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Issue No. 01 - Jan. (2015 vol. 14)
ISSN: 1536-1233
pp: 194-207
Zhonghong Ou , Department of Computer Science and Engineering, Aalto University, Espoo, Finland
Jiang Dong , Department of Computer Science and Engineering, Aalto University, Espoo, Finland
Shichao Dong , Department of Computer Science and Engineering, Aalto University, Espoo, Finland
Jun Wu , Department of Computer Science and Engineering, Aalto University, Espoo, Finland
Antti Yla-Jaaski , Department of Computer Science and Engineering, Aalto University, Espoo, Finland
Pan Hui , Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
Ren Wang , Systems and Software Research at Intel Labs, 2111 N.E. 25th Avenue, Hillsboro, OR
Alexander W. Min , Systems and Software Research at Intel Labs, 2111 N.E. 25th Avenue, Hillsboro, OR
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 real-time, 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 pseudo-identical, 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 percent compared to the conventional opportunistic scheduling, i.e., schedule transmissions only based on instantaneous channel conditions.
INDEX TERMS
Power demand, Throughput, Multiaccess communication, Spread spectrum communication, High definition video, Smart phones
CITATION

Z. Ou et al., "Utilize Signal Traces from Others? A Crowdsourcing Perspective of Energy Saving in Cellular Data Communication," in IEEE Transactions on Mobile Computing, vol. 14, no. 1, pp. 194-207, 2015.
doi:10.1109/TMC.2014.2316517
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