ARD): This column contains float values representing the average diameter of the plant's roots. Root diameter can affect the plant's ability to absorb water and nutrients from the soil.

Average dry weight of the root (ADWR): This column contains float values representing the average dry weight of the plant's roots. Dry weight is a measure of the plant's biomass after removing water content and is an indicator of the root's structural and storage capacity.

Percentage of dry matter for vegetative growth (PDMVG): This column contains float values representing the percentage of dry matter in the vegetative parts of the plant. This metric indicates the proportion of the plant's biomass that is not water, which can be crucial for understanding its structural and nutritional status.

Average root length (ARL): This column contains float values representing the average length of the plant's roots. Root length can influence the plant's ability to explore and absorb nutrients and water from the soil.

Average wet weight of the root (AWWR): This column contains float values representing the average wet weight of the plant's roots. Wet weight includes the water content in the roots, indicating their overall biomass and water retention capacity.

Average dry weight of vegetative plants (ADWV): This column contains float values representing the average dry weight of the vegetative parts of the plant. This me...

Synthetic Traffic Flow and Incident Data dataset by Synthetic IDD offers a comprehensive collection of synthetic traffic metrics and incident reports from around the globe. With 100 million lines of data, this dataset provides an extensive resource for researchers, urban planners, and developers interested in understanding traffic patterns, congestion points, and incident occurrences.

Usage:

This dataset can be used for a variety of purposes, including but not limited to: - Analyzing traffic patterns and congestion hotspots globally - Building predictive models for traffic management and incident prediction - Researching the impact of road conditions and incidents on traffic flow - Developing applications for real-time traffic monitoring and navigation

License:

The dataset is provided under the CC0 (Public Domain) license, allowing users to freely use, modify, and distribute the data without any restrictions.

1.Introduction

Sales data collection is a crucial aspect of any manufacturing industry as it provides valuable insights about the performance of products, customer behaviour, and market trends. By gathering and analysing this data, manufacturers can make informed decisions about product development, pricing, and marketing strategies in Internet of Things (IoT) business environments like the dairy supply chain.

One of the most important benefits of the sales data collection process is that it allows manufacturers to identify their most successful products and target their efforts towards those areas. For example, if a manufacturer could notice that a particular product is selling well in a certain region, this information could be utilised to develop new products, optimise the supply chain or improve existing ones to meet the changing needs of customers.

This dataset includes information about 7 of MEVGAL’s products [1]. According to the above information the data published will help researchers to understand the dynamics of the dairy market and its consumption patterns, which is creating the fertile ground for synergies between academia and industry and eventually help the industry in making informed decisions regarding product development, pricing and market strategies in the IoT playground. The use of this dataset could also aim to understand the impact of various external factors on the dairy market such as the economic, environmental, and technological factors. It could help in understanding the current state of the dairy industry and identifying potential opportunities for growth and development.

2. Citation

Please cite the following papers when using this dataset:

1. I. Siniosoglou, K. Xouveroudis, V. Argyriou, T. Lagkas, S. K. Goudos, K. E. Psannis and P. Sarigiannidis, "Evaluating the Effect of Volatile Federated Timeseries on Modern DNNs: Attention over Long/Short Memory," in the 12th International Conference on Circuits and Systems Technologies (MOCAST 2023), April 2023, Accepted

3. Dataset Modalities

The dataset includes data regarding the daily sales of a series of dairy product codes offered by MEVGAL. In particular, the dataset includes information gathered by the logistics division and agencies within the industrial infrastructures overseeing the production of each product code. The products included in this dataset represent the daily sales and logistics of a variety of yogurt-based stock. Each of the different files include the logistics for that product on a daily basis for three years, from 2020 to 2022.

3.1 Data Collection

The process of building this dataset involves several steps to ensure that the data is accurate, comprehensive and relevant.

The first step is to determine the specific data that is needed to support the business objectives of the industry, i.e., in this publication’s case the daily sales data.

Once the data requirements have been identified, the next step is to implement an effective sales data collection method. In MEVGAL’s case this is conducted through direct communication and reports generated each day by representatives & selling points.

