Internet of Things ger människor möjligheter att genom tjänster dra nytta av sensorer och andra enheter som tillsammans skapar ett brett utbud av lösningar som smart hem, smart transport, äldreomsorg och mycket mer. Den senaste innovationen av Bluetooth SIG är Bluetooth mesh topologi som tillåter att ansluta trådlösa enheter i ett många till många förhållanden. I denna avhandling utvecklade vi ett inomhusspårningssystem baserat på Bluetooth Low Energy teknik och mesh topologi för att undersöka de potentiella fördelar som Bluetooth Low Energy nätverk har att erbjuda för inomhusspårningssystemet. Systemet är utvecklat för att spåra Bluetooth beacon i en inomhus kontorsmiljö. Received Signal Strength används för att beräkna avståndet till beacon, medan positionen av beacon beräknas med Extended Min-Max och Trilateration algoritmer. Beräkningar utförs på servern. Resultaten analyseras genom jämförelse av Root Mean Square Error av båda algoritmerna. I denna avhandling utvärderas inomhusspårning som en del av ett uppdrag som ges av u-blox. / The Internet of Things brings connectivity of people, services, sensors and other devicesenabling a wide range of applications like smart home, smart transport, elder care andmuch more. The latest innovation of Bluetooth SIG is the Bluetooth mesh topology thatallows us to connect wireless devices in a many-to-many relationship. To investigate thepotential benefit that the BLE mesh network has to offer for the indoor tracking system.In this thesis, we developed an indoor tracking system based on Bluetooth Low Energytechnology and mesh topology. The system is developed to track Bluetooth beacon inan indoor office environment. Received Signal Strength is used to calculate the distance tothe beacon, while the position of the beacon is calculated using Extended Min-Max andTrilateration algorithms. Calculations are performed on the server. The results are analysed through comparison of Root Mean Square Error of both algorithms. In this thesis, indoor tracking is evaluated as part of an assignment given by u-blox.

When choosing what IoT protocol to use today, there are lots of choices. If a mesh type network is chosen, Bluetooth mesh might be a possible candidate. Bluetooth mesh without correctly configured parameters can however suffer from congestion and packet loss if the network is very dense or consists of many nodes. This can be counteracted by choosing which nodes should be relays more carefully, as well as setting the re-transmission count and Time To Live (TTL) based on the current topology of the network. If the nodes in the network change position or are added/removed regularly, it is impossible to set the parameters optimal for all the possible network layouts. This is where a dynamic configuration comes in handy. In this master thesis a custom control model was created which implemented the K2 Pruning algorithm for relay selection, custom heartbeats for a dynamic TTL on each node, and a static re-transmission count for message originators and relays. A possible way to implement a dynamic re-transmission count is also discussed, as well as how the dynamic configuration could be autonomous without the need of physical interaction when reconfiguring the network. The implemented dynamic configuration tested on a physical system of 33 nodes was partly unsuccessful, but still provided improved Packet Delivery Ratio (PDR), reduced message delay, and useful knowledge for future implementations of a dynamic configuration. The K2 Pruning algorithm failed in choosing relays correct and quickly due to congestion during the neighbor information exchange needed to run the algorithm. Therefore, a different relay selection algorithm is suggested for future models or the refrain of acknowledged messages during the neighbor information exchange phase.