Exploration and Implementation of Wireless Protocol Platforms

Suet-Fei Li, Ph.D. Thesis

The focus of the thesis research is on the implementation of flexible energyefficient wireless protocols, and the corresponding design methodologies. In the first part of the 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, meets the application’s need on flexibility while achieving energy efficient solutions. Using PicoRadio as the design driver, the proposed formal top-down design methodology yields superior results compared to traditional bottom-up ad-hoc approaches.

In the second half of the thesis, we focus on one particular but very important part of the protocol implementation strategy, that is the energy-efficient management for event-driven heterogeneous systems. Traditional general-purpose Operating Systems, acting as the system manager and scheduler, are not efficient or in many cases not sufficient for the targeted types of complex real time, power-critical domain specific systems. By deploying a reactive 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 requirement, and a 12x reduction in power over the general-purpose implementation. Our proposed solution utilizes a system management framework; it exploits the reactive event-driven nature of the systems, and deploys 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 encompass 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 have studied different power management algorithms for the network level. Adaptive algorithms seem to be good solutions since they are able to explore the temporal correlations in the traffic streams, handle environmental changes and are relatively simple to implement. However, simple constant threshold algorithms perform better for critical controller nodes and systems with high wakeup overhead. Our experimentation on the various adaptive algorithms lead us to speculate that there is a performance limit to any adaptive algorithm that only has the  knowledge of the recent inter-arrival history. 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 packets, as well as adjusting the sleep thresholds based on known topology information.