Indoor Localization Using Local Node Density In Ad Hoc WSNs

Indoor Localization using Local Node Density
in Ad-Hoc Wireless Sensor Networks

Proyecto Final de Carrera
Ingeniería de Telecomunicación
Ingeniería Técnica en Informática de Sistemas

Joaquín González Guerrero
2. Octubre. 2009 Escuela Politécnica Superior
Universidad San Pablo CEU

Table of Contents
1. Objective and thesis contribution
2. Wireless Sensor Networks (WSNs)
3. Problem statement
4. State of the Art: Location Systems for WSNs
5. Localization algorithms overview
6. Simulation
7. Experimental evaluation
8. Conclusions
9. Future Work

Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 2

Thesis contribution
 Objective:
Deployment and performance characterization of indoor distributed location
algorithms for ad-hoc wireless sensor networks.

 Contributions:
Detailed study of indoor positioning system based on Radio Signal Strength
(RSSI) range estimation.
First implementation and performance evaluation of novel Local Node
Density-based (LND) algorithm using simulation and real hardware.
Exhaustive comparison of LND against two distributed positioning algorithms
(DV-Hop, DV-Dist) over single self-developed simulation platform.
Quantitative performance analysis of five distributed positioning alternatives
in real indoor testbed environment.
Computational, communication and power cost associated to LND algorithm.


Table of Contents
1. Objectives and thesis contribution

6. Simulation
8. Conclusions
9. Future Work


Wireless Sensor Networks (WSNs)
 Collection of autonomous, spatially distributed devices.
 Nodes have sensing capabilities.
 Can communicate with each other to establish a network.
 Resources limitations: size, cost, energy, computation, memory.

Applications:
Monitor physical conditions
Agriculture control, species monitoring
Forest fire surveillance
Detect structural damage
Early detection of leakages


Table of Contents

6. Simulation
8. Conclusions
9. Future Work


Problem statement
Goal:
Determine the location of individual sensor
nodes without relying on external infrastructure.
GPS unsuitable: unrealistically high costs,
coverage problems indoors.
WSNS: optimal alternative  non-obstrusive,
infrastructure-free and low-cost
implementation.
Figure 1. Structural damage detection.

Motivation:
A myriad of applications rely on location data to
perform their tasks.
Physical measurements meaningless without
associated origin position.
Geographic and context-based routing protocols.

Figure 2. Forest fire surveillance


Table of Contents

6. Simulation
8. Conclusions
9. Future Work


Localization in WSNs: Overview
Area of intense research activity in the past years.
Broad spectrum of location techniques proposed.
Most proposals utilize a fraction of anchors with known positions.
Unknowns perform physical measurements to infer location.

Anchor
Unknown


Measurement Techniques
1. Distance related
Received Signal Strength Indicator (RSSI)
Time of Arrival (ToA)
Time Difference of Arrival (TDoA)

2. Angle of Arrival (AoA) Figure 3. Angulation based on two anchors [24].
Beamforming
Phase interferometry
Subspace-based

3. Scene analysis
RSSI-profiling (RADAR[6])

4. Connectivity-based (hop-count)
Figure 4. Hop-count measurement in anisotropic network.


Location Systems for WSNs
One-hop Multihop

Range-free Range-based

Centralized Distributed

Active Badge [1] DV-Distance [8]
Active Office [2] N-hop multilateration [15]
DV-Hop [8]
Cricket [3] MDS range-based [12] Robust positioning [16]
Amorphous [9]
GPS-less [4] SDP range-based [13] Coordinate stitching [17,18]
SDP [10]
APIT [5] Simulated Annealing [14] Particle filters
MDS [11]
RSSI-profiling Kalman [19]
RADAR [6] Bayesian [20,21]
LANDMARK [7] Montecarlo [22,23]

Localization in WSNs:
General trends
Complexity/cost and accuracy tradeoff
Selection highly dependent on specific application requirements.

Accuracy Complexity Specialized HW Cost
Range-based ✓ ✗ Yes ✗
Range-free ✗ ✓ No ✓

Centralized vs Distributed localization algorithms
Implementation Accuracy Energy Cost
complexity consumption*
Centralized ✓ ✓ ✓ ↔it > hops ✗
Distributed ✗ ✗ ✓ ↔it < hops ✓
* It = Nº of iterations in distributed algorithm; hops = Avg. Nº of hops to central processing unit [25].


Table of Contents

6. Simulation
8. Conclusions
9. Future Work


Evaluated algorithms
Common features:
Truly distributed  no external infrastructure or centralized processing unit.
Communication protocol based on local broadcast transmissions.
Scalable to large WSNs (100+).
No specialized hardware requirements.

