Adaptive energy conserving algorithms for neighbor discovery in opportunistic Bluetooth networks

In this paper, we introduce and evaluate novel adaptive schemes for neighbor discovery in Bluetooth-enabled ad-hoe networks. In an ad-hoc peer-to-peer setting, neighbor search is a continuous, hence battery draining process. In order to save energy when the device is unlikely to encounter a neighbor, we adaptively choose parameter settings depending on a mobility context to decrease the expected power consumption of Bluetooth-enabled devices. For this purpose, we first determine the mean discovery time and power consumption values for different Bluetooth parameter settings through a comprehensive exploration of the parameter space by means of simulation validated by experiments on real. devices. The fastest average discovery time obtained is 0.2 s, while at an average discovery time of 1 s the power consumption is just 1.5 times that of the idle mode on our devices. We then introduce two adaptive algorithms for dynamically adjusting the Bluetooth parameters based on past perceived activity in the ad-hoc network. Both adaptive schemes for selecting the discovery mode are based only on locally-available information. 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 static power-conserving scheme.