computing with biosensors
DESCRIPTION
Computing with Biosensors. Gul Agha University of Illinois http://osl.cs.uiuc.edu. Biosensor Computing Systems. Natural biosensors work in a complex context Need to create hybrid computer/biosensor networks. Routing and Group Communication. - PowerPoint PPT PresentationTRANSCRIPT
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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