Scalable Sensor Data Management under Frequent Content Changes in Peer-to-Peer Network

Abstract

Scalable data management is essential to share real-time sensor data in a worldwide sensor web. A peer-to-peer (P2P) network is suitable for it because of its scalability, flexibility, and robustness. When we share sensor data from the physical world in a global P2P network, overhead in data updates may be very large because sensor data frequently change in general. However, traditional works in P2P network do not cope with the problem. In this paper, we propose a method for load balancing on a structural P2P network supporting range queries based on geographic location. By using computer simulation for performance evaluation, we show that our proposed method achieves effective load balancing even under the non-uniform rates of data update and query access by geographic location.