With the tremendous growth in wireless network deployment and increasing use of mobile devices, e.g., smartphones and tablets, improving energy efficiency in such devices, especially with communication driven workloads, is critical to providing a satisfactory user experience. Studies show that signal strength plays an important role on energy consumption of cellular data communications. While energy consumption can be minimized by accurately predicting signal strengths and reacting to it in realtime, the dynamic nature of wireless environments makes signal strengths highly unpredictable. In this paper, after analyzing in detail the signal strength variation and its impact on energy consumption, we propose to use crowdsourcing approach to optimize mobile devices' energy efficiency by utilizing signal strength traces reported/shared by other users/devices in cellular networks. Via a comprehensive measurement study, we observe that signal strength traces collected from different devices are pseudoidentical, and they even exhibit similar threshold-based behaviors in the relationship between signal strength and device power consumption. Based on our observations, we propose a predictive scheduling algorithm that: (i) selects the right set of signal strength traces based on its location, (ii) applies a filter to smooth out signal strengths and hide abrupt changes, (iii) digitizes the signal strength to "good" and "bad" areas, and (iv) schedules transmissions based on power-throughput characteristics to optimize the transmission energy efficiency. To demonstrate the efficacy of the proposed algorithms, we prototype the crowdsourcing-based predicative scheduling algorithm on Android-based smartphones. Our experiment results from real-life driving tests demonstrate that, by leveraging others' signal traces, mobile devices can save energy up to 35 % compared to the conventional opportunistic scheduling, i.e., schedule transmissions only based on instantaneous channel conditions.

The cellular network bandwidth increases significantly in the past few years, stimulated by many popular networkintensive applications, such as video streaming and cloud storage usages. Meanwhile, more and more users enjoy the multitasking feature of mobile devices and concurrently run a number of applications. Given these two trends and the fact that extended battery life remains to be a critical factor for small form factor devices, e.g. smartphones and tablets, it is imperative to understand the energy impact of multiple applications running concurrently on such platforms.In this paper, we characterize and understand the energy and performance impact of concurrent applications via a comprehensive set of carefully designed experiments. Specifically, we focus on network-intensive applications since most usage models today are driven by always-on communication activities. We make several significant contributions to shed light on understanding the energy behavior of concurrent applications. Firstly, we find out that running multiple network-intensive applications concurrently can significantly improve energy efficiency, up to 2.2X compared to running them separately. Secondly, we observe that power consumption from CPU and System on Chip (SoC) are the primary culprits of power dynamic for network-intensive applications; while communication components, including Network Interface Card (NIC), poses very little power consumption variation with different throughput. Thirdly, we investigate, in detail, the significant impact of signal strength on the energy consumption and throughput performance. Our findings and analysis can be applied to provide helpful guidance for a wide range of research aiming to optimize mobile Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. device energy efficiency, e.g. transmission scheduling and protocol design in cellular networks.

Based on the current situation of large volume and single detection function of the underwater acoustic beacon detection device, combined with the requirements of multi-parameter detection of underwater acoustic beacon function and performance, an embedded underwater acoustic beacon multi-parameter detection device is developed. The detection device uses ARM as the core processor, carries Linux embedded system, and establishes QT graphical user interface. The detection device can realize the acoustic signal of underwater acoustic beacon Frequency, pulse width, period, sound source level, spectrum, and other multi-parameter detection and graphical display of the test results.