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A Low-Power Mobile Sensing Architecture

  • Author(s): Dutta, Prabal
  • Advisor(s): Culler, David E
  • Shenker, Scott
  • et al.

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


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.

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