Monitoring your Wireless Network

Access to your data in real-time is becoming increasingly critical. The more data you push over our networks, the more you need to apply the lessons of Smart IoT. Allowing a network monitoring solution, asset health solution, or just some remote ‘thing’ you are pulling data from in your IoT strategy to utilize a majority of the bandwidth just isn’t acceptable, particularly if there is a smarter way to access that data while limiting the possibility of network disruption.

Solutions that monitor mobile clients from the infrastructure can present several distinct disadvantages. Determining the problem in an area of disconnection area can be frustrating. Most monitoring tools monitor only the infrastructure devices. The infrastructure device will be able to give valuable information while the mobile client is connected, but when it goes dark, infrastructure monitoring can’t help. You need to know what is happening where the client is.

Another form of infrastructure monitoring will contact the clients periodically, gathering connectivity information. These tools often use SNMP, or maybe even SSH to gather information from the client device. This solution can present a couple of problems of its own.

First, these devices still can’t show you what the client is seeing, when the client is disconnected. Often, this type of monitoring ends up with either a best guess at the root cause, or with a fieldtrip by the troubleshooter in an attempt to recreate the problem.

There is one other problem here. The extra network traffic required to make these connections to the client device can troublesome. In some situations, this additional traffic can drastically increase the network load simply through the act of monitoring.

Making a connection to the device, requesting information, and finally downloading that information produces a lot of very small, uncompressed packets. Each of these packets take up valuable air time, creating significant overhead. Utilized air time equates to utilized throughput. The result is a network that sees a significant reduction in available capacity.

When you start to deploy a solution that uses these protocols over an entire wireless network, it becomes quickly apparent that the scale of this monitoring negatively impacts network performance, with the net result being opposite the desired, in that monitoring didn’t help you prevent problems, but actually created them.

At 3D-P, we have seen this type monitoring solution double the amount of packets on the network. That is more than just a significant increase. That is the type of increase that can bring a network to its knees.

In contrast, a Smart IoT solution utilizes edge computing capabilities to monitor the client devices connectivity, from the client, recording the actual environment regardless of whether the device is on or offline. This data is compressed and sent to the monitoring platform when the device is back in communication. Managing this data in this manner not only provides the critical outage data that isn’t available through the infrastructure solution, but allows management of how and when this data is sent, reducing the network load to a fraction of the overhead required through the connect, request, and download method.

Comparing Traditional vs Smart Monitoring

In the graph below, you can see two monitoring solutions. The line in blue is the actual traffic being generated by the applications connected through the client device. The grey line is a network monitoring solution utilizing SSH to connect and gather critical performance information from the networked client. The line in yellow, at the bottom of the graph is network data being gathered at the edge on the 3D-P IEP.

It’s essentially the same critical data being monitored in both solutions, and then sent to the office for analysis. How can the network demand between these two solutions be so vastly different yet provide the same information? The answer is as simple as smartly using the power of edge computing to collect the data, compress the data, and then smartly offloading the data.

How many clients can you feasibly roll this out to until your data is being held back? In a deployment where the network is in high demand, such as an autonomy capable network, that grey line is the type of behavior that should be keeping your wireless network management team up at night. After all, the purpose of the network is to allow the transfer of your critical application data, not solely the data generated by the tools deployed to monitor that network.

Start monitoring smartly with 3D-P Insight

As you look at different monitoring solutions, asset health solutions, or other IoT solutions, it’s important to realize the impact each can have on your network, and ask if it is capable of monitoring with any respect for the shared medium of your wireless client network.

Unfortunately, many application developers don’t have the domain knowledge of mining applications, or in some cases, even wireless technology, to properly deliver the solution. 3D-P designed its monitoring and data collection tools to have minimal impact on your network, using custom collectors for remote assets, and smartly offloading that data with the right protocols and timing. This keeps your clients quiet and leaves much more time for your actual data to get out to end devices and back to your servers.

Optimal network performance starts with the right tools

It doesn’t take long for a wireless medium to get filled with data that doesn’t need to be there, and when it gets full it can be a costly and time consuming project to get things back to working order. Hopefully this highlights what you need to consider before choosing a network monitoring platform and things you may need to consider in your highly mobile, high-demand environment.

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