Energy-preserving maintenance of k-centers on large wireless sensor networks

Recently, large wireless sensor networks have been used in many applications. Analyzing data detected by numerous sensors is one of the prominent issues in these applications. However, the power consumption of sensors is the major bottleneck of wireless sensor network lifetime. Energy-preserving data collection on large sensor networks becomes an important problem. In this thesis, we focus on continuously maintaining k-centers of sensor readings in a large sensor network. The goal is to preserve energy in sensors while the quality of k-centers is retained. We also want to distribute the clustering task into sensors, so that raw data and many intermediate results do not need to be transmitted to the server. We propose the reading reporting tree as the data collection and analysis framework in large sensor networks. We also introduced a uniform sampling method, a reporting threshold method and a lazy approach to achieve good quality approximation of k-centers.