The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dalam makalah ini, kami mengkaji masalah memproses pertanyaan julat berterusan dalam rangkaian penderia wayarles hierarki. Baru-baru ini, apabila saiz rangkaian sensor meningkat disebabkan oleh pertumbuhan persekitaran pengkomputeran di mana-mana dan rangkaian wayarles, membina rangkaian sensor wayarles dalam hierarki konfigurasi dikemukakan sebagai pendekatan praktikal. Berbeza dengan pendekatan tradisional membina rangkaian dalam struktur "rata" menggunakan peranti penderia dengan keupayaan yang sama, pendekatan hierarki menggunakan peranti berkeupayaan lebih tinggi dalam peringkat yang lebih tinggi, iaitu, peringkat yang lebih dekat dengan pelayan. Walaupun pemprosesan pertanyaan dalam rangkaian penderia rata telah dikaji secara meluas, kajian mengenai pemprosesan pertanyaan dalam rangkaian penderia hierarki telah tidak mencukupi. Dalam rangkaian penderia wayarles, kos utama yang perlu dipertimbangkan ialah tenaga untuk menghantar data dan storan untuk menyimpan pertanyaan. Terdapat pertukaran antara kedua-dua kos ini. Berdasarkan ini, kami mula-mula mencadangkan a pemprosesan progresif kaedah yang berkesan memproses sejumlah besar pertanyaan julat berterusan dalam rangkaian penderia hierarki. Kaedah yang dicadangkan menggunakan teknik penggabungan pertanyaan yang dicadangkan oleh Xiang et al. sebagai asas. Di samping itu, kaedah ini mempertimbangkan pertukaran antara kedua-dua kos. Lebih khusus lagi, ia berfungsi ke arah mengurangkan kos penyimpanan pada nod peringkat rendah dengan menggabungkan lebih banyak pertanyaan dan ke arah mengurangkan kos tenaga pada nod peringkat lebih tinggi dengan menggabungkan lebih sedikit pertanyaan (dengan itu mengurangkan "penggera palsu"). Kami kemudiannya membentangkan cara membina rangkaian sensor hierarki iaitu optimum berkenaan dengan jumlah wajaran kedua-dua kos. Ini membolehkan kawalan sistematik berasaskan kos bagi pertukaran berdasarkan kepentingan relatif antara storan dan tenaga dalam persekitaran rangkaian dan aplikasi tertentu. Keputusan eksperimen menunjukkan bahawa kaedah yang dicadangkan mencapai kawalan yang hampir optimum antara penyimpanan dan tenaga dan mengurangkan kos sebanyak 1.002 -- 3.210 kali berbanding dengan kos yang dicapai menggunakan tetapan rata (iaitu, bukan hierarki) seperti dalam kerja oleh Xiang et al.
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Salinan
Jeong-Hoon LEE, Kyu-Young WHANG, Hyo-Sang LIM, Byung SUK LEE, Jun-Seok HEO, "Progressive Processing of Continuous Range Queries in Hierarchical Wireless Sensor Networks" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 7, pp. 1832-1847, July 2010, doi: 10.1587/transinf.E93.D.1832.