It is also important for MEVGAL to ensure that the data collection process conducted is in an ethical and compliant manner, adhering to data privacy laws and regulation. The industry also has a data management plan in place to ensure that the data is securely stored and protected from unauthorised access.

The published dataset is consisted of 13 features providing information about the date and the number of products that have been sold. Finally, the dataset was anonymised in consideration to the privacy requirement of the data owner (MEVGAL).

File	Period	Number of Samples (days)
product 1 2020.xlsx	01/01/2020–31/12/2020	363
product 1 2021.xlsx	01/01/2021–31/12/2021	364
product 1 2022.xlsx	01/01/2022–31/12/2022	365
product 2 2020.xlsx	01/01/2020–31/12/2020	363
product 2 2021.xlsx	01/01/2021–31/12/2021	364
product 2 2022.xlsx	01/01/2022–31/12/2022	365
product 3 2020.xlsx	01/01/2020–31/12/2020	363
product 3 2021.xlsx	01/01/2021–31/12/2021	364
product 3 2022.xlsx	01/01/2022–31/12/2022	365
product 4 2020.xlsx	01/01/2020–31/12/2020	363
product 4 2021.xlsx	01/01/2021–31/12/2021	364
product 4 2022.xlsx	01/01/2022–31/12/2022	364
product 5 2020.xlsx	01/01/2020–31/12/2020	363
product 5 2021.xlsx	01/01/2021–31/12/2021	364
product 5 2022.xlsx	01/01/2022–31/12/2022	365
product 6 2020.xlsx	01/01/2020–31/12/2020	362
product 6 2021.xlsx	01/01/2021–31/12/2021	364
product 6 2022.xlsx	01/01/2022–31/12/2022	365
product 7 2020.xlsx	01/01/2020–31/12/2020	362
product 7 2021.xlsx	01/01/2021–31/12/2021	364
product 7 2022.xlsx	01/01/2022–31/12/2022	365

3.2 Dataset Overview

The following table enumerates and explains the features included across all of the included files.

Feature	Description	Unit
Day	day of the month	-
Month	Month	-
Year	Year	-
daily_unit_sales	Daily sales - the amount of products, measured in units, that during that specific day were sold	units
previous_year_daily_unit_sales	Previous Year’s sales - the amount of products, measured in units, that during that specific day were sold the previous year	units
percentage_difference_daily_unit_sales	The percentage difference between the two above values	%
daily_unit_sales_kg	The amount of products, measured in kilograms, that during that specific day were sold	kg
previous_year_daily_unit_sales_kg	Previous Year’s sales - the amount of products, measured in kilograms, that during that specific day were sold, the previous year	kg
percentage_difference_daily_unit_sales_kg	The percentage difference between the two above values	kg
daily_unit_returns_kg	The percentage of the products that were shipped to selling points and were returned	%
previous_year_daily_unit_returns_kg	The percentage of the products that were shipped to

NB-IoT vs. LTE-M: Measurement Data of the Energy Consumption of LPWAN Technologies

This dataset contains the raw energy measurements as well as R scripts to reproduce the energy consumption plot for the corresponding paper.

Each .csv file contains a specific set of measurements and we provide a script to read, process and plot the contained data.

Figure 3

Mean energy consumption of the different phases for Authentication for NB-IoT and LTE-M.

Due to the fact that the duration of Idle Connected in the measurement scripts was 30 seconds and 60 seconds for Idle Not Connected, the D-value and the mean power consumption are divided by 2.

Data – energy_measurements_fig3.csv

Code – fig3.R

Figure 4

Mean energy consumption of the different phases for Data Connection and Download for NB-IoT and LTE-M for 1KB of data in HTTP.

The delay between the measurements for Figure 4 were all 30 seconds long, but the identified Standby and Idle phases have different lengths. Therefore, the Idle phase values for both access technologies have been normalized and calculated for 20 seconds each.

Data – energy_measurements_fig4.csv

Code – fig4.R

Figure 5

Mean energy consumption of the different phases for Data Connection and Download for HTTP and MQTT for 1KB of data in NB-IoT.

