3GPP NTN: supporting potentially 120 million IoT connections by 2034, but possibly improving 1.5 billion
- 3GPP
- Hyperconnectivity
- IoT
- NTN
- Non-terrestrial networks
- satellite
The 3GPP Non-Terrestrial Networks (NTN) initiative aims to expand cellular connectivity beyond traditional terrestrial networks by integrating satellite and high-altitude platform systems into the 5G framework. This report examines the potential market for NTN connectivity. In total, the report identifies over 120 million potential NTN connections, including 57.7 million Notional Connections.
- Hyperconnectivity
- Internet of Things
The leading AI models for (A)IoT
- AIoT
- ALBERT
- AlexNet
- Artificial Intelligence
- BERT (Bidirectional Encoder Representations from Transformers)
- Convolutional Neural Network
- CNN
- DistilBERT
- Edge AI
- EfficientNet
- GoogLeNet aka Inception-v1
- HuBERT (Hidden Unit BERT)
- Large Language Models
- LLaMA
- LLMs
- MiniLM (Minimalistic Language Model)
- MobileBERT
- MobileNet
- Neural Networks
- Random Forest
- Recurrent Neural Network
- ResNet (Residual Network)
- RNN
- SSD (Single Shot Multibox Detector)
- SVM (Support Vector Machine)
- T5 (Text-to-Text Transfer Transformer)
- TinyBERT
- Wave2Vec2
- Whisper
- XGBoost (eXtreme Gradient Boosting)
- YAMNet (Yet Another MobileNet)
- YOLO.
Artificial Intelligence (AI) models are a critical component of any AI system. They are the brains that support AI-enabled systems to analyse, decide, and perform the action for which the system is developed.
- Google
- Meta
- Sima.ai
- Syntiant
- Internet of Things
- Artificial Intelligence
Agentic AI: next generation AI that works autonomously
- Artificial Intelligence
- Agentic AI
- AI Agents
- AIoT
- Edge AI
- Generative AI
Agentic AI is making waves in the technology world, capturing the attention of both vendors and enterprises. Unlike the generative AI systems that have garnered so much attention in recent years, these autonomous systems operate with little to no human intervention, working toward a user’s end goal. With many systems built using a fusion of advanced large language models, natural language processing, reinforcement learning, and machine learning, agentic AI is capable of planning, reasoning, acting, and learning. This enables it to continuously fine-tune and enhance its performance, setting it apart from many conventional AI systems. It can autonomously orchestrate complex tasks and create and manage smaller tasks dynamically to achieve the desired outcome. Advanced agents have contextual understanding and can determine a course of actions and decide what tools (including external databases, APIs, and other AI agents) and assistance are required to reach their desired outcomes. While generative AI primarily focuses on content creation, including generating text, videos, or audio, and analysing vast datasets to uncover insights, agentic AI goes a step further. It is designed to tackle complex problems, make real-time decisions, and act independently. Businesses are beginning to adopt agentic AI to boost productivity, optimise operations, and drive innovation, making it a game-changer in the AI landscape. This report discusses how agentic AI is different from traditional AI systems (especially generative AI) and highlights the use cases for which agentic AI is being deployed in support of a range of enterprise functions. The report also lists some examples of leading companies that are already deploying the technology within these functions. Furthermore, the report discusses the key challenges and issues that could hinder the adoption of agentic AI.
