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Towards Energy Optimization Using Joint Data Rate Adaptation for BSN and WiFi Networks

机译:使用联合数据速率适配的BSN和WiFi网络实现能源优化

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Body sensor networks (BSNs) and WiFi networks have been widely investigated due to the availability of sensor motes and WiFi devices, but they are commonly deployed separately. In this paper we propose to optimize the total communication energy consumption of BSN and WiFi (BSN-WiFi) networks using joint data rate adaptation. More specifically, we first elaborate the BSN-WiFi network system in four consecutive phases. Then based on the system, we analyze the communication energy consumption, throughput and time delay, and provide a signal-to-noise ratio and packet delivery ratio (SNR-PDR) mappings of BSN and WiFi networks. Next, we build an energy optimization model with constraints of SNR-PDR mappings, throughput, and time delay to minimize the total communication energy consumption in BSN-WiFi networks. With the input of SNR values, we solve this model by cvx to obtain the output of optimal data rates associated with SNR values, which are then tabulated for online data rate adaptation. Finally, we collect 20-minute traces from a specific BSN-WiFi network system for performance evaluation, and the results demonstrate that our optimal data rate solution achieves up to 86% energy savings comparing with the solutions using fixed data rates.
机译:由于传感器微粒和WiFi设备的可用性,人体传感器网络(BSN)和WiFi网络已被广泛研究,但是它们通常单独部署。在本文中,我们建议使用联合数据速率适配来优化BSN和WiFi(BSN-WiFi)网络的总通信能耗。更具体地说,我们首先在四个连续的阶段详细阐述BSN-WiFi网络系统。然后,基于该系统,我们分析了通信能耗,吞吐量和时延,并提供了BSN和WiFi网络的信噪比和分组传输比(SNR-PDR)映射。接下来,我们建立一个具有SNR-PDR映射,吞吐量和时间延迟约束的能量优化模型,以最小化BSN-WiFi网络中的总通信能量消耗。使用SNR值的输入,我们通过cvx求解该模型,以获得与SNR值相关的最佳数据速率的输出,然后将其制成表格以进行在线数据速率调整。最后,我们从特定的BSN-WiFi网络系统收集了20分钟的迹线以进行性能评估,结果表明,与使用固定数据速率的解决方案相比,我们的最佳数据速率解决方案可节省多达86%的能源。

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