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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
IntroductionSystem model and AssumptionsProblem statementRelated workThermal Aware Routing AlgorithmSimulations and ImplementationConclusion and Future Work
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
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
Specific to biological bodies, Pennes bio heat equation [6]
gives rate of rise in temperature.
perfusion. blood ofeffect cooling theis B
,metabolism basal theis A
re, temperatuflow blood theisT
space and timeoffunction a as re temperatu theis T
ty,conductivi thermal theisK
heat, specific theis C
density, theis
)T-B(TA K T/
0
b
b02
sourcePTtC
System model
B
Sensor node
Gateway node
B Base station
Communication is done through radio frequency
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
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
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
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 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
A D
C
B
EGateway
TARA- Setup Phase
A D
C
B
EGateway
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}
A D
C
BE
Gateway1
2
2
3
TARA- operation phase
1 4
3
2
5Gateway
{2,2}{3,2}
{4,1}{1,3}
{4,1}{1,3}
{5,0}{3,2}{2,2}
Data
TARA- operation phase
1 4
3
2
5Gateway
{2,2}{3,2}
{4,1}{1,3}
{4,1}{1,3}
{5,0}{3,2}{2,2}
Data
?
?
TARA-FDTD
sourceb PTTBATKC )(02tT/
Pennes equation
we denote j)(i,nT as temperature at location i,j and at time n
=tjiji /)T (T tT/ ),(
n),(
1n
Similarly for xT/
xjiji /)T (T xT/ ),(n
),1(n , Similarly for y
xxxx jijijiji /)/)T (T - /)T ((T /T ),1(n
),(n
),(n
),1(n22
/T /T T 22222 yx
TARA-FDTD Substituting the discretized values in the bioheat
equation, the bioheat equation becomes
For all (i,j),
)
sourcebj)(i,n
0 P )T -B(T-A
xxx jijijiji /)/)T (T - /)T ((T ),1(n
),(n
),(n
),1(n
j)(i,n
j)(i,1n T T
(t/C
yyy jijijiji /)/)T (T - /)T ((T )1,(n
),(n
),(n
)1,(n
c.37 T oj)(i,
0
TARA-FDTD
1 4
3
2
5
Node 1 and 4 can calculate the temperature rise using FDTD.
TARA - Cordoning
8
4
6
5
11
1
2 3
10
9
7
12
13{9,temp residue}
Gateway-ve
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
3D plot of temperature rise across the network using TARA
6X6 grid topology with source at 1,1 and gateway at 6,6.
Simulation
3D plot for temperature rise across the network using shortest-hop
Simulation100X100 mm
Placement is predetermined
Average rise in temperature
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
Seconds 1000 2000 3000 4000 5000 6000
tem
pe
ratu
re r
ise
in C
TARA
Shortest-hop
Simulation
Highest rise in temperature
0
0.05
0.1
0.15
0.2
0.25
Seconds 1000 2000 3000 4000 5000 6000
Te
mp
era
ture
ris
e in
C
TARA
Shortest-Hop
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.
Implementation
0
20
40
60
80
100
120
Deadline 350 360 370 380 390 400 410 420 430 440 450 460 470
Deadline in msec
% o
f p
ac
ke
ts m
ee
tin
g d
ea
dlin
e
Shortest-hop
TARA
Implementation
performance of Tara and shortest hop for low traffic
0
10
20
30
40
50
60
70
80
90
100
Deadline 100 200 300 400 500 600
Deadline in msec
% o
f p
ac
ke
ts m
ee
tin
g d
ea
dlin
es
Tara
Shortest-hop
Implementation
Performance of TARA and shortest-hop for high traffic
0
10
20
30
40
50
60
70
80
Deadline 100 200 300 400 500 600
Deadline in msec
% o
f p
ac
ke
ts m
ee
tin
g d
ea
dlin
e
Tara
Shortest-hop
Implementation
Average delay at each node in a scenario
0
50
100
150
200
250
300
350
400
450
Node 1 2 3 4 5 6 7 8 9 10 11
Nodes
Av
era
ge
De
lay
Shortest-Hop
TARA
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
Extend the protocol to route in real-time considering soft and hard real time deadlines.
Enhance the protocol to work in restrictive scenarios.
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 temperaturein the resting human forearm”, J. Appl. Physiol. Vol 1, 1948.
Demonstration Scenario
4
59
67
8
3
Problem statementGiven a biosensor network, BSN=<V,E> |V|=k.E = set of links; V = set of nodes;
tempij is the temperature residue across link ijT- 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 destinationWe 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
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713 Gateway
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