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Initially, data quality evaluation indicators and their measurement methods in wireless sensor networks were introduced.
Numbers of researchers have proposed neural network (NN) and fuzzy logic based data aggregation methods in Wireless Environment.
The results presented show that Kernel Regression is an efficient solution for such scenarios, thus motivating further usage of Kernel methods in wireless modeling.
Furthermore, most of the conventional cooperation methods in wireless networks including [11] and [12] were discussed only in the link or/and physical layer.
This implies that the traditional subcarrier assignment methods in wireless communication system can be used in FBMC based CR network, which reduces the CR system complexity.
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Thus, this paper puts forward a node importance evaluation method in wireless sensor network based on energy field model.
We implement point-to-point simulations because the master synchronization is a physical-layer method in wireless sensor networks.
In addition, the optimum wordlength searched by the proposed method yields 30% lower hardware implementation costs than the sequential search method in wireless demodulators.
This article proposes a novel moment-based local cluster division optimization method in wireless sensor networks, and improves the energy efficiency of local cluster area with uneven nodes distribution.
This paper proposes a node importance evaluation method in wireless sensor network based on the energy field model, in which the influence transfer refers to the data transmission between different nodes and the transmission process refers to the process of influence transferring along the network topology.
As stated earlier, the current methods in the wireless telecommunications studies use zero degrees of freedom.
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