Wireless Sensor Network Resource Management And Scheduling

In wireless sensor networks, the use of network resources which are mainly referring to the sensor resources is associated with the implementation of tasks closely. The implementation of tasks needs to spend a certain amount of network sensor resources. But mostly as a result of wireless sensor networks can not be run in close proximity, the poor and even dangerous environment, and its huge number of sensor nodes and the node energy is limited, making the task for the successful implementation of rational, scientific and efficient sensor resource management is necessary. In addition, in the process of sensor management, if the hardware and software architecture of the wireless sensor network has been set, ensuring the completion of tasks under the premise of the use of dynamic energy management in order to reduce sensor node energy, thereby extending the life of the entire network is also necessary.Based on multi-agent and strategies technology, this paper introduces the sensor distribution model. We use an improved double-particle swarm algorithm to deal with this distribution model, makeing all the tasks implemented and maximizing the performance utility value. 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 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 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 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 resources.5, In the process of 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.