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A Low-Power Mobile Sensing Architecture
- Dutta, Prabal
- Advisor(s): Culler, David E;
- Shenker, Scott
Abstract
The system and network architecture for stationary sensornets is
largely solved today with many commercial solutions now available and
standardization efforts underway at the IEEE, IETF, ISA, and within
many industry groups. However, the existing techniques for reliable,
low-power communications in stationary sensornets fail on both counts
when confronted with mobility. In this dissertation, we argue that
awareness of real or potential mobility enables a solution that
handles the mobile case well, and supports stationary networks as a
special case. This dissertation addresses micropower mobiscopes, a
nascent class of mobile sensornets -- small, embedded, and
battery-powered systems -- that experience unpredictable but
structured mobility and are severely energy-constrained. We show how
awareness of mobility can simplify their communication challenges,
enable low-power operation, and enhance the reliability of data
delivery.
We introduce the MOV metric, a measure of mobility, and present
techniques to gather it on a near nano-power budget. We also present
iCount, a regulator-integrated energy meter design that allows nodes
to introspect their own energy usage, and adapt their behavior to the
actual energy availability and consumption. Integrating the pieces,
we present three concrete hardware platforms that support our mobile
sensing architecture. We develop a novel asynchronous neighbor
discovery algorithm called Disco that allows nodes to operate their
radios at very low duty cycles and yet still discover neighbors
without any external synchronization information. Recognizing the
necessity of beaconing in mobile networks, and the need for
mobile-stationary node interactions, we design a link layer
synchronization primitive, Backcast, and a receiver-initiated link
layer, HotMac, that are suitable for mobile sensing, but also work
for stationary networks across a range of conventional data collection
workloads and a broad range of duty cycles.
We evaluate our thesis with three mobile sensing applications that
embody our proposed architecture. The three applications --
AutoWitness, SleepTrack, and CommonSense -- are representative of
asset tracking, health and fitness, and participatory urban sensing,
and they each stress different aspects of the architecture, including
motion detection, neighbor discovery, communications, interaction
patterns, energy management, and data transport. These design points
illustrate that our architecture is general enough to enable a range
of applications but specific enough to support them well.
Main Content
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