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研究生： 朱威誠
Chu, Wei-Cheng
論文名稱： 在無位置資訊及移動式蒐集點的無線感測網路中實現具時效性資料匯集和邊界偵測演算法
Delay-constrained Data Aggregation and Boundary Detection in Location-free Mobile-sink Wireless Sensor Networks
指導教授： 斯國峰
Ssu, Kuo-Feng
學位類別： 博士
Doctor
系所名稱： 電機資訊學院 - 電腦與通信工程研究所
Institute of Computer & Communication Engineering
論文出版年： 2014
畢業學年度： 102
語文別： 英文
論文頁數： 95
中文關鍵詞： 無線感測網路 、無位置資訊 、資料整合 、邊界偵測
外文關鍵詞： wireless sensor network, location-free, data aggregation, boundary detection
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    • 資料匯集在無線感測網路中已被研究一段時間。於中繼節點中進行資料整合能夠降低封包傳輸量，進而減少傳輸所需的成本。以往的相關研究著重在靜態的網路中，所以並不適合應用於擁有移動式蒐集點的環境。為了在移動式蒐集點的環境中實現資料匯集的技術，本論文發展出一以叢集為基礎的移動式蒐集點搜尋演算法（CMSE），將資料封包有效率地導引至蒐集點。在移動式蒐集點的環境中搜尋蒐集點並建立節點到蒐集點之間的傳輸路徑是一項很大的挑戰，尤其是在無位置資訊的限制下。頻繁地更新蒐集點位置至網路上的節點會造成節點的電量耗損過大。基於此，如何降低更新成本便值得探討。在CMSE中，一資料來源端能不利用蒐集點位置資訊的情況下判斷出蒐集點所在，並且節點會建立多條由自己到蒐集點間的傳輸路徑，以提升網路壽命。由於節點功能失效或是新節點的加入，網路拓樸會因此產生變動，所以CMSE也提出了一維護的機制，以維持CMSE的正常運作。實驗結果顯示，相較於其它演算法，CMSE能夠在不同的環境中節省更多電量消耗，進而提升網路壽命。
    基於CMSE，本論文發展出一個具時效性的資料匯集演算法（DCDA）以用於移動式蒐集點的環境中。在DCDA裡，節點可動態調整自己對資料的保留時間以增進資料整合的效率。此外，考慮到兩節點間傳輸時的連線品質，DCDA利用ACK的機制以確認封包傳輸是否成功。從實驗結果可看出DCDA提供了比其它作法更佳的資料整合率以及網路壽命。
    DCDA的效能有大半取決於網路上運作正常的節點數量。雖然在CMSE中已提供一維護的機制，但網路仍可能需要進行空洞修補或是重新佈點。為了判斷網路是否有此需求，邊界偵測演算法便有其存在的必要。偵測且標示邊界與網路中許多功能具有很大的相依性，像是繞徑協定、覆蓋率確認等。之前許多基於拓樸架構的邊界偵測演算法，都未曾考慮到移動式節點的環境。當網路拓樸經過變動後，這些演算法必須重新偵測並建立所有的邊界。基於此，本論文提出一分散式邊界偵測演算法（DBD）以標示障礙物及網路的邊界。在DBD中，每個節點只會蒐集三步鄰居的資訊，其它訊息（如節點位置資訊）並不需要。一個節點能夠利用DBD以分散式的方法自行判斷自己是否是邊界節點。根據實驗後的比較，DBD能夠在靜態及動態網路環境中精確地偵測出邊界。本論文同時也針對DBD進行實作，以顯示其在真實環境中的可行性。

    Data aggregation in Wireless Sensor Networks (WSNs) has been studied recently. Aggregating data at intermediate nodes can reduce the number of exchanged messages and consequently lower communication cost. Previous data aggregation schemes were developed for static sink environments. Therefore, they are not suitable for mobile sink environments. To aggregate data in a mobile sink environment, a cluster-based mobile sink exploration (CMSE) scheme is developed to guide data packets efficiently to mobile sinks. Searching for a sink and determining routing paths are challenging tasks in a mobile sink environment, especially in a location-free environment. Updating the sink location frequently by transmitting messages to preserve a route greatly increases the energy consumption of sensor nodes. Therefore, methods to lower updating costs should be investigated. In CMSE, a source node can identify the sink location without knowledge of node locations, and multiple routing paths are established from a sensor node to the sink to enhance network longevity. A network topology might change because of either sensor incapacitation or the addition of a new sensor. Thus, CMSE presents a maintenance mechanism to allow the scheme. Simulation results show that compared with the use of previous methods, using the CMSE scheme helps save more energy and increases network longevity under various scenarios.

