隨著手機技術的發展，手機上配備有更多的感測器。這些感測
器的資訊被廣泛的使用和開發在情境感知的應用程式(Context-Aware
applications)。利用感測器資訊推論外在環境狀況或使用者的活動情
形。目前的手機系統未提供整合性的感測器排程，這些情境感知的應
用程式會各自獨立操作感測器的開關及資料的讀取，導致不必要的電
量消耗。在本篇論文中，我們強調有效的整合應用程式並找出最佳的
感測器使用方法。對於單一手機，我們提出一個middleware介於應用
程式和手機硬體之間，用以溝通應用程式並規劃和控制感測器的開
關。目前手機被廣泛的使用於日常生活中，我們更進一步的讓感測
器排程整合更多手機上或是基礎設備中的感測器，並將此想法應用
到crowdsensing系統中。首先，我們對單一手機設計、實作和分析一
個middleware，此middleware 權衡感測器的電量消耗和情境感知的精準
度，找出最佳的感測器使用方法。我們將問題分成兩種並用數學式子
表示: (1) 滿足應用程式的要求，最小化電量消耗和(2)在有限的電量
下，最大化情境感知的精準度。我們分別提出兩個最佳化演算法和快
速的演算法並用Java開發模擬器。從實驗結果表示，快速的演算法可
以即時的解決問題、節省電量消耗和最佳化演算法平均只有∼ 3%的
差距。我們將演算實做到Android系統上並成功節省電量的消耗。在
論文的第二部分，我們推廣感測器排程的概念到多隻手機上並將其應
用到crowdsensing 系統中。我們設計一個crowdsensing系統，系統依照
手機使用者的位址和能力(ex: 剩餘電量)，找出最佳的工作分配方式
以降低碳排放的量。我們用數學式子表示問題並提出兩個最佳化/快
速的演算法。利用Java開發的模擬器所得到的結果表示，快速的演算
法可以減少364倍的碳排放量、加速工作完成(8倍)和最佳化演算法只
有∼ 2%的差距。

Existing context-aware mobile applications directly control sensors in the
mobile devices in an uncoordinated and non-optimized manner, which leads
to redundant sensor activations and energy waste. Optimal and coordinated
sensor usage dictates a comprehensive mobile middleware solution with sensor
scheduling on single device to bring together the information from all
applications/sensors and intelligently select the best set of sensors to activate.
While the widespread use of smartphones, we cooperate the sensors on multiple
smartphones and infrastructure sensors to build a novel crowdsensing
system.
In Chap. 3, we design, implement, and evaluate a novel green sensor management
middleware for single device that rigorously makes tradeoffs between
energy consumption of sensors and accuracy of inferred contexts. The
problem is formulated rigorously as mathematical optimization problems that
(i) minimize the total energy consumption while achieving the required accuracy
and (ii) maximize the overall accuracy under a given energy budget. Two
optimal algorithms for these two optimization problems are proposed, which
provide the performance bounds. As they may lead to prohibitively long running
time, two efficient heuristic algorithms are then presented, which run in
real-time. Extensive trace-driven simulations are conducted using traces from
real Android users to evaluate the performance of the proposed middleware
and algorithms. The simulation results indicate that the heuristic algorithms:
(i) always terminate in real-time, (ii) result in small optimization gap of up
to ∼ 2%, and (iii) lead to better performance for larger problems. We also
implement and evaluate the proposed middleware and algorithms on real Android
smartphones, showing their practicality and efficiency.
For the extension, we consider the sensor scheduling on multiple smartphones
and infrastructure sensors in Chap. 4. We apply the extensive consideration
to crowdsensing system. We present a Smartphone Augmented
Infrastructure Sensing (SAIS) system that offers better situation awareness to
officials and civilians for minimizing the amount of generated carbon dioxide.
The SAIS system minimizes the carbon footprint by solving the task
assignment problem. We mathematically formulate the problems and optimally
solve it using optimization problem solvers, and we also proposed an
efficient task assignment algorithm (ETA) for lower running time. Our tracedriven
simulations show the results of our efficient algorithm: (i) saves up to
364 times in carbon footprint, (ii) outperforms by up to 8 times in responding
time, and (iii) achieves a small optimization gap of ∼ 2%.

