In this page it is possible to find some examples of filters that can be applied to the data provided by Accuware Wi-Fi Location Monitor. The examples are referred to an hypothetical installation inside a shopping mall. Obviously the same concepts can be adapted to any other environment.

Filter on the opening hours

This kind of filtering mechanism checks the time-stamp of detection of the Wi-Fi devices. The nodes used by Accuware Wi-Fi Location Monitor detect Wi-Fi devices for all the time they are kept On (and they can be potentially kept On 24/7). Despite this, a shopping mall is usually open only for a specific time window every day. For this reason you may want to create a list of Wi-Fi devices that are detected when the shopping mall is closed to the public and exclude them from your analysis.

Filter on the locations with respect to the area of the nodes

This kind of filtering mechanism compares the locations (latitude and longitude) estimated by WiFi Location Monitor against the area with the nodes. You need to keep in mind that the nodes are able to detect Wi-Fi devices within a very wide radius (up to 150 feet  indoors). Despite this wide detection radius, you need to be aware that the only accurate locations computed are those related to Wi-Fi devices that are physically located inside (or very close to) the ideal perimeter defined by connecting the most outer nodes. The locations computed for Wi-Fi devices that are physically outside the nodes perimeter are not very accurate. For this reason you may want to create a list of Wi-Fi devices whose locations fall outside the area of the shopping mall and exclude them from your analysis. To develop such a mechanism we recommend to use the Google Maps API. Google provides:

  • this function to define a geo-fence (defined as a polygon with an unlimited number of vertexes).
  • this function that allows you to know if a set of latitude and longitude is inside or outside the geo-fence (the polygon).

Filter on the locations with respect to the level of the nodes (multi-floor buildings)

You need to be aware that Accuware Wi-Fi Location Monitor can work on multi-floor buildings, allowing to distinguish Wi-Fi devices located on multiple floors/levels. Anyway, the attenuation introduced by the floors is not enough to ensure that the Wi-Fi devices detected are on the same level in which the nodes are installed. This because the propagation of the Wi-Fi signal is spherical and the nodes are able to detect the Wi-Fi signal of devices within a spherical radius of 100-150 feet. This means that Accuware Wi-Fi Location Monitor triangulates the locations of Wi-Fi devices located not only on the floor in which the nodes are installed, but also on the floors above and below.

In order to create a distinction between Wi-Fi devices located on multiple floors, there are 2 options:

  1. the first (more reliable) solution is to install a comparable number of nodes on the floors above and below the floor of interest. The nodes must be placed inside the Accuware dashboard on different logical Levels according to the physical floors in which they are installed (this can be done by adding new levels using the Levels section of the dashboard). In addition by keeping the same vertical alignment of the Wi-Fi access points of different levels the results will be better (reducing the possibility of jumps between the levels)
  2. the second solution is to analyze the RSS value provided by the Accuware Wi-Fi Location Monitor “station” API (or provided inside the CSV files) to create a filter that considers on a different floor, all the Wi-Fi devices with a very low RSS value (i.e. minor or equal to -80 dBm) and thus physically on the floor above or below. Please take a look at the RSS fields returned in the EXAMPLE 2 of this page.

Even if it is true that the RSS value, for a Wi-Fi device, decreases as the distance from the nodes increases. It is also true that the RSS value does not decrease linearly and it is also affected by different materials in the environment. For these reasons we suggest to perform some empirical tests to determine how the RSS varies with the distance from the nodes in your specific environment. These kind of empirical tests can be performed following the instructions in this support page.

PLEASE READ:

  • The field “Altitude (meters)” that can be added for each “Level” is just for your reference but it is not taken into account by the Accuware Wi-Fi Location Monitor algorithm at this time.
  • For each Site you will find the default level with LevelID 0. This level can not be eliminated.
  • Using the Accuware dashboard it is possible to create up to 30 Levels (in addition to the Level 0). Additional levels can be created using this PUT API call.

Filter on the location changes over time

This kind of filtering mechanism compares a chosen number of consecutive locations over time. People who carry their Wi-Fi devices are supposed to move inside the environment. For this reason you may want to create a list of Wi-Fi devices that are detected in the same location of the shopping mall for a long period and exclude them from your analysis (e.g. printers, laptop, IP camera etc..) by creating a geofence around each Wi-Fi device and by comparing a chosen number of consecutive locations. To develop such a mechanism we recommend to use the Google Maps API. Google provides:

  • this function to define a geo-fence (defined as a polygon with an unlimited number of vertexes).
  • this function that allows you to know if a set of latitude and longitude is inside or outside the geo-fence (the polygon).

Filter on the number of detection over time

This kind of filtering mechanism checks the number of detection of each Wi-Fi device over time. People who carry their Wi-Fi devices are supposed to be detected only for a specific amount of time to be considered “visitors”. For this reason you may want to create a list of Wi-Fi devices that are detected constantly (e.g at least once every 5 minutes) inside the shopping mall for a long period (e.g. for 10 hours) and exclude them from your analysis (e.g. printers, laptop, IP camera or persons who work inside the shopping mall).

Filter on the manufacturer of the Wi-Fi chip

This kind of filtering mechanism checks the first 3 bytes of the MAC address detected. The first 3 bytes can be used to identify the Wi-Fi chip manufacturer of a Wi-Fi device and there are some manufacturers that do not produce Wi-Fi chips for smartphones. For this reason you may want to create a list of Wi-Fi devices with a Wi-Fi chip that is produced by manufacturers that produce Wi-Fi chips for specific devices you are not interested in (e.g. printers/IP cameras etc…) and exclude them from your analysis. At this link it is possible to find the official repository of manufacturers prefixes. To find the manufacturer of a Wi-Fi chip of a specific device, you have to use the first 3 bytes (the first 6 digits) of the device’s MAC address for a look-up in the register (e.g AC:86:74:XX:XX:XX –> Open Mesh Inc.).