Full Test

  • The probe constantly measures the availability and performance of the services using scripted tests
  • The set of predefined scripted tests can be extended, creating new scripts that implement interaction and validation of specific items of the customer architecture
  • The relevant data for each measure are stored inside a time series database and can be visualised concisely to allow the easy pinpoint of the anomalies
  • Detailed technical logs of the measures and .pcap files are stored and are directly accessible for an effective drill down to investigate the root cause of the behaviour

Edge & Cloud

The collected data are stored inside the device flash memory and synced to the cloud.
The full UI is available both on cloud and on-device for immediate diagnosis.
The edge device UI can be directly accessed over its:

  • RJ45 Ethernet port
  • MicroUSB port as a RNDIS/NCM network device
  • Internal Wi-Fi HotSpot/AP
The cloud sync can be activated on the Ethernet connection, via UMTS/LTE or on the WiFi itself during the measure and do not require any other link

Secured Platform

The full platform is highly secured by:

  • Elliptic curve ciphers for the dialog between Cloud and Edge
  • Strong authentication based on digital certificate for both Edge and Cloud
  • Mandatory strong key of 256bit or more
  • Containerization of the measure execution for security isolation
  • Storage encryption for the Edge with TPM based secure boot
Even in case of physical tampering of the edge device, it's not possible to extract the access credential or any sensitive data

Numeric Indicators

Besides the summary graphs and the detailed logs of the system activities, a set of numeric indicators are computed and collected:

  • RSSI: Received Signal Strength Indication
  • SCORE: indicates the service level of the ESSID/BSSID
  • OVERLAP INDEX: indicates the overlapping on the channel frequency with other Wi-Fi networks

Artificial Intelligence diagnostic

Wi-Fi networks operation depends on the correct working of different network components, both hardware and software. Wi-Fi diagnostic can be quite demanding and time consuming.
WiFiProbe leverages powerful Neural Network (CNN, RNN) models on the cloud, to map the measure results to a comprehensive ontology for fault diagnosis and remedy.
Using these modern AI modules, WiFiProbe can also issue the proper commands to restore the service state or instruct human operators with basic skills to do the actions needed to fix the anomalies.
With WiFiProbe you can improve the service quality and availability, while at the same time decrease the cost of operation.

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