Execution divided into three stages:
Phase 1: Node-to-anchor distance estimation.
Phase 2: Initial node positions computation.
Phase 3: Iterative refinement (optional).


Algorithms overview
Range-based:
Local Node Density-based (LND)
DV-Dist
RSSI-based techniques (RSSI1 and RSSI2)

Range-free:
DV-Hop

Phase LND Algorithm A Algorithm B Algorithm C Algorithm D
1a. Range DIN DIN - RSSI-Approx1 RSSI-Approx2

1b. Distance Sum-dist DV-Dist DV-Hop RSSI-Approx1 RSSI-Approx2

1c. Distance FCH - - - -
correction
2. Initial Multilateration Multilateration Multilateration Multilateration Multilateration
position
3. Refinement PIV PIV PIV - -


LND Algorithm
Phase 1a. DIN internodal range
Local node density information to estimate distances
Execution procedure (pair of nodes nA, nB):
1. Exchange neighbour tables
2. Determine number of nodes in union (Ku) and intersection (Ku) areas
3. Calculate area relationship H(dn) = Ai/Au
4. Yield distance estimate (normalized distance ∙ R) dAB = dn ∙ R = f(H(dn)) ∙ R

nB
nA


LND Algorithm
3. Calculate area relationship H(dn) = Ai/Au

R

R


LND Algorithm
3. Calculate area relationship H(dn) = Ai/Au ≈ Ki/Ku

Ki = 4
Ku = 13

Intersection nodes

+ Union nodes


LND Algorithm

Ki = 4
Ku = 13

H(dn) = 4/13
Intersection nodes

+ Union nodes


LND Algorithm

Ki = 4
Ku = 13

dAB H(dn) = 4/13

dAB = dn ∙ R
Intersection nodes

28.4 H n  92.6 H n  118.4 H n  76.5H n  27.8H n  7.5H n  1.9, ki  ku
6 5 4 3 2


+ Union nodes dn   1
 , ki  ku
 ki  1


LND Algorithm
Phase 1b. Initial node-to-anchor distance estimation (Sum-dist)
Flood connectivity and distance data (distance-vector approach).
Process initiated at anchors.
Propagation control: forward packets with non-stale information.
[x1,y1,0] nC

nA

nB
nG nH

nD

nF

nE Anchor

Unknown


LND Algorithm

Flooding procedure case scenario (1 hop)

[x1,y1,1,dCA]
[x1,y1,0] nC

nA

nB
nG nH
[x1,y1,1,dBA] [x1,y1,1,dDA]

nD

nF

nE Anchor

Unknown


LND Algorithm

Flooding procedure case scenario (2 hops)

[x1,y1,1,dCA]
[x1,y1,0] nC

nA

nB [x1,y1,2,dCA+dGC]
nG nH
[x1,y1,1,dBA] [x1,y1,1,dDA]

nD

nF
[x1,y1,2,dDA+dED]
nE Anchor

Unknown


LND Algorithm

Flooding procedure case scenario (Complete)

[x1,y1,1,dCA]
[x1,y1,0] nC

nA

nB [x1,y1,2,dCA+dGC]
nG nH
[x1,y1,1,dBA] [x1,y1,1,dDA]
[x1,y1,3,dCA+dGC+dHG]
nD
[x1,y1,3,dDA+dED+dFE]
nF
[x1,y1,2,dDA+dED]
nE Anchor

Unknown


LND Algorithm
Phase 1c. Factor Correction Hop (FCH)
1. Anchors capture network propagation error in correction factors (ci).
n n

d
j 1
r ,ij  d e,ij 
j 1
ij

hij hij
ci    avg. error per hop, j  i
n 1 n 1
2. Flood distance correction data throughout WSN.
3. Unknown corrects initial node-to-anchor distance to aj (de,ij) using cj and nº hops (hij).

de' ,ij  de,ij  (hij  c j )


LND Algorithm
Phase 2. Initial node positions via multilateration
Computational method to solve system of linearized equations (Ax=b).
Linear equations from anchor coordinates (xi,yi) and distance estimates (di).
Minimum nº of equations: n > Dim (e.g., bidimensional space n > 2).
Overdetermined system  counter range error with redundancy (least squares).
Simultaneous execution with Sum-dist and FCH stages (Phases 1b & 1c).

Figure 5. Trilateration visualization example.


