Multi-cluster Head Algorithm And Simulation, Wireless Sensor Networks

Wireless Sensor Networks are networks of tiny, battery powered sensor nodes with limited on-board processing, storage and radio capabilities, which can perceive, collect and process the information of perceived objects in the coverage and send it to the observer. As the sensor nodes are usually battery-driven, once put into practice, it is difficult to supplement the energy again. So how to make good use of the limited energy resource to improve the efficiency of the entire network and prolong the lifetime of the whole network is a very important aspect in the research of WSN.In this dissertation, based on the application diversity and complexity of the environment in the wireless sensor networks, our main work is researching the energy consumption model and clustering algorithm in order to improve the energy efficiency and prolong the lifetime of the networks––an optimal energy consumption model and self-adaptive algorithm based on multi-cluster head are proposed. The main work of this dissertation includes the following aspects:1. Analyzing the development situation, research focus and problems in the wireless sensor networks. At present, there are a lot of research achievements in the field of sensor networks, but most of that are only improvements based on one or several parameters which has some theoretical reference value, but less project applied. So, how to form a unified protocol standard in sensor network research has become a major trend.2. Analyzing the characteristics of sensor networks, from the perspective of energy consumption, we analyze the energy consumption efficiency and propose the optimal energy consumption model––OECM. The new energy model is more general and project applied which establishes the basis for topology design and routing algorithm.3. Under the optimal consumption model, we propose Self- Adaptive Algorithm Based on Multi-Cluster Head––SABMH. Considering the complexity of sensor network and large-scale nature, there is a greater inconsistency in local network behavior, such as different energy consumption rate and various sizes of cluster structures and so on. Based on this point, SABMH algorithm can better coordinate the behavior of the sensor network and the state, to achieve greater energy efficiency and prolong the lifetime of the whole network.Simulation results show that: under the OEMC, the clustering of network is more accurate. The number of clusters is not only related with nodes n, but also with the network radius R and the task of the network which greater improves the project applied and has a good theoretical reference value. In SABMH algorithm, compared with LEACH algorithm, the network extends the effective lifetime of 16%, while the unstable period is shortened by 30% ~ 50% and the energy load parameters reduce 25% ~ 40%. Therefore, SABMH algorithm greatly improved the efficiency of energy consumption and prolonged the effective life cycle of the sensor network.