This thesis presents EcoSlend, a power management framework for resource-constrained wireless sensor platforms running a power-hungry application. It exploits some hardware functionalities to optimize the energy consumption of a node. EcoSlend implements several power management mechanisms including time synchronization, threshold detection, and low-power listening to make nodes wake up only when they require. Furthermore, EcoSlend offers several sleep / wakeup policies with different duty cycles. Programmers can select the most appropriate policy according to the characteristic of the application running on the nodes. EcoSlend also provides a battery monitor component called the Power Monitor to help programmers get the battery status during run-time. To ease programmers' burden, the implementation of this framework is packed into several APIs so that programmers can apply EcoSlend to their applications with just through a few function calls. We apply EcoSlend to two “real-running” wireless sensor network (WSN) applications to evaluate the efficiency of our framework. The experimental results show that both applications can save at least 70% of current consumption after applying the appropriate EcoSlend policies with only a few hundred bytes code memory overhead.

本論文提出了EcoSlend，針對資源有限且執行一個耗電的應用程式之無線感測平台所設計的電源管理框架。EcoSlend利用一些硬體的功能來對無線感測器的耗電進行優化，並且實做了幾個電源管理機制，包括時間同步(Time Synchronization)、臨界值偵測(Threshold Detection)以及低耗電封包監聽(Low-power Listening)。

針對不同的工作週期，EcoSlend設計了三種醒睡的策略，使用者可以根據無線感測器上執行的應用程式之特性來套用最合適的策略。另外，EcoSlend也提供了一個電池監控元件來幫助使用者動態的取得目前電池的狀態。為了減輕使用者負擔，本論文的實做都被包成應用程式介面(Application Program Interface)的形式，使用者只需透過數個函式呼叫便可套用我們設計的電源管理框架到無線感測平台上。

為了驗證EcoSlend的效能，我們將此框架套用到兩個實際運作的無線感測網路(WSN)系統中。實驗結果顯示，兩個系統在套用合適的策略之後都節省了至少百分之七十以上的耗電，並且只額外佔用少量的記憶體空間。

