Energy Consumption vs Battery LifeFor current smartphones, uploading small-size data via 3Gonce only consumes about 0.1% battery (e.g. Nokia N95with a 950mAh battery). However, energy-saving schemesstill play a role in effSense, because the battery drain canincrease for two reasons. First, more uploadings (i.e.shorter delay) and larger data will drain more battery.Second, the most ‘friendly’ data-plan users will help other2∼3 non-data-plan users upload data per collectionaccording to our experiment, which incurs extra batterydrain. E.g., as six 200KB data uploadings in the daytime(i.e. about 3-hour delay) drain about 1.5% battery, themost ‘friendly’ data-plan users will drain up to 5% batteryper day in data uploading. By introducing energy-savingschemes into effSense, the most ‘friendly’ data-plan userscan decrease the battery drain to around 1%.

Additional Critical EventsDue to the dataset limitation, we cannot capture all theuseful critical events in the experiment. For example,whether a phone is charging or not plays a vital role indeciding if data should be offloaded or kept. In our futurework, we plan to introduce the phone charging event andother useful events into effSense.

Other Issues to Improve effSenseWe can also improve effSense in many other directions,such as using a more precise energy estimation mechanismand an advanced user mobility/call prediction method.

ConclusionIn this paper, we investigate how to reduce both energyconsumption (for data-plan users) and mobile data cost(for non-data-plan users) caused by data uploading inmobile crowdsensing, and design the effSense frameworkto improve both users’ experience in delay-tolerant mobile

crowdsensing. The experiment results on MIT Reality andNodobo datasets verified the effectiveness of effSense.

References[1] Balasubramanian, N., Balasubramanian, A., and Venkataramani, A.

Energy consumption in mobile phones: a measurement study andimplications for network applications. In IMC (2009), 280–293.

[2] Bell, S., McDiarmid, A., and Irvine, J. Nodobo: Mobile phone as asoftware sensor for social network research. In Vehicular TechnologyConference (2011), 1–5.

[3] Chu, D., Lane, N. D., Lai, T. T.-T., Pang, C., Meng, X., Guo, Q., Li, F.,and Zhao, F. Balancing energy, latency and accuracy for mobile sensordata classification. In SenSys (2011), 54–67.

[4] Eagle, N. The reality mining data readme:http://www.media.mit.edu/ventures/EPROM/data/RealityMining_ReadMe.pdf.

[5] Eagle, N., and (Sandy) Pentland, A. Reality mining: sensing complexsocial systems. Personal Ubiquitous Comput. 10 (2006), 255–268.

[6] Hull, B., Bychkovsky, V., Zhang, Y., Chen, K., Goraczko, M., Miu, A.,Shih, E., Balakrishnan, H., and Madden, S. CarTel: a distributed mobilesensor computing system. In SenSys (2006), 125138.

[7] Kjrgaard, M. B., Bhattacharya, S., Blunck, H., and Nurmi, P.Energy-efficient trajectory tracking for mobile devices. In MobiSys(2011), 307–320.

[8] Musolesi, M., Piraccini, M., Fodor, K., Corradi, A., and Campbell, A. T.Supporting energy-efficient uploading strategies for continuous sensingapplications on mobile phones. In Pervasive Computing, P. Floren,A. Krger, and M. Spasojevic, Eds., no. 6030 in LNCS. 2010, 355–372.

[9] Nurminen, J. Parallel connections and their effect on the batteryconsumption of a mobile phone. In CCNC (2010), 1–5.

[10] Perrucci, G., Fitzek, F., and Widmer, J. Survey on energy consumptionentities on the smartphone platform (2011). 1–6.

[11] Phithakkitnukoon, S., Dantu, R., Claxton, R., and Eagle, N.Behavior-based adaptive call predictor. ACM Transactions onAutonomous and Adaptive Systems (TAAS) 6, 3 (2011), 21.

[12] Ra, M.-R., Paek, J., Sharma, A. B., Govindan, R., Krieger, M. H., andNeely, M. J. Energy-delay tradeoffs in smartphone applications. InMobiSys (2010), 255–270.

[13] Rachuri, K. K., Mascolo, C., Musolesi, M., and Rentfrow, P. J.SociableSense: exploring the trade-offs of adaptive sampling andcomputation offloading for social sensing. In MobiCom (2011), 73–84.

[14] Rana, R. K., Chou, C. T., Kanhere, S. S., Bulusu, N., and Hu, W.Ear-phone: an end-to-end participatory urban noise mapping system. InIPSN (2010), 105–116.

[15] Sheng, X., Tang, J., and Zhang, W. Energy-efficient collaborativesensing with mobile phones. In INFOCOM (2012), 1916–1924.

[16] Sherchan, W., Jayaraman, P. P., Krishnaswamy, S., Zaslavsky, A., Loke,S., and Sinha, A. Using on-the-move mining for mobile crowdsensing. InMDM (2012), 115–124.

[17] Vu, L., Do, Q., and Nahrstedt, K. Jyotish: A novel framework forconstructing predictive model of people movement from jointwifi/bluetooth trace. In PerCom (2011), 54–62.

Workshop – PUCAA: 1st International Workshop on Pervasive Urban Crowdsensing Architecture and Applications

UbiComp’13 Adjunct, September 8–12, 2013, Zurich, Switzerland

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