Researches and simulations of wireless sensor network clustering algorithms

Wireless sensor networks are formations of a large number of nodes, which can be considered combinations of wireless networks and sensors. In wireless sensor networks, clustering algorithm is widely used to extend network life and reduce data redundancy. The present paper simulates and analyzes the existing algorithms, which are more applicable to static networks due to the lack of adaptability to topology changes. However the dynamic network will introduces mass data thereby making the implementation unprocurable. So, the paper proposes a double-heads-tradeoff clustering algorithm based on the minimum ID algorithm further choosing the cluster head from the tradeoff between the ID and the power. The algorithm can rebuild the clusters in the random event areas thereby to reduce data redundancy and improve network efficiency. We also offer a simulation model and test the efficiency of the algorithm, and analysis the trace and dynamic clustering situation of nodes. We found the double-head-trade-off clustering algorithm is better than classic algorithms in cluster head distribution and energy consumption equalization, and can be efficient in the network with a large number of mobile nodes.