中文摘要i
Abstract ii
1 Introduction 1
1.1 Sensor Scheduling for Single Device . . . . . . . . . . . . . . . . . . . . 2
1.2 Sensor Scheduling for Multiple Devices . . . . . . . . . . . . . . . . . . 3
1.3 Contributions of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Related Work 6
2.1 Sensor Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Crowdsensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3 Sensor Scheduling for Single Mobile Device 9
3.1 Framework :OSM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.1.1 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.1.2 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2 Sensor Scheduling Problem Formulations . . . . . . . . . . . . . . . . . 12
3.2.1 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.2.2 Problem Formulations . . . . . . . . . . . . . . . . . . . . . . . 15
3.3 Sensor Scheduling Algorithms . . . . . . . . . . . . . . . . . . . . . . . 15
3.3.1 Optimal Sensor Scheduling Algorithms (EMA/AMA) . . . . . . 16
3.3.2 Efficient Energy Minimization Algorithm (EEMA) . . . . . . . . 17
3.3.3 Efficient Accuracy Maximization Algorithm (EAMA) . . . . . . 18
3.3.4 Heterogeneous Frequency and Sampling Rate . . . . . . . . . . . 19
3.4 Trace-Driven Simulations for Single Device . . . . . . . . . . . . . . . . 21
3.4.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.4.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.5.1 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.5.2 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4 Sensor Scheduling for Multiple Devices: CrowdSensing 33
4.1 Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.2 Task Scheduling Problem . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.2.1 System Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.2.2 Problem Formulations . . . . . . . . . . . . . . . . . . . . . . . 35
4.2.3 Optimal Task Scheduling Algorithm (OPT) . . . . . . . . . . . . 35
4.2.4 Efficient Task Scheduling Algorithm (ETA) . . . . . . . . . . . . 36
4.3 Trace-Driven Simulations for Multiple Devices . . . . . . . . . . . . . . 37
4.3.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.3.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . 38
5 Conclusion and FutureWork 42
Bibliography 44

[1] IPInfoDb. http://www.ipinfodb.com/index.php.
[2] User’s guide, 66321B/D mobile communications dc source. http://cp.literature.agilent.com/litweb/pdf/5964-8184.pdf.
[3] V. Antila, J. Polet, A. Sarjanoja, P. Saarinen, and M. Isomursu. Contextcapture:Exploring the usage of context-based awareness cues in informal information sharing.
In Proc. of International Academic MindTrek Conference: Envisioning Future Media Environments(MindTrek ’11), pages 269–275, Tampere, Finland, September 2011.
[4] M. Baldauf, S. Dustdar, and F. Rosenberg. A survey on context-aware systems.International Journal of Ad Hoc and Ubiquitous Computing, 2(4), 2007.
[5] Y. Chon, N. Lane, Y. Kim, F. Zhao, and H. Cha. A large-scale study ofmobile crowdsourcing with smartphones for urban sensing applications. In Proc. of ACM International
Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’13),Zurich, Switzerland, September 2013.
[6] Y. Chon, N. Lane, F. Li, H. Cha, and F. Zhao. Automatically characterizing places with opportunistic crowdsensing using smartphones. In Proc. of ACM Conference
on Ubiquitous Computing (UbiComp’12), pages 481–490, Pittsburgh, Pennsylvania, September 2012.
[7] Y. Chon, E. Talipov, H. Shin, and H. Cha. Mobility prediction-based smartphone energy optimization for everyday location monitoring. In Proc. of ACM Conference on Embedded Networked Sensor Systems (SenSys’11), Seattle, WA, 2011.
[8] E. Chuvieco and R. Congalton. Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sensing of Environment,29(2):147–159, August 1989.
[9] Context-aware applications. what are they and how can they help your business? http://tinyurl.com/kjqq2bo.
[10] ContextMenus. http://developer.chrome.com/extensions/
contextMenus.html.
[11] V. Coric and M. Gruteser. Crowdsensing maps of on-street parking spaces. In Proc. of IEEE International Conference on Distributed Computing in Sensor Systems
(DCOSS’13), pages 115–122, Cambridge, MA, May 2013.
