Number Of Bluetooth Low Energy Devices Research Articles

Energy Modeling of Neighbor Discovery in Bluetooth Low Energy Networks.

Given that current Internet of Things (IoT) applications employ many different sensors to provide information, a large number of the Bluetooth low energy (BLE) devices will be developed for IoT systems. Developing low-power and low-cost BLE advertisers is one of most challenging tasks for supporting the neighbor discovery process (NDP) of such a large number of BLE devices. Since the parameter setting is essential to achieve the required performance for the NDP, an energy model of neighbor discovery in BLE networks can provide beneficial guidance when determining some significant parameter metrics, such as the advertising interval, scan interval, and scan window. In this paper, we propose a new analytical model to characterize the energy consumption using all possible parameter settings during the NDP in BLE networks. In this model, the energy consumption is derived based on the Chinese remainder theorem (CRT) for an advertising event and a scanning event during the BLE NDP. In addition, a real testbed is set up to measure the energy consumption. The measurement and experimental results reveal the relationship between the average energy consumption and the key parameters. On the basis of this model, beneficial guidelines for BLE network configuration are presented to help choose the proper parameters to optimize the power consumption for a given IoT application.

Efficient Advertiser Discovery in Bluetooth Low Energy Devices

Bluetooth Low Energy (BLE) has become ubiquitous in the majority of mobile devices that connect wirelessly. With the increase in the number of devices, the probability of congestion also increases in a network. Data channels of the BLE use frequency hopping, but it is not available for advertising channels. The capability of the BLE for providing a wide range of parameters settings ensures the impressive potential for BLE devices to customize their discovery latency. But communication before connection setup is not synchronous and both the scanning devices and the advertising devices are unaware of the timing parameters of each other. This can lead to inefficient advertiser device discovery. To resolve this issue, an algorithm is proposed to reduce the average latency per advertiser experienced due to the increase in the number of BLE devices in a vicinity. It is observed that the average latency has shown improvement in the range of 35% to 55%, depending on different simulated scenarios. Due to this improvement the overall energy consumption is also reduced.