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Nowadays, it has become common to equip a device with Bluetooth. As such devices become pervasive in the world; much work has been done on forming them into a network, however, almost all the Bluetooth Scatternet Formation Algorithms assume devices are homogeneous. Even the exceptional algorithms barely mentioned a little about the different characteristics of devices like computational abilities, traffic loads for special nodes like bridge nodes or super nodes, which are usually the bottleneck in the scatternet. In this paper, we treat the devices differently not only based on the hardware characteristics, but also considering other conditions like different classes, different groups and so on. We use a two-phase Scatternet Formation Algorithm here: in the first phase, construct scatternets for a specified kind of devices; in the second phase, connect these scatternets by using least other kinds of devices as bridge nodes. Finally, we give some applications to show the benefit of classification.


Nowadays, it has become common to equip a device with Bluetooth. As such devices become pervasive in the world; much work has been done on forming them into a network, however, almost all the Bluetooth Scatternet Formation Algorithms assume devices are homogeneous. Even the exceptional algorithms barely mentioned a little about the different characteristics of devices like computational abilities, traffic loads for special nodes like bridge nodes or super nodes, which are usually the bottleneck in the scatternet. In this paper, we treat the devices differently not only based on the hardware characteristics, but also considering other conditions like different classes, different groups and so on. We use a two-phase Scatternet Formation Algorithm here: in the first phase, construct scatternets for a specified kind of devices; in the second phase, connect these scatternets by using least other kinds of devices as bridge nodes. Finally, we give some applications to show the benefit of classification.