ICS TERMS AND ACRONYMS
- OT (Operational Technology) This term often refers to the use of hardware and software to monitor and control physical processes, devices, and infrastructure in industries such as manufacturing, energy, and transportation.
IoT (Internet of Things): A network of interconnected physical devices, objects, or “things” that are embedded with sensors, software, and connectivity, allowing them to collect and exchange data over the internet.
Sensor: A device that detects and measures physical or environmental characteristics, such as temperature, humidity, light, motion, or pressure, and converts this data into digital information.
Actuator: A device that takes digital signals from a controller or system and converts them into physical actions or adjustments, like opening a valve or turning on a motor.
Edge Computing: Processing data on or near the IoT device itself rather than sending all data to a centralized cloud server, which can reduce latency and bandwidth usage.
Gateway: A device or software component that connects IoT devices to a network, often serving as a bridge between the local network and the internet.
IoT Platform: A software or cloud-based system that provides the infrastructure and tools for managing, analyzing, and controlling IoT devices and data.
M2M (Machine-to-Machine): Communication between IoT devices or machines without human intervention, often used for data exchange or control.
Protocol: A set of rules and standards that govern how IoT devices communicate with each other and with networks. Examples include MQTT, CoAP, and HTTP.
IoT Ecosystem: The collection of interconnected components, including devices, platforms, and networks, that work together to enable IoT solutions.
Telemetry: The process of collecting and transmitting data from remote IoT devices for monitoring, analysis, or control.
Firmware: Software embedded in an IoT device’s hardware, responsible for controlling device functions and operations.
API (Application Programming Interface): A set of rules and protocols that allows different software applications or devices to communicate with each other.
Cloud Computing: Storing and processing data in remote servers (the cloud) rather than on local devices, often used in IoT for data storage and analysis.
Big Data: Large volumes of data generated by IoT devices that require specialized tools and technologies for storage, processing, and analysis.
IoT Security: Measures and practices designed to protect IoT devices and data from unauthorized access, tampering, and breaches.
Machine Learning: A subset of artificial intelligence (AI) that allows IoT systems to learn and make predictions or decisions based on data without explicit programming.
OTA (Over-the-Air) Updates: The ability to update IoT device software remotely, ensuring security patches and feature enhancements can be delivered without physical access to the device.
Zigbee: A wireless communication protocol commonly used in IoT for short-range, low-power, and low-data-rate applications.
LoRa (Long-Range): A low-power wide-area network (LPWAN) technology designed for long-range communication in IoT applications.
5G: The fifth generation of mobile networking technology, which promises faster and more reliable connectivity for IoT devices.
Smart Home: A residential IoT ecosystem that includes devices like smart thermostats, lighting, security cameras, and appliances for automation and control.
Smart City: The application of IoT and digital technologies to improve the efficiency and quality of urban services, including transportation, energy, and public safety.
Asset Tracking: Using IoT devices to monitor the location and condition of assets, such as vehicles, inventory, or equipment.
Predictive Maintenance: Using IoT data and analytics to predict when equipment or machinery is likely to fail, enabling proactive maintenance.
Digital Twin: A virtual representation of a physical object or system, created and maintained with real-time data from IoT devices, for simulation and analysis.
Machine Learning (ML)
Supervised Learning: In this approach, a model is trained on labeled data, where the algorithm learns to map input data to the correct output based on examples. Common algorithms include linear regression, decision trees, and neural networks.
Unsupervised Learning: Unsupervised learning involves training a model on unlabeled data to discover patterns or structures in the data. Common techniques include clustering and dimensionality reduction.
Semi-Supervised Learning: A combination of supervised and unsupervised learning, this approach uses a small amount of labeled data along with a larger amount of unlabeled data.
Reinforcement Learning: In reinforcement learning, agents learn to make sequences of decisions by interacting with an environment. They receive rewards or penalties based on their actions and use this feedback to improve their decision-making.
Deep Learning: A subfield of machine learning that involves artificial neural networks with multiple layers (deep neural networks). Deep learning has been particularly successful in tasks like image and speech recognition.
Feature Engineering: The process of selecting, transforming, and engineering the most relevant features (input variables) for a machine learning model.
Model Evaluation: Techniques and metrics used to assess the performance and generalization of machine learning models, such as accuracy, precision, recall, F1 score, and cross-validation.
Bias and Fairness: Concerns about the potential bias in machine learning models, which can result from biased training data or algorithmic biases. Ensuring fairness and equity in machine learning is an important topic.
Overfitting and Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, resulting in poor generalization. Underfitting happens when a model is too simple to capture the underlying patterns in the data.
Hyperparameter Tuning: The process of adjusting the settings (hyperparameters) of a machine learning algorithm to optimize its performance.
Ensemble Learning: Combining the predictions of multiple machine learning models to improve overall accuracy and reduce overfitting. Common ensemble techniques include bagging and boosting.
Natural Language Processing (NLP): A subfield of machine learning and AI that focuses on the interaction between computers and human language, enabling tasks like text classification, sentiment analysis, and language translation.
Acronyms in OT
- IoT – Internet of Things
- IoTaaS – IoT as a Service
- M2M – Machine-to-Machine communication
- LPWAN – Low-Power Wide Area Network
- 5G – Fifth Generation of mobile networking
- RFID – Radio-Frequency Identification
- NFC – Near Field Communication
- GPS – Global Positioning System
- Zigbee – A low-power wireless communication protocol
- BLE – Bluetooth Low Energy
- MQTT – Message Queuing Telemetry Transport
- CoAP – Constrained Application Protocol
- API – Application Programming Interface
- SDK – Software Development Kit
- FOTA – Firmware Over-The-Air updates
- OTA – Over-The-Air updates
- RTLS – Real-Time Location System
- SaaS – Software as a Service
- PaaS – Platform as a Service
- IaaS – Infrastructure as a Service
- SoC – System on a Chip
- CPS – Cyber-Physical System
- AR – Augmented Reality
- VR – Virtual Reality
- ML – Machine Learning
- AI – Artificial Intelligence
- BI – Business Intelligence
- GDPR – General Data Protection Regulation
- HTTP – Hypertext Transfer Protocol
- HTTPS – Hypertext Transfer Protocol Secure
- PKI – Public Key Infrastructure
- TLS/SSL – Transport Layer Security/Secure Sockets Layer
- DNN – Deep Neural Network
- RNN – Recurrent Neural Network
- CNN – Convolutional Neural Network
- IoE – Internet of Everything (a broader concept than IoT)
- WAN – Wide Area Network
- LAN – Local Area Network
- MAN – Metropolitan Area Network
- VAN – Very Large Area Network
- SCADA – Supervisory Control and Data Acquisition.