- Aqua
- Bioaccess
- Bud Financial
- Fujitsu
- HotelPlanner
- Lemonade
- Meta
- Microsoft
- NYU Langone Health
- OpenAI
- OpenTable
- Owkin
- Ramp
- Relevance AI
- SafetyCulture
- Salesforce
- Shamrock Capital
- SharkNinja
- VIT
Schneider Electric: Digital Transformation capabilities assessment
- 3D Printing and Additive Manufacturing
- Internet of Things
- Artificial Intelligence
- Hyperconnectivity
- Human Machine Interface
- Data Sharing
- Autonomous Robotic Systems
- Distributed Ledger
- Edge Computing
- Robotic Process Automation
- Product Lifecycle Management
- Manufacturing Execution Systems (MES)
- Computer-Aided Design (CAD)
- Computer-Aided Manufacturing (CAM)
- Precision Specialist Robots
- Autonomous Robots
- cobots
- Suruchi Dhingra
- Paras Sharma
This report examines the capabilities of Schneider Electric in Digital Transformation. It provides a comprehensive review of the products, services, and capabilities of Schneider Electric across 11 technology areas and dozens of functions, to determine its core strengths for meeting enterprise needs. The 11 technology families in which the vendors capabilities are assessed are IoT, Hyperconnectivity, Human Machine Interface, Artificial Intelligence, Distributed Ledger, Data Sharing, Product Lifecycle Management, Robotic Process Automation, Edge Computing, Autonomous Robotic Systems, and 3D Printing & Additive Manufacturing. While these might not encompass every possible technology that organisations might need in order to purse a Digital Transformation, they certainly represent the most disruptive, and therefore the ones of which enterprises should be most aware. The report includes rating across each of the technology areas and functional capabilities (specialised hardware, general hardware, software products, integrated solutions, application development, systems integration and project management, specialist services, field & operational services) using Transforma Insight’s four-level universal rating system for vendors in Digital Transformation. Internet of Things, for instance, spans hardware, software, application development, implementation, field services and specialist services. For each of the 92 combinations of function and technology, Schneider Electric is rated for whether its capabilities are ‘Emerging’, ‘Significant’ or ‘Market Leading’ (or ‘None’). This rating is based on both the credibility of the solution and the position of the offering in the market (e.g. market share).
ATMs: A mature IoT market with 4 million devices by 2034 and a focus on providing digital financial service activities
- Internet of Things
- IoT
- Hyperconnectivity
- ATMs
- Automated Teller Machine
- Banking
- Cashless
- Cellular
- Contactless payment
- Digital payment
- LPWA
- Payment Terminals
- POS Devices
This report provides Transforma Insights’ view on the IoT market relating to Automated Teller Machines (ATMs). Included are details of how ATMs have evolved not only as providers of financial services, but also as a means of processing, collecting, and transmitting important consumer data faster, offering services such as scheduling appointments and interactions with bank employees, delivering personalised offers and service messages, and supporting digital signage capabilities for advertising. ATMs have been a permanent fixture on high streets for decades and their nature requires them to be connected. However, the availability of various cashless payment modes has reduced the need for cash, and therefore ATMs, in many countries. Yet ATMs continue to hold a potential for growth, albeit in developing countries. In developed markets such as the US and Europe the number of ATMs per capita is higher, but gradually declining. Connectivity allows modern ATMs to support video interaction, faster data processing, security monitoring, software updates, advertisements, and processing loans and mortgages and other financial service activities. The report provides a detailed definition of the sector, analysis of market development and profiles of the key vendors in the space. It also provides a summary of the current status of adoption and Transforma Insights’ ten-year forecasts for the market. The forecasts include analysis of the number of IoT connections by geography, the technologies used (including splits by 2G, 3G, 4G, 5G, LPWA, short range, satellite and others), as well as the revenue split between module, value-added connectivity and services. A full set of forecast data, including country-level forecasts, sector break-downs and public/private network splits, is available through the IoT Forecast tool.
- Barclays
- BRED Banque Populaire
- Caja Rural Granada
- Dataiku
- Diebold Nixdorf’s
- Federal Deposit Insurance Corporation (FDIC)
- Hongdian Corporation
- Milesight IoT
- OptConnect
- Royal Bank of Scotland
- Siam Commercial Bank (SCB)
- Tata Communication
- Teltonika Networks
- Valiant Bank AG
- Ventus
- World Bank
- Hyperconnectivity
- Internet of Things
Automotive Batteries: An EV charging alternative worth USD3 billion in 2034
- Internet of Things
- IoT
- Battery Swapping Stations
- Swappable Batteries
This report provides Transforma Insights’ view on the use of IoT in the Automotive Batteries market. This Application Group covers vendor tracking of swappable batteries used by electric vehicles. The growing number of electric vehicles will increase the need for vehicle charging facilities. Currently, there are a limited number of charging stations, some of which can take up to half a day to completely charge a vehicle. Battery swapping is an alternative to recharging a vehicle’s battery in situ. Swapping a depleted battery for a fully charged currently takes about 5 minutes and has a similar subscription cost to EV charging. China has been one of the early adopters of battery swapping with leading vendors such as Gogoro and Nio now expanding their geographic presence across other large markets such as Europe and the US. The rest of the market has been dominated by small and medium sized companies manufacturing swappable batteries for two and three wheelers that are popular in Southeast Asian countries. Most of these vehicles tend to be used for commercial activities such as food and package delivery. One of the main obstacles to widespread adoption is whether automakers will collaborate to develop standardised batteries that can be used across multiple brands. The likes of Mercedes, BMW, and Volkswagen fear that standardising batteries could reduce their competitive edge by turning them into a commodity, undermining the performance of their vehicles. This hesitancy, combined with the substantial investment required to redesign vehicles for swappable batteries, and the long development cycles in the automotive industry, has slowed widespread adoption of these batteries, especially in the developed markets.