In this scenario the delay between the measurements were different again. For MQTT the delay was 150 seconds and for HTTP 30 seconds. Therefore, the data during the Idle and Standby (only for MQTT) phase is normalized and calculated for 20 seconds and 10 seconds, respectively. During the MQTT Idle phase measurements, the device disconnects. This is not taken into account for the evaluation, which is why these energy values are discarded for this figure.

Data – energy_measurements_fig5.csv

Code – fig5.R

Contact

For questions or issues with this code, please contact Viktoria Vomhoff (viktoria.vomhoff@uni-wuerzburg.de) or any of the authors of the related publication.

Problem Statement

👉 Download the case studies here

Hospitals and healthcare providers faced challenges in ensuring continuous monitoring of patient vitals, especially for high-risk patients. Traditional monitoring methods often lacked real-time data processing and timely alerts, leading to delayed responses and increased hospital readmissions. The healthcare provider needed a solution to monitor patient health continuously and deliver actionable insights for improved care.

Challenge

Implementing an advanced patient monitoring system involved overcoming several challenges:

Collecting and analyzing real-time data from multiple IoT-enabled medical devices.

Ensuring accurate health insights while minimizing false alarms.

Integrating the system seamlessly with hospital workflows and electronic health records (EHR).

Solution Provided

A comprehensive patient monitoring system was developed using IoT-enabled medical devices and AI-based monitoring systems. The solution was designed to:

Continuously collect patient vital data such as heart rate, blood pressure, oxygen levels, and temperature.

Analyze data in real-time to detect anomalies and provide early warnings for potential health issues.

Send alerts to healthcare professionals and caregivers for timely interventions.

Development Steps

Data Collection

Deployed IoT-enabled devices such as wearable monitors, smart sensors, and bedside equipment to collect patient data continuously.

Preprocessing

Cleaned and standardized data streams to ensure accurate analysis and integration with hospital systems.

AI Model Development

Built machine learning models to analyze vital trends and detect abnormalities in real-time

Validation

Tested the system in controlled environments to ensure accuracy and reliability in detecting health issues.

Deployment

Implemented the solution in hospitals and care facilities, integrating it with EHR systems and alert mechanisms for seamless operation.

Continuous Monitoring & Improvement

Established a feedback loop to refine models and algorithms based on real-world data and healthcare provider feedback.

Results

Enhanced Patient Care

Real-time monitoring and proactive alerts enabled healthcare professionals to provide timely interventions, improving patient outcomes.

Early Detection of Health Issues

The system detected potential health complications early, reducing the severity of conditions and preventing critical events.

Reduced Hospital Readmissions

Continuous monitoring helped manage patient health effectively, leading to a significant decrease in readmission rates.

Improved Operational Efficiency

Automation and real-time insights reduced the burden on healthcare staff, allowing them to focus on critical cases.

Scalable Solution

The system adapted seamlessly to various healthcare settings, including hospitals, clinics, and home care environments.

