17 Jan Beacon Data Needs Fog Computing
The IoT is moving toward fog computing. That means faster results and less bulky data. What could this mean for beacons and industry?
For many, the end goal of beacons is data generation. Bluetooth tags and beacons are able to capture thorough real-world data. Many data collection methods, though incredibly useful, are rather archaic in the scope of the IoT. Surveys and interviews leave huge information gaps, and even many technologically-based methods waste time and money by always having to consult the cloud.
Industry leaders are already noting the importance (and inevitability) of fogging with beacons. If beacons are going to lead the IoT revolution and connect the physical and digital world, it will need fog computing. Will moving everything to the edge stimulate and support the futuristic and interoperable infrastructures promised by the IoT?
What is fog computing (AKA edge computing) with beacons?
Normally, data collected through sensors or other means are analyzed in the cloud. That means, it is first sent from the initial collecting device on the edge of the network to the cloud. After being analyzed, it is then sent back to the edge device for following actions. With fog computing, everything happens on the edge. That end device does the collecting, analyzing, and acting. Data isn’t regularly stored here because it isn’t needed.
Industrial gateway technology, without being filtered in any way, is likely to simply flood networks and create far more hassle than they may be worth. While this method isn’t practical for collecting huge amounts of data, it is practical for using data the moment it is generated.
Not all data needs to be stored in the cloud. IoT and beacons need data at the edge.
Less Wasted Space Through Fog Computing
Beacons are popularly known as interactivity tools used for marketing purposes. They allow retailers to engage with customers, gamify shopping experiences, and push new offers or deals. This is not, however why the biggest and most innovative companies use them. Beacons offer the chance to collect real-world data that would normally never be captured, and that data is big. Just like other big data applications, beacons will have to face the problem of space and speed.
Beacon infrastructures are practically made to be modern upgrades for existing RTLS systems. Manufacturers and healthcare professionals will turn to Bluetooth beacons in order to generate more data more affordably and keep a closer eye on day-to-day activities. Of course, beacon infrastructures can also generate huge amounts of data. With every asset being checked regularly, that data is going to take up a lot of space physically. Cloud server components are in need of regular replacement and work, and the actual pricetag associated with hosting data on every asset would be enormous compared to the pay-off.
What if we move data processing to the edge?
Not all data needs to be stored in the cloud—for many, almost none of it does. By processing data at the edge instead of in the cloud, managers waste less money on servers and even unnecessary data analysis. In many scenarios, big data is great, but it can also simply be unwieldy. There is no point to holding onto all of it or collecting it in the first place. Data is often most useful in infrastructures because it is actionable. It is about the change of state or the moment an action occurs, not what minor changes occurred six months in the past. Understanding the flow of assets or even employees can bring huge insights into how to optimize operations, but that is not the primary, daily function of a beacon infrastructure.
Faster Responses at The Edge
Using beacons just as they are now to transfer and store huge amounts of data will not only waste money, it will also drastically reduce the most meaningful part of the data: relevance. If a patient is in danger or an asset is moving in the wrong direction, managers will want to know immediately, not in several hours. Data can and should often be analyzed before being sent off to a silo. There simply isn’t always time to wait for data to be sent, analyzed, and then relayed. Sometimes, responses need to happen immediately.
Data can move fast. The time it takes for information to be taken in, sent off, returned, and put to use is not necessarily going to be hours or days. It’s up to solution providers to make this process go as quickly and smoothly as possible, and putting it to use immediately is the best way to accomplish that.
With the edge, triggers and alerts could become even more meaningful. Triggers take that immediate data and create value by triggering an action. For example, an asset is passing through the production line. Apart from under a specific study, no one needs to know the exact moment the asset passes through each doorway or gets picked up. When that asset is registered in a predefined area, an alert can tell the nearest employee what’s happened. With fog computing, this will happen directly on the edge—no cloud required.
Better Reliability and Less Latency
Because fog computing relies on data being stored and accessed locally, there is a greatly decreased risk of the system going down. With no cloud or connection to worry about, fog computing will bring a huge amount of reliability to complex infrastructures where complete transparency and dependability are paramount. This is made clear in use cases where a few seconds can make a huge difference in terms of safety. If an asset is traveling at high speeds, those recognizing and correcting an error would benefit everyone involved. This is only possible when computing moves to those edge devices. Even when there is no immediate danger involved, poor connection quickly drain funds and slow processes.
Fog computing is already beginning to happen with beacons, and there will be a slow move in this direction as its benefits gains attention and traction—and beacons won’t be the only technology making this change. Many of the moving parts in the Internet of Things will also benefit from a more dispersed system.