- Aulton
- Better Place
- BMW
- Ford
- General Motors
- Gogoro
- Honda
- Hongqi
- Mercedes Benz
- Nio
- Oyika
- Porsche
- Renault
- Sun Mobility
- Swobbee
- Tesla
- Voyah
- Xpeng
- Zotye
- Electricity, Gas, Steam & A/C
- Manufacturing
- Transportation & Storage
Introducing AIoT: AI onboard IoT devices
- AI
- AIoT
- Artificial Intelligence
- IoT
- Internet of Things
Artificial Intelligence (AI) and the Internet of Things (IoT) are two of the most impactful and far-reaching technology developments of our time. Increasingly these two technologies are deployed together and the term ‘AIoT’ has come to the fore. IoT devices can generate significant amounts of potentially valuable data and AI can be applied to this data in many locations, including ‘cloud’ data centres, various network edge locations, and on board the actual IoT devices themselves. All of these approaches have the potential to unlock significant value and new insights from IoT data. When we refer to ‘AIoT’ we mean specifically the deployment of AI Use Cases on board IoT devices. Applying AI to IoT data on board the source IoT devices can bring significant benefits, including improved performance, enhanced compliance, privacy and security and potentially reduced operational costs. In this report we provide an introductory overview of AIoT, including the benefits of AIoT, the market potential for AIoT, requirements for platforms to support AIoT, and chipsets that are optimised for AIoT.
- Axis Communications
- Clearway
- Hymax Technologies
- SiMa.ai
- Syntiant
- WINT
- Artificial Intelligence
- Internet of Things
Parking Space Monitoring: 5.5 million IoT devices in 2034, curbing emissions and reducing congestion
- ANPR
- Automated Number Plate Recognition
- Car Parks
- Parking
- Parking Lots
- Parking Space
- Parking Spots
- Smart Parking
- Parking Barriers
- Smart City
This report provides Transforma Insights’ view on the Parking Space Monitoring market. This segment comprises of both public and private parking spaces. Drivers spend an average of 20 minutes daily looking for a parking space; this is responsible for almost 30% of all traffic congestion in urban areas and causes 28 million tonnes of CO2 emissions every year. Smart parking solutions are being introduced to counter this situation and help drivers find a parking spot easily and without any delays. These solutions have been shown to reduce CO2 emissions by more than 2% and traffic volume by 8% in Europe. Smart Parking devices use a host of Short Range technologies, such as RFID (for scanning tags on the vehicles), Wi-Fi, NB-IoT, and other Cellular technologies to communicate the status of parking lots to drivers and operators in real-time. The report provides a detailed definition of the sector, analysis of market development and profiles of the key vendors in the space. It also provides a summary of the current status of adoption and Transforma Insights’ ten-year forecasts for the market. The forecasts include analysis of the number of IoT connections by geography, the technologies used (including splits by 2G, 3G, 4G, 5G, LPWA, short range, satellite, and others), as well as the revenue split between module, value-added connectivity, and services. A full set of forecast data, including country-level forecasts, sector break-downs and public/private network splits, is available through the IoT Forecast tool.