[12] IBM ILOG CPLEX optimizer. http://www-01.ibm.com/software/integration/optimization/cplex-optimizer/.
[13] R. Ganti, F. Ye, and H. Lei. Mobile crowdsensing: Current state and future challenges. IEEE Communication Magazine, 49(11):32–39, November 2011.
[14] Gartner says context-aware technologies will affect $96 billion of annual consumer spending worldwide by 2015. http://www.gartner.com/newsroom/id/1827614.
[15] Gimbal. https://developer.qualcomm.com/mobile-development/mobile-technologies/context-aware-gimbal.
[16] GoogleMap. https://maps.google.com/.
[17] D. Hasenfratz, O. Saukh, S. Sturzenegger, and L. Thiele. Participatory air pollution monitoring using smartphones. In Proc. of International Workshop on Mobile
Sensing, Beijing, China, April 2012.
[18] P. Holub, H. Rudov´a, and M. Liˇska. Data transfer planning with tree placement for collaborative environments. Constraints, 16(3), July 2011.
[19] J. Hsieh, S. Yu, Y. Chen, and W. Hu. Automatic traffic surveillance system for vehicle tracking and classification. Intelligent Transportation Systems, 7(2):175–187, June 2006.
[20] K. Kaer. A survey of context-aware middleware. In Proc. of Conference on IASTED International Multi-Conference: Software Engineering, Innsbruck, Austria, 2007.
[21] S. Kang, J. Lee, H. Jang, H. Lee, Y. Lee, S. Park, T. Park, and J. Song. SeeMon:scalable and energy-efficient context monitoring framework for sensor-rich mobile
environments. In Proc. of the International Conference on Mobile Systems, Applications, and Services (MobiSys’08), Breckenridge, CO, 2008.
[22] D. Kim, Y. Kim, D. Estrin, and M. Srivastava. SensLoc: sensing everyday places and paths using less energy. In Proc. of ACM Conference on Embedded Networked Sensor Systems (SenSys’10), Zurich, Switzerland, 2010.
[23] N. Lane, Y. Chon, L. Zhou, Y. Zhang, F. Li, D. Kim, G. Ding, F. Zhao, and H. Cha. Piggyback crowdsensing (pcs): energy efficient crowdsourcing of mobile sensor data by exploiting smartphone app opportunities. In Proc. of ACM Conference on Embedded Networked Sensor Systems (SenSys’13), page 7, Rome, Italy, November 2013.
[24] J. Lee and U. Chandra. Mobile phone-to-phone personal context sharing. In Proc. of IEEE International Symposium on Communications and Information Technology(ISCIT’09), pages 1034–1039, Incheon, Korea, September 2009.
[25] C. Liao and C. Hsu. A detour planning algorithm in crowdsourcing systems for multimedia content gathering. In Proc. of Workshop on Mobile Video(MoVid’13), pages 55–60, Oslo, Norway, February 2013.
[26] C.-L. Lin. An Energy/Accuracy-Optimized Framework for Context Sensing on Smartphones. Master’s thesis, National Tsing Hua University, Taiwan, 2013.
[27] K. Lin, A. Kansal, D. Lymberopoulos, and F. Zhao. Energy-accuracy trade-off for continuous mobile device location. In Proc. of International Conference on Mobile
systems, Applications, and Services (MobiSys’10), San Francisco, CA, 2010.
[28] J. Liu, E. Chu, and P. Tsai. Fusing human sensor and physical sensor data. In Proc. of IEEE International Conference on Service-Oriented Computing and Applications
(SOCA’12), pages 1–5, Taipei, Taiwan, December 2012.
[29] H. Lu, J. Yang, Z. Liu, N. Lane, T. Choudhury, and A. Campbell. The Jigsaw continuous sensing engine for mobile phone applications. In Proc. of ACM Conference on Embedded Networked Sensor Systems (SenSys’10), Zurich, Switzerland, 2010.
[30] Y. Ma, R. Hankins, and D. Racz. iLoc: a framework for incremental location-state acquisition and prediction based on mobile sensors. In Proc. of ACM Conference on Information and Knowledge Management (CIKM’09), Hong Kong, China, 2009.
