| The focus of the thesis research is on the implementation of flexible energy-efficient wireless protocols and the corresponding design methodologies. In the first part of this thesis, we propose a formal top-down, platform-based design methodology, targeting complex systems with a high level of integration and heterogeneousity. Our methodology relies on a formal Model of Computation (MOC). It supports architecture exploration and meets the application's need for flexibility, while achieving energy efficient solutions. Using PicoRadio as the design driver, the proposed formal top-down design methodology yields superior results when compared to traditional bottom-up ad-hoc approaches.;In the second half of this thesis, we focus on one very important aspect of the protocol implementation strategy: the energy-efficient management of event-driven heterogeneous systems. Traditional general-purpose Operating Systems, acting as the system manager and scheduler, are not efficient or not sufficient for the targeted types of complex real-time, power-critical, domain specific systems. By deploying an OS that specifically targets the reactive nature of the applications, we are able to achieve an 8x improvement in performance, 2x and 30x improvement in instruction and data memory size, and a 12x reduction in power, when compared to the general-purpose implementation. Our proposed solution utilizes a system management framework, exploiting the reactive event-driven nature of the systems and deploying aggressive power management. The hierarchical structure of the framework enhances design scalability, supports concurrency, and enables power control at various granularities. The scope of our power management algorithm is not limited to individual nodes; instead, it aims to serve the interest of the network as a whole. State space partitioning is deployed to execute our power management algorithm in two phases: network level power management and the node level power scheduling.;We conclude by studying different power management algorithms on the network level. Adaptive algorithms are appealing because they are able to explore the temporal correlations in traffic streams, handle environmental changes and lead to simple implementation. Experimental evidence supports the speculation that there is a performance limit to any adaptive algorithm that only has knowledge of the recent inter-arrival history. Surprisingly, simple constant threshold algorithms perform better for critical controller nodes and systems with high wakeup overhead. A global paradigm that incorporates information on the network neighborhood is needed to achieve major breakthroughs. In the future, we would like to explore such approaches by appending dedicated power management fields to existing packet formats, as well as adjusting the sleep thresholds based on known topological information. |
