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Why Is Data Wrangling Necessary for IoT Analytics?

Machine learning (ML) is an application of artificial intelligence that enables systems to learn and improve from experience without human intervention. The goal of machine learning is to create systems that can access and learn from data without explicit programming.

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Machine Learning

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Technology

Connectivity technologies, Bluetooth or LPWAN for example, enable devices to communicate and exchange data through the Internet. Connectivity technologies vary in what they bring to individual IoT solutions and devices, each with its advantages and drawbacks.

Connectivity

Blockchain is a distributed ledger technology that allows records to be stored globally across many participating computers, ensuring that it cannot be forged or altered without changing all subsequent blocks. Blockchain technology can enable IoT devices to transfer and store data in a tamper-proof format.

Blockchain

New technologies mean new standards and regulations. Make sure you're staying up-to-date and complying with the latest.

Standards & Regulations

Research and development activities by companies, organizations, governments, and people to develop new IoT products and services and improve existing ones.

Research and Development

Artificial intelligence (AI) enables machines and programs to analyze data and detect patterns, allowing machines to make decisions using real-time data. AI is said to unlock the true power of IoT and is a necessary part of many automated systems.

Artificial Intelligence

Augmented reality (AR) combines a user's view of the real-world with a computer-generated image, often to provide contextual information. Through the integration of IoT, AR systems can request and relay data in real time.

Augmented Reality

Cloud software leverages data or programs over the Internet, enabling programs to utilize remote systems or components. Through cloud computing, IoT deployments can utilize resources remotely to simplify on-site installation and maintenance.

Cloud Software

Device management is a vital part of an IoT solution's installation and continued maintenance. Device management is the process by which devices are added to a network, monitored, and maintained. This process includes provisioning, configuration, diagnostics and regular software maintenance and updates.

Device Management

Edge computing allows data to be processed at the source, rather than the cloud. Edge computing is especially useful in situations where Internet access is intermittent or where latency is a concern.

Edge Computing

Hardware components are integral to the success of any IoT deployment or service. Hardware components cover a vast array of devices that can be used to gather, process, store and transmit data. What devices or sensors you need are highly dependent on the goals of your IoT system and Applications.

Hardware

Integration services help you get the most out of your existing infrastructure while upgrading with new IoT solutions or devices. IoT integration helps to lower the cost of integrating new IoT devices by allowing you to utilize existing infrastructure, rather than replace it.

Integration Services

Location technologies are vital to any IoT applications that require monitoring the location of assets or people. Which location technology is best for your solution depends on your needs - your access to the Internet and power, the range at which you need to track assets, and more.

Location Technology

Mixed reality (MR) uses components of both virtual reality and augmented reality to create environments where both real and virtual objects coexist. Through integration with IoT, MR systems are able to access real-time data and connect with other devices to create more immersive and robust systems.

Mixed Reality

Understanding network standards and protocols is vital to choosing the connectivity technology to power your IoT solution or device. Some of the common IoT protocols to know are BLE, Cellular Connectivity, LPWANs, Mesh protocols, and RFID - each has its advantages and disadvantages and which one to use will be determined by your particular Applications.

Network and Protocols

Virtual reality (VR) immerses users in a virtual environment and enables them to interact with it, typically, in a physical way. Applications integrating IoT and VR include simulating control of something in the physical world, like a remote surgical robot, or enabling users to connect remotely in a virtual space.

Virtual Reality

Voice interaction, or voice user interfaces (VUI), enables a user to control a machine via voice commands. VUI enables touchless control of devices, even from a short distance away, and is a common control method for IoT devices in the smart home. Specifically via voice assistants like Google Assistant or Alexa.

Voice Interaction

Data analytics is the result of the process of data analysis in which raw data is inspected, cleaned, transformed, and processed into more useful and organized formats. The goal is to find useful and new correlations and information that can inform tactical and strategic decision-making.

