The more complex a wireless network, the more likely an access point or several of them will fail. Using self-healing techniques provides a way to keep inevitable downtimes from affecting your data.
Aislelabs works with some of the largest access point (AP) deployments on the planet. The data collected from these wireless networks is vital for marketing and operations teams to drive business value. Any lack or distortion of data from failing APs can have profound effects on understanding key metrics. But how does a business guarantee 100% uptime?
In short: they can’t
However, Aislelabs Smart Fill allows large enterprises to self-heal any downtime that the network experiences.
The issues with AP downtime
No hardware is immune from downtime and any individual AP or multiple APs could be down for a few hours to a few days. That means no data can be collected by these devices which presents several issues. The most obvious being that marketing and operations teams will not have access to this information.
The more problematic issue, however, comes with aggregating data. If the network is down for a day in March, for example, that means there is only thirty days of collected information rather than thirty-one days, artificially lowering the numbers for March. For large shopping centres, that could equal a hundred-thousand missing visitors.
Since any loss in data or error in deployment can distort the reports, it’s critical that there be some way to correct any inaccuracies. Here’s how the Aislelabs platform overcomes these types of errors.
Using Smart Fill to self heal errors
When an AP or several APs go down, it is impossible to collect real-time data, which remains essential in established workflows and may lead to underreporting or inaccuracies. However, that is not to say that there is no data regarding the zones where the devices failed. Smart Fill allows businesses to overcome this common hurdle by deducing and reconstructing data through a deep learning model built from the historical data. Various factors such as the median historical data, time of day, events and even the weather are analyzed so the previously lost information can be synthesized, remediating any downtime.
Fortunately, detecting when an AP is down is relatively straightforward which provides a timestamp for Smart Fill to self-heal the missing data. This gives operations and marketing teams the peace of mind that no matter what the status of the IT infrastructure, the data they need to plan and execute projects is there.
To learn more about remediation of errors on large wireless networks, we also have an overview on artificial intelligence in IT operations (AIOps) available here.
