Wireless Networking Solutions for

Smart Sensor Biomedical Applications

 

Vision

Smart sensors offer the promise of significant advances in medical treatment. Networking multiple sensors into an application-specific solution to combat disease is a promising approach, which will require research with a different perspective to resolve an array of novel and challenging problems

Research Challenges

  • Bio-Compatibility
  • Energy Efficiency
  • Application Specifics Wireless Networking Issues
  • Processing Issues
  • Low Power Operation
  • Sensor Material Constraints
  • Robustness and Fault Tolerance
  • Security and Interference

Research Approach

  • Tissue Channel Modeling
  • Energy Aware Cross-Layer Design
  • Application Characterization
  • Wireless Communication Protocols
  • In-Network Processing
  • Energy Management
  • MEMS Design
  • Self-Healing Recovery Schemes
  • Secure Key Distribution, MAC, etc.

Broader Impact

  • Support General Use of Wireless Sensors
  • Improved Quality of Life
  • Increased Vitality and Longevity
  • Economic Benefits to Society
  • Interdisciplinary Educational and Research Opportunities
  • Attractive Application of Computer Technology Motivating potential Computer Science Students

 

Our Achievements

Characterization of Wireless Channel within the Human Body

Propagation model plays a very important role in designing wireless communication systems. For wireless communication inside the human body, the tissue medium acts as a channel through which the information is sent as electromagnetic (EM) radio frequency (RF) waves. A propagation model is necessary to determine the losses involved in the form of absorption of EM wave power by the tissue. Absorption of EM waves by the tissue body, which consists of mostly saline water, accounts for a major portion of the propagation loss. In this work we present a propagation loss model (PMBA) for homogeneous tissue bodies. We have verified the model for the frequency range of our interest (900MHz to 3GHz) using a 3D EM Simulation Software, HFSSTM, and experimental measurements using saturated salt water.

Publication

Y. Prakash, S. Lalwani, S.K.S Gupta, E. Elsharawy,  L. Schwiebert, Towards a Propagation Model for Wireless Biomedical Applications, IEEE International Conference on Communications, 2003. ICC '03. Volume: 3, 11-15 May 2003,Page(s): 1993-1997. [PDF | PPT]

 

Energy Efficient Coding Scheme

Energy consumed by transcevers of sensor nodes plays an important role in improving the energy efficiency and maximizaing network lifetime. In this work, we propose an On-Off Keying based minimal energy coding. The basic idea is using fewer high bits to reduce transmission energy. We derived a closed-form expression of the BER performance of that scheme over an AWGN channel with either a coherent receiver or a noncoherent receiver. Our results show that the performance is better than BPSK only when the codeword length is greater than 63. It is shown that hard-decision decoding outperforms BPSK only when the SNR is higher than a certain threshold and that soft-decision decoding outperforms BPSK regardless of the SNR value.

 

Publication

Y. Prakash, S.K.S Gupta, Energy Efficient Source Coding and Modulation for Wireless Applications, IEEE Wireless Communications and Networking Conference, 2003. WCNC 2003. Volume: 1, 16-20 March 2003, Page(s): 212-217. [PDF | PPT]

Q. Tang, S. K. S. Gupta, and L. Schwiebert, Ber performance analysis of an on-off keying based minimum energy coding for energy constrained wireless sensor application, in IEEE ICC2005, accepted for publication. [PDF ]

 

Communication Scheduling to minimize thermal effects of implanted biosensor networks.

Biosensors can be organized into clusters where most of the communication takes place within the clusters, and long range transmissions to the base station are performed by the cluster leader to reduce the energy cost. In some applications, the tissues are sensitive to temperature increase and may be damaged by the heat resulting from normal operations and the recharging of sensor nodes. Our work is the first to consider rotating the cluster leadership to minimize the heating effects on human tissues. We explore the factors that lead to temperature increase, and the process for calculating the Specific Absorption Rate (SAR) and temperature increase of implanted biosensors by using the Finite-Difference Time-Domain (FDTD) method. We improve performance by rotating the cluster leader based on the leadership history and the sensor locations. We propose a simplified scheme, Temperature Increase Potential, to efficiently predict the temperature increase in tissues surrounding implanted sensors. Finally, a genetic algorithm is proposed to exploit the search for an optimal temperature increase sequence.

System Model of a Cluster of Implanted Sensors

Difference scheduling sequence result

in difference temperature rise

 

 

Publication

Q. Tang, N. Tummala, S. K. S. Gupta, and L. Schwiebert, Communication scheduling to minimize thermal effects of implanted biosensor networks in homogeneous tissue, IEEE Tran. Biomedical Eng., accepted for publication. [PDF ]

 

Thermal-Aware routing

One of the major challenges of continuous in-vivo sensing is the heat generated by the implanted sensors due to communication radiation and circuitry power consumption. This work addresses the issues of routing in implanted sensor networks. We propose a thermal-aware routing protocol that routes the data away from high temperature areas (hotspots). With this protocol each node estimates temperature change of its neighbors and routes packets around the hotspot area by a withdraw strategy. The proposed protocol can achieve a better balance of temperature rise and only experience a modest increased delay compared with shortest hop, but thermal- awareness also indicates the capability of load balance, which leads to less packet loss in high load situations.

Difference between area hotspot and link hotspot (a) all links are broken, node 2 is not accessible (b) some links are broken, node 2 is accessible.

Temperature distribution of TARA: temperature rise is relatively even in the whole area  since the thermal-awareness of TARA avoids introducing the overheated area.

Publication

Q. Tang, N. Tummala, S. K. S. Gupta, and L. Schwiebert, TARA: Thermal-Aware Routing Algorithm for Implanted Sensor Networks, In Proc. of Intl Conference on Distributed Computing in Sensor Systems (DCOSS), 2005 [ pdf ]

 

 



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