Research Challenges in Wireless Networks of Biomedical Sensors *

1. Research Challenges in Wireless Networks of Biomedical Sensors* Loren Schwiebert Wayne State University Department of Computer Science Sandeep K. S. Gupta Arizona State University Department of Computer Science and Engineering Jennifer Weinmann Wayne State University Department of Electrical and Computer Engineering Additional Authors: Ayad Salhieh, Vikram Shankar, Valliappan Annamalai, Manish Kochhal, and Greg Auner. *This material is based upon work supported by the National Science Foundation under Grants ANI-0086020 and DGE-9870720 and the Kresge Eye Institute.

2. “Typical” Future Sensor Node • Matchbox Size • Battery Powered • Power-conserving processor – 100’s of MHz • 10’s of MB of program and data memory • Radio modem using TDMA • Capable of running a scaled down version of Palm OS or Windows CE

3. Biomedical Smart Sensors?? • Smart Sensor – Biomedical sensor/actuator with integrated circuitry • Biomedical – Implanted in the human body • Form – Small with limited power, encased in inert material • Function – Alleviate chronic diseases and disabilities, monitor health

4. Example Applications • Glucose Level Monitor • Transplant Organ Viability Monitor • Blood Monitor • Cancer Detection/Monitor • Health Monitor • Retinal and Cortical Prosthesis

5. Sensor-Based Visual Prostheses Retinal Implant Cortical Implant

6. Data Processing and Communication

7. Smart Sensor Retinal Interface

8. Multidisciplinary Research Smart Sensors and Integrated Devices Materials Characterization (Microstructure, optical, electrical) Materials Development Materials Simulation, Device Simulation, Design, and Testing Device Development and Prototyping Device Simulation Design and Testing Materials Processing (Special lithography and device fabrication development) Electronic Integration Design Data Communications and Interface Design VLSI Circuit Development Intelligent system Design and Development (Neuronet, logic) Hybrid Technology and Packaging Device Characterization, Testing, and Evaluation

9. Biomedical Sensor Constraints • Limited Computation and Data Storage • Ultra Low Power Consumption • Wireless Communication • Continuous Operation • Inaccessibility

10. Biomedical Sensor Requirements • Bio-Compatibility – Material Constraints • Robustness and Fault Tolerance • Secure Data Communications • Regulatory Requirements Combination of Features Makes Biomedical Sensor Networks Unique!

11. Research Approach • Optimize across protocol layers • Organize communication among sensor nodes • Develop application-specific solutions • Take advantage of biomedical sensor features – fixed topology, pre-defined communication, and known membership • Generalize these solutions

12. Communication Requirements of a Biosensor Application • Intra-sensor communication • Data aggregation • Distributed decision making • Sensors to External controller (basestation) communication • Downlink: control operations. • Uplink: feedback.

13. Designing for Energy-Efficiency • Sensor-sensor communication: • nearest-neighbor • ad hoc • Sensor-base station communication: • periodic • long-distance sensor to base station communication • Not energy-efficient to use the same routing protocol for both types of communication.

14. Research on Fixed Topologies • Vary # of Neighbors • Trade-offs Exist • Number of Hops • Number of Receivers • Amount of Contention • Evaluate Power Usage • Test Power-Aware Routing

15. Perf. Results: Fixed Topologies • Power-Aware Routing reduces Power Usage • 3D is better than 2D • 4 Neighbor Topology has lower Power Use • Reason is always fewer Receptions

16. Cluster-Based and Tree-Based Approaches

17. Perf. Results: Sensor-Base Station • Cluster-based approach provides better energy-efficiency than the tree-based approach. • True for a wide range of path loss exponents. • For high path loss exponents, fewer clusters is better. .

18. Current Research Emphasis • Strict Power Management • Efficient Wireless Spectrum Use • Scalability – Support as Many Sensing Elements as Possible • Support Diagnostic Functionality • Standardize Design with Other Research Groups

19. Wireless Networking is Key • Novel Sensing Materials Exist • Low-Power Electronics are Available • Wireless Communication is Next Step • Should Interoperate with Other Wireless Protocols • Enormous Potential for Social Benefit

20. Retina and Cortical Implant Project Ophthalmology Gary Abrams, MD Raymond Iezzi, MD Alexander Dizoor, PhD Neurosurgery Pat McAllister, PhD Robert Johnson, MD Janet Miller, B.S. Hun Park, MD, PhD Todd Frances, M.S. Veterinarian Liz Dawe, D.V.M. Arizona State University Sandeep K.S. Gupta, PhD Valliappan Annalmalai, grad student (NSF ITR) Karthik Jayaraman, grad student (NSF ITR) Suresh Lalwani, grad student (NSF ITR) Vikram Shankar, grad student (NSF ITR) Smart Sensors and Integrated Microsystems Gregory W. Auner, PhD Pepe Siy, PhD Loren Schwiebert, PhD Vaman Naik, PhD Ratna Naik, PhD Lowell Wenger, PhD Xiaoyan Han, PhD Yuriy Danylyuk, grad student Dan Durisin, engineer Francette Fey, grad student (NSF IGERT) Sam George, research assistant Changhe Huang, PhD Chantelle Hughes, grad student (NSF IGERT) Changli Jiao, grad student Manish Kochhal, grad student (NSF ITR) Michael Lukitsch, grad student (NSF IGERT) Marvie Nickola, grad student (NSF IGERT) Mona Safadi, grad student (NSF IGERT) Ayad Salhieh, grad student (NSF ITR) David Sant, grad student Flaminia Serina, M.S. (NSF IGERT) Margarita Thompson, PhD Jennifer Weinmann, grad student (NSF IGERT) Jie Xu, professor Song Xu, PhD Feng Zhong, grad student