Embedded systems are widely used, from consumer electronics, to industrial equipment, to spacecraft. With embedded systems becoming more complex, new challenges are presented to application developers. In this dissertation, we focus on three of the most important: (i) Network simulation tools are widely used for sensor network testing and evaluation. Simulation performance affects the efficiency of the application developers who use these tools. The performance of a single host system represents a performance bottleneck for large-scale network simulation. A distributed simulator offering higher performance is needed to support fast, large-scale network simulation. (ii) Single event upsets (SEUs), which occur when a high-energy ionizing particle passes through an integrated circuit, can change the value of a single bit, causing damage and potentially catastrophic system failures. Modern SEU detection and correction approaches typically introduce additional hardware, increasing execution overhead and cost. Given the nature of resource-lean embedded systems, a software-based protection approach must be lightweight. (iii) Writing device drivers for serial-based peripherals is a repetitive task, given that microprocessors operate most such devices in the same way, issuing commands and parsing corresponding responses. A serial device driver generation tool must be capable of accommodating various microprocessors and devices with varying characteristics (e.g., UART settings, device response times, etc.), while producing drivers that offer performance at least as good as functionally equivalent, handwritten drivers. In this dissertation, we focus on the design and implementation of approaches to distributed sensor network simulation, embedded memory protection, and automated serial device driver generation. The first challenge is to effectively emulate sensor network systems with high fidelity using a distributed simulation system. This is achieved by developing a distributed version of SnapSim, D-SnapSim, which runs on a cluster. D-SnapSim relies on multiple physical systems to achieve enhanced speed and scalability, while providing flexibility to execute on clusters of varying size and computational power. The performance of D-SnapSim is evaluated as a function of network size, bitrate, and cluster configuration relative to SnapSim. The second challenge is to protect embedded system memory from SEUs with a software-only approach. Traditional SEU prevention and correction strategies rely on hardware extensions to the target system. We present a software-only approach that detects and corrects SEUs in RAM. This is achieved by extending the AVR-GCC compiler to protect the system stack from SEUs through duplication, validation, and recovery. Four applications are used to verify our approach, and the time and space overhead characteristics are evaluated. The third challenge is to automatically generate serial device drivers, eliminating the repetitive, error-prone work involved in serial device driver development. We present DriverGen, a configuration-based tool developed to provide automated serial device driver generation. Three applications are used to evaluate the performance of the generated drivers, both in terms of space and execution time. A user study is conducted to evaluate the usability of our tool in comparison with driver development in C
