Sensor Networks for Structural Monitoring

Ananth Grama

Parallel and Distributed Systems Lab Department of Computer Sciences

Purdue University

Goal

Designing sensing infrastructure for real-time, high fidelity measurement Test infrastructure: three story 50′ × 30′ structure Physical phenomenon to be measured:

Linear displacement Multi-axial acceleration Others...

Heterogenous Wireless Sensor Network

Hierarchical network Varying communication technologies: FM, Bluetooth, 802.11b Varying data rates: ≈ 30Kbps to > 1Mbps Varying CPU power: 8MHz (Atmega128 μc), 400MHz (XScale), 1-2GHz (Pentium)

Instrumentation: Displacement

Laser displacement sensor (Acuity AR600)

Accuracy up to 0.04 inch Sampling rate up to 1.25KHz

Laser sensors around the building accurately measure building displacement in different axis

Higher sampling rates require an accompanying network node with higher processing capability and memory

Instrumentation: Acceleration

ADXL202 Micro-electro-mechanical (MEMs) 2-D accelerometer on mica motes

Range +/ − 2g

Sensitivity 12.5%/g, 2mg resolution at 60Hz Low power < 0.6mA at ∼ 3V Low cost

Can use several motes dispersed in the building Need to design efficient data processing software for motes Need to correlate data from several sensors

System Architecture

Data centric distributed system architecture Goal: Allow engineers to macro-architect sensing infrastructure Compose a distributed data processing application at run-time from configurable software components

Data processing at source

Aggregation and in-network processing at hops Routing layer: push/pull data from source to sinks

… while meeting fidelity requirements

Data Processing at Source

Resource limitation: CPU, RAM Need to minimize data transmission cost: constrained battery power Signal processing: filter noise from data Temporal correlation: compress data Fourier analysis using an extremely lean integer-based FFT library (currently under development).

Data Processing at Source

Fourier analysis at data source: Provides both filtering and compression Separates software code from data semantics

Allows building reusable software component Gives engineers control over data filtering and resolution using run-time configurations

Allows macro-architecting & flexibility Challenges: Implementation on motes

Need optimization due to CPU & power limitation (8-bit, 8MHz, 4KB Data RAM, no FPU)

Our immediate deliverable will be a TinyFFT library for the motes. We expect to follow it with TinyKalman and other such signal processing and aggregation methods.

– p. 8/14

Aggregation & In-Network Processing

Utilize spatial correlation to avoid congestion on paths to sink

Leads to further noise filtering and compression

Makes redundant deployment of cheap nodes more feasible

Can combine multiple views of same physical phenomenon

Infer larger trends

Increase certainty in observations (Kalman filtering)

Utilize powerful nodes as data traverses up the hierarchy of nodes in the network

Aggregation & In-Network Processing

Challenges: Lack of (or incomplete) spatial information of nodes Asynchrony amongst nodes (leading to phase shifts) Increase in computational requirement due to agregation of data

Routing

Routing data from n sources to m sinks Exploit hierarchy in the network nodes

Low diameter network Increased energy cost function optimization Resilience to random failure due to graph non-uniformity

Expose priority based multi-class data handling Assured delivery: archival data Expedited delivery: “interesting” data, events, or control messages

Automated inference of class of data based on in-network data processing Can be designed to aid spatial correlation of data

– p. 11/14

Routing

Challenges: Communication reliability High energy cost of tx & rx Need to rely on local information due to bandwidth and buffer limitations

Current Status

All of the sensors have been calibrated, a network of these sensors has been built and data can be collected from these sensors. (Note that all of the aforementioned work addresses practical issues of scalability, power savings, and noise reduction).

Primary primitives of multicast and convergecast have been implemented.

TinyFFT has been developed and benchmarked on a linux PC. The performance characteristics are encouraging – i.e., it appears likely that we will be able to perform 128 point integer-based FFTs on the motes.

TinyFFT is being ported to the Atmega microcontroller.

– p. 13/14

Immediate Goals

Complete TinyFFT and TinyKalman libraries. Complete in-network spatial aggregation. Validate the entire communications infrastructure and deploy the network.

Our target for getting this done is end of summer.