The document proposes a new method to improve geolocation precision by using Wi-Fi signal strength instead of GPS. It describes developing an Android app to log Wi-Fi access points and signals. Various clustering algorithms are tested on the log data to group locations. The final algorithm uses K-means clustering on latitude/longitude to assign Area Codes, hierarchical clustering on access point visibility to assign Visibility Codes, and signal strength differences to assign Power Codes. Code and diagrams generated in R are discussed as validating the location grouping method.

1. Abstract
As the technology advancements stride across the globe, there seems to be a fair need of
improvement in precision levels of Geolocation of technology users. Global Positioning System
(GPS) or Google maps geolocation APIs based location services are used presently for tracking
location, but its precision lacks to provide location under the roof i.e. within the infrastructures,
in such case, it provides the last tracked location via GPS satellites. Considering the need for an
edge over existing services, this paper explores the methods to identify the use of Wi-Fi devices
and factors such as its signal strength to find out and improvise geolocation.
Introduction
GPS as well as Google maps geolocation APIs based services for android applications is
presently used for tracking location, in most of the devices. Considering their incapability to
track the location of the devices within the infrastructures, this paper proposes a more reliable
method, which solely focus on Network availability, its signal strength and a logging
application which provides important parameters in a log file. The developed algorithm uses the
required parameters as per the need of logic and works on R (language and environment for
statistical computing and graphics) for proving and validation of proposed logic.
Figure 1: Overview of work-flow

2. Requirement of data for analysis and development is met by an android application which solely
probes and logs the available APs and write the log file to device, running the application. The
application was developed using Eclipse software environment for android development in
which uses predefined structure for development, which allows development as per the
requirements. Mainly there were four java classes which were called using service and Intent
feature of android development and permissions for inbuilt Location Manager was accessed by
Android Manifest along with other basic permissions. Location manager was used to give
Latitude and Longitudes of the Scanning device.
Motivation and objective behind the project
GPS based location services and its drawbacks
The Global Positioning System (GPS) is a network of about 30 satellites orbiting the Earth at
an altitude of 20,000 km. The system was originally developed by the US government for
military navigation but now anyone with a GPS device, be it a SatNav, mobile phone or
handheld GPS unit, can receive the radio signals that the satellites broadcast. Wherever you are
on the planet, at least four GPS satellites are ‘visible’ at any time.
But these radio signals stop communication once it starts facing obstacles, which means
within the buildings we cannot rely on GPS for locating the device. In this case it provides
the last tracked location of the device.
Therefore, the following method tries to overcome this drawback by shifting the basic
principle from GPS to “Network Availability” which has an edge over former, as its signals
propagate through infrastructures. Routers (Access Points) are the basis of the proposed
algorithm Principles.

3. Work Plan to meet the objective
Development of Logging Application and its use
Requirement of data for analysis and development is met by an android application which solely
probes and logs the available APs and write the log file to device, running the application.
The log file provides the scanned results in following parameters:
 UNIX Time (POSIX time), for better understanding of logging time periods
 Logging Date
 Time Stamp (every single stamp in milliseconds at which the logging is done)
 Latitude of the logging device (geo latitudinal component of co-ordinate, which gives
precise location on 2-D mapped earth surface)
 Longitude of the logging device (geo longitudinal component of co- ordinate, which
gives precise location on 2-D mapped earth surface)

4.  BSSID (Basic Service Set Identifier), which is Unique for any network providing device
 RSSI (Received Signal Strength Indicator) at a particular lat-long from all scanned
APs
 SSID (Signal Strength Indicator) or the Identity name of the AP, this can be same for
more than one APs
Although Latitude-Longitude pairs are integrated in the application, but their sensitivity and
randomness makes it worthless to use these in algorithm building, which is being concluded after
analysis and testing.
Work Description
Some Methodologies (Trials for the Algorithm Development)
From the data collection, the basic idea was to use the parameters such that they could be
used in the known classification methods for the cluster formation, such as K-Means, K-
Nearest Neighbor, Hierarchical Clustering and others, so the methods then tried are as follow:
1. Using Latitude-Longitude location and corresponding RSSI values.
On consideration of the fact that mapped ID’s cannot be used directly in clusterization,
Method for classification was tried using RSSI values at different lat-long pairs. The idea
was to assume the ID with the maximum signal strength value at a particular location as
the most probable AP and proceeding that manner would have resulted in regions with
the probable IDs.
But factors such as randomness, error and sensitivity of the location based on lat- long and
lack of finding distance between each pairs led to reject this idea.
2. Histogram Formation f r o m M a p p e d BSSIDs
After mapping the BSSIDs using lookup methods, this unique parameter was used for
histogram formation for analysis, but this could only give the different ranges of IDs with

