In the process of optimal scheduling of sensor, we use an improved method of dynamic energy management to save energy consumption of the sensor node and extend life cycle of the network. This study includes the following sections:1, Based on multi-agent and strategy technology, maximizing the performance utility value, meeting the constraint matching conditions of the sensor cheap evening dresses and the task, we establish the distribution model of sensor.2, In accordance with the established distribution model of sensor, we use double-particle swarm algorithm for optimal scheduling.3, This paper improve double-particle swarm algorithm and use test laboratories to verify ralph lauren polo shirts that the improved algorithm can effectively avoid local optimization and improve the ability of global optimization.4, In accordance with an example of sensor allocation, we use Matlab simulation men's nike shoes experiments to show that, compared with the original double-particle swarm algorithm and genetic algorithms, applying improved double-particle swarm algorithm to sensor resources allocation of the wireless sensor network, we can achieve the better allocation results of sensor women's nike shoes resources.5, In the process of Lacoste Polo Shirts optimal scheduling of sensor resources, we introduce an improved dynamic energy management in order to reduce energy consumption of sensor node and extend the life cycle of the whole network.
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