As students and administrators seek anytime, anywhere access to the cloud, higher ed IT teams must face their fears and get to work.
Machine-to-machine (M2M) networks already are changing the way we live and do business. Examples abound: Radio-frequency identification technology has revolutionized supply chains. Networked sensors feed health information to real-time analytic applications that can save lives. Building sensors, devices and infrastructure applications exchange information to reduce energy use and secure facilities.
Each of those implementations represents the first steps toward a true Internet of Things (IoT) — joining those billions of devices, from robots on factory floors to coffeemakers in family kitchens, that are equipped with microprocessors and Internet connections. Despite rapidly multiplying examples of M2M communication at work, realizing the promise of the IoT will require much more time and research, says analyst and technology writer Brian Proffitt.
"Reaping the benefits of large-scale, machine-to-machine communications is orders of magnitude more complicated than the deployments that exist today," says Proffitt, who also is an adjunct professor in the University of Notre Dame's Mendoza College of Business.
University research centers, businesses, government agencies such as the National Institute for Standards and Technology, and industry organizations like the Institute of Electrical and Electronics Engineers (IEEE) are all working to create standards for the wireless technologies, data types and application processing interfaces that make M2M communication possible. Once standards are in place, developers will be able to build APIs based on a single model (a sort of common language for M2M communication), but plenty of work remains. Purdue University's M2M Lab has created a model called HARMS that identifies the components that need to interface in an M2M network: humans, software agents, robots, machines and sensors, says Eric Matson, an assistant professor and researcher in the M2M Lab.
"Purdue is developing its own API for the HARMS infrastructure, but building it for all the machines out there would take more time than we have left in the world," Matson says. "New devices come out every day. New infrastructure comes out every day. When you look at how devices consume meaning, content and context and their semantics, the problem becomes even more complex."
M2M networks are challenged by a lack of common protocols, says Stephen Miles, a research scientist at the Massachusetts Institute of Technology's Auto-ID Labs, where the phrase "Internet of Things" was coined a decade ago. The real-time analytics, along with the APIs that deliver data from sensors and other devices, are closely held, as are applications that manage the flood of data created by M2M networks. When an organization purchases a new network-connected device, it does not expect to purchase entirely new network management systems for each device, Miles says.
"There are significant closed-loop, proprietary RFID implementations that are very successful, but the notion of an open Internet of Things where everything can talk to everything will be a longer haul," Miles says. "There are many business barriers as well as technological barriers to agreements about sharing events in a business context."
Growing recognition of the promise of M2M networks will ensure the continuation and spread of university research on related topics, and demand for related classes already is increasing, Proffitt says.