Energy Optimization for Wireless Communications on Mobile Devices

Open Access
Yang, Yi
Graduate Program:
Computer Science and Engineering
Doctor of Philosophy
Document Type:
Date of Defense:
December 15, 2017
Committee Members:
  • Guohong Cao, Dissertation Advisor
  • Guohong Cao, Committee Chair
  • George Kesidis, Committee Member
  • Sencun Zhu, Committee Member
  • Dinghao Wu, Outside Member
  • Wireless network
  • Energy optimization
  • Mobile devices
Mobile devices such as smartphones and smartwatches are becoming increasingly popular accompanied with a wide range of apps. Those apps usually require data communications through wireless interfaces, which will drain the battery quickly. Thus, it is of great value to characterize the energy consumption of wireless communications and propose energy saving solutions. The specific goal of this dissertation is to optimize the energy consumption of wireless communications on mobile devices. Specifically, this dissertation has four foci. First, we propose network quality aware prefetching algorithms to save energy for in-app advertising. The cellular interface on smartphones continues to consume a large amount of energy after a data transmission (referred to as the {\em long tail problem}). Then periodically fetching ads through the cellular network may lead to significant battery drain on smartphones. To reduce the tail energy, we can predict the number of ads needed in the future and then prefetch those ads together. However, prefetching unnecessary ads may waste both energy and cellular bandwidth, and this problem becomes worse when the network quality is poor. To solve this problem, we propose network quality aware prefetching algorithms. We first design a prediction algorithm which generates a set of prefetching options with various probabilities, and then we propose two prefetching algorithms to select the best prefetching option by considering the effect of network quality. Second, we generalize the prefetching problem, where the goal is to find a prefetching schedule that minimizes the energy consumption of the data transmissions under the current network condition. To solve the formulated nonlinear optimization problem, we first propose a greedy algorithm, and then propose a discrete algorithm with better performance. Third, we consider the context information when offloading tasks for wearable devices. Considering the low energy consumption of the Bluetooth data transmissions, wearable devices usually offload computationally intensive tasks to the connected smartphone via Bluetooth. However, existing smartphones cannot properly allocate CPU resources to these offloaded tasks due to lack of context information, resulting in either energy waste on smartphones or high interaction latency on wearable devices. To address this issue, we propose a context-aware task offloading framework, in which offloaded tasks can be properly executed on the smartphone or further offloaded to the cloud based on their context, aiming to achieve a balance between good user experience on wearable devices and energy saving on the smartphone. Finally, we characterize and optimize Bluetooth energy consumption on smartwatches. Bluetooth is used for data communications between smartwatches and smartphones, but its energy consumption has been rarely studied. To solve the problem, we first establish the Bluetooth power model and then we perform an in-depth investigation of the background data transfers on smartwatches. We found that those data transfers consume a large amount of energy due to the energy inefficiency attributed to the adverse interaction between the data transfer pattern (i.e., frequently transferring small data) and the Bluetooth energy characteristics (i.e., the tail effect). Based on these findings, we propose four techniques to save Bluetooth energy for smartwatches.