| This dissertation proposes a novel scheme for neighbour discovery in Bluetooth that achieves fast discovery times and low power consumption. To tune the parameter values of the scheme, we develop a simulator to comprehensively explore the large parameter space and then validate the simulation results for selected parameter values through measurements with real devices. Our results show a two-fold improvement in both mean discovery time and power consumption, at the same time, over the previously best known scheme.; The evaluation of our neighbour discovery scheme reveals that there is a trade-off in the selection of its parameters between energy efficiency and discovery time. In the second part of this thesis, we propose two adaptive algorithms for dynamically switching these parameters, based on past activity. We evaluate these algorithms in a node mobility simulation. Our adaptive algorithms reduce energy consumption by 50% and have up to 8% better performance over a naive, power-conserving scheme. |
