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

• Published in: IEEE transactions on mobile computing 2015-01, Vol.14 (1), p.194-207
• Main Authors: Zhonghong Ou, Jiang Dong, Shichao Dong, Jun Wu, Yla-Jaaski, Antti, Pan Hui, Ren Wang, Min, Alexander W.
• Format: Magazinearticle
• Language: English
• Subjects: Algorithms; Crowdsourcing; Devices; Energy conservation; Energy consumption; Energy efficiency; Energy management; High definition video; Mobile communication systems; Multiaccess communication; Power demand; Scheduling; Signal strength; Smart phones; Smartphones; Spread spectrum communication; Throughput; Wireless networks
• ISSN: 1536-1233; 1558-0660
• DOI: 10.1109/TMC.2014.2316517

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.