Computing with Biosensors

Gul Agha

University of Illinois http://osl.cs.uiuc.edu

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Biosensor Computing Systems

• Natural biosensors work in a complex context

• Need to create hybrid computer/biosensor networks

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Routing and Group Communication

• Routing delivers messages to a specific node in the network– Multi-hop, ad hoc– Old problem, but needs new

approach in the biosensor network environment

• Group communication (multicast) delivers messages to a subset of nodes in the network– Needed to communicate to groups of biosensors

• Parameters: reliability, efficiency,power consumption

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Data Aggregation

• Combines data from many biosensors into a more compact form before forwarding to a location for processing

• Needed to handle the large amount of data generated in sensor networks

• Parameters: efficiency, speed

traffic vs. distance from sinkwithout data aggregation

AggregationForwarding

vs.

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Localization

• Determine the physical locations of the biosensors– Biosensors may be mobile

• If thousands of sensors aredeployed, don’t want to entertheir locations by hand

• Use sensing or network connectivity to infer physical location

• Parameters: precision, efficiency

proximity

triangulation

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Fault Tolerance

• Some sensors may fail• Due to the large number of

sensors, faults are common: not an exception but the rule

• The network needs to keep working, even if with diminished capacity

• Parameters: resiliency, response time

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Simulation

• Event-based simulator for sensors, network and target environment

• Now: sensors on the ground– Simulates 1000’s of biosensor nodes

faster than real-time on a standard PC.

• Future: structure model for environment• Use combination of simulated, recorded and

live inputs to drive virtual or real sensor network for more realistic testing

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Programming Models for Biodigital Hybrid Computers

• Hybrid systems with biological and digital components require new programming models– Massive parallelism – Continuous variables– Statistical abstractions

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Some Opportunities

• Bioinspired models of computing– Adaptation– Resilience

• Cooperative computing

• Shift from logical to statistical view of computing