Skip to main content

Machine-to-Machine M2M Market Forecast

A recent ABI Research study of the cellular machine-to-machine communications market compared the current breakdown of usage with projected utilization patterns for 2010.
Today, telematics accounts for nearly a third of all M2M equipment shipments, with manufacturing automation, monitoring and control accounting for less than a fifth. One of the most significant changes the research showed over the next five years, is the comparative increase in fixed M2M relative to telematics. "Telematics isn't going to disappear -- it will continue to expand and to play a significant role, especially with the growth of trailer tracking and other fleet management applications," says Erik Michielsen, the company's director. "But the fundamentals of commercial telematics can also be applied to cellular M2M in the area of fixed monitoring and control." How does this kind of M2M work? Consider utility meters that communicate their measurements to the power or gas company via cellular calls. Think of temperature and pressure monitoring of industrial equipment, particularly in the field. Think of pipeline flow monitoring. Some sensors can help optimize performance of equipment on the factory floor. Cellular M2M becomes especially useful where an outside vendor, without access to their customer's own network, is responsible for correct operation of industrial plant.

Popular posts from this blog

The AI Application Integration Challenge

Artificial intelligence (AI) has rapidly become the defining force in business technology development, but integrating AI into applications remains a formidable challenge. According to a recent Gartner survey, 77 percent of engineering leaders identify AI integration in apps as a major hurdle for their organizations. As demand for AI-powered solutions accelerates across every industry, understanding the tools, the barriers, and the opportunities is essential for business and technology leaders seeking to evolve. The Gartner survey highlights a key trend: while AI’s potential is widely recognized, the path to useful integration is anything but straightforward. IT leaders cite complexities in embedding AI models into existing software, managing data pipelines, ensuring security, and maintaining compliance as persistent obstacles. These challenges are compounded by a shortage of skilled AI engineers and the rapid evolution of AI technologies, which can outpace organizational readiness and...