IELS: Indoor Equipment Localization System: Discussion (Part 5/6) (IoT)


When this project started, the idea was simple. The goal was to localize objects inside a building environment in an easy and affordable way.

This  idea  turned out to  be  a wide  research area, and lead  to  try different approaches as explained in this chapter.

We think adding walking steps, compass and gyros  sensor to predict moving direction would help creating extra  virtual position. This would add extra  position for our calculation process. We did few experiment in this direction but decided to leave this for future development.

In addition, concepts like fingerprinting and dead reckoning will add value to the system, both should be considered for future improvement.

We have  at  some point considered machine learning for clustering prediction. We think this can be an interesting area of research, for its own.

In our experiment the  user  hold the smartphone in his hand, but  how about collecting data while having the  smartphone in a pocket, at leg level or on a table etc. This could be interesting to clarify,  as it is our  ultimate goal to predict objects while having our smartphones in a pocket.

We thought about adding extra  smartphones in a fixed position that collects data permanently to improve stability of results.

Another interesting aspect could be to try the  same approach with  less iBeacons and try it with directional iBeacons to cover  specific regions. We have  tried to work with eight iBeacons, but we observed that our  software fails to return results.  An- other approach is to  try with  only  four  iBeacons at  each corner of the  room and predict region position estimation instead of x and y position.  We have  at the end chosen to focus on our existing setup with 14 iBeacons and decided to leave this for further research.

Power consumption is important to bring up,  this means a lot for end-users. We do not want to let our  app drain battery from  a user’s smartphone.  Therefore, it is ideally to collect data periodically. For instance, we collect data once only  when a user or the smartphone is stable and not in a movement. We recollect data again when a user or the smartphone change position status. First of all that would reduce noise, improve accuracy and improve battery operation time.

We thought in the long term, that if our app should cover  big buildings, we could easily combining iBeacon data and Wi-Fi data to cover  most of the building without necessarily deploying iBeacon all over the building.

It is true that our  algorithm is tested, works  and deliver the  results we present here in this  report, but  we would like to mention that, it has a place for further improvement. We will mention some of the improvements, that we observed with our algorithm. While we developed the algorithm we did not think of efficiency and running time. This might be possible to improve. The sorting process of iBeacons strongest signal does not guarantee that the strongest signal is the  nearest iBeacon. This  has  therefore reduced the overall accuracy results.  This is left for future improvement.

An interesting test would be with the use of real evaluation with real end-users in a real environment out of the IT-University. Especially the input from  the end-users and the business owners would be a valuable information to have.

It is our  ultimate goal to use  the  smartphone as a crowdsensing device, but nevertheless, this  will rise a new  question about privacy issues. For example: will end-users accept using their smartphones to collect his/her position in which we would be able to use it for localizing trackable objects? Is there a mechanism to use crowdsensing for prediction object location without keeping the data from  a smartphone? These questions need to be studied and clarified.

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