Research And Implementation Of Scheduling Algorithms For Wireless Sensor Networks

Wireless Sensor Networks mainly consists of a lagre number of micro sensor nodes, sensing andtransmitting the information. Due to the shortage of battery energy of the nodes, how to balance theenergy to extend the lifetime of networks has become the key point. Node scheduling is such aneffective solution. It doesn’t only effectively reduce the network energy consumption, but also canavoid the channel conflicts in the communication process, improving the performance of networks.At present, many achievements of node scheduling have been obtained, but mainly aboutomni-directional sensing model. Directed research in sensor networks are relatively less. In thisthesis, the related problems are studied. The nodes in the directional sensor networks are deployedby direction, and the corresponding scheduling is called directional node scheduling. And two kindsof scheduling algorithms, which are based on two-step optimization algorithm and quantum geneticalgorithm, are put forward.Based on two-step optimization algorithm, the scheduling algorithm puts forward theoptimizing standard of the directions of nodes, according to the influencing factors, then determinesthe optimal node perception directions through the calculation results of the utility value ofperception directon of nodes. At last the nodes are scheduled by grouping optimization algorithm.Theoretical analysis and simulation results show that two-step optimization algorithm is better thanthe greedy algorithm, and could effectively prolong networks lifetime.Directional sensor networks based on the quantum genetic algorithm takes a further research onthe basis of two-step optimization algorithm. The problem of node scheduling is formulated as anoptimization problem. It combines the quantum genetic algorithm with the directional nodescheduling. With the help of building the chromosome model and making the practical problemsabstracting by quantum coding, it puts forward the optimal solution. This method is simple and easyto operatation and realization. Theoretical analysis and simulation results show that it is better thantwo-step optimization algorithm.