Research Of Wireless Sensor Network Multicast Model Based On Hierarchical Windmill Structure

Wireless Sensor Network consists of a large number of sensor nodes deployed in themonitoring area. It is a multi-hop self-organizing network system, by way of wirelesscommunication. However, due that sensor nodes are of limited energy, how to reduce powerconsumption of sensor nodes and prolong the survival time of the whole network is of greatsignificance.There are more application scenarios of multiple receiving nodes appearing in Wireless SensorNetwork. Unicast and broadcast may increase the amount of data transmission and block thenetwork. While, multicast communication has characteristics of high bandwidth utilization,energy-saving, as well as reducing the repetition of sending a message. Therefore, applyingmulticast in the one-to-many application scenario of Wireless Sensor Network can reduce energyconsumption of sensor nodes and extend the survival time of the network.On the basis of existing research results, a new multicast model named HWSM (HierarchicalWindmill Structure Multicast Model) is put forward here in the thesis. Hierarchical structure as wellas the idea of clustering is combined in this model. HWSM uses Kruskal algorithm to find theoptimized path to forward data to specific region. HWSM also uses three performance indicators toelect cluster leader with better performance in each cluster, which enables the elected cluster leadersevenly distributed in the monitoring area. The performance indicators are as follows: remainingenergy of sensor node, energy consumption speed and the distance to Sink. After the election,cluster leaders form a multicast tree and forward data via the neighbor nodes in the tree, which hasan advantage of reducing the energy consumption of long-distance data transfer. Secondly,RS-HWSMRP (Region Specific Hierarchical Windmill Structure Multicast Routing Protocol) isalso presented based on HWSM. Lastly, simulation results are analysed and discussed. They verifythat HWSM and RS-HWSMRP can reduce and balance energy consumption of sensor nodes andprolong the survival time of the network.