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The problem of acquiring critical points from the monitored physical world is also defined. 2.
Therefore, the algorithms for retrieving the critical data points from the monitored physical world are quite important for WSNs.
Moreover, none of them could support the requirement of retrieving the critical points from the monitored physical world.
Since the variation of the monitored physical world in the future is unknown, the posterior estimation is adopted, that is, the history sensory data collected in the current time will be used to estimate the variation of the monitored physical world in the future.
The experimental results show that both of the precision and recall rate of our proposed algorithms are quite high to retrieve δ -approximate extremum point and δ -approximate inflection point from the monitored physical world.
For any given error bound ε, the authors proved that the result returned by their algorithm is O approximate to the monitored physical world, where ε can be arbitrarily small.
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In his living room, the Weather Channel monitors the physical world.
The coverage directly reflects the capability of network to monitor the physical world, and energy consumption determines the lifetime of the wireless sensor networks.
Suppose that t s and t f denote the start and final time in monitoring the physical world by a WSN, respectively.
Therefore, VSNs offers integrated communication protocols for effectively monitoring the physical world, especially in urban areas where a high concentration of vehicles equipped with onboard sensors is expected [5, 6].
In smart factories cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions.
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