Wireless Sensor Networks (WSNs) that use tiny wireless devices capable of communicating, processing, and sensing promise to have applications in virtually all fields. Smart homes and smart cities are just few of the examples that WSNs can enable. Despite their potential, WSNs suffer from reliability and energy limitations. In this study, we address the problem of designing Distributed Data Storage Systems (DDSSs) for WSNs using decentralized erasure codes. A unique aspect of WSNs is that their data is inherently decentralized. This calls for a decentralized mechanism for encoding and decoding. We propose a distributed data storage framework to increase data survivability in WSNs. The framework utilizes Decentralized Erasure Codes for Data Survivability (DEC-DS) which allow for determining the amount of redundancy required in both hardware and data to allow sensed data to survive failures in the network. To address the energy limitations, we show two approaches to implement the proposed solution in an energy efficient manner. The two approaches employ Random Linear Network Coding (RLNC) to exploit coding opportunities in order to save energy and in turn prolong network life. A routing based scheme, called DEC Encode-and-Forward (DEC-EaF), applies to networks with routing capability, while the second, DEC Encode-and-Disseminate (DEC-EaD), uses a variation of random walk to build the target code in a decentralized fashion. We also introduce a new decentralized approach to implement Luby Transform (LT)-Codes based DDSSs. The scheme is called Decentralized Robust Soliton Storage (DRSS) and it operates in a decentralized fashion and requires no coordination between sensor nodes. The schemes are tested through extensive simulations to evaluate their performance. We also compare the proposed schemes to similar schemes in the literature. The comparison considers energy efficiency as well as coding related aspects. Using the proposed schemes can greatly improve the reliability of WSNs especially under harsh working conditions. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2013-09-30 22:43:04.509