- AppyParking
- AppyWay
- ARM
- China Mobile
- Cleverciti
- DTMobile
- Get My Parking
- Inrix Parking
- Interparking Group
- Jieshun
- Jieshun Zhicheng
- Liftshare
- ParkPilot
- PArkU
- Smart Parking
- Telstra
- Urbiotica
- Internet of Things
- Hyperconnectivity
- Transportation & Storage
- Administrative
- Retail & Wholesale
- Accommodation & Food Service
- Government
Environment Monitoring: 89.5 million connected devices by 2034, generating USD698 million revenue
- Internet of Things
- IoT
- Hyperconnectivity
- Smart Monitoring
- Air Quality Monitoring
- Water Quality Monitoring
- Dust Monitoring
- Noise Monitoring
- Pollution
- Pollutants
- Sustainability
- Environment
- Climate Change
- Greenhouse Gases
- Paras Sharma
- Matt Arnott
- Nikita Singh
This report provides Transforma Insights’ view on the Environment Monitoring market. This segment comprises industrial air, water, noise, nuclear radiation, odour detection, river water pollution, and dust monitors placed across industrial sites to monitor pollutants discharged from industrial processes. The segment also includes earthquake, flood, hurricane, and tsunami monitors placed across vulnerable areas to send real-time data to monitoring centres. Industrial pollution affects millions of lives around the world. Globally, governments are ramping up efforts to lower pollution levels by using alternative sources of energy and monitoring pollution levels using smart environment monitors. By using environment monitors, industries and governments get a better view of the situation and can take appropriate actions to mitigate the risks of air, water, and noise pollution. Monitoring floods, tsunamis, and earthquakes can help in reducing potential damage these natural disasters can cause. Sensor data can alert monitoring centres and communities of potential risks and provide adequate safety measures. The report provides a detailed definition of the sector, analysis of market development and profiles of the key vendors in the space. It also provides a summary of the current status of adoption and Transforma Insights’ ten-year forecasts for the market. The forecasts include analysis of the number of IoT connections by geography, the technologies used (including splits by 2G, 3G, 4G, 5G, LPWA, short range, satellite and others), as well as the revenue split between module, value-added connectivity and services. A full set of forecast data, including country-level forecasts, sector break-downs and public/private network splits, is available through the IoT Forecast tool.
- Aeroqual
- Airly
- Breeze Technologies
- Ecomesure
- Ellab
- Libelium
- Masan Group
- Monnit
- Manx Technology Group
- Oizom
- Internet of Things
- Hyperconnectivity
- Mining & Quarrying
- Manufacturing
- Electricity, Gas, Steam & A/C
- Water Supply & Waste Management
- Construction
- Transportation & Storage
- Government
LPWA Case Study Analysis
- LoRa
- LoRaWAN
- Low Power Wide Area
- LPWA
- LTE-M
- mMTC
- NB-IoT
- Sigfox
- Joydeep Bhattacharyya
- Nikita Singh
- Rohan Bansal
This report provides an overview of the different applications and industries that are leveraging Low Power Wide Area (LPWA) technologies. It includes details of the adoption of LPWA solutions, based on their use case as well as geography. In some places our analysis is further divided into specific LPWA technologies that are being used in different cases. The report identifies the key opportunity areas for LPWA solutions and highlights the major applications for LPWA in different industries. Additionally, the report includes a detailed overview of the adoption of LPWA by different verticals, key vendors supporting the deployments, and regional adoption of the technology.
- Actility
- Adeunis
- Deutsche Telekom
- Libelium
- LoRa Alliance
- Milesight
- Quectel
- Sensoneo
- Sigfox
- Smart Parking System
- Telia
- Thinxtra
- UnaBiz
- Vodafone
- Hyperconnectivity
- Internet of Things
Below is a list of Transforma Insights' research reports on Digital Transformation, IoT, AI and other disruptive technologies. Our 'Essential' subscribers can access a select sub-set of the reports as 'Essential Reading'. User Group members can access exclusive 'User Group' content. Some reports (e.g. Peer Benchmarking) are only available to 'Corporate' users. For details on how to upgrade your subscriptions, check your Profile page. If you would like to speak with our analysts about the content of any report, or any other topic, please contact enquiries@transformainsights.com.
