In-Network Coding for Resilient Sensor Data Storage and Efficient Data Mule Collection
Source: Stony Brook University
In a sensor network of n nodes in which k of them have sensed interesting data, the authors perform in-network erasure coding such that each node stores a linear combination of all the network data with random coefficients. This scheme greatly improves data resilience to node failures: as long as there are k nodes that survive an attack, all the data produced in the sensor network can be recovered with high probability. The in-network coding storage scheme also improves data collection rate by mobile mules and allows for easy scheduling of data mules.