Abstract: In this paper, we study the problem of processing continuous range queries in a hierarchical wireless sensor network. Recently, as the size of sensor networks increases due to the growth of ubiquitous computing environments and wireless networks, building wireless sensor networks in a hierarchical configuration is put forth as a practical approach. Contrasted with the traditional approach of building networks in a "flat" structure using sensor devices of the same capability, the hierarchical approach deploys devices of higher-capability in a higher tier, i.e., a tier closer to the server. While query processing in flat sensor networks has been widely studied, the study on query processing in hierarchical sensor networks has been inadequate. In wireless sensor networks, the main costs that should be considered are the energy for sending data and the storage for storing queries. There is a trade-off between these two costs. Based on this, we first propose a progressive processing method that effectively processes a large number of continuous range queries in hierarchical sensor networks. The proposed method uses the query merging technique proposed by Xiang et al. as the basis. In addition, the method considers the trade-off between the two costs. More specifically, it works toward reducing the storage cost at lower-tier nodes by merging more queries and toward reducing the energy cost at higher-tier nodes by merging fewer queries (thereby reducing "false alarms"). We then present how to build a hierarchical sensor network that is optimal with respect to the weighted sum of the two costs. This allows for a cost-based systematic control of the trade-off based on the relative importance between the storage and energy in a given network environment and application. Experimental results show that the proposed method achieves a near-optimal control between the storage and energy and reduces the cost by 1.002 -- 3.210 times compared with the cost achieved using the flat (i.e., non-hierarchical) setup as in the work by Xiang et al.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.1832/_p
Salinan
@ARTICLE{e93-d_7_1832,
author={Jeong-Hoon LEE, Kyu-Young WHANG, Hyo-Sang LIM, Byung SUK LEE, Jun-Seok HEO, },
journal={IEICE TRANSACTIONS on Information},
title={Progressive Processing of Continuous Range Queries in Hierarchical Wireless Sensor Networks},
year={2010},
volume={E93-D},
number={7},
pages={1832-1847},
abstract={In this paper, we study the problem of processing continuous range queries in a hierarchical wireless sensor network. Recently, as the size of sensor networks increases due to the growth of ubiquitous computing environments and wireless networks, building wireless sensor networks in a hierarchical configuration is put forth as a practical approach. Contrasted with the traditional approach of building networks in a "flat" structure using sensor devices of the same capability, the hierarchical approach deploys devices of higher-capability in a higher tier, i.e., a tier closer to the server. While query processing in flat sensor networks has been widely studied, the study on query processing in hierarchical sensor networks has been inadequate. In wireless sensor networks, the main costs that should be considered are the energy for sending data and the storage for storing queries. There is a trade-off between these two costs. Based on this, we first propose a progressive processing method that effectively processes a large number of continuous range queries in hierarchical sensor networks. The proposed method uses the query merging technique proposed by Xiang et al. as the basis. In addition, the method considers the trade-off between the two costs. More specifically, it works toward reducing the storage cost at lower-tier nodes by merging more queries and toward reducing the energy cost at higher-tier nodes by merging fewer queries (thereby reducing "false alarms"). We then present how to build a hierarchical sensor network that is optimal with respect to the weighted sum of the two costs. This allows for a cost-based systematic control of the trade-off based on the relative importance between the storage and energy in a given network environment and application. Experimental results show that the proposed method achieves a near-optimal control between the storage and energy and reduces the cost by 1.002 -- 3.210 times compared with the cost achieved using the flat (i.e., non-hierarchical) setup as in the work by Xiang et al.},
keywords={},
doi={10.1587/transinf.E93.D.1832},
ISSN={1745-1361},
month={July},}
Salinan
TY - JOUR
TI - Progressive Processing of Continuous Range Queries in Hierarchical Wireless Sensor Networks
T2 - IEICE TRANSACTIONS on Information
SP - 1832
EP - 1847
AU - Jeong-Hoon LEE
AU - Kyu-Young WHANG
AU - Hyo-Sang LIM
AU - Byung SUK LEE
AU - Jun-Seok HEO
PY - 2010
DO - 10.1587/transinf.E93.D.1832
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2010
AB - In this paper, we study the problem of processing continuous range queries in a hierarchical wireless sensor network. Recently, as the size of sensor networks increases due to the growth of ubiquitous computing environments and wireless networks, building wireless sensor networks in a hierarchical configuration is put forth as a practical approach. Contrasted with the traditional approach of building networks in a "flat" structure using sensor devices of the same capability, the hierarchical approach deploys devices of higher-capability in a higher tier, i.e., a tier closer to the server. While query processing in flat sensor networks has been widely studied, the study on query processing in hierarchical sensor networks has been inadequate. In wireless sensor networks, the main costs that should be considered are the energy for sending data and the storage for storing queries. There is a trade-off between these two costs. Based on this, we first propose a progressive processing method that effectively processes a large number of continuous range queries in hierarchical sensor networks. The proposed method uses the query merging technique proposed by Xiang et al. as the basis. In addition, the method considers the trade-off between the two costs. More specifically, it works toward reducing the storage cost at lower-tier nodes by merging more queries and toward reducing the energy cost at higher-tier nodes by merging fewer queries (thereby reducing "false alarms"). We then present how to build a hierarchical sensor network that is optimal with respect to the weighted sum of the two costs. This allows for a cost-based systematic control of the trade-off based on the relative importance between the storage and energy in a given network environment and application. Experimental results show that the proposed method achieves a near-optimal control between the storage and energy and reduces the cost by 1.002 -- 3.210 times compared with the cost achieved using the flat (i.e., non-hierarchical) setup as in the work by Xiang et al.
ER -