[1] MicaZ. http://www.xbow.com/Products/Product_pdf_files/Wireless_pdf/MICAZ_Datasheet.pdf.
[2] nRF24LE1 product specification. http://www.nordicsemi.no/files/Product/data_sheet/nRF24LE1_Product_Spec_v1_3.pdf.
[3] Understanding ZigBee RF4CE. http://www.zigbee.org/imwp/download.asp?ContentID=16212.
[4] ALIPPI, C., ANASTASI, G., GALPERTI, C., MANCINI, F., AND ROVERI, M. Adaptive sampling for energy conservation in wireless sensor networks for snow monitoring applications. In Proc. IEEE International Workshop on Mobile Ad Hoc and Sensor Systems for Global and Homeland Security (MASS-GHS 2007), Pisa, Italy (2007), Citeseer.
[5] ANASTASI, G., CONTI, M., DI FRANCESCO, M., AND PASSARELLA, A. Energy conservation in wireless sensor networks: A survey. Ad Hoc Networks 7, 3 (2009), 537–568.
[6] BENBASAT, A., AND PARADISO, J. An inertial measurement framework for gesture recognition and applications. Gesture and Sign Language in Human-Computer Interaction (2002), 77–90.
[7] BENINI, L., BOGLIOLO, A., AND DE MICHELI, G. A survey of design techniques for system-level dynamic power management. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 8, 3 (2000), 299–316.
[8] FASOLO, E., ROSSI, M., WIDMER, J., AND ZORZI, M. In-network aggregation techniques for wireless sensor networks: a survey. IEEE Wireless Communications 14, 2 (2007), 70–87.
[9] GANERIWAL, S., KUMAR, R., AND SRIVASTAVA, M. Timing-sync protocol for sensor networks. In Proceedings of the 1st international conference on Embedded networked sensor systems (2003), ACM, p. 149.
[10] HE, T., KRISHNAMURTHY, S., STANKOVIC, J., ABDELZAHER, T., LUO, L., STOLERU, R.,YAN, T., GU, L., HUI, J., AND KROGH, B. Energy-efficient surveillance system using wireless sensor networks. In Proceedings of the 2nd international conference on Mobile systems, applications, and services (2004), ACM, pp. 270–283.
[11] HUI, J., REN, Z., AND KROGH, B. Sentry-based power management in wireless sensor networks. In Information Processing in Sensor Networks (2003), Springer, pp. 551–551.
[12] KESHAVARZIAN, A., LEE, H., AND VENKATRAMAN, L. Wakeup scheduling in wireless sensor networks. In Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing (2006), ACM, p. 333.
[13] KIM, S., PAKZAD, S., CULLER, D., DEMMEL, J., FENVES, G., GLASER, S., AND TURON, M. Wireless sensor networks for structural health monitoring. In Proceedings of the 4th international conference on Embedded networked sensor systems (2006), ACM, p. 428.
[14] KIMURA, N., AND LATIFI, S. A survey on data compression in wireless sensor networks. In Information Technology: Coding and Computing, 2005. ITCC 2005. International Conference on (2005), vol. 2.
[15] LIU, H., CHANDRA, A., AND SRIVASTAVA, J. eSENSE: Energy efficient stochastic sensing framework for wireless sensor platforms. In Information Processing in Sensor Networks, 2006. IPSN 2006. The Fifth International Conference on (2006), pp. 235–242.
[16] PARK, C., AND CHOU, P. Eco: Ultra-wearable and expandable wireless sensor platform. In Wearable and Implantable Body Sensor Networks, 2006. BSN 2006. International Workshop on (2006), p. 4.
[17] PARK, G., ROSING, T., TODD, M., FARRAR, C., AND HODGKISS, W. Energy harvesting for structural health monitoring sensor networks. Journal of Infrastructure Systems 14 (2008), 64.
[18] PASSOS, R., COELHO, C., LOUREIRO, A., AND MINI, R. Dynamic power management in wireless sensor networks: An application-driven approach. In Wireless On-demand Network Systems and Services, 2005. WONS 2005. Second Annual Conference on (2005), pp. 109–118.
[19] PILLAI, P., AND SHIN, K. Real-time dynamic voltage scaling for low-power embedded operating systems. In Proceedings of the eighteenth ACM symposium on Operating systems principles (2001), ACM, p. 102.
[20] POLASTRE, J., SZEWCZYK, R., AND CULLER, D. Telos: enabling ultra-low power wireless research. In Information Processing in Sensor Networks, 2005. IPSN 2005. Fourth International Symposium on (2005), pp. 364–369.
[21] POTTIE, G., AND KAISER, W. Wireless integrated network sensors. Communications of the ACM 43, 5 (2000), 51–58.
[22] RABAEY, J., AMMER, J., KARALAR, T., LI, S., OTIS, B., SHEETS, M., AND TUAN, T. PicoRadios for wireless sensor networks: the next challenge inultra-low power design. In 2002 IEEE International Solid-State Circuits Conference, 2002. Digest of Technical Papers. ISSCC (2002), vol. 1.
[23] RAGHUNATHAN, V., SCHURGERS, C., PARK, S., AND SRIVASTAVA, M. Energy-aware wireless microsensor networks. IEEE Signal Processing Magazine 19, 2 (2002), 40–50.
[24] ROBERT, A. Batteries for low power electronics. Proceedings of the IEEE 83, 4 (1995), 687.
[25] SINHA, A., CHANDRAKASAN, A., AND MIT, C. Dynamic power management in wireless sensor networks. IEEE Design & Test of Computers 18, 2 (2001), 62–74.
[26] VAN DAM, T., AND LANGENDOEN, K. An adaptive energy-efficient MAC protocol for wireless sensor networks. In Proceedings of the 1st international conference on Embedded networked sensor systems (2003), ACM, p. 180.
[27] WEISER, M., WELCH, B., DEMERS, A., AND SHENKER, S. Scheduling for reduced CPU energy. Mobile Computing (1996), 449–471.
[28] WERNER-ALLEN, G., JOHNSON, J., RUIZ, M., LEES, J., AND WELSH, M. Monitoring volcanic eruptions with a wireless sensor network. In Wireless Sensor Networks, 2005. Proceeedings of the Second European Workshop on, pp. 108–120.
[29] XU, Y., HEIDEMANN, J., AND ESTRIN, D. Geography-informed energy conservation for adhoc routing. In Proceedings of the 7th annual international conference on Mobile computing and networking (2001), ACM, p. 84.
[30] YE, W., HEIDEMANN, J., AND ESTRIN, D. Medium access control with coordinated adaptive sleeping for wireless sensor networks. IEEE/ACM Trans. Netw. 12, 3 (2004), 493–506.