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Many mobile sensing frameworks have been developed to help researcher doing their mobile sensing research. However, energy consumption is still an issue in the mobile sensing research, and the existing frameworks do not provide enough solution for solving the issue. We have surveyed several mobile sensing frameworks and carefully chose one framework to improve. We have designed an adaptive sampling module for a mobile sensing framework to help solve the energy consumption issue. However, in this study, we limit our design to an adaptive sampling module for the location and motion sensors. In our adaptive sampling module, we utilize the significant motion sensor to help the adaptive sampling. We experimented with two sampling strategies that utilized the significant motion sensor to achieve low-power consumption during the continuous sampling. The first strategy is to utilize the sensor naively only while the second one is to add the duty cycle to the naive approach. We show that both strategies achieve low energy consumption, but the one that is combined with the duty cycle achieves better result.

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기사명 저자명 페이지 원문 목차
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Significant Motion-Based Adaptive Sampling Module for Mobile Sensing Framework Muhammad Fiqri Muthohar, I Gde Dharma Nugraha, Deokjai Choi p.948-960

Integrated Lighting Enabler System Using M2M Platforms for Enhancing Energy Efficiency Maman Abdurohman, Aji Gautama Putrada, Sidik Prabowo, Catur Wirawan Wijiutomo, Asma Elmangoush p.1033-1048

Predicting the Unemployment Rate Using Social Media Analysis Pum-Mo Ryu p.904-915

QP-DTW : Upgrading Dynamic Time Warping to Handle Quasi Periodic Time Series Alignment Imen Boulnemour, Bachir Boucheham p.851-876

Clustering Algorithm Considering Sensor Node Distribution in Wireless Sensor Networks Boseon Yu, Wonik Choi, Taikjin Lee, Hyunduk Kim p.926-940

A New Perspective to Stable Marriage Problem in Profit Maximization of Matrimonial Websites Aniket Bhatnagar, Varun Gambhir, Manish Kumar Thakur p.961-979

Arm Orientation Estimation Method with Multiple Devices for NUI/NUX Yunsick Sung, Ryong Choi, Young-Sik Jeong p.980-988

Gait Recognition Algorithm Based on Feature Fusion of GEI Dynamic Region and Gabor Wavelets Jun Huang, Xiuhui Wang, Jun Wang p.892-903

Analysis of Academic Evaluation Indicators Based on Citation Quality Mingyue Zhang, Jin Shi, Jin Wang, Chang Liu p.916-925

A Solution towards Eliminating Transaction Malleability in Bitcoin Ubaidullah Rajput, Fizza Abbas, Heekuck Oh p.837-850

Motion-Blurred Shadows Utilizing a Depth-Time Ranges Shadow Map MinhPhuoc Hong, Kyoungsu Oh p.877-891

An Improved Spin Echo Train De-noising Algorithm in NMRL Feng Liu, Shuangbao Ma p.941-947

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Restful Web Services Composition Using Semantic Ontology for Elderly Living Assistance Services Sheik Mohammad Mostakim Fattah, Ilyoung Chong p.1010-1032

Measuring the Degree of Content Immersion in a Non-experimental Environment Using a Portable EEG Device Nam-Ho Keum, Taek Lee, Jung-Been Lee, Hoh Peter In p.1049-1061

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참고문헌 (28건) : 자료제공( 네이버학술정보 )