 

Data Analytics

Cybersecurity is the software side of the protection of computer systems and networks from bad actors looking to commit cybercrimes and data leakage like information disclosure. It is often used to prevent interruptions of service and also hardware, software, and data theft or damage.

Cybersecurity

Digital transformation is the adoption of digital technology to digitize non-digital products, services, or operations. The goal is to increase value through innovation, invention, customer experience, and efficiency.

Digital Transformation

Mobile technology refers to portable devices like smartphones and tablets that are connected through wireless networks and allow users to communicate, access information, and use various applications from almost anywhere.

Mobile

Embedded systems are networked or otherwise connected computer systems made up of processors, memory, and input/output peripheral devices that have a dedicated function within a larger mechanical or electronic system.

Embedded Systems

IoT platforms are software suites designed to manage or control IoT systems, either on-premise or from the cloud. They often incorporate a dashboard for viewing analytics, network status, and sensor and device activity. An IoT platform can also include functions that allow remote control and management of these endpoints, gateways, and networks. As of 2021, there are about 700 IoT Platforms on the market, depending on criteria.

IoT Platforms

Low-code development is a way to make software without needing to write lots of code. You can use a platform with easy-to-use visual tools and drag-and-drop features instead of complicated programming languages.

Low Code Software

Electric vehicles (EVs) are cars that run on electric motors using electricity instead of gasoline or diesel. IoT can help EV owners remotely monitor and control their cars through apps or web interfaces. IoT can also enable communication between EVs, charging stations, and energy grids so that EV owners can easily find charging for their cars.

Electric Vehicles

Computer vision relies on a combination of algorithms and artificial intelligence to process information like shapes, colors, and textures to understand what is before it.

Computer Vision

A set of practices that unifies machine learning (ML) system development and operations (Ops). MLOps aims to streamline the end-to-end machine learning lifecycle, from data collection and model training to deployment and monitoring, ensuring faster, scalable, and reproducible ML outcomes.

MLOps

Focused on the seamless deployment, monitoring, and governance of machine learning and AI models in production environments. ModelOps ensures that models are effectively integrated into business processes, remain performant over time, and can be updated or retired as needed.

ModelOps

Data that is artificially generated rather than collected from real-world sources. Synthetic data is created using statistical or computational techniques to mimic the characteristics and patterns of real data while ensuring the privacy and confidentiality of sensitive information. It finds applications in scenarios where access to actual data is restricted or when data privacy concerns arise.

Synthetic Data

Deep learning is a subfield of machine learning that focuses on neural networks with multiple layers (or deep neural networks). Deep neural networks can learn from vast amounts of data and automatically discover complex features and representations of the data. This makes them well-suited for tasks that involve large quantities of data.

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Deep Learning

A category of machine learning in which an algorithm learns from labeled training data to make predictions or decisions. In supervised learning, the algorithm is provided with input-output pairs, where the input represents the features or attributes of data, and the output is the desired target or label.

Supervised Learning

A category of machine learning in which algorithms are trained on unlabeled data to discover patterns, structures, or relationships within the data. Unlike supervised learning, unsupervised learning does not rely on predefined output labels. Instead, it seeks to identify inherent structures or groupings in the data.

Unsupervised Learning

Generative Adversarial Networks (GANs) are a class of artificial intelligence models used in unsupervised machine learning. They were introduced by Ian Goodfellow and his colleagues in 2014 and have since become a powerful tool in various applications, including image generation, style transfer, text-to-image synthesis, and more. GANs consist of two neural networks, the generator and the discriminator, which are trained together through a competitive process.

Generative Adversarial Networks (GANs)

A machine learning paradigm where an agent learns to make sequential decisions by interacting with an environment. In reinforcement learning (RL), the agent takes actions to maximize a cumulative reward signal, which is a measure of the long-term success of its decisions.

Reinforcement Learning

A subfield of reinforcement learning (RL) that focuses on training RL agents using human-generated feedback rather than traditional reward functions. In RLHF, humans provide feedback in the form of preferences, rankings, or comparisons to guide the learning process of RL agents.

Machine Learning

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