LND Algorithm
Phase 3. Positioning Iterative Vector (PIV) refinement
Increase accuracy of node position estimates in iterative manner.
Local information used to recompute initial estimate: neighbour coordinates ( xit , yit )
and DIN internodal ranges ( d it ).
At each iteration t+1, node updates its estimated coordinates ( xet , yet ):

1 k dit  ei t
x t 1
e x  
t
e ( xi  xe )
t

k i 0 2dit
1 k dit  ei t
y t 1
e y  
t
e ( yi  ye )
t

k i 0 2dit

Correction principle: minimize mismatch between real ( ei ) and virtual ranges
(estimated distance d it ).

Stop condition:
Fixed number of iterations.
Update magnitude lower threshold δ. ( xe1  xe )  ( ye1  ye )  
t t t t


LND Algorithm – PIV Example
Execution
1. Exchange neighbour data
2. Update position
8 R

5
9
3

PIV refinement procedure case scenario


5 (x0,y0) = (5,9)
Execution
2. Update position
8

5 (xr,yr) = (3,6)
8
(2,7) 9
9 (6,5)
3

Real position
(1,2) 3
Estimated position


5 (x0,y0) = (5,9)
Execution
5
2. Update position
(x1,y1) = (4.47,7.54)
8 1 3 dit  ei t
x1  x0   t
( xi  xe )  ...  5  0.53  4.47
t

3 i  0 2d i
1 3 dit  ei t
5 (xr,yr) = (3,6) y1  y0   t
( yi  ye )  ...  9  1.45  7.55
t

8 3 i  0 2d i
(2,7) 9
9 (6,5)
3
Position error iter. 0 (ξ0 = 3.6)
Position error iter. 1 ( ξ1 = 2.13)

Relative improvement (%)

3  0  1
(1,2)  r (%)  100  40.87%
0


Alternative hop-by-hop algorithms
DV-Dist:
Simplified version of LND (Sum-dist ≈ DV-Dist, FCH suppressed).

DV-Hop:
Connectivity-based distance estimation.
1. Anchors compute calibration factors (single-hop length estimation)

ci 
 ( xi  x j )2  ( yi  y j ) 2
,i  j
 hj
2. Unknowns derive extended ranges using nº hops (hj) de,ij  hij  c j

Note:
Main difference: node-to-anchor distance estimation technique.
Phases 2 and 3 identical to LND (Multilateration + PIV).


RSSI-based algorithms
Range estimation: Relate RSSI and distance to sender.

dist  f ( RSSI )
Preliminary study: transmission pattern analysis of ScatterWeb Modular Sensor Board (MSB)*.

40
45
40-45
50
45-50
55
60 50-55
-dBm
65 55-60

70 60-65
75 65-70
80 70-75
85 75-80
5
3,75
4,5

80-85
3,75
3

2,5

m
2,25
1,5

1,25

m
0,75

0

Figure 6. Signal strength measurements from the Spectrum Analyzer. Figure 7. Spectrum Analyzer RSSI measurements.
Tx power 0x01, node on lower-right corner. TX power 0x01, node on central position.


RSSI-based algorithms
Use RSSI empirical data to yield 2 approximation range functions (MatLab):

f ( x) RSSI1  0.0127 x 2  0.3697 x  2.2688
f ( x) RSSI2  0.2996 x 2  1.407 x  33.7234

*Remarks empirical RSSI analysis
High spatial & temporal variability (no uniform
circular model!).
Chipcon CC1020 transceiver limited sensitivity
(5-15dBm difference vs Spectrum analyzer).
Figure 8. RSSI approximations for transmission power 0x01 indoors Tx power 0x01: higher spatial resolution.
using partial mapping.

Note:
Phase 2 identical to LND (Multilateration).
Lack Phase 3 (PIV refinement stage).


Table of Contents

6. Simulation
8. Conclusions
9. Future Work


Simulation environment
Self-implemented C-based simulator.
Simplified radio propagation: circular transmission model.
Absence of propagation effects  best-case scenario.

Standard scenario:
L x L = 50 x 50 units square area.
Grid configuration.
Anchors at the edges (throughout perimeter).
PIV iterations = 200.

Variable network conditions:
L L
Transceiver communication radio (R)  R  L, R =
10 10
Number of references (A) A=4,8,16
Nº unknowns (N) 15  N  100, N =5


LND simulation results
Phase 1a: DIN
Best performance: low transmission radios.
Underestimation tendency: ↑ R  ↓erel
R=L/3  |Er| < 1.84m, Stdv < ± 1.3m.

Figure 9. DIN ranging estimation error using 16 anchors
under varying number of deployed nodes.

Phase 1b: Sum-dist
2 opposite trends:
Indirect paths  overshooting.
Distance-vector  shortest path  undershooting.
↑ R or ↑ N  ↓erel  ↑ |Er|
Best results: R < L/5  |Er| < 7.89m, Stdv < ± 4.56m.
Figure 10. DIN ranging estimation error using 16 anchors
under varying number of deployed nodes.


