This page describes the key components and the measurement workflow of the WiFiMon system.
WiFiMon Architectural Components
Mobile User Device and Monitoring Probes
In addition, WiFiMon offers hardware probes (WiFiMon Hardware Probes, WHP) which are installed on small form factor hardware devices (e.g. Raspberry Pi's) and placed in fixed positions within the monitored WiFi network.
The WiFiMon admin is the network administrator managing the WiFi network and the WiFiMon system. Using the WiFiMon UI, they can perform queries to the Elastic ELK Cluster and get information about the performance of the wireless network.
Figure 1: WiFiMon Architectural Components
Elastic ELK Cluster
The WiFiMon Analysis Station (WAS) uses an ELK cluster for performance measurement data processing and visualisation. Elasticsearch is a full-text, distributed NoSQL search engine which enables storing of raw measurement data, high-speed access and processing. Raw measurement data received from multiple measurement data sources are filtered and correlated in the Logstash and stored in the cluster. The WiFiMon Admin uses Kibana as a web user interface and for cluster management. Another component of ELK Stack is Filebeat, which is installed as an agent on the servers and is used to stream the log data. Filebeat monitors log files for new content, collects log events and forwards them to Logstash.
- Collected Raw Measurement Data
- Additional (Exported) Raw Data
WiFiMon can be deployed within any WiFi network, but may provide additional benefits if used in an eduroam environment, where through very detailed performance assessments can be obtained through analysing the eduroam logs. If eduroam is used, the mobile user will be authenticated at the home organisation (IdP) and authorised at the end-user's location (SP) on an IEEE 802.1X based WiFi network. In order to make a thorough analysis of the WiFi network performance (e.g. per access point) it is necessary to gather additional data from services including eduroam and DHCP which reveal the IP addresses particular users have, the access point they are associated to, etc. These logs are fetched from Filebeat agents and are then sent to Logstash pipelines for filtering and editing, before being stored in the WAS.
WiFiMon Agent / UI
WiFiMon AgentProcesses collected raw and exported measurement data to provide insight on the wireless network performance per client or AP. The results of correlations are stored in the ELK Cluster.
WiFiMon UIProvides temporal graphs of all the performance metrics, allows queries to retrieve information about the measurements initiated from the WSP or WHP associated to a specific access point and within a specific time period. Other queries could be to retrieve the information about specific IP ranges, test tools, user browsers etc.
The main WiFiMon service delivery model assumes that the WiFiMon user installs all the WiFiMon components in their premises. Therefore all the data remains in the user's possession all the time and the WiFiMon team does not have any access to it. All the traffic in transit between the WiFiMon components is TLS encrypted. Furthermore, sensitive and potentially personally identifiable information data such as mobile user IP or MAC address are hashed before being stored in the WAS. The correlation procedure mentioned in the WiFiMon Agent section is performed over the hashed data.
WiFiMon Building Blocks and Measurement Workflow
WiFiMon Test Server (WTS)
WiFiMon uses active probing in order to obtain WiFi network performance metrics. Active probe packets are exchanged between the WSP and WHP on one side and the WiFiMon Test Server on the other. WTS uses NetTest, Akamai Boomerang and LibreSpeed Speedtest measurement software which can provide information about the packet round trip times and network performance data including bandwidth and latency (represented in Figure 2 as lines marked as 2.1, 2.2, and 2.5).
One of the methodologies that WiFiMon tests use is to measure the upload and download time for a file that the WiFiMon administrator chooses. By choosing a larger file, more accurate measurements can be obtained, because of the ramp-up time of TCP congestion control/windowing mechanisms and slow start. However there is a trade-off because larger transfer sizes mean introducing a larger traffic overhead and this way a potential negative impact to the WiFi network. Therefore it is recommended that the file size is comparable to the size of a typical web page (up to 5MB). This way one measurement would push to the network and user's device probe traffic comparable to one web site visit and the obtained measurements would show the throughput and the time to load such a page as the user sees it.
The obtained throughput metrics should not be considered as the maximum achievable throughput of the WiFi network at the moment of measurement, but the WiFi network administrator should rather look at the relative differences between the measurements in order to get the insight into the quality of the user's experience with the WiFi network uses. The location of the WTS and its topological distance from the monitored WiFi network has a major impact on the crowdsourced measurement accuracy and reliability. You can read more about this here.
Figure 2: WiFiMon Measurement Workflow
WiFiMon Software Probe (WSP)
WiFiMon Hardware Probe (WHP)
The WiFiMon Hardware Probes (WHP's) are set up on small form factor devices, such as the Raspberry Pi. The WHP probe may be viewed as an end-user logged in to the eduroam wireless network, but monitoring continuously from a fixed point. It measures bandwidth, latency, the average values of bit rate, the signal level, the link quality and the transmission power. Moreover, probes collect system metrics and perform TWAMP measurements towards the WiFiMon Test Server. The WiFiMon team recommends setting up a WHP on a Raspberry Pi v3 Model B+ or on a v4 Pi.
WiFiMon Analysis Server (WAS)
The WiFiMon Analysis Server (WAS) processes the performance results of crowdsourced and hardware probe measurements received from WSP's and WHP's respectively. The WAS consists of the WiFiMon agent, Elasticsearch, Logstash and the WiFiMon UI that leverages on Kibana for customising reports and their visualisation.
Websites monitored by WiFiMon are not only visited by end users residing in the monitored subnets, but also from end users external to them. Therefore, it is important to prevent the WAS from processing excessive traffic and measurements which are not related to the WiFi network that is being monitored. To that end, WiFiMon maintains a list of registered subnets. Upon accepting a connection from an end user, the WiFiMon Test Server checks whether this end user is within a monitored subnet by obtaining the list of registered subnets from the WAS (2.3, 2.4). For WSP's/WHP's residing within the registered subnets, network performance metrics are calculated and streamed from the end devices to the WiFiMon Analysis Server (2.6) to be processed. Afterwards, the WAS correlates performance data with client IPs and AP-IDs (2.7/2.8). Results are stored in the ELK stack.