Probabilistic Data Propagation in Wireless Sensor Networks

We focus on the problem of data propagation in distributed wireless sensor networks. We present two characteristic methods for data propagation: the first one performs a local, greedy optimization to minimize the number of data transmissions needed, while the second creates probabilistically optimized redundant data transmissions to trade-off energy efficiency with fault-tolerance. Both approaches make use of randomization at both the algorithmic design and analysis; this demonstrates the suitability for sensor network algorithms of probabilistic techniques, because of their simplicity, locality and load balancing features.