Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Laboratory of Broadband Wireless Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Sensors (Basel). 2019 Nov 16;19(22):4997. doi: 10.3390/s19224997.
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.
鉴于当前物联网 (IoT) 应用程序使用许多不同的传感器来提供信息，将开发大量用于物联网系统的蓝牙低能 (BLE) 设备。开发低功耗和低成本的 BLE 广告商是支持如此多数量的 BLE 设备的邻居发现过程 (NDP) 的最具挑战性的任务之一。由于参数设置对于实现 NDP 的所需性能至关重要，因此在确定一些重要参数指标（如广告间隔、扫描间隔和扫描窗口）时，BLE 网络中邻居发现的能量模型可以提供有益的指导。在本文中，我们提出了一种新的分析模型，用于描述 BLE 网络中 NDP 期间使用所有可能参数设置的能耗。在该模型中，基于中国剩余定理 (CRT) 推导了 BLE NDP 期间广告事件和扫描事件的能耗。此外，还建立了一个实际的测试平台来测量能耗。测量和实验结果揭示了平均能耗与关键参数之间的关系。在此模型的基础上，提出了 BLE 网络配置的有益指导方针，以帮助选择适当的参数来优化给定 IoT 应用程序的功耗。
