Today, your neighborhood might have a few thousand devices all connecting to a mobile wireless network. But if predictions become reality, by 2025 that number will increase — ultimately reaching well over a million devices in the same area.
While this proliferation of connected things holds promise for everything from our health to home security, there’s a major barrier standing in the way of the widespread adoption of the internet of things: bandwidth. Today’s 4G and LTE networks, while powerful, simply can’t accommodate the needs of millions of new connections.
Thankfully, the advent of 5G — the next generation of wireless technology, which could operate at a throughput that is 10 to 1000 times faster than current networks — has arrived just in time for the explosion of the IoT. 5G will allow mobile data networks to open the door for all manner of new services, but they’ll come at a cost: a much higher likelihood of widespread network congestion.
For 5G to have a far-reaching impact on data not only at the scale but also the speed required by the devices set to connect, it will need a team up with a new type of computing.
Clearing the Bottleneck on the Edge
Edge computing is a concept that moves processing power from the center of the network (traditional servers) to the edge, closer to where the data is consumed (by a computer, phone, or some other device). By putting smaller, decentralized servers with computing power nearer to their use, congestion and strain on the network decreases — increasing performance for everyone.
According to Ihab Tarazi, a former telecom engineer and the new CTO of cloud hosting company Packet, with 5G, this increased performance is essential because there will soon be so many devices connecting to the network that a traditional, centralized design would cause everything to grind to a halt. The network would be so busy that “you simply wouldn’t be able to connect.”
“Today’s telecommunications architecture is very traditional, with calls and data transferred from tower to tower and compute power located within centralized server farms. With edge computing we can push processing closer and closer to the tower.”
This will redefine cloud technologies, minimize latency issues and allow users to benefit from the increased speed that 5G promises. Without the edge, 5G won’t be much different than its predecessors (with many more devices trying to run on it).
Upgrading to the Edge
Experts agree that while the edge is top of mind for telcos, its implementation will take time.
In an ideal universe, every cell tower radio would be upgraded with its own computing system, a micro-server of sorts that could provide muscle and serve commonly-used data without having to call back to a server farm in Iowa. But with over 200,000 cell towers and other stations in the U.S. alone, it isn’t financially feasible to retrofit every tower in the country in this fashion.
“The right answer probably lies with regional data centers and on-board computing [rising] as we slowly migrate compute power to the edge over time,” says Joe Madden, lead analyst with Mobile Experts, which has been studying the economics of 5G for the last four years.
The upshot for this move to an edge is that bottlenecks will be eased, decreasing sluggish data delivery in the last mile — transforming the customer experience and their expectations of what the network can do. We’re not just talking about getting movies downloaded faster, either. The combination of 5G and edge computing could reinvent everything from real-time machine control systems to autonomous vehicles.
Like most technology areas, edge computing has its own lexicon. Here are brief definitions of some of the more commonly used terms
Edge devices: These can be any device that produces data. These could be sensors, industrial machines or other devices that produce or collect data.
Edge: What the edge is depends on the use case. In a telecommunications field, perhaps the edge is a cell phone or maybe it’s a cell tower. In an automotive scenario, the edge of the network could be a car. In manufacturing, it could be a machine on a shop floor; in enterprise IT, the edge could be a laptop.
Edge gateway: A gateway is the buffer between where edge computing processing is done and the broader fog network. The gateway is the window into the larger environment beyond the edge of the network.
Fat client: Software that can do some data processing in edge devices. This is opposed to a thin client, which would merely transfer data.
Edge computing equipment: Edge computing uses a range of existing and new equipment. Many devices, sensors and machines can be outfitted to work in an edge computing environment by simply making them Internet-accessible. Cisco and other hardware vendors have a line of ruggedized network equipment that has hardened exteriors meant to be used in field environments. A range of compute servers, converged systems and even storage-based hardware systems like Amazon Web Service’s Snowball can be used in edge computing deployments.
Mobile edge computing: This refers to the buildout of edge computing systems in telecommunications systems, particularly 5G scenarios.source
As we are going to move toward the future there will be more depends over the Edge computing there will the product be like smart cities, there will more in the medical era.
cognitive is a term which is a mental process to learn, read.., etc.
what is cognitive computing?
cognitive computing is a process of analyzing, recognition, under the major platforms machine learning, reasoning, natural language processing, speech recognition and vision (object recognition), human-computer interaction, dialog and narrative generation, among other technologies.which are near to the human brain.
Adaptability is a term which analyzes through Machine learning and which also adapt to different contexts with minimal human supervision
2. Natural language interaction:
Natural language interaction which is used for interact and data insight with humans through Chatbot.
Yes this my experience working under Amazon lex#chatbot. Today is my first day in creating a demo bot with Amazon Lex, I didn't think it will be very similar than any other platform
I have just tried with a video tutorial to develop a bot for booking for car rental. The most successful experience on bot out there makes one thing clear from the very beginning of the experience: that the user is chatting with a robot, not with another human. which I just confused whether I'm speaking to bot or human it just like another human which is artificially made by human to reduce the human’s work.