||Preserving Target Area Coverage in Wireless Sensor Networks by Using Computational Geometry
||Master's Program in Networking and Communications, Department of Computer Science and Information En
Target area coverage
||在 Wireless Sensor Networks (WSNs)中，要如何安排Sensor Nodes 成為Active模式，進行感測重要區域的任務，並有效地延長WSNs 的工作時間，是一個非常重要的議題。本篇論文提出一個以計算幾何學中Voronoi Diagram 以及 Delaunay Triangulation 為理論基礎， 所設計之分散式工作排程機(Geometric-Base Activity Scheduling Scheme，GAS)。Sensor Nodes 可以自我決定何時進入Sleep 狀態，或是為了維持Coverage 的需求而進入Active 狀態。在GAS中，可以找到數量盡量少的Sensor Nodes 來負責維持感測任務。然而，我們可以知道尋求Sensor Nodes 的最少數量，是一個組合最佳化(Combinatorial Optimization)的問題，而此問題又是一個NP-Complete 的問題。最後透過實驗模擬結果顯示，GAS 能有效地規畫Sensor Nodes 何時在Active 以及Sleep 模式之間做切換，且當Sensor Nodes 分布不平均時，更能顯示出GAS 的效果，有效地延長網路的有效感測時間。
||The activity scheduling of sensors to alternately wake up for sensing obligation such that the network lifetime can be efficiently prolonged is a very important issue in wireless sensor networks (WSNs). Target area coverage is a new coverage problem in wireless sensor networks. The paper addresses the target area coverage problem and schedules sensors to alternatively wake up to collaboratively cover and sense the target area.
A geometric-based activity scheduling scheme, named GAS scheme, for WSNs to fully cover a target area is proposed. By means of computational geometry, the sensors can self-determine when to sleep or wake up while preserving the sensing coverage. GAS is able to find as few sensors as possible to cover the target area, which is termed a cover set. In addition, GAS can find as many number of cover sets as possible to be alternately in charge of the sensing task. Simulation results show that GAS can efficiently schedule the sensor when to switch between active and sleep modes. Furthermore, the network lifetime can be prolonged significantly in comparison with the state-of-the-art schemes.
||1 Abstract 1
2 Preliminaries 7
2.1 Problem description 8
2.2 Geometry 11
2.3 Basic concept 14
3 Cover Set discovery 17
3.1 Cover Set basic conditions 19
3.2 Cover Set in the establishment 25
3.3 Sensor Nodes of adding time 29
3.4 Algorithm 33
4 Scheduling mechanism 34
5 Simulation 37
5.1 Experiment 1: Special Scene 38
5.2 Experiment 2: normal distribution 44
6 Conclusion 53
List of Figures
Figure 2.1 Voronoi diagram 12
Figure 2.2 Delaunay Triangulation 13
Figure 2.3 Operation time architecture 14
Figure 2.4 Cover Set Schematic 15
Figure 3.1 Coverb Set of the set up 23
Figure 3.2 Cover Set The Ideal point 25
Figure 3.3 Cover Set Member option 27
Figure 3.4 s2 from the establishment of Cover Set 27
Figure 3.5 sensor nodes initiation time 30
Figure 3.6 sensor nodes of adding time 31
Figure 3.7 Cover Set of the set up time 32
Figure 4.1 Initiator active arranged according to the length of time remaining capacity 35
Figure 5.1 Sensor remaining capacity diagram 39
Figure 5.2 Sensor’s power consumption 41
Figure 5.3 Activity points 42
Figure 5.4 Effective working time of sensor (LifeTime) 45
Figure 5.5 Activities per unit of time 46
Figure 5.6 The activities of the general distribution points 48
Figure 5.7 All nodes in the network, the average power 49
Figure 5.8 GAS and FANS in the coverage point of Q the average residual power 50
Figure 5.9 The number can be identified Cover Set 51
Figure 5.10 Composition of Cover Set 52
List of Tables
Table 5.1 Simulation Parameters 38
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