Improving the energy efficiency and reliability of wireless sensor networks using coding techniques

Abstract

Wireless sensor networks (WSNs) are rapidly being adopted in a wide range of applications, from continuous health monitoring to automated industrial infrastructures, and soon will have a major environmental, financial and societal impact. Some of the main technical challenges in designing and deploying WSNs are meeting their communications reliability and energy consumption requirements. In order to address these two challenges, this thesis proposes new coding schemes and communication protocols, a novel paradigm for information acquisition, and the design and implementation of specific, circuits architectures. The reliability and energy efficiency trade-offs of splitting the inserted redundancy in multiple layers of the network stack are investigated through analysis and over-the-air experiments. Not only appropriate and efficient coding schemes for each layer are examined, but their interaction and synergistic functioning are explored. The energy benefits of each approach are quantified by designing a low-power custom transmitter using a 65nm TSMC process, integrating the first hardware implementation of a multi-rate forward error correction (FEC) and random linear network coding (RLNC) accelerator. In addition, a physical layer (PHY) independent partial packet reception (PPR) scheme is proposed for asymmetric networks, i.e. WSNs with a star topology, called packetized rateless algebraic consistency (PRAC). PRAC reduces the number of retransmissions by harnessing information contained in partial packets. Experiments with off-the-shelf transceivers validate our analysis results on the data reliability and energy consumption benefits of the proposed scheme. Apart from communicating information, acquiring the signals of interest can account for a significant fraction of the power consumption of a sensor node. For this reason, the thesis proposes a nonuniform sampling scheme in order to exploit the inherent compressibility and sparse structure of typical signals encountered in many WSNs. Simulations results with real datasets and an energy comparison against the state-of-the-art sampling schemes demonstrate its rate and energy efficiency advantages. Finally, the thesis studies the joint fundamental performance bounds of acquiring and transmitting sparse signals through noisy channels. An integrated source representation-to-transmission scheme, called AdaptCast, is proposed and, using rate distortion analysis, its asymptotically optimal performance is proved. Based on simulation results in the context of a health monitoring application, AdaptCast's performance benefits are demonstrated against other coding schemes and PHY architectures in terms of the provided data reliability and reconstruction distortion.