LND simulation results
Phase 1c: FCH
Tackles undershooting.
Avg. improvement not ensured  robust (?)
Distance mismatch reduction dependent on ability to capture
propagation error  anchor placement critical.
Good performance: R=3L/10, 4L/10. Most cases: ∆=4.76-73.55%.

Phase 2: Multilateration
Sensitive to transmission range, insensitive to anchor fraction.
Error peaks  insatisfactory FCH behaviour in given topology.
a) Multilateration
Best performance: low-medium communication radios.
L/10 < R < L/2  Er < 5m (<42.69%), Stdv < ± 2.7m
Why? R < L/2 most accurate DIN ranges  best NTA distances!

Phase 3: PIV refinement
Performance highly dependent on DIN ranges accuracy.
Favourable conditions: low tx radios, high anchor fraction.
Most improvement: 30-40 first iterations (!).
Not robust: accuracy degradation in certain topologies.
b) PIV
Competitive final results Figure 11. Position error before and after PIV
R=3L/10, 4L/10  Er < 4.78m(22.88%) Stdv < ± 1.71m refinement phase (A=4).


Simulation performance comparison
Algorithms: LND, DV-Dist, DV-Hop.
Phase 1. Node-to-anchor distance estimation
Low-medium tx. radio (R ≤ L/2): comparable results 5 ≤|Et| ≤10m.
High tx radio (R > L/2):
DV-Hop: best performer. Stable and predictable behaviour, slight overshooting.
DV-Dist: performance degradation, dramatic undershooting (poorer DIN range estimates!).
Sum-dist/FCH: in most cases counters negative bias, excessive correction in certain scenarios.

a) Absolute distance error – 4A b) Relative distance error – 8A
Figure 12. Node-to-anchor distance estimation error for varying node transmission radio deploying 75 unknowns.


Phase 2. Initial position estimation (multilateration)
Low-medium tx radios (R ≤ L/2): similar accuracies.
DV-Hop usually worst performer.
Range-based?  FCH generally outperforms DV-Dist

High tx radios (R > L/2):
DV-Dist  usually poorest results |Et| ≤ 17m.
FCH  accuracy enhancement not ensured.
DV-Hop  most satisfactory estimates |Et| ≤ 11m.
a) 75 Nodes

Phases 1+2 conclusions
R ≤ L/2
accurate DIN ranges  Range-based algorithms ✓
DV-Dist vs Sum-dist/FCH  inconclusive results, captured
propagation error?

R ≤ L/2
DV-Hop best performer  stable, predictable.
Range-based  degradation due to poor DIN estimates.

b) 100 Nodes
Figure 13. Position error for varying node transmission radio using 4 anchors.


Phase 3. PIV iterative refinement (it. 200)
Equalize performance  convergence to almost identical final estimates.
DV-Dist cheapest method (communication, computation)  most suitable for implementation!
Final accuracy most related with quality of internodal ranges (it → ∞).
Improvement ∆(%) dependent on:
a. Initial avg. accuracy. b. DIN neighbour distance estimates.

DV-Dist: moderate accuracy enhancements (10-40%) under most scenarios.
DV-Hop: benefit constrained to low tx radios (30-55%). High radios  accuracy degradation!
Sum-dist/FCH: highly variable improvement.

Figure 14. PIV position error under varying node transmission radios Figure 15. PIV position improvement (%) under varying node
using 8 anchors and deploying 75 unknowns. transmission radios using 4 anchors and deploying 50 unknowns.


Table of Contents
6. Simulation

8. Conclusions
9. Future Work


Testbed setup
8 x 9m indoor area (seminar room).
Network configuration: uniform, horseshoe.
Nº unknowns (N): 50, 100.
Nº anchors (A): 4, 8.
Node model: ScatterWeb Modular Sensor Board (MSB).
Algorithms: LND, DV-Hop, DV-Dist, RSSI-based methods (RSSI1, RSSI2).

a) Horseshoe configuration b) Uniform configuration

Figure 16. Experimental testbed overview pictures.


Overview horseshoe configuration

Anchor

Unknown


Implementation on real WSN hardware
Artificial circular transmission radio (R ≈ 3m):
RSSI threshold of 33 (-42.5dBm)
Chipcon CC1020 radio transceiver to tx. power 0x01 (-5dBm).

Collision avoidance (DIN, Sum-dist/DV-Hop/DV-Dist, FCH, PIV): round-robin oriented
communication protocol.

Central control unit functionality:
Experimental data retrieval.
Indication of algorithm phase execution initiation.
Monitoring