    Based on CMSE, a delay-constrained data aggregation (DCDA) scheme is developed for mobile sink environments. The DCDA scheme dynamically adjusts the data holding time of each node to improve aggregation performance. The DCDA scheme considers link qualities and uses ACK messages to examine transmission results. Simulations indicate that DCDA provides better data aggregation rate and network longevity than previous schemes.

    The performance of DCDA is typically proportional to the number of available sensor nodes. Even though the maintenance mechanism has been developed in CMSE, the network might need a hole healing or redeployment method to preserve its performance. To accomplish it, a boundary detection scheme is required. Detecting and locating boundaries have a great relevance for network services, such as routing protocol, coverage verification, and so on. Previous designs, which adopt topology-based approaches to recognizing obstacles or network boundaries, did not consider the environment with mobile sensor nodes. When a network topology changes, a topology-based approach has to reconstruct all boundaries. This thesis develops a distributed boundary detection (DBD) algorithm for identifying the boundaries of obstacles and networks. Each node only requires the information of its three-hop neighbors. Other information (e.g., node locations) is not needed. A node with DBD can determine whether itself is a boundary node by a distributed manner. The DBD approach further identifies the outer boundary of a network. Performance evaluation demonstrates that DBD can detect boundaries accurately in both static and mobile environments. This study also includes experiments to show that DBD is applicable in a real sensor network.

    1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Cluster-based Mobile Sink Exploration . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Delay-constrained Data Aggregation Scheme . . . . . . . . . . . . . . . . . . 2 1.3 Distributed Boundary Detection . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Thesis Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4.1 Cluster-based Mobile Sink Exploration . . . . . . . . . . . . . . . . . 6 1.4.2 Delay-constrained Data Aggregation Scheme . . . . . . . . . . . . . . 7 1.4.3 Distributed Boundary Detection . . . . . . . . . . . . . . . . . . . . . 7 2 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1 Cluster-based Mobile Sink Exploration . . . . . . . . . . . . . . . . . . . . . . 9 2.1.1 Rendezvousbased Approaches . . . . . . . . . . . . . . . . . . . . . 9 2.1.2 Backbonebased Approaches . . . . . . . . . . . . . . . . . . . . . . . 10 2.2 Delay-constrained Data Aggregation Scheme . . . . . . . . . . . . . . . . . . 11 2.3 Distributed Boundary Detection . . . . . . . . . . . . . . . . . . . . . . . . . 12 3 Cluster-based Mobile Sink Exploration . . . . . . . . . . . . . . . . . . . . . . 14 3.1 Network Model and Assumption . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.2 Overview of CMSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.2.1 Perspective of a Cluster . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.2.2 Perspective of a Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.3 Cluster Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.4 Cluster Broadcasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.5 Registration Web Construction . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.5.1 Cluster Flooding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.5.2 Registration Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.6 Exploration Path . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.7 Maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.7.1 Sensor Entry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.7.2 Sensor Exit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.8 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.8.1 Registration Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.8.2 Exploration Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.8.3 Size of the Registration Web . . . . . . . . . . . . . . . . . . . . . . . 33 3.8.4 Delivery Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4 Delay-constrained Data Aggregation Scheme . . . . . . . . . . . . . . . . . . . 36 4.1 Network Model and Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 4.2 Aggregation Timer Assignment . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.3 Transmission Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.4 Timer Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 5 Distributed Boundary Detection . . . . . . . . . . . . . . . . . . . . . . . . . . 40 5.1 Network Assumptions and System Model . . . . . . . . . . . . . . . . . . . . 40 5.2 The Boundary of an Obstacle . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 5.3 Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 5.3.1 Initialization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 5.3.2 Detection Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 5.3.3 Pruning Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.4 Dynamic Boundary Detection . . . . . . . . . . . . . . . . . . . . . . . . . . 47 5.4.1 Neighbor Exit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.4.2 Neighbor Entry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.5 Outer Boundary Determination . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.5.1 Boundary Classification . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.5.2 Outer Boundary Recognition . . . . . . . . . . . . . . . . . . . . . . . 50 5.6 Proof of Correctness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 6 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 6.1 Cluster-based Mobile Sink Exploration . . . . . . . . . . . . . . . . . . . . . . 55 6.2 Delay-constrained Data Aggregation Scheme . . . . . . . . . . . . . . . . . . 65 6.2.1 The First type of Network Longevity . . . . . . . . . . . . . . . . . . 66 6.2.2 The Second type of Network Longevity . . . . . . . . . . . . . . . . . 68 6.3 Distributed Boundary Detection . . . . . . . . . . . . . . . . . . . . . . . . . 71 7 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 8 Conclusion and FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 8.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 8.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Vita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

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