5. their corresponding frequencies. But this gave a new idea for using mapped IDs for
developing distance function.
3. Using K-Means for clustering known distance parameters.
Simplest classification could be done by taking lat-long pairs for scanned APs, i.e. the
positions where APs were scanned and we can conclude that they are close to the position of
the logging device. We considered this for further processing even if there were
randomness errors in position, because it presented clusters on the basis of intra-cluster
closeness and inter-cluster separation, which is hardly affected by small errors.
After these trial, from the experiences and ideas from each one, the principle for
final Algorithm is developed.
Implementation Details
Final Model and Algorithm Development
The Final Principle is based on developing a distance function which can be used in one
of the clusterization methods. The idea is to develop three codes based on their
precision levels, named as A.Code (Area Code), V.Code (Visibility Code) And H.Code
(Power code), such that each individual group or cluster of BSSIDs would have a unique code
of tags.
First the A.Code is find by using K-means and lat-long pairs as distance function, as
described in the last trial step. So all those possessing same A.code are at least close to a
particular lat-long pair.
Following this is the step for V.Code that is classification on the basis of visibility, which means
that those clusters of BSSIDs and their time stamps which possess same visibility code got
scanned in small interval of time or are highly visible in two or more time stamps so are
closer to each other.

6. The steps are as follows
 Segregating the BSSIDs at each time stamp
 Finding an affinity matrix based on a visibility closeness formula:
1)
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐶𝑜𝑚𝑚𝑜𝑛 𝑢𝑛𝑖𝑞𝑢𝑒 𝐵𝑆𝑆𝐼𝐷𝑠 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑎 𝑝𝑎𝑖𝑟 𝑜𝑓 𝑝𝑜𝑖𝑛𝑡𝑠
𝑇𝑜𝑡𝑎𝑙 𝑠𝑐𝑎𝑛𝑛𝑒𝑑 𝐵𝑆𝑆𝐼𝐷𝑠 𝑎𝑡 𝑒𝑎𝑐ℎ 𝑝𝑜𝑖𝑛𝑡
 After the affinity matrix so formed, there comes a distance function which now can be used
in one of the clustering methods.
So the best function for the defined data is Hierarchical Clustering
which used the matrix to find Euclidean distance and functions on Average value method to
cluster the timestamps. From the cluster analysis or Dendrogram visualization, an optimal
number of clusters can be concluded either as per required clusters or on the basis of
optimal value function. For this method, Bayesian Information Criteria (BIC) has been used.
Following the above steps to find V.Code, the algorithm seeks to cluster the already found
clusters on the basis of RSSI values.
The principle is as follows:
 For H.Code formulation, considering one cluster at a time, in which for each pair of
timestamps having some number of BSSIDs, following formula function has been
developed-
 Only the common BSSIDs are to be considered.
 For an individual ID, at two different timestamps in a pair, there can be two
different value of Signal strength, due to different positions of logging or any such
factor.
 So taking the difference of these two values for each common ID, there forms a
table for each one of it.
 Amongst the found values, only those values are to be considered that form with in
the range or under the defined threshold value of differences.

7.  This threshold value is given by-
2)
𝑆𝑢𝑚 𝑜𝑓 𝑎𝑙𝑙 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒𝑠
𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑜𝑚𝑚𝑜𝑛 𝐼𝐷𝑠
 Thus, we get number of IDs within defined range, which is used in following steps
of algorithm.
 For affinity matrix formation on the basis of RSSI values, the function is defined as
follows-
3)
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐼𝐷𝑠 𝑖𝑛 𝑟𝑎𝑛𝑔𝑒
𝑇𝑜𝑡𝑎𝑙 𝑐𝑜𝑚𝑚𝑜𝑛 𝐵𝑆𝑆𝐼𝐷
 From each pair’s value, a single affinity matrix forms which implies that there will be as
many as K matrices, where K is the number of clusters, either given by user or found
optimally.
 Out of these k matrices, those which can further be classified, uses the same clustering
function used for V.Code, thus concluding final H.Code.
Result and Discussion
The Code and corresponding output tables and diagrams are self
sufficient for the discussion:
R (language and environment for statistical computing and graphic) is extensively used for
operating the data in the excel file format for various basic calculations required analysis.
Following are related diagrams of manual operation which are being carried out within the
code for particular data set.

8. 1) Overall Log-file showing a sample of collected dataset.

9. 2 Segregating BSSIDs on the basis of time stamps:

10. 3 R-Code, using Hierarchical Clustering method for Classification and using library “hclust”.
4) Phase one completion with result as Affinity matrix for V.Code (sample)

11. 5) Concluding clusters on the basis of dendrogram formation and bordering techniques

12. Conclusion
The developed algorithm, therefore propose a more reliable method, which has an edge over
existing geolocation methods and gives a new hope of exploring indoor location services simply
based on Access Point availability.
Although the proposed algorithm proves out to be the best for indoor and 3-Dimensional geo
location for now, but there are certain factors which are responsible for its accuracy,
especially in noisy environments. But certain factors can be taken care of, such as possibility
to increase accuracy of geolocation using Wi-Fi SSIDs in a planned manner along with
applying data mining techniques accordingly.

13. Figure 2: Work Flow

14. Table of Content
1) Abstract
2) Introduction
3) Motivation and objective behind the project
 Development of Logging Application and its use
4) Work Description
 Some Methodologies
5) Implementation Details
 Final Model and Algorithm development
6) Result and Discussion
7) Conclusion