A. Wang

Wireless distributed microsensor systems will enable fault tolerant monitoring and control of a variety of applications. Due to the large number of microsensor nodes that may be deployed and the need for long system lifetimes, replacing the battery is not an option. Sensor systems must utilize the minimal possible energy while operating over a wide range of operating scenarios. This paper presents an overview of the key technologies required for low-energy distributed microsensors. These include power aware computation/communication component technology, low-energy signaling and networking, system partitioning based on computation and communication tradeoffs, and a power aware software infrastructure.

Wireless distributed microsensor systems will enable the reliable monitoring and control of a variety of applications that range from medical and home security to machine diagnosis, chemical/biological detection and other military applications. The sensors have to be designed in a highly integrated fashion, optimizing across all levels of system abstraction, with the goal of minimizing energy dissipation. This paper addresses some of the key design considerations for future microsensor systems including the network protocols required for collaborative sensing and information distribution, system partitioning considering computation and communication costs, low energy electronics, power system design and energy harvesting techniques.

Minimizing energy requires scaling supply voltages below device thresholds. Logic and memory design techniques allowing subthreshold operation are developed and demonstrated. The fabricated 1024-point FFT processor operates down to 180mV using a standard 0.18/spl mu/m CMOS logic process while using 155nJ/FFT at the optimal operating point.

Wireless microsensor networks lend themselves to trade-offs in energy and quality. In these networks, the individual sensor data per se are not necessarily important to the end user. Rather, it is the combined knowledge of all the sensors that describes what is occurring in the environment. By allowing the algorithms and protocols to adapt the quality of this description, with a corresponding change in energy dissipation, sensor networks can be flexible to the end-user's requirements. In this paper, we provide models for predicting quality and energy and show the advantages of trading off these two parameters. By ensuring that the system operates at a minimum energy for each quality point, the system can achieve both flexibility and energy efficiency, allowing the end-user to maximize system lifetime.

Networks of distributed microsensors are emerging as a compelling solution for a wide range of data gathering applications. Perhaps the most substantial challenge facing designers of small but long-lived microsensor nodes is the need for significant reductions in energy consumption. We propose a power-aware design methodology that emphasizes the graceful scalability of energy consumption with factors such as available resources, event frequency, and desired output quality, at all levels of the system hierarchy. Our architecture for a power-aware microsensor node highlights the collaboration between software that is capable of energy-quality tradeoffs and hardware with scalable energy consumption.