Simulators are important tools for analyzing and evaluating different design options for wireless sensor networks (sensornets) and hence, have been intensively studied in the past decades. However, existing simulators only support evaluations of protocols and software aspects of sensornet design. They cannot accurately capture the significant impacts of various hardware designs on sensornet performance.  As a result, the performance/energy benefits of customized hardware designs are difficult to be evaluated in sensornet research. To fill in this technical void, in first section, we describe the design and implementation of SUNSHINE, a scalable hardware-software emulator for sensornet applications. SUNSHINE is the first sensornet simulator that effectively supports joint evaluation and design of sensor hardware and software performance in a networked context. SUNSHINE captures the performance of network protocols, software and hardware up to cycle-level accuracy through its seamless integration of three existing sensornet simulators: a network simulator TOSSIM, an instruction-set simulator SimulAVR and a hardware simulator GEZEL. SUNSHINE solves several sensornet simulation challenges, including data exchanges and time synchronization across different simulation domains and simulation accuracy levels. SUNSHINE also provides hardware specification scheme for simulating flexible and customized hardware designs. Several experiments are given to illustrate SUNSHINE's simulation capability. Evaluation results are provided to demonstrate that SUNSHINE is an efficient tool for software-hardware co-design in sensornet research. Even though SUNSHINE can simulate flexible sensor nodes (nodes contain FPGA chips as coprocessors) in wireless networks, it does not estimate power/energy consumption of sensor nodes. So far, no simulators have been developed to evaluate the performance of such flexible nodes in wireless networks. In second section, we present PowerSUNSHINE, a power- and energy-estimation tool that fills the void. PowerSUNSHINE is the first scalable power/energy estimation tool for WSNs that provides an accurate prediction for both fixed and flexible sensor nodes. In the section, we first describe requirements and challenges of building PowerSUNSHINE. Then, we present power/energy models for both fixed and flexible sensor nodes. Two testbeds, a MicaZ platform and a flexible node consisting of a microcontroller, a radio and a FPGA based co-processor, are provided to demonstrate the simulation fidelity of PowerSUNSHINE. We also discuss several evaluation results based on simulation and testbeds to show that PowerSUNSHINE is a scalable simulation tool that provides accurate estimation of power/energy consumption for both fixed and flexible sensor nodes. Since the main components of sensor nodes include a microcontroller and a wireless transceiver (radio), their real-time performance may be a bottleneck when executing computation-intensive tasks in sensor networks. A coprocessor can alleviate the burden of microcontroller from multiple tasks and hence decrease the probability of dropping packets from wireless channel. Even though adding a coprocessor would gain benefits for sensor networks, designing applications for sensor nodes with coprocessors from scratch is challenging due to the consideration of design details in multiple domains, including software, hardware, and network. To solve this problem, we propose a hardware-software co-design framework for network applications that contain multiprocessor sensor nodes. The framework includes a three-layered architecture for multiprocessor sensor nodes and application interfaces under the framework. The layered architecture is to make the design of multiprocessor nodes' applications flexible and efficient. The application interfaces under the framework are implemented for deploying reliable applications of multiprocessor sensor nodes. Resource sharing technique is provided to make processor, coprocessor and radio work coordinately via communication bus. Several testbeds containing multiprocessor sensor nodes are deployed to evaluate the effectiveness of our framework. Network experiments are executed in SUNSHINE emulator to demonstrate the benefits of using multiprocessor sensor nodes in many network scenarios. / Ph. D.