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번호 참고문헌 국회도서관 소장유무
1 T. Faetti and R. Paradiso, "A novel wearable system for elderly monitoring," Advances in Science and Technology, vol. 85, pp. 17-22, 2013. 미소장
2 An Android-Based Heart Monitoring System for the Elderly and for Patients with Heart Disease 네이버 미소장
3 Android and ODK based data collection framework to aid in epidemiological analysis. 네이버 미소장
4 Mobile Health: Revolutionizing Healthcare Through Transdisciplinary Research 네이버 미소장
5 HMM-Based Human Fall Detection and Prediction Method Using Tri-Axial Accelerometer 네이버 미소장
6 Distinguishing the causes of falls in humans using an array of wearable tri-axial accelerometers 네이버 미소장
7 P. Zhou, Y. Zheng, and M. Li, "How long to wait? Predicting bus arrival time with mobile phone based participatory sensing," in Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, Low Wood Bay, UK, 2012, pp. 379-392. 미소장
8 R. LiKamWa, Y. Liu, N. D. Lane, and L. Zhong, "Can your smartphone infer your mood," in Proceedings of International Workshop on Sensing Applications on Mobile Phone (PhoneSense), Seattle, WA, 2011, pp. 1-5. 미소장
9 A. Bogomolov, B. Lepri, and F. Pianesi, "Happiness recognition from mobile phone data," in Proceedings of 2013 International Conference on Social Computing (SocialCom), Alexandria, VA, 2013, pp. 790-795. 미소장
10 Mining large-scale smartphone data for personality studies 네이버 미소장
11 V. K. Singh, L. Freeman, B. Lepri, and A. S. Pentland, "Predicting spending behavior using socio-mobile features," in Proceedings of 2013 International Conference on Social Computing, Alexandria, VA, 2013, pp. 174-179. 미소장
12 N. Maisonneuve, M. Stevens, M. E. Niessen, and L. Steels, "NoiseTube: measuring and mapping noise pollution with mobile phones," in Information Technologies in Environmental Engineering. Heidelberg: Springer, 2009, pp. 215-228. 미소장
13 H. M. Thang, V. Q. Viet, N. D. Thuc, and D. Choi, "Gait identification using accelerometer on mobile phone," in Proceedings of 2012 International Conference on Control, Automation and Information Sciences (ICCAIS), Ho Chi Minh, Vietnam, 2012, pp. 344-348. 미소장
14 Secure and Privacy Enhanced Gait Authentication on Smart Phone 네이버 미소장
15 J. A. Burke, D. Estrin, M. Hansen, A. Parker, N. Ramanathan, S. Reddy, and M. B. Srivastava, "Participatory sensing," in Proceedings of the 1st Workshop on World-Sensor-Web: Mobile Device Centric Sensory Networks and Applications (WSW 2006), Boulder CO, 2006. 미소장
16 IEEE Communications Magazine 네이버 미소장
17 Mobile cloud sensing, big data, and 5G networks make an intelligent and smart world 네이버 미소장
18 K. K. Rachuri, C. Mascolo, M. Musolesi, and P. J. Rentfrow, "Sociablesense: exploring the trade-offs of adaptive sampling and computation offloading for social sensing," in Proceedings of the 17th Annual International Conference on Mobile Computing and Networking, Las Vegas, NV, 2011, pp. 73-84. 미소장
19 Google Developers, "About Android," [Online]. Available: https://developer.android.com/about/. 미소장
20 GitHub Inc., "funf-core-android," 2016 [Online]. Available: https://github.com/funf-org/funf-coreandroid/wiki/Configuration. 미소장
21 K. Katevas, H. Haddadi, and L. Tokarchuk, "Poster: Sensingkit: a multi-platform mobile sensing framework for large-scale experiments," in Proceedings of the 20th Annual International Conference on Mobile Computing and Networking, Maui, HI, 2014, pp. 375-378. 미소장
22 MSF: An Efficient Mobile Phone Sensing Framework 네이버 미소장
23 G. Cardone, A. Cirri, A. Corradi, L. Foschini, and R. Montanari, "Activity recognition for smart city scenarios: Google play services vs. MoST facilities," in Proceedings of 2014 IEEE Symposium on Computers and Communication (ISCC), Funchal, Portugal, 2014, pp. 1-6. 미소장
24 Google Developers, "Sensors overview," [Online]. Available: https://developer.android.com/guide/topics/sensors/sensors_overview. 미소장
25 Google Developers, "Sensor types," [Online]. Available: https://source.android.com/devices/sensors/sensor-types#significant_motion. 미소장
26 Google Developers, "Android dashboard," [Online]. Available: https://developer.android.com/about/dashboards/. 미소장
27 OpenSignal, "Android fragmentation (August 2015)," [Online]. Available: https://opensignal.com/reports/2015/08/android-fragmentation/. 미소장
28 K. S. Narendra and M. A. Thathachar, Learning Automata: An Introduction. Mineola, NY: Dover Publications, 2012. 미소장

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