移動感測系統的省電技術研究

黃有德

doi:10.6342/NTU201700519

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移动感测系统的省电技术研究

Energy Efficient Enhancements for Mobile Sensing Systems

黄有德 , 博士　　导师：朱浩华　　

英文 DOI： 10.6342/NTU201700519

可适性工作周期；省电技术；目标追踪；轨迹记录；环境感测；全球定位系统； adaptive duty-cycle scheme ； energy efficiency ； target tracking ； trace logging ； environmental sensing ； Global Positioning System

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摘要 │ 参考文献 (38) │ 国际计量

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Mobile location sensing applications (MLSAs) represent an emerging genre of applications that exploit Global Positioning System (GPS) technology and facilitate location-based services. The design of MLSAs must incorporate a trade-off between information accuracy and energy efficiency because GPS technology is energy expensive and unaffordable for most MLSA platforms, which are battery-powered and therefore resource-constrained. Each scenario has different requirements and presents unique challenges. For example, the hiker tracking scenario requires timely and accurate location information, and as many location coordinates as possible must be collected. Based on our observation that the reception of GPS signals is spatially and temporally correlated, we propose an algorithm called the Adaptive Duty Cycle (ADC) scheme to exploit the spatio-temporal localities in the design of GPS scheduling algorithms. Using a comprehensive set of evaluations, as well as realistic hiker mobility traces, we evaluate the ADC scheme in terms of data granularity and power consumption. The results demonstrate that the scheme can achieve the Pareto optimum in all test cases.

For the trace logging scenario, using mobile devices to continuously log a trace over a period of time presents many opportunities for emerging applications. Most such applications are related to recreational activities that use mobile devices instead of paper maps to determine precise locations. GPS is preferred over GSM or Wi-Fi based position systems because of its accuracy. Duty-cycling GPS provides a trade-off between positioning accuracy and lower energy consumption. However, a non-uniform trace will make interpretation of the logging trace more challenging. To address these issues, we present Budget-based Duty Cycle (BDC) scheduling for time-bounded tracking. The method enables a mobile device to effectively log a complete trace over a period of time, while consuming a given amount of the device’s energy. More importantly, BDC uses a series of techniques that preserve power to ensure that the trace is completed and its sampling interval is uniform. BDC was motivated by our observation that GPS locks are not always successful during the GPS duty-cycle, and the power cost of a failed lock is greater than that of a successful lock. The method uses budget power to preserve power for failed GPS locks and automatically calculates the time interval of a lock from the remaining energy. Budget power employs the BDC-Hybrid function, which is a combination of two functions, namely, the BDC-Linear and BDC-Step functions. The former is a naive method that is used when GPS locks succeed, while the latter is more analytical and is used when GPS locks fail. Budget power is concerned with power preservation as well as the uniformity of the sampling of a trace.

For the environmental sensing scenario, we propose an algorithm called Adaptive Return-to-Home Sensing (ARS) for a drone sensing system deployed in an open area to conduct periodic environmental sensing. The ARS scheme can perform as many rounds of environmental sensing as necessary without drastic oscillations between consecutive sensing attempts and still conserve sufficient energy for the drone to return home. We also present a parameter-tuning algorithm that combines Naive Bayes Classification (NBC) and Binary Search (BS) to adapt the ARS scheme parameters effectively on the fly. Finally, we evaluate the ARS scheme under a variety of environmental difficulties. The results demonstrate that the scheme is effective in mitigating oscillations of spatial distance between consecutive sensing attempts. The NBC enhanced ARS scheme is better able to guarantee the Return-To-Home (RTH) feature, and it is more cost-effective in terms of parameter tuning than other machine learning based approaches. Moreover, the ADC, BDC and ARS schemes are simple, effective, and generalizable to other mobile location sensing applications in different scenarios.

参考文献 ( 38 ) 〈TOP〉

[1] J. Asama, M. R. Burkhardt, F. Davoodi, and J. W. Burdick. Design investigation of a coreless tubular linear generator for a moball: A spherical exploration robot with wind-energy harvesting capability. In IEEE International Conference on Robotics and Automation, 2015.
连结：
[2] C. Balocco, Y. Pan, M. C. Rosamond, and E. H. Linfield. Design and performance of a micro-rectenna focal plane array for thermal energy harvesting. In URSI Asia- Pacific Radio Science Conference, 2015.
连结：
[3] Y. Chon, Y. Kim, H. Shin, and H. Cha. Adaptive duty cycling for place-centric mobility monitoring using zero-cost information in smartphone. IEEE Transactions on Mobile Computing, 13(8):1694–1706, August 2014.
连结：
[4] S. B. Eisenman, E.Miluzzo, N.D. Lane, R.A. Peterson, G.-S. Ahn, and A.T. Campbell. BikeNet: A Mobile Sensing System for Cyclist Experience Mapping. ACM Transactions on Sensor Networks, 6(1):6:1–6:39, 2009.
连结：
[5] D. El-Damak and A. P. Chandrakasan. Solar energy harvesting system with inte- grated battery management and startup using single inductor and 3.2nw quiescent power. In IEEE Symposium on VLSI Circuits, 2015.
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引用含有DOI的文献

DOI可强化引用精确性、增强学术圈连结，并给予使用者跨平台的良好使用经验，目前在全世界已有超过五千万个对象申请DOI。 如想对DOI的使用与概念有进一步了解，请参考 （ ） 。

DOI可强化引用精确性、增强学术圈连结，并给予使用者跨平台的良好使用经验，目前在全世界已有超过五千万个对象申请DOI。如想对DOI的使用与概念有进一步了解，请参考（）。