A node mobility clustering method and system
A mobility and node technology, applied in the field of communication engineering, can solve the problems that cluster head selection cannot achieve global optimization, shorten the network life cycle, and too many factors of manual intervention.
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Embodiment 1
[0049] Such as figure 1 As shown, a node mobility clustering method provided in this embodiment includes:
[0050] Step 101: According to the set mutation probability, perform a genetic mutation operation on the optimal cluster to obtain a dominant subgraph and a non-dominated subgraph, and store the non-dominant subgraph in the first set corresponding to the current iteration number, and set The dominant subgraph is stored in the second set corresponding to the current number of iterations; wherein the optimal cluster is the optimal solution; the optimal solution is obtained by sequentially inputting the clusters in the initial population into the multi-objective optimization function Obtained; the optimal solution is a cluster comprising only one cluster head node; the multi-objective optimization function is an objective function of maximizing residual energy, minimizing connection distance, and maximizing connection time; the initial population includes multiple clusters;...
Embodiment 2
[0066] In order to improve the clustering accuracy of the large-scale wireless sensor network, on the basis of Embodiment 1, an additional step is added to adjust the mutation probability and the selection probability, and repeat steps 101 to 107. Wherein, in this embodiment, no initial population construction and multi-objective optimization function construction are required, and the total number of iterations remains unchanged.
Embodiment 3
[0068] Such as figure 2 As shown, a node mobility clustering system provided in this embodiment includes:
[0069] The storage module 100 is configured to perform a genetic mutation operation on the optimal cluster according to a set mutation probability to obtain a dominant subgraph and a non-dominated subgraph, and store the non-dominant subgraph in the first set corresponding to the current iteration number , store the dominant subgraph in the second set corresponding to the current iteration number; wherein, the optimal cluster is the optimal solution; the optimal solution is obtained by sequentially inputting the clusters in the initial population into the multi-objective Obtained in the optimization function; the optimal solution is a cluster that only contains one cluster head node; the multi-objective optimization function is an objective function that maximizes the remaining energy, minimizes the connection distance, and maximizes the connection time; the initial Th...
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