1 / 37

Thermal Aware Routing in Implanted Sensor Networks

Thermal Aware Routing in Implanted Sensor Networks. Masters thesis by Naveen Tummala Advising Committee: Dr. Sandeep Gupta Dr. Arunabha Sen Dr. Partha Dasgupta. Outline. Introduction

alexis-guerrero
Download Presentation

Thermal Aware Routing in Implanted Sensor Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Thermal Aware Routing in Implanted Sensor Networks Masters thesis by Naveen Tummala Advising Committee: Dr. Sandeep Gupta Dr. Arunabha Sen Dr. Partha Dasgupta

  2. Outline • Introduction • System model and Assumptions • Problem statement • Related work • Thermal Aware Routing Algorithm • Simulations and Implementation • Conclusion and Future Work

  3. Wireless Sensor Networks • Minute devices used for sensing. • Low power, battery operated devices • Typically transmit data in multi-hop • Several routing techniques based on application • Focus on energy efficiency, lifetime and latency.

  4. Medical Biosensor Networks • A Medical biosensor is a device that detects, records and transmits information regarding a physiological change in biological environment. • How are they different from environment sensors? - Operating environment is sensitive - Invasive – alternative power, less maintenance - Continuous monitoring • Applications: Prosthesis, Organ monitoring, Cancer Detection, Glucose monitoring

  5. Heating in biological bodies • Specific to biological bodies, Pennes bio heat equation [6] gives rate of rise in temperature.

  6. System model Sensor node Gateway node B Base station B • Communication is done through radio frequency

  7. Assumptions • The neighbor set of a node is constant • Protocol is operated in a homogeneous tissue environment • Nodes are aware of their location • Each node has a forwarding path to the gateway • Heat does not have effect on sensor processor speed

  8. Problem Statement Given a biosensor network, BSN=<V,E> |V|=k. E = set of links; V = set of nodes; for each k ε V, the problem is to route the data from k to the gateway node by - keeping the temperature rise caused by communication within a safe value - Achieving the minimum possible delay caused by tradeoff for thermal efficiency.

  9. Related work- Dosimetry • Hirata et al. [1] calculated the temperature rise in human eye when exposed to ISM frequency radiation. • Lazzi et al. [2] simulated temperature increase in a head/eye model containing retinal prosthesis.

  10. Related Work - Routing • On demand routing protocols like AODV, [3] ODMRP are not suitable due to large amount of control messages involved in finding route. • Energy efficiency protocols [4] doesn’t necessary reduce the radiation exposure of a tissue area. • Geographic routing protocols [5] are used in a similar scenario like a biosensor network – static, known location but doesn’t consider the radiation effects.

  11. Thermal Aware Routing AlgorithmTARA Salient features • Routing is done based on - temperature residue in tissue at forwarding node - forwarding node’s proximity to gateway • Use Finite Differential Time Domain (FDTD) to estimate the temperature at neighbors. • Use cordoning to prevent communication in hotspots. • Two phases: setup, operation.

  12. TARA- Setup Phase B Gateway E A D C

  13. TARA- Setup Phase B Gateway E A D C

  14. TARA- Setup Phase At the end of setup phase, each node has Hop number – number of hops to gateway Neighbor set {neighbor id, neighbor hop no} 2 B Gateway 1 E A D 3 C 2

  15. TARA- operation phase 2 Gateway {4,1} {1,3} 5 Data 1 4 {5,0} {3,2} {2,2} {2,2} {3,2} 3 {4,1} {1,3}

  16. TARA- operation phase 2 ? Gateway {4,1} {1,3} 5 Data 1 4 {5,0} {3,2} {2,2} {2,2} {3,2} ? 3 {4,1} {1,3}

  17. TARA-FDTD Pennes equation we denote as temperature at location i,j and at time n = Similarly for , Similarly for y

  18. TARA-FDTD • Substituting the discretized values in the bioheat equation, the bioheat equation becomes • For all (i,j),

  19. TARA-FDTD 2 5 1 4 3 Node 1 and 4 can calculate the temperature rise using FDTD.

  20. TARA - Cordoning 12 9 -ve 10 Gateway 13 7 8 {9,temp residue} 11 4 5 6 1 3 2

  21. Simulations • Model a human body in a small region and calculate the effect of temperature using MATLAB • Goal is to demonstrate the significance of thermal aware routing. • Compare our protocol with a shortest hop routing protocol.

  22. Simulation 6X6 grid topology with source at 1,1 and gateway at 6,6. 3D plot of temperature rise across the network using TARA

  23. Simulation 3D plot for temperature rise across the network using shortest-hop

  24. Simulation 100X100 mm Placement is predetermined

  25. Simulation

  26. Implementation • Goal of implementation is to demonstrate the tradeoffs the protocol makes with delay. • mica2 motes and tinyos. • Issues with using motes - motes have limited memory capability. - motes are difficult to debug. - motes transmission is unpredictable and wide ranged.

  27. Implementation

  28. Implementation

  29. Implementation

  30. Implementation

  31. Conclusion • Thermal effects of wireless sensors should be considered during the design of communication protocols for medical biosensor network. • Proposed a protocol, TARA for routing in wireless biosensor network. • TARA is compared with shortest-hop - causes less exposure of radiation to the tissue. - Performs better at higher traffic.

  32. Future Work • Extend the protocol to route in real-time considering soft and hard real time deadlines. • Enhance the protocol to work in restrictive scenarios.

  33. References [1] A.Hirata, G.Ushio and T.Sciozawa. “Calculation of temperature rises in the human eye for exposure to EM waves in the ISM frequency bands.” IEICE Transactions on Communications, vol.E83-B, no.3, pp.541-548,2000. [2] G.Lazzi, S.C. Demarco, W.Liu and M.Humayun. “Simulated Temperature Increase in a Head/Eye Model Containing an Intraocular Retinal Prosthesis.” IEEE Int'l Symp. Antennas and Propagation Society, vol.2,pp.72-75,July 2001. [3] http://moment.cs.ucsb.edu/AODV/aodv.html [4] W.R.Heinzelmann, A.Chandrakasan and H.Balakrishnan. “Energy-efficient Communication for Wireless Microsensor Networks”, In Hawaii Int'l Conf. System Sciences, 2000. [5] B.Karp and H.T.Kung. “Greedy Perimeter Stateless Routing for Wireless Networks”, Mobicom 2000. [6] H.H.Pennes. “Analysis of tissue and arterial blood temperature in the resting human forearm”, J. Appl. Physiol. Vol 1, 1948.

  34. Demonstration Scenario 6 3 7 4 9 5 8

  35. Thank you

  36. Problem statement Given a biosensor network, BSN=<V,E> |V|=k. E = set of links; V = set of nodes; tempij is the temperature residue across link ij T- temperature rise due to communication of 1 data unit. xij is the total data units to be forwarded across link Tcutoff is the maximum safe temperature at tissue Hf -number of hops the node f is away from destination We introduce a cost function, fnij which determine the selection of forwarding node. fnij((xij*T) + tempij, hij). With reference to the cost function which determines the selection of forwarding node, the problem can be written as for all ij ε E, minimize the fnij(..) subject to the following constraints (xij*T) + tempij < Tcutoff

  37. Appendix -1 9 10 Gateway 13 7 8 11 4 5 6 1 3 2

More Related