The LTE IoT market is experiencing robust growth, driven by the increasing adoption of connected devices across diverse sectors. A 25.23% CAGR from 2019-2033 signifies significant expansion, fueled by factors such as the need for enhanced data security in IoT applications, improved network coverage and reliability offered by LTE-M and NB-IoT technologies, and the declining cost of these technologies. The market segmentation reveals a diverse landscape, with professional services commanding a larger share compared to managed services, reflecting the current preference for customized solutions. Within product types, NB-IoT likely holds a larger market share initially due to its cost-effectiveness, although LTE-M's higher bandwidth capacity will likely drive its growth in the long term. The IT & Telecommunication sector currently leads in adoption, followed by Consumer Electronics and Retail (Digital Ecommerce) showing promising growth due to the increasing demand for smart devices and online tracking. Healthcare and Industrial sectors are emerging as key growth areas, driven by applications like remote patient monitoring and smart manufacturing. Geographic distribution likely shows North America and Europe as mature markets, while Asia Pacific demonstrates high growth potential due to its massive population and rapid technological advancements. Leading companies, including Qualcomm, Gemalto, u-blox, Ericsson, and Cisco (Jasper), are actively shaping the market through innovation and strategic partnerships. The continued expansion of the LTE IoT market hinges on several crucial factors. Further technological advancements, particularly in low-power wide-area network (LPWAN) technologies, will continue to drive cost reductions and performance improvements. Government initiatives promoting IoT adoption across various sectors will play a significant role in fostering growth. Furthermore, the development of robust security protocols and data management solutions will be crucial to address concerns around data privacy and security, increasing trust and adoption. Competition among service providers and device manufacturers will intensify, potentially leading to more competitive pricing and innovative solutions. The long-term growth trajectory is highly promising, with significant opportunities for expansion across all segments and regions. Recent developments include: June 2022 - System Loco selected the Aeris Intelligent IoT network to provide next-generation connectivity that includes LTE-M, NB-IoT, LTE, and 2G/3G coverage from 600 carriers globally to offer a dynamic and flexible connectivity solution that ensures that all demands from current and future networks are met to support and manage the worldwide track and trace of smart pallets employed by System Loco's customers throughout the world., April 2022 - UScellular Collaborated with Qualcomm and Inseego to Launch 5G mmWave High-Speed Internet Service to provide high-speed internet access wirelessly to customers' homes or businesses. Inseego Wavemaker FW2010 outdoor CPE delivers multi-gigabit download speeds for data-hungry applications and supports 5G sub-6 GHz and Cat 22 LTE, making it great for a wide range of locations and applications.. Key drivers for this market are: Growing Demand for High-speed Broadband Connectivity, Rising Demand for the Industrial IoT among End-user Industries. Potential restraints include: Growing Demand for High-speed Broadband Connectivity, Rising Demand for the Industrial IoT among End-user Industries. Notable trends are: Industrial Sector is Expected to Grow at a Significant Rate.

The LTE IoT market is experiencing robust growth, fueled by the increasing adoption of connected devices across diverse sectors. The 25.23% CAGR from 2019 to 2025 indicates significant market expansion, driven primarily by the need for reliable, low-power wide-area networks (LPWAN) in applications demanding extended battery life and cost-effectiveness. Key drivers include the proliferation of smart city initiatives, the rise of industrial IoT (IIoT) applications in manufacturing and logistics, and the growing demand for remote monitoring and asset tracking solutions in healthcare and other industries. The market segmentation reveals a strong presence of both professional and managed services, with NB-IoT and LTE-M technologies leading the product landscape. The IT & telecommunication sector, along with consumer electronics and retail (digital e-commerce), represent major end-user industries, indicating broad application across various verticals. While precise regional breakdowns are unavailable, North America and Europe are likely to hold significant market shares, given their advanced technological infrastructure and early adoption of IoT technologies. However, the Asia-Pacific region is projected to experience substantial growth in the coming years due to increasing urbanization and digital transformation initiatives. Competition is fierce, with established players like u-blox, Ericsson, and Qualcomm competing alongside other key technology providers. The forecast period (2025-2033) suggests continued expansion, although the CAGR might moderate slightly as the market matures. Factors such as regulatory frameworks, technological advancements (e.g., 5G's impact on LTE IoT), and the overall economic climate will influence future growth. However, the long-term outlook remains positive, given the inherent scalability and cost-effectiveness of LTE IoT solutions. The continuous innovation in device miniaturization, power efficiency improvements, and enhanced security measures will further propel market expansion, creating lucrative opportunities for both established and emerging players in the LTE IoT ecosystem. Strategic partnerships and mergers & acquisitions will likely shape the competitive landscape, leading to consolidation and further market concentration. Recent developments include: June 2022 - System Loco selected the Aeris Intelligent IoT network to provide next-generation connectivity that includes LTE-M, NB-IoT, LTE, and 2G/3G coverage from 600 carriers globally to offer a dynamic and flexible connectivity solution that ensures that all demands from current and future networks are met to support and manage the worldwide track and trace of smart pallets employed by System Loco's customers throughout the world., April 2022 - UScellular Collaborated with Qualcomm and Inseego to Launch 5G mmWave High-Speed Internet Service to provide high-speed internet access wirelessly to customers' homes or businesses. Inseego Wavemaker FW2010 outdoor CPE delivers multi-gigabit download speeds for data-hungry applications and supports 5G sub-6 GHz and Cat 22 LTE, making it great for a wide range of locations and applications.. Key drivers for this market are: Growing Demand for High-speed Broadband Connectivity, Rising Demand for the Industrial IoT among End-user Industries. Potential restraints include: Reduction in PC Demand. Notable trends are: Industrial Sector is Expected to Grow at a Significant Rate.

