Around 2015, there were 260 IoT platforms in the market. In just one year, the number rose to 360! By 2017, there were 450 IoT platforms and today, there are over 600. This exponential growth in the number of IoT platforms proves that IoT has already become mainstream.
But this large number can lead to a lot of confusion when it comes to selecting the right platform for a particular business. There are so many factors that need to be considered and assessed. One needs to keep in mind at least the following 15 things while selecting an IoT platform.
1. Vendor/parent company
You need to ensure that the platform you are choosing comes from a reliable and credible vendor—one that has been in the market for a considerable amount of time. You can try checking out other products from the same vendor and see if they have a good life expectancy in the market. You can consider looking at clients and customers of the vendor as well, especially those who have invested in the same platform as you are planning to. Trust is a key factor in the IoT platform market—you are trusting a platform with your data after all.
2. Scalability
A scalable system is a stable system. Over time, businesses grow, expand and sometimes even pivot. Your IoT platform must be scalable enough and should accommodate all these changes— especially the large influx of data as a company matures in the market. To make your selection process easier, check how the IoT platform has grown from the time it was launched. Did it compete with the changing technologies? Has it adopted newer protocols?
Diese Geschichte stammt aus der October 2022-Ausgabe von Open Source For You.
Starten Sie Ihre 7-tägige kostenlose Testversion von Magzter GOLD, um auf Tausende kuratierte Premium-Storys sowie über 8.000 Zeitschriften und Zeitungen zuzugreifen.
Bereits Abonnent ? Anmelden
Diese Geschichte stammt aus der October 2022-Ausgabe von Open Source For You.
Starten Sie Ihre 7-tägige kostenlose Testversion von Magzter GOLD, um auf Tausende kuratierte Premium-Storys sowie über 8.000 Zeitschriften und Zeitungen zuzugreifen.
Bereits Abonnent? Anmelden
Modelling Toeplitz Networks with SageMath
A Toeplitz network refers to a graph that has a comparable regularity in its structure. SageMath is an excellent tool for facilitating the creation, analysis, and visualisation of graphs. Hence, SageMath can be used to effectively model Toeplitz networks and get insights into their structural characteristics, leading to advancements in network design and analysis.
It's the Age of AI Agents!
Businesses must get ready to work with AI agents if they want to stay competitive. Many have already adopted them, while others are gearing up to do so. These agents will soon be part of almost every organisation, making up a large global digital workforce.
Building Machine Learning Models with Scikit-learn
Scikit-learn scores over other machine learning libraries because it is easy to use, comes with a comprehensive feature set, has strong community support, and is customisable. Here's a quick look at its features and use cases.
SageMath: Deeper Insights into Cybersecurity
In the previous article in this SageMath series (published in the January 2025 issue of OSFY), we concluded our discussion of classical encryption techniques and moved on to the exploration of modern cryptography by looking at symmetric-key cryptography. In this ninth article in the series, we will continue the focus on symmetric-key cryptography.
Why You Should Go for Grafana
Explore the main characteristics of Grafana, the open source analytics and visualisation tool for application in the Internet of Things, and see how it compares with other similar popular tools.
Metaverse and Digital Twins: Partnering to Innovate
Let's explore Al-powered digital twin technology and the Metaverse, delving into what they promise, their limitations, and how large language models and generative Al help address these challenges.
How Open Source LLMs are Shaping the Future of AI
The future of AI isn't locked behind proprietary paywalls—it's open and collaborative, with open source LLMs giving businesses the power to innovate on their own terms.
Netbooting a Large Language Model-based OS in an Ubuntu Live Server
This brief tutorial explores the wireless netbooting of the LLM model Gemini AI in an Ubuntu server.
NLP: Text Summarisation with Python
Here's a simple Python method based on the Natural Language Toolkit for extractive text summarisation in natural language processing.
MLOps vs AlOps: What, Where, and Why
MLOps and AIOps excel at driving efficiency and innovation in an organisation. Let's find out what they are, where they can be used, and why we should do so.
