Peer-To-Peer Data Clustering In Self-Organizing Sensor Networks

This work proposes and evaluates distributed algorithms for data clustering in self-organizing ad-hoc sensor networks with computational, connectivity, and power constraints. Self-organization is essential in environments with a large number of devices, because the resulting system cannot be configured and maintained by specific human adjustments on its single components. One of the benefits of in-network data clustering algorithms is the capability of the network to transmit only relevant, high level information, namely models, instead of large amounts of raw data, also reducing drastically energy consumption. For instance, a sensor network could directly identify or anticipate extreme environmental events such as tsunami, tornado or volcanic eruptions notifying only the alarm or its probability, rather than transmitting via satellite each single normal wave motion. The efficiency and efficacy of the methods is evaluated by simulation measuring network traffic, and comparing the generated models with ideal results returned by density-based clustering algorithms for centralized systems.