Transcript TriopusNet: Automating Wireless Sensor Network Deployment

IPSN 2012
Yu Wang, Rui Tan, Guoliang Xing, Jianxun Wang, and Xiaobo Tan
NSLab study group 2012/07/02
Reporter: Yuting
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Introduction
System Model
Movement Scheduling
Evaluation
Conclusion
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Goal
◦ Detect and monitor aquatic environments
◦ Diffusion profile:
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Concentration contour maps
Elapsed time of diffusion
Total amount of discharged substance
Location of original source
Movement Scheduling
◦ Improve the profiling accuracy
◦ Constraints on sensor mobility and energy budget
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System Model
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Diffusion Process
MLE-based Diffusion Profiling
Profiling Accuracy Metric
Two scheduling algorithm
Experiments
◦ Validation of the diffusion model
◦ Evaluation by real data traces (on telosB) simulation
using MATLAB
◦ Impact of several factors on profiling accuracy
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Introduction
System Model
Movement Scheduling
Evaluation
Conclusion
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Fickian diffusion-advection model:
◦ t: time elapsed since the discharge of substance
◦ c: substance concentration
◦ D: diffusion coefficient
 Characterize speed of diffusion, depend on (1) species
of solvent (2) discharge substance (3) environment
factors (ex: temperature)
◦ u: advection speed
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Usually Dx=Dy, and Dz can be omitted
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Assume some initial condition
◦ A total of A cm3 of substance is discharged at
location (xs,ys) and t=0
 t>0: (x0,y0) = (xs+uxt , ys+uyt)
◦ Distance from any location (x, y) to the source:
d = d(x, y) =
◦ Concentration at (x , y): c(d,t)
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 Diffusion profile Θ = {x0, y0 ,α, β} (β->t, α->A)
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Can't use Bayesian (need prior probability)
Assume constant-speed advection, then
reading of sensor i : zi = c(di , t)+bi +ni
◦ bi : bias
◦ ni : noise ~ N(0, ς2), assume {ni} are independent
◦ Takes K samples in a short time and average them,
then zi ~ N( c(di , t)+bi , σ2), where σ2 = ς2 /K
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 Log-likelihood:
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=>
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A theoretical lower bound on the variance of
parameter estimators (Θ here)
Inverse of the Fisher information matrix (FIM)
J, J =
,
is taken
over all z
= CRB(Θk)
(xi,yi): coordinates of sensor i
LX1 , LY1 are 1×N vectors
LX2 , LY2 are N×1 vectors
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Previous works take det(J) as the metric, but it's
too problem-dependent
This paper use a novel metric based on CRB
Larger ω indicates more accurate estimation of
x0 and y0 ( Can also use CRB(α), CRB(β) )
ω is function of (x0,y0), (xi,yi), for all i
=> use estimated (x0,y0) instead
If sensors are randomly distributed around the
diffusion source => ε=0
=>
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Introduction
System Model
Movement Scheduling
Evaluation
Conclusion
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Assumption
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Profiling iteration has short time duration
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Problem statement: max 𝜔 s.t.
◦ Sensors move straight in each profiling iteration
◦ Moving distance is always multiple of l meters, where l is
referred to as "step"
◦ Sensor’s straight movement does not cause significant
errors
𝑚𝑖 ,𝜑𝑖
◦ M: total steps that can be allocated (in one iteration)
◦ Li: the largest distance sensor i can move (in one
iteration)
◦ l: unit of step
◦ mi: # of steps (in one iteration)
◦ φi: movement orientation (in one iteration)
◦ Complexity:
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φi = ∠(∇iω)
||∇iω||: steepness of the metric ω
Proportionally allocate the movement steps
according to sensor’s gradient magnitude:
Complexity: O(N)
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Fx the sensor closest to the estimated source location
Only schedule other sensors
di* = arg max ω𝑖 =
𝑑𝑖
,where
Dynamic programming algorithm:
Ω(i, m): maximum ω when the first i sensors are
allocated with m steps
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Complexity: O((N-1)M2) ~ O(N3)
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SNR-based
◦ Move toward the estimated source location to
increase SNR
◦ Complexity: should be O(N)
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Simulated Annealing
◦ Given movement orientations {φi |∀i}, uses brutalforce search to find the optimal step allocation
◦ Then search for optimal movement orientations by
simulated annealing algorithm
◦ Complexity: exponential with respect to N
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Introduction
System Model
Movement Scheduling
Evaluation
Conclusion
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The performance of profiling are affected by
these errors (GPS, motor)
Iterative approach avoid error accumulation
◦ Sensors update their positions and report to cluster
head (in each iteration)
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Average GPS error: 2.29(m)
Robotic fish speed: expect 2.5m/min when
tail beats at 23° amplitude and 0.9Hz
frequency
◦ Error not mentioned in the paper
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Fig4: Simple clustering method
Nodes randomly assigns itself a cluster ID
Average of results from all clusters
12cm from the water surface
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Greedy algorithm does not account for the
interdependence of sensors in providing the
overall profiling accuracy
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10 sensors, 15 profiling iterations
Greedy and radial: curves with and without
simulated movement control and localization errors
almost overlap => no error accumulation
Radial:
better than annealing
in terms of both
time complexity
and optimality
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The variances decrease with increasing A
Both the greedy and radial algorithms can
achieve a high accuracy
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(Fig12) δ: source location bias
◦ Diffusion source appears at (δ, 0)
◦ Sensors are not randomly deployed around source
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(Fig 14) Fix each di and randomly deploy sensors in
different quadrant of plane
Deployment with max ω is still an open issue
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Shortest distance path from sensors to cluster head
Trace-driven simulations
◦ Nodes transmit packet to the next hop with success p = PRR
retrieved from the communication traces
◦ Nodes re-transmit the packet for 10 times before it is dropped
until success
◦ Packet to the cluster head includes:
sensor ID, current position, measurement
◦ Packet to the sensor includes:
moving orientation, distance
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# of packet (re-)transmissions in an iteration:
mean 158, standard deviation 28
(30 sensors are randomly deployed)
Delay will be within seconds at most
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Introduction
System Model
Movement Scheduling
Evaluation
Conclusion
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Strength
◦ Reduce computation and hardware cost
◦ Real hardware implementation of lots of
mathematical model
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Weakness (also their future work)
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Cluster head needed
May not work on wavy environments
GPS and Zigbee may not work in deep water
It seems that the system can't be done in real time