Abstract
Distributed storage coding has been widely applied on data gathering over unreliable wireless sensor networks (WSNs), where it is essential to ensure the data persistence in case of massive sensor failures caused by battery run-out or some physical damage problems surroundings. How to efficiently and scalably disseminate and collect the sensing data over WSNs is a key challenge yet. In this study, assumed that there are K sensor nodes equipped sensing apparatus within N storage sensors, these K numbers of sensors can sense environmental changes and disseminate coded (by Fountain codes) time-series data over WSNs using the simple random walk. That is, each sensor will receive others’ data, randomly select d (which according to the chosen degree distribution) of them, and encode into an encoded data then store it. In this thesis, we employ two types of Fountain codes: Luby Transform (LT) codes and Repairable Fountain (RF) codes to maintain the level of data persistence. In order to perform the Fountain codes over WSNs, the question is to disseminate data in the long range of random walks to preserve the randomness so as to boost the source decoded rate. In other words, the hop count would be long enough such that the decoding process can be perfectly completed using less amount of redundancy (say 10%). In this thesis, a framework of less communication cost is proposed due to the temporal dependency of time-series data. The concept is simple: the complete decoding is not necessary for most of time-series data since the missing portions can be compensated by neighbors if exists. Our framework works as follows. We separate time-series data into two categories. For a given number t, the data corresponding to the numbers divisible by t will be totally recovered with high probability; however, the data corresponding to the other time slots is set as partially recovered. As mentioned, the missing one can be interpolated by the nearby neighboring data through temporal dependency. The proposed framework employs LT codes and RF codes to increase the level reliability. Besides, a mathematical model to estimate the appropriate source decoded rate is proposed to reduce the transmission cost (hop count) while maintaining tolerable level (< 4% normalized root-mean-square error (NRMSE)) of errors as well.