無線感測網路之多重路徑感測器區域同步化

隨著科技的發展，內嵌無線通訊、精密感測計算等感測器裝置的研究技術成熟，無線感測器系統已經普遍應用在環境監測、自動化控制或是災害預防中。無線感測器能提供大量即時的監測資訊，如何收集、管理和應用這些資料是一大挑戰。另外，由於考量成本，感測器僅擁有受限的能源和資料處理能力，如何在資源受限的條件下，設計高效能的資料收集、資料傳輸和資料處理，成為另一個研究焦點。目前，最常見的資料聚合方式為樹狀式的聚合計算，但是樹狀式聚合計算的缺點在於高通訊失敗率，因此近年來研究無線感測器在多路徑傳遞的應用。多路徑聚合計算使得感測器廣播資料到多個感測器，因為多筆副本在網路中傳遞與計算，唯有所有副本都遺失才會造成其聚合值遺失，此方法大大的提升通訊的容錯能力。在廣大的監測環境中，因為感測器處於不同的區域，感測器有不同的取樣頻率(sampling frequency fi)，例如在森林火災預防中，鄰近硫磺礦區的環境溫度變化較大，此區的感測器需更為頻繁的取樣而得到較精確的溫度變化資料。不過，因為彼此取樣頻率的不同，子節點感測器傳輸資料可能會因為父節點感測器正在休眠而傳輸失敗。為了避免傳輸失敗，需調整感測器的工作頻率，使得兩兩感測器同步化，這個新的工作頻率稱為連通頻率(network frequency Fi)。為了解決這個不同步問題，全域同步化設計(global synchronization)，將所有感測器的連通頻率設為取樣頻率最大值。因為此法以高頻率工作而消耗電力過多，另一個研究[WY11]提出區域同步化設計(local synchronization)，區域同步化使得感測器兩兩同步化，能降低子節點的工作頻率，節省電力消耗。由於之前的研究僅能達成樹狀網路結構的感測器區域同步化，我們進一步探討多重路徑網路結構的感測器區域同步化。

關鍵字

區域同步化；無線感測器網路；能源使用；取樣頻率

並列摘要

Wireless sensor network (WSN) has been applied in difficult environments for long. In a WSN, the base station collects information from a number of sensors, taking measurements such as temperature, humidity and light. Since the sensors are equipped with limited power energy, they work in cycles. That is, the sensors are active in a fixed duration and sleep until next cycle. The frequencies of cycles may vary due to different accuracy or environmental requirements, determining the portion of active time and energy consumption. In order to improve energy efficiency, an existing work [WY11], local synchronization (LS), enables each sensor synchronizing with its neighbors. However, this work deals with local synchronization only in tree topology, not graph topology, and is not robust against sensor failures. To overcome the weakness, we propose a query processing technique for locally synchronized multi-path WSN. Specifically, given a routing graph and the sampling frequencies of sensors, we present a dynamic programming algorithm to find out the optimal network frequencies of sensors such that the WSN is locally synchronized. Furthermore, we demonstrate the experimental results both in real and synthetic datasets, validating the theoretical results and advantages of the proposed approach.

並列關鍵字

local synchronization ； wireless sensor network ； energy consumption ； sampling frequency

參考文獻

[CL04] J. Considine, F. Li, G. Kollios, and J. Byers, Approximate Aggregation Techniques for Sensor Databases, In Proc. of ICDE, 2004.

[DG04] A. Deshpande, C. Guestrin, S. Madden, J. Hellerstein, and W. Hong. Model-Driven Data Acquisition in Sensor Networks. In VLDB, 2004.

[MF05] S. Madden, M.J. Franklin, J.M. Hellerstein, W. Hong, "TinyDB: an acquisitional query processing system for sensor networks," ACM Transactions on Database Systems, vol. 30, no 1, pp. 122–173, 2005.

[MF03] S. Madden, M.J. Franklin, J. Hellerstein, The Design of an Acquisitional Query Processor for Sensor Networks, SIGMOD, 2003.

[MF02] S. Madden, M.J. Franklin, the Stream: An Architecture for Queries over Streaming Sensor Data, ICDE, 2002.

國際替代計量

無線感測網路之多重路徑感測器區域同步化

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