This month will see the release of the Samsung Galaxy Note Edge which, with its cousin, the Galaxy Note 4, brings Quad-HD phones (2560x1440) to the masses. Although Apple doesn’t have a mobile device with such resolution, the iPhone 6 (1334x750) and 6 Plus (1920x1080) represent devices with larger numbers of pixels than earlier models.
Last week, the FCC sued AT&T for deceptively throttling the data rates of unlimited users after hitting a certain allocation of monthly bits. There is a good reason for the wireless company to do something which could eventually give them a big public black-eye and potentially cost them millions of dollars. The demand for bandwidth – especially from smartphones, is growing and will continue to grow, not only because screen-sizes are growing but because these better screens mean the phones become portable televisions.
Compression technology is nothing new – even the fax machine of the early 1980’s with its low-powered processor, was capable of squishing data streams to save precious bandwidth. More accurately, at the time, a fax had to use a phone line and this was a fixed amount of bandwidth which had to be optimized. Fast-forward a few decades and a new company called Centri Technology has emerged to help carriers with their bandwidth problem.
Centri was developed based on technology from the University of Mississippi which has led to 10+ international patents pending and a number of trials this year. Gartner in fact decided to laud the company with its ‘Cool Vendor’ status over a year ago.
To learn more, I spent time with CEO and President Vaughan Emery and right off the bat he told me he thinks the cloud has matured to the point where the company’s technology is able to help. He sees a world of countless connected devices against finite resources. As a result – as I outlined above, there will be network capacity issues.
Where Centri sits in the network
To solve this problem, the company advocates a 64K distributed byte-level cache named BitSmartCX which sits in the data flow and looks for repeating patterns in M2M sensors, the network core or elsewhere. They can also apply a stream cipher against the data set if the interest is also better security and network efficiency.
The example below shows a savings of 54 percent based on pattern recognition technology
Bottom line, the technology can be applied to cloud-apps, M2M, IoT, Fog Computing and anywhere carriers are looking to more efficiently transmit data.
Edited by Stefania Viscusi