Introduction
The Internet of Things (IoT) is defined as the connection of devices to each other and to a common network. It’s a technology that’s been around for decades, but it’s only recently become popular due to advances in wireless connectivity and data analytics. Due to its potential for massive growth in the coming years, many companies are investing heavily in edge computing: a new way of thinking about data processing. Before we dive into this topic further, let’s first understand what exactly edge computing is all about.
Edge computing is a new way of thinking about data processing. Unlike traditional cloud computing, edge computing uses small devices that are closer to the source of the data to process and analyze it.
Edge computing is a new way of thinking about data processing. Unlike traditional cloud computing, edge computing uses small devices that are closer to the source of the data to process and analyze it. These devices can be mobile, such as wearable sensors or drones; stationary, like an energy meter on your home; or even static but equipped with sensors (like your refrigerator).
Edge computing has many benefits over traditional cloud-based solutions: it reduces latency and improves security by keeping sensitive information within a firewall; reduces costs by eliminating unnecessary traffic between networks; and allows for more tailored solutions because each application has access only to its own data set rather than having access to all available information in one place (which could be overwhelming).
These devices are often mobile, such as wearable sensors or drones. They can also be small and stationary, such as smart cameras or small-scale energy systems.
Edge computing is a term that describes the distribution of data and services closer to where it’s needed. This means, for example, that instead of sending all your information about device health back to a central processing center for analysis–and then waiting for that analysis before taking action–you can run those analytics right on or near the devices themselves.
This gives rise to a new set of questions: What type(s) of devices are involved? Where will they be located? How much processing power do they need? Are they fixed or mobile; small or large; stationary or mobile; physical, virtual, or some combination thereof?
It turns out these questions are not only interesting from an engineering perspective but also critical for developers who want their applications built on top of IoT infrastructure because different types of edge computing architectures require different programming models and languages
The advantage of moving some of the data processing to these devices is that they can produce much more timely results, because they’re in close proximity to the real-time source of the data.
The advantage of moving some of the data processing to these devices is that they can produce much more timely results, because they’re in close proximity to the real-time source of the data.
For example, imagine you have an IoT device monitoring a shipment of goods on a truck. If its job is simply to report when it arrives at its destination so you can update your inventory system, then there’s no need for it to perform any complex computations before sending out its signal (and thus no reason for it not be located closer). But if you want more details about how well your delivery was handled by FedEx–was anything damaged? Was there enough fuel?–then having this information sooner will help ensure better service going forward and reduce costs overall by helping you avoid making unnecessary trips back into town when something goes wrong (or even worse).
Edge computing is becoming increasingly important because most IoT applications rely on real-time data, or near real-time data. Even when an application can afford a delay before getting its results (for example when it’s used to generate sales statistics), often there are other factors that make real time processing essential.
Edge computing is becoming increasingly important because most IoT applications rely on real-time data, or near real-time data. Even when an application can afford a delay before getting its results (for example when it’s used to generate sales statistics), often there are other factors that make real time processing essential.
In the case of autonomous vehicles, edge computing allows the car to make decisions based on information gathered from sensors in its immediate vicinity rather than waiting for the signal to travel back to headquarters before making decisions about how best to deal with obstacles on the road ahead. This means that cars can react much more quickly than they would if all of their decision making had been done at headquarters; this also reduces lag time between events occurring and those being reported by users/drivers so that companies like Uber or Lyft can better assess risks associated with different routes based on historical traffic patterns and other factors affecting each particular route’s safety level at any given moment in time
For example, consider a home security system that detects motion in your living room via a network camera. If it takes several minutes for this information to reach your smartphone, you probably won’t notice any intruders who came by during those minutes anyway!
Imagine a home security system that detects motion in your living room via a network camera. If it takes several minutes for this information to reach your smartphone, you probably won’t notice any intruders who came by during those minutes anyway!
Real-time data is essential for many IoT applications–and real-time processing is important because it can prevent accidents and save lives. Real-time processing also helps companies make better decisions, avoid delays in getting their results, and improve customer satisfaction levels.
Conclusion
Edge computing is one of the most exciting fields in data processing today. It has the potential to transform how we use our devices and interact with each other, as well as how we think about our physical environment. As more applications become available on edge devices–such as smart speakers or augmented reality glasses–we’ll see even more opportunities for this technology to change our lives for the better.
