Utilize Signal Traces from Others? A Crowdsourcing Perspective of Energy Saving in Cellular Data Communication

Abstract

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.

MLStar

“…The energy optimizations are moving into the realm of monitoring and optimizing the application behavior in the user systems. Crowdsourcing where the profilers on the user systems send exchange data are gaining popularity as they allow accelerating application characterization and offering instantaneous benefit to the users for existing apps [8,9,18]. Crowdsourcing has been also applied to optimize system setting to automatically improve energy efficiency [21] for end users.…”

Cloud-Assisted Live Streaming for Crowdsourced Multimedia Content

“…For example, LiFS (Locating in Fingerprint Space) was developed for wireless indoor localization with smartphones based crowdsourcing [4]. Ou et al [5] used crowdsourcing approach to optimize mobile devices' energy efficiency by utilizing signal strength traces shared by other devices in cellular networks. For video applications, a scalable system that allows users to perform content-based searches on continuous collection of crowdsourced video was proposed in [7].…”

“…5(b), the cloud site is preferred by the live crowdsourcers and , i.e., for . According to the direction edges (4,2) and (4,5), we can have the service migration vectors (5,2) and (5,5) for the live crowdsourcers , and (6,2) and (6,5) for the live crowdsourcers . Define as the set of all service migration vectors that are generated from the given distribution graph.…”

“…We utilize 5 videos with different bitrates listed in Table III. 5 Each dedicated sourcer stores one of these videos as its own live feed.…”

Spoofing Detection in Automatic Speaker Verification Systems Using DNN Classifiers and Dynamic Acoustic Features

“…Phase-based features, e.g., relative phase shift, group delay (GD), modified GD (MGD), baseband phase difference, and cosine normalized phase features, were investigated in [17]- [19]. Spectrum and phase-based cepstral coefficients features, such as linear-frequency cepstral coefficients (LFCC), cochlear filter cepstral coefficients, mel-frequency cepstral coefficients (MFCC), cosine-normalized phase-based cepstral coefficients, and MGD filter-bank cepstral coefficients were studied in [17] and [20]- [24]. Other kinds of features, such as local binary patterns, pitch pattern features, utterance level i-Vectors [3], and modulation features were also discussed in [19] and [25]- [27].…”