Toward Joint Compression-Transmission Optimization for Green Wearable Devices: An Energy-Delay Tradeoff

Small-size and light-weight, as the modern design concept for the emerging wearable devices, has become a trend. However, such trend puts physical limitations to the battery, and the resulting short battery lifetime becomes the bottleneck for most wearable devices today. In this paper, we aim to optimize the energy usage through data compression and transmission rate control. We propose a novel joint compression-transmission approach, which not only minimizes the energy consumption of both compression and transmission, but also maintains the corresponding data distortion and transmission delay within a certain tolerant level. By adopting the Lyapunov framework, we develop an online algorithm to minimize the one-slot drift-plus-penalty function. We conduct numerical analysis and experimental study for our proposed approach. The results show that the size of queuing buffer has the significant impact on the energy cost. Next, we verify a fundamental tradeoff between the energy expenditure and transmission delay, and derive the theoretical performance bounds. After that, we show that the energy cost is also determined by the wireless channel gain and the data compression ratio. Finally, compared to a strategy without compression, our approach can save up to 92% of energy.