Today’s networks are an essential part of business, education, government, and home communications. Many residential, business, and mobile Internet Protocol (IP) networking trends are being driven largely by the combination of video, social networking, and advanced collaboration applications, termed “visual networking.” In fact, total Internet traffic has experienced dramatic growth in the past decade alone. Take a look at this interactive infographic from Cisco that shows key trends and forecasts the growth of global IP traffic from 2013 to 2018. You can choose a category and filter the geographic regions in the map to view the impact of global IP traffic. According to Cisco’s Visual Networking Index (VNI), globally, there will be 20.6 billion networked devices by 2018, up from 12.4 billion in 2013. VNI is part of Cisco’s ongoing effort to forecast and analyze the growth and use of IP networks worldwide. VNI also forecasts that global Internet Protocol (IP) traffic will increase nearly three-fold over the next five years due to more Internet users and devices, faster broadband speeds and increased video viewing. Global IP traffic for fixed and mobile connections is expected to reach an annual run rate of 1.6 zettabytes – more than one and a half trillion gigabytes per year by 2018.
So who and what are responsible for the projected increase in overall internet traffic?
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Tags: broadband, Cisco, global ip traffic, Internet of Everything, IoE, ip traffic, M2M, Machine to Machine, Service Provider, visual networking index, vni, wi-fi, wifi, zettabyte
The Internet of Everything (IoE) describes machine-to-machine (M2M) compute entities that track and measure real-time data that can be used to build out a data history for analytics that could be used to optimize the quality of life. The opportunity is represented by devices used in a person’s everyday life that are connected to the Internet, have the ability to learn a person’s consumption behavior, and embody the goal to improve the efficacy of services and goods delivery and consumption. Cisco Systems CEO John Chambers says that the Internet of Everything could be a $19 trillion opportunity. 1 Read More »
Tags: #ciscochampion, cloud, Internet of Everything, M2M, Machine to Machine
Your smart sprinkler system is happily pumping water to your lawn in highly efficient sprays that are “aware” of the soil, the climate, the weather, the time of day, and even whether or not your kids are playing in the backyard on a Saturday. Suddenly, a faulty valve bursts and an uncontrolled geyser erupts. One part of your property is about to be ruined by flooding while the rest of the lawn is left to yellow in the sun.
You and your family are miles away, yet you know all about it. Sensors throughout the system alert your smartphone. At the same time, machine-to-machine signals shut down the pumps, and an expert from the sprinkler company is dispatched to your home with the precise replacement part and the real-time knowledge to fix the system.
It’s a great example of how the Internet of Everything (IoE) may soon funnel precise information in real time to the people — or machines — that need it most. Many of these “remote expert” technologies are either already here or on the horizon.
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Tags: Big Data, Cisco, Cisco Consulting Services, collaboration, Internet of Everything, IoE, M2M, Machine to Machine, remote expert
At the recent Cisco Live 2013 event in Orlando, I talked about the business value of converging operations technology (OT)—used for industrial automation systems—with IT business networks, in order to create more secure, end-to-end, standard communications and control. Regarding business value of IT/OT convergence for machine builders/integrators and consequently their manufacturing customers, I referenced a case study involving Comau Group that Al Presher from DesignNews recently picked up in a blog entitled “Connectivity Enabling Smart Manufacturing.”
Comau is a leading supplier and partner for most global automakers, integrating welding and assembly lines that coordinate dozens of robots and ancillary automation across multiple stations.
The order-to-engineering sign-off cycle requires months and the consequent build and commissioning to full production adds many more months for a new or refreshed manufacturing line.
Multiple fieldbus protocols at the device level complicate both design and implementation, requiring more integration services—time and money—to make the system work.
By designing a converged IT/OT “Connected Machine” solution that utilizes IP-standards-based, off-the-shelf modularity with a network architecture validated for both business and controls topologies, Comau has been able to reduce engineering cycles and cut integration time by more than two-thirds. Quoting an Engineering Manager from the company, “Installation, commissioning and debugging for 10 stations with 12-15 robots takes a couple days, rather than 1-2 weeks.” Read More »
Tags: Automotive, comau, Industrial Automation, internet of things, IoE, IoT, ip, IT, IT/OT, M2M, machine builders, Machine to Machine, operational technology, OT, standards
In a previous post, we looked at examples of new business opportunities enabled by the Internet of Everything and the importance of evaluating Data in Motion in new ways.
Naturally, the Internet of Everything brings its share of IT challenges. Data collection starts at the network edge, including a multitude of endpoint devices and sensors in everyday objects that automatically collect, analyze and transmit data—including video—on a massive scale.
For the most part, it is data that has previously gone untapped—a giant superset of the persistent data that is the subject of Big Data today. The velocity and volume of this data make it difficult to bring it together into one place and extract value from it in a timely fashion. A key IT challenge is deciding what data to store (which can be costly) and what data to ignore (which can be a lost opportunity).
For example, high-definition video surveillance cameras combined with data analysis offer retailers insight into everything from facial recognition to age, gender and socioeconomic indicators. Retailers can also use video intelligence to create augmented reality mirrors or spot customers in need and send associates to assist them. However, not all the data from these devices needs to be stored or even analyzed, but rather used in the moment to create interactive engagements with the customers.
To address these challenges, intelligence and automated data processing must be embedded in the network. This intelligence takes the guesswork out of selecting the correct data from the torrent, because the network can filter based on relevance. At the same time, it can prioritize what data to retain and what data to discard based on value policies. This requires a flexible infrastructure where compute, storage, network and security resources can be assigned on the fly where and when needed. In most cases with Data in Motion, the application moves to where the data is, not the other way round.
Another key challenge is security, which remains paramount all the way from the edge to the cloud and back. The rapid deployment of Internet of Things and M2M technologies is leading to a proliferation of devices whose variety, data, complexity and vulnerability go beyond the traditional IT landscape. Along with the tremendous value that can be extracted from Data in Motion come new risks that require network-centric security approaches.
The Internet of Everything brings together people, process, data and things to make networked connections more relevant and valuable than ever before, thus providing unprecedented economic opportunity for businesses, individuals and countries. We are still in the early stages of evolution for Data in Motion and the impact it will have on all of us. But it is clear that the more knowledge we have, based on meaningful information pulled from a variety of data sources, the more wisdom we can gain and apply. It will profoundly change the world.
Tags: cloud, data in motion, Internet of Everything, internet of things, Machine to Machine