Low power body sensor network design based on relaying of creeping waves in the unlicensed 2.4GHz band

Body Sensor Networks are an important enabling technology for future applications in remote medical diagnostics. Practical deployments of these systems have only recently edged closer to viability, due in part to advances in low power electronics and System-On-Chip devices. Wireless communication between these sensors remains a daunting challenge, and designers typically leverage existing industrial standards designed for applications with significantly different communications requirements. This Thesis proposes a wireless communications platform designed specifically for body mounted sensors, exploiting a phenomenon in electromagnetic wave propagation known as a creeping wave. Relaying of these waves leads to a highly reliable body sensor network with very low power consumption in the unlicensed 2.4 GHz band. A link budget is derived based on the creeping wave component of the transmitted signal, which is then used to design a spread spectrum wireless transceiver. Significant attention is given to interference mitigation, allowing the system to co-exist with other wireless devices on the internationally unlicensed band. Fading statistics from both anechoic and high multipath scenarios are used to define a channel model for the system. The link budget and channel model lead to the proposed use of relaying as a power savings technique, and this concept is a core feature of the design. This technique is shown to provide reliable total body coverage with very low transmission power, a result that has eluded body sensor networks to date. Various relaying topologies are discussed, and robust operation for highly mobile users is achieved via sensor handoffs, a concept that resembles a similar solution in cellular networks. The design extends to define a polling protocol and packet structures. Objective performance metrics are defined, and the proposed system is evaluated in line with these metrics. The power reduction of the suggested approach is analyzed by comparing the network lifetime and energy-per-bit to those of a reference system offering the same quality of service without relaying. The analysis results in generic closed form expressions of significant gains. The improvement in network lifetime increases with the number of sensors and settles at approximately 8x104, 7x106, 7x107 and 3x108 for 2,4,6 and 8 relaying nodes respectively. The energy-per-bit is shown to decrease by 2, 116, 828 and 2567 for 2, 4, 6 and 8 relay nodes respectively

Runtime Adaptation in Embedded Computing Systems using Markov Decision Processes

During the design and implementation of embedded computing systems (ECSs), engineers must make assumptions on how the system will be used after being built and deployed. Traditionally, these important decisions were made at design time for a fleet of ECSs prior to deployment. In contrast to this approach, this research explores and develops techniques to enable adaptation of ECSs at runtime to the environments and applications in which they operate. Adaptation is enabled such that the usage assumptions and performance optimization decisions can be made autonomously at runtime in the deployed system. This thesis utilizes Markov Decision Processes (MDPs), a powerful and well established mathematical framework used for decision making under uncertainty, to control computing systems at runtime. The resulting control is performed in ways that are more dynamic, robust and adaptable than alternatives in many scenarios. The techniques developed in this thesis are first applied to a reconfigurable embedded digital signal processing system. In this effort, several challenges are encountered and resolved using novel approaches. Through extensive simulations and a prototype implementation, the robustness of the adaptation is demonstrated in comparison with the prior state-of-the-art. The thesis continues by developing an efficient algorithm for conversion of MDP models to actionable control policies - a required step known as solving the MDP. The solver algorithm is developed in the context of ECSs that contain general purpose embedded GPUs (graphics processing units). The novel solver algorithm, Sparse Parallel Value Iteration (SPVI), makes use of the parallel processing capabilities provided by such GPUs, and also exploits the sparsity that typically exists in MDPs when used to model and control ECSs. To extend the applicability of the runtime adaptation techniques to smaller and more strictly resource constrained ECSs, another solver - Sparse Value Iteration (SVI) is developed for use on microcontrollers. The method is explored in a detailed case study involving a cellular (LTE-M) connected sensor that adapts to varying communications profiles. The case study reveals that the proposed adaptation framework outperforms a competing approach based on Reinforcement Learning (RL) in terms of robustness and adaptation, while consuming comparable resource requirements. Finally, the thesis concludes by analyzing the various logistical challenges that exist when deploying MDPs on ECSs. In response to these challenges, the thesis contributes an open source software package to the engineering community. The package contains libraries of MDP solvers, parsers, datasets and reference solutions, which provide a comprehensive infrastructure for exploring the trade-offs among existing embedded MDP techniques, and experimenting with novel approaches

A Framework for Design and Implementation of Adaptive Digital Predistortion Systems

Digital predistortion (DPD) has important applications in wireless communication for smart systems, such as, for example, in Internet of Things (IoT) applications for smart cities. DPD is used in wireless communication transmitters to counteract distortions that arise from nonlinearities, such as those related to amplifier characteristics and local oscillator leakage. In this paper, we propose an algorithm-architecture-integrated framework for design and implementation of adaptive DPD systems. The proposed framework provides energy-efficient, real-time DPD performance, and enables efficient reconfiguration of DPD architectures so that communication can be dynamically optimized based on time-varying communication requirements. Our adaptive DPD design framework applies Markov Decision Processes (MDPs) in novel ways to generate optimized runtime control policies for DPD systems. We present a GPU-based adaptive DPD system that is derived using our design framework, and demonstrate its efficiency through extensive experiments.acceptedVersionPeer reviewe