Artificial Intelligence Gives M2M a Brainby OMA | Wednesday, January 2, 2013
Innovation Generation 12/26/2012 Artificial Intelligence Gives M2M a Brain …You’re in a hurry to deliver your quarterly sales presentation to your boss, and you send copies to the printer. When you arrive at the printer, that annoying flashing light is indicating low toner, and you’re forced to go to the meeting empty-handed. Now imagine a world where the printer had alerted you to replace the toner two days earlier, so you could have averted this disaster. It may not be life or death, but it’s one of the many things that can be solved by the union of a machine-to-machine (M2M) system with an artificial intelligence (AI) entity.
The AI provides the decision making function that foresees the problem and takes the appropriate action to avert it. The primary function of a Machine to Machine (M2M) system is to collect data from end devices, analyze that data and then take appropriate action as programmed. One outcome of this data analysis is to prevent the system from either malfunctioning or breaking down entirely. Pre-programmed artificial Intelligence (AI) embedded in the system determines what actions should be taken depending on the nature of the problem to prevent an undesired end result. Detection of an object before it fails to function can improve the efficiency of a process or product. But these processes and product efficiencies are directly related to the efficiency of a service technician, manufacturing plant or organization that is leased to maintain certain service level agreements on the installed equipment. In those cases, when a process or product is connected to humans directly or indirectly, it becomes a critical part in deciding the level of quality of life.
The services function is the common denominator of all present and future M2M applications that many Communication Service Providers (CSPs) are in the midst of either deploying or considering deploying. Given the finite number of humans on the planet, there is an upper limit on the number mobile devices that can be in circulation at any given time. However, in the case of M2M, the deployment scenarios are now emerging rapidly because of favorable conditions such as everywhere wireless coverage, enterprise willingness to offer new applications that improve customer stickiness and productivity, innovative application developers, and miniaturization of sensors and modules. Because of these favorable conditions, there is no foreseeable end in sight to the growing number of end device connections. Such deployments will put pressure on the services sector to maintain them at the agreed upon performance level.
The services activity that is being referred to here is not the same as the service provided by an end device. For example, the service provided by a laser printer (an end device) is the printing of paper that is fed to it by a user. On the other hand an example of a service function that is performed by a human or a machine is keeping the laser printer printing well and uninterrupted, so that it meets the expectations of the user.
Uninterrupted services require the monitoring and gathering of data such as toner level, number of pages printed, paper stack level, paper jam, parts malfunctions, and communication link status between the computer and printer. The collection and analysis of such data and the resulting decision is the function of Artificial Intelligence (AI) embedded in the system. Such an action helps a service provider to maintain an assured level of product availability for the end user. This AI function provides needed alerts to monitoring stations and on the job service technicians as a heads-up before a process failure takes place. An M2M system with a combination of an AI function makes a perfect union for ensuring an uninterrupted service. This integration will result in an exponential improvement of productivity of technicians resulting in tremendous cost savings for the service provider while creating a better user experience.