[31] E. Miluzzo, N. Lane, K. Fodor, R. Peterson, H. Lu, S. Eisenman, X. Zheng, and A. Campbell. Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application. In Proc. of ACM Conference on Embedded Networked Sensor Systems (SenSys’08), Raleigh, NC, 2008.
[32] My Act. http://xyo.net/android-app/my-act-cXMTrVs/.
[33] S. Nath. ACE: exploiting correlation for energy-efficient and continuous context sensing. In Proc. of International Conference on Mobile Systems, Applications, and Services (MobiSys’12), Low Wood Bay, UK, 2012.
[34] Nike. https://play.google.com/store/apps/details?id=com.nike.plusgps.
[35] V. Paschos. A survey of approximately optimal solutions to some covering and packing problems. Journal of ACM Computing Surveys, 29(2), 1997.
[36] G. R.K., F. Ye, and H. Lei. Mobile crowdsensing: current state and future challenges. Communications Magazine, IEEE, 49(32-39), November 2011.
[37] A. Saeed and T.Waheed. An extensive survey of context-awaremiddleware architectures. In Proc. of IEEE International Conference on Electro/Information Technology
(EIT’10), Normal, IL, 2010.
[38] S. Sahni. Approximate algorithms for the 0/1 knapsack problem. Journal of the ACM, 22(1), 1975.
[39] M. Schirmer and H. H¨opfner. SENST*: approaches for reducing the energy consumption of smartphone-based context recognition. In Proc. of International and Interdisciplinary
Ionference on Modeling and Using Context (CONTEXT’11), Berlin, Heidelberg, 2011.
[40] M. Talasila, R. Curtmola, and C. Borcea. Improving location reliability in crowd sensed data with minimal efforts. In Proc. of Joint IFIP Wireless and Mobile Networking Conference (WMNC’13), Dubai, United Arab Emirates, April 2013.
[41] P. Troubil and H. Rudov´a. Integer linear programming models for media streams planning. Lecture Notes in Management Science, 2011(3), August 2011.
[42] Ultra-low-power, context-aware motion-recognition platform to result from stmicroelectronics
and movea cooperation. http://www.st.com/web/en/press/
t3468.
[43] Y. Wang, J. Lin, M. Annavaram, Q. Jacobson, J. Hong, B. Krishnamachari, and N. Sadeh. A framework of energy efficient mobile sensing for automatic user state
recognition. In Proc. of International Conference on Mobile Systems, Applications, and Services (MobiSys’09), Krak´ow, Poland, 2009.
[44] G. Xing, R. Tan, B. Liu, J. Wang, X. Jia, and C. Yi. Data fusion improves the coverage of wireless sensor networks. In Proc. of the 15th Annual International
Conference on Mobile Computing and Networking (MobiCom’09), pages 157–168, Beijing, China, 2009.
[45] T. Yan, V. Kumar, and D. Ganesan. Crowdsearch: exploiting crowds for accurate real-time image search on mobile phones. In Proc.of the International Conference
on Mobile Systems, Applications, and Services (MobiSys’10), pages 77–90, San Francisco, CA, USA, June 2010.
[46] Z. Yan, H. Jeung, D. Chakraborty, A. Misra, and K. Aberer. SAMMPLE: Detecting semantic indoor activities in practical settings using locomotive signatures. In Proc.
of International Symposium on Wearable Computers (ISWC’12), Newcastle, UK, 2012.
[47] Z. Yan, V. Subbaraju, D. Chakraborty, A. Misra, and K. Aberer. Energy-efficient continuous activity recognition on mobile phones: An activity-adaptive approach. In
Proc. of International Symposium on Wearable Computers (ISWC’12), Newcastle, UK, 2012.
[48] M. Yuen, I. King, and K. Leung. A survey of crowdsourcing systems. In Proc. of IEEE International Conference on Social Computing (SocialCom’11), pages 766–
773, Boston, MA, USA, October 2011.
[49] P. Zhou, Y. Zheng, and M. Li. How long to wait?: Predicting bus arrival time with mobile phone based participatory sensing. In Proc. of ACM International Conference on Mobile Systems, Applications, and Services (MobiSys’12), pages 379–392, Lake District, UK, June 2012.