Data Gathering with Tunable Compression in Sensor Networks

We study the problem of constructing a data gathering tree over a wireless sensor network in order to minimize the total energy for compressing and transporting information from a set of source nodes to the sink. This problem is crucial for advanced computation-intensive applications, where traditional "maximum" in-network compression may result in significant computation energy. We investigate a tunable data compression technique that enables effective tradeoffs between the computation and communication costs. We derive the optimal compression strategy for a given data gathering tree and then investigate the performance of different tree structures for networks deployed on a grid topology as well as general graphs. Our analytical results pertaining to the grid topology and simulation results pertaining to the general graphs indicate that the performance of a simple greedy approximation to the Minimal Steiner Tree (MST) provides a constantfactor approximation for the grid top...

• Type: Journal
• Year: 2008
• Where: TPDS
• Authors: Yang Yu, Bhaskar Krishnamachari, Viktor K. Prasanna
• Keywords: General Graphs, Grid Topology, Simple Greedy Approximation, TPDS 2008