Real-Time Location System Case Study
assignment for the Real Time Location System (RTLS) unit is a case study using
the data that is available for the Nolan and Lang textbook website:
example of one (the first) line of the data within the file is shown below:
the first line of the data file, and the components of the line are organized
as shown in Table 1, page 7, of the Nolan and Lang book and described
below. The variable t
indicates the timestamp of the data being gathered. The timestamp is in units of milliseconds and
represents the number of milliseconds since midnight, January 1, 1970 UTC. The variable id indicates the MAC address of the
scanning device. The variable pos
indicates the physical coordinates of the scanning device. The variable degree indicates the orientation
of the user carrying the scanning device in degrees. The remainder of the data contains a
quadruplet consisting of the MAC address of a responding peer with its
corresponding values for received signal strength in dBm, the channel frequency
and the devices mode of operation (either 3 for access point or 1 for device in
case study for this unit, we will be analyzing this data using the k-nearest
neighbors to determine locations and to determine potential issues with
decisions made regarding the use, and non-use, of the data. Section 1.5 of
Nolan and Lang provides a basic k-nearest neighbors approach to determining
location assuming the floor plan for the building (see Figure 1.1) is accurate.
The floor plan shows six access points; however, the data contains seven access
points with roughly the expected number of signals. In the analysis presented in Nolan and Lang,
the access points were matched to their locations, and the decision was made to
keep the access point with MAC address 00:0f:a3:39:e1:c0 and to eliminate the
data corresponding to MAC address 00:0f:a3:39:dd:cd.
a more thorough data analysis into these two MAC addresses including
determining locations by using data corresponding to both MAC addresses. Which of these two MAC addresses should be
used and which should not be used for RTLS? Which MAC address yields the best
prediction of location? Does using data
for both MAC addresses simultaneously yield more, or less, accurate prediction
of location? (Note: this portion is derived from Exercise Q.9 in Nolan and
k-nearest neighbors has proven to be a good approach to determining location,
alternate approaches have been proposed.
One simple alternative approach is to use weights on the received signal
strength, where the weight is inversely proportional to the “distance” from the
test observation. This allows for the
“nearest” points to have a greater contribution to the k-nearest neighbor
location calculation than the points that are “further” away.
this alternative prediction method. For
what range of values of weights are you able to obtain better prediction values
than for the unweighted k-nearest neighbor approach? Use calcError() to compare
this approach to the simple average.
an iPython Notebook including code output and graphics for all of your work.
an introduction to explain the case study, explain the approach used to
complete the case study and explain the output achieved. Explanations of output should be included as
close to the output or figures as possible.
references used, including the book by Nolan and Lang.
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