The potential for collaborative, robust networks of microsensors has attracted a great deal of research attention. For the most part, this is due to the compelling applications that will be enabled once wireless microsensor networks are in place; location-sensing, environmental sensing, medical monitoring and similar applications are all gaining interest. However, wireless microsensor networks pose numerous design challenges. For applications requiring longterm, robust sensing, such as military reconnaissance, one important challenge is to design sensor networks that have long system lifetimes. This challenge is especially difficult due to the energyconstrained nature of the devices. In order to design networks that have extremely long lifetimes, we propose a physical layer driven approach to designing protocols and algorithms. We first present a hardware model for our wireless sensor node and then introduce the design of physical layer aware protocols, algorithms, and applications that minimize energy consumption of the system. Our approach prescribes methods that can be used at all levels of the hierarchy to take advantage of the underlying hardware. We also show how to reduce energy consumption of non-ideal hardware through physical layer aware algorithms and protocols.

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.

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An increasingly important figure-of-merit of a VLSI system is "power awareness," which is its ability to scale power consumption in response to changing operating conditions. These changes might be brought about by the time-varying nature of inputs, desired output quality, or just environmental conditions. Regardless of whether they were engineered for being power aware, systems display variations in power consumption as conditions change. This implies, by the definition above, that all systems are naturally power aware to some extent. However, one would expect that some systems are "more" power aware than others. Equivalently, we should be able to re-engineer systems to increase their power awareness. In this paper, we attempt to quantitatively define power awareness and how such awareness can be enhanced using a systematic technique. We illustrate this technique by applying it to VLSI systems at several levels of the system hierarchy-multipliers, register files, digital filters, dynamic voltage-scaled processors, and data-gathering wireless networks. It is seen that, as a result, the power awareness of these preceding systems can be significantly enhanced leading to increases in battery lifetimes in the range of 60-200%.

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Distributed networks of thousands of collaborating microsensors promise a maintenance-free, fault-tolerant platform for gathering rich, multi-dimensional observations of the environment. As a microsensor node must operate for years on a tiny energy source, innovative energy management techniques are needed. Widespread device deployment makes battery replacement infeasible, requiring energy to be scavenged from the environmente.g., conversion of ambient vibrations to electric energy. Computation and communication must be optimized for very low duty cycles, making issues such as standby leakage and start-up overhead critical. All levels of the communication hierarchy, from the physical and link layer to routing protocols, must be tuned for energy efficiency. A total-system approach is required for reliable, self-powered microsensor networks that deliver maximal system lifetime in the most challenging environments.

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. O u r architecture for a power-aware microsensor node highlights the collaboration between software that is capable of energy-quality tradeoffs a n d hardware with scalable energy consumption.

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