The energy consumed by video streaming includes the energy consumed for data transmission and CPU processing, which are both affected by the CPU frequency. High CPU frequency can reduce the data transmission time but it consumes more CPU energy. Low CPU frequency reduces the CPU energy but increases the data transmission time and then increases the energy consumption. In this paper, we aim to reduce the total energy of mobile video streaming by adaptively adjusting the CPU frequency. Based on real measurement results, we model the effects of CPU frequency on TCP throughput and system power. Based on these models, we propose an Energy-aware CPU Frequency Scaling (EFS) algorithm which selects the CPU frequency that can achieve a balance between saving the data transmission energy and CPU energy. Since the downloading schedule of existing video streaming apps is not optimized in terms of energy, we also propose a method to determine when and how much data to download. Through trace-driven simulations and real measurement, we demonstrate that the EFS algorithm can reduce 30 percent of energy for the Youtube app, and the combination of our download method and EFS algorithm can save 50 percent of energy than the default Youtube app.

Modern mobile devices are equipped with multicore-based processors, which introduce new challenges on computation offloading. With the big.LITTLE architecture, instead of only deciding locally or remotely running a task in the traditional architecture, we have to consider how to exploit the new architecture to minimize energy while satisfying application completion time constraints. In this paper, we address the problem of energy-efficient computation offloading on multicore-based mobile devices running multiple applications. We first formalize the problem as a mixed-integer nonlinear programming problem that is NP-hard, and then propose a novel heuristic algorithm to jointly solve the offloading decision and task scheduling problems. The basic idea is to prioritize tasks from different applications to make sure that both application time constraints and task-dependency requirements are satisfied. To find a better schedule while reducing the schedule searching overhead, we propose a critical path based solution which recursively checks the tasks and moves tasks to the right CPU cores to save energy. Simulation and experimental results show that our offloading algorithm can significantly reduce the energy consumption of mobile devices while satisfying the application completion time constraints.

Cellular network enables pervasive data access, but it also increases the power consumption of smartphones due to the long tail problem, where the cellular interface has to stay in the high-power state for some time after each data transmission. To reduce the tail energy, data that will be used in the future can be prefetched. However, prefetching unnecessary data may waste energy, and this problem becomes worse when the network quality is poor. In this paper, we generalize and formulate the prefetch-based energy optimization 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 this nonlinear optimization problem, we first propose a greedy algorithm, and then propose a discrete algorithm with better performance. We have implemented and evaluated the proposed algorithms in two apps: in-app advertising and mobile video streaming. Evaluation results show that the proposed algorithms can significantly reduce the energy consumption.

Wearable devices such as smartwatches do not have enough power and computation capability to process computationally intensive tasks. One viable solution is to offload these tasks to the connected smartphone. Existing Android smartphones allocate CPU resources to a task according to its performance requirement, which is determined by the context of the task. However, due to lack of context information, smartphones cannot properly allocate resources to tasks offloaded from wearable devices. Allocating too few resources to urgent tasks (related to user interaction) may cause high interaction latency on wearable devices, while allocating too many resources to unimportant tasks (unrelated to user interaction) may lead to energy waste on the smartphone. To solve this problem, we propose a context-aware task offloading (CATO) 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. To validate our design, we have implemented CATO on the Android platform and developed two applications on top of it. Experimental results show that CATO can significantly reduce latency for urgent tasks and save energy for other unimportant tasks.

Smartwatches are quickly gaining popularity, but their limited battery life remains an important factor that adversely affects user satisfaction. To provide full functionality, smartwatches are usually connected to phones via Bluetooth. However, the Bluetooth power characteristics and the energy impact of Bluetooth data traffic have been rarely studied. To address this issue, we first establish the Bluetooth power model based on extensive measurements and a thorough examination of the Bluetooth implementation on Android smartwatches. Then we perform the first in-depth investigation of the background data transfers on smartwatches, and find that they are prevalent and consume a large amount of energy. For example, our experiments show that the smartwatch's battery life can be reduced to one third (or even worse) due to background data transfers. Such high energy cost is caused by many unnecessary data transfers and 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 the identified causes, we propose four energy optimization techniques, which are fast dormancy, phone-initiated polling, two-stage sensor processing, and context-aware pushing. The first one aims to reduce tail energy for delay-tolerant data transfers. The latter three are designed for specific applications which are responsible for most background data transfers. Evaluation results show that jointly using these techniques can save 70.6% of the Bluetooth energy.

In-app advertising provides a monetization solution for free apps, but also consumes lots of energy due to the long tail problem in cellular networks. To reduce the tail energy, we can predict the number of ads needed in the future and then prefetch those ads together instead of periodically. However, prefetching unnecessary ads may waste both energy and cellular bandwidth, and this problem becomes worse when the network quality is poor. In this paper, we propose network quality aware prefetching algorithms. First, we design a prediction algorithm which generates a set of prefetching options with various probabilities. Second, with these prefetching options, we propose two prefetching algorithms to reduce the energy consumption by considering the effect of network quality, where the energy-aware prefetching algorithm aims to minimize the energy consumption, and the energy-and-data aware prefetching algorithm considers the data usage to achieve a tradeoff between energy and data usage. Evaluation results show that, compared to traditional ways of fetching ads periodically, our energy-aware prefetching algorithm can save 80% of energy under various network quality, and our energy-and-data aware prefetching algorithm can achieve similar energy saving with less data usage.

Computationally intensive applications may quickly drain mobile device batteries. One viable solution to address this problem utilizes computation offloading. The tradeoff is that computation offloading introduces additional communication, with a corresponding energy cost. Yet, previous research into computation offloading has failed to account for the special characteristics of cellular networks that impact mobile device energy consumption. In this paper, we aim to develop energy efficient computation offloading algorithms for cellular networks. We analyze the effects of the long tail problem on task offloading, formalize the computation offloading problem, and use Dijkstra's algorithm to find the optimal decision. Since this optimal solution relies on perfect knowledge of future tasks, we further propose an online algorithm for offloading. We have implemented this latter algorithm on Android-based smartphones. Both experimental results from this implementation and trace-driven simulation show that our algorithm can significantly reduce the energy of computation offloading in cellular networks.