Here, all field measurements acquired for the article entitled: ''A contribution to real-time space weather monitoring based on scintillation observations and IoT'', by Santos Freitas et al (2022) in Advances in Space Research are made available. The Scintapp is also available for download. Details on the data format can be found in section 2 of the aforementioned article.

In this dataset

Snapshot img

The industrial predictive maintenance market share in APAC is expected to increase by USD 7.44 billion from 2021 to 2026, and the market’s growth momentum will accelerate at a CAGR of 34.71%.

This industrial predictive maintenance market in APAC research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers the industrial predictive maintenance market in APAC segmentation by End-user (oil and gas, chemical and petrochemical, aerospace and defense, power generation, and others), deployment (cloud and on-premises), and geography (China, Japan, India, and Rest of APAC). The industrial predictive maintenance market in APAC report also offers information on several market vendors, including General Electric Co., Huawei Investment and Holding Co. Ltd., International Business Machines Corp., Oracle Corp., Robert Bosch GmbH, SAP SE, SAS Institute Inc., Siemens AG, Splunk Inc., and TIBCO Software Inc. among others.

What will the Industrial Predictive Maintenance Market Size in APAC be During the Forecast Period?

Download the Free Report Sample to Unlock the Industrial Predictive Maintenance Market Size in APAC for the Forecast Period and Other Important Statistics

Industrial Predictive Maintenance Market in APAC: Key Drivers, Trends, and Challenges

The developments in customized industrial predictive maintenance is notably driving the industrial predictive maintenance market in APAC, although factors such as low investments in the latest machinery and measuring equipment may impede market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the industrial predictive maintenance industry in APAC. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key Industrial Predictive Maintenance Market Driver in APAC

One of the key factors driving the global industrial predictive maintenance market growth is the developments in customized industrial predictive maintenance. Countries such as China, Japan, and South Korea are leading the automation industry in the region, which is creating opportunities for the growth of the industrial predictive maintenance market in APAC. The rise in the adoption of advanced technologies, such as IoT, industrial IoT (IIoT), AI, and big data, as well as investments in improving product quality and production assets in APAC, are expected to lead to the increased adoption of industrial predictive maintenance during the forecast period. Therefore, vendors such as SAP SE, International Business Machines Corp., and Oracle Corp. provide custom-made industrial predictive maintenance solutions and services based on the needs of specific end-users, which will protect their critical equipment and enable them to gain a competitive edge in productivity.

Key Industrial Predictive Maintenance Market Trend in APAC

Shift from reactive to predictive maintenance is one of the key industrial predictive maintenance market trends that is expected to impact the industry positively in the forecast period. The integration of business information along with sensor data and enterprise asset management (EAM) systems is allowing end-user industries to move away from reactive and shift to predictive maintenance services and solutions. The development of IoT solutions that use real-time machinery data to determine the operational efficiency and condition of the equipment, with the support of sophisticated analytics, helps to predict failures early, unlike preventive maintenance. The disadvantages associated with preventive maintenance are the key factors for the shift to predictive maintenance, as preventive maintenance does not prevent catastrophic failures, is labor-intensive, and needs unnecessary maintenance, which causes damage to equipment and components. Such factors will further support the market growth during the forecast years.

Key Industrial Predictive Maintenance Market Challenge in APAC

One of the key challenges to the global industrial predictive maintenance market growth is the low investments in the latest machinery and measuring equipment. Industrial predictive maintenance requires that the software solutions and services exhibit better performance and have a better impact on industrial assets and production. In addition, there are difficulties in retrofitting existing and older industrial machinery with sensors and monitoring equipment. End-user industries such as oil and gas, chemical and petrochemical, and power generation generally still operate using older machinery, which will, in turn, hamper the adoption of predictive maintenance solutions and services. Moreover, the adoption of industrial predictive maintenance is currently low in develo