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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

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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
Outline

  • Introduction

  • System model and Assumptions

  • Problem statement

  • Related work

  • Thermal Aware Routing Algorithm

  • Simulations and Implementation

  • Conclusion and Future Work


Wireless sensor networks
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.


Medical biosensor networks
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


Heating in biological bodies
Heating in biological bodies

  • Specific to biological bodies, Pennes bio heat equation [6]

    gives rate of rise in temperature.


System model
System model

Sensor node

Gateway node

B

Base station

B

  • Communication is done through radio frequency


Assumptions
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


Problem statement
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.


Related work dosimetry
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.


Related work routing
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.


Thermal aware routing algorithm tara
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.


Tara setup phase
TARA- Setup Phase

B

Gateway

E

A

D

C


Tara setup phase1
TARA- Setup Phase

B

Gateway

E

A

D

C


Tara setup phase2
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


Tara operation phase
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}


Tara operation phase1
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}


Tara fdtd
TARA-FDTD

Pennes equation

we denote

as

temperature at location i,j and at time n

=

Similarly for

, Similarly for y


Tara fdtd1
TARA-FDTD

  • Substituting the discretized values in the bioheat equation, the bioheat equation becomes

  • For all (i,j),


Tara fdtd2
TARA-FDTD

2

5

1

4

3

Node 1 and 4 can calculate the temperature rise using FDTD.


Tara cordoning
TARA - Cordoning

12

9

-ve

10

Gateway

13

7

8

{9,temp residue}

11

4

5

6

1

3

2


Simulations
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.


Simulation
Simulation

6X6 grid topology with source at 1,1 and gateway at 6,6.

3D plot of temperature rise across the network using TARA


Simulation1
Simulation

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


Simulation2
Simulation

100X100 mm

Placement is predetermined



Implementation
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.






Conclusion
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.


Future work
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.


References
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.


Demonstration scenario
Demonstration Scenario

6

3

7

4

9

5

8



Problem statement1
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


Appendix -1

9

10

Gateway

13

7

8

11

4

5

6

1

3

2


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