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Titlebook: Optimal Coverage in Wireless Sensor Networks; Weili Wu,Zhao Zhang,Ding-Zhu Du Book 2020 Springer Nature Switzerland AG 2020 group Steiner

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51#
發(fā)表于 2025-3-30 12:13:16 | 只看該作者
52#
發(fā)表于 2025-3-30 15:48:06 | 只看該作者
53#
發(fā)表于 2025-3-30 18:38:24 | 只看該作者
erent theoretical framework for analysis and covers state-of-the-art methods. The book describes practical methods that can be used for voltage security assessment and offers a variety of examples. This is a first attempt to condense the technical papers and reports on this subject into a single, co
54#
發(fā)表于 2025-3-30 21:37:14 | 只看該作者
Fundamental Issues, detecting disasters, examining human’s bodies, and collecting data from hostile area, etc. In many applications, the coverage is a fundamental requirement and hence becomes an important issue in study of sensor systems, especially wireless sensor networks. For example, when a request comes to ask f
55#
發(fā)表于 2025-3-31 02:19:22 | 只看該作者
Connected Sensor Cover,Das, and Gu. They presented a greedy algorithm with performance ratio . where . is the number of sensors and . is the . of the sensor network, i.e., for any two sensors . and . with a sensing point in common, there exists a path between . and . with hop distance at most . in communication network. Z
56#
發(fā)表于 2025-3-31 08:59:28 | 只看該作者
57#
發(fā)表于 2025-3-31 10:21:38 | 只看該作者
Grid-Based Deployment,area) coverage? It is not hard to show that the optimal solution is the triangle grid such that each cell is an equilateral triangle. However, when the requirement is the full coverage together with others, such as .-connectivity, the optimal solution would be nontrivial. Actually, there exist a lot
58#
發(fā)表于 2025-3-31 16:33:04 | 只看該作者
59#
發(fā)表于 2025-3-31 19:24:18 | 只看該作者
60#
發(fā)表于 2025-3-31 22:13:04 | 只看該作者
Probabilistic Coverage, However in the real world, a sensor covers targets with a certain probability, called the ., due to various issues which cause the uncertainty and form various probabilistic models, including the probabilistic network model, the probabilistic sensing model, the random noise model, the random fault
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