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Computing with Biosensors

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.

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Computing with Biosensors

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  1. Computing with Biosensors Gul Agha University of Illinois http://osl.cs.uiuc.edu

  2. Biosensor Computing Systems • Natural biosensors work in a complex context • Need to create hybrid computer/biosensor networks Agha - Computing with Biosensors

  3. Routing and Group Communication • Routing delivers messages to a specific node in the network • Multi-hop, ad hoc • Old problem, but needs newapproach 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 Agha - Computing with Biosensors

  4. Aggregation Forwarding 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 vs. Agha - Computing with Biosensors

  5. proximity triangulation 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 Agha - Computing with Biosensors

  6. 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 Agha - Computing with Biosensors

  7. Simulation • Event-based simulator for sensors, network and target environment • Now: sensors on the ground • Simulates 1000’s of biosensor nodesfaster 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 Agha - Computing with Biosensors

  8. Programming Models for Biodigital Hybrid Computers • Hybrid systems with biological and digital components require new programming models • Massive parallelism • Continuous variables • Statistical abstractions Agha - Computing with Biosensors

  9. Some Opportunities • Bioinspired models of computing • Adaptation • Resilience • Cooperative computing • Shift from logical to statistical view of computing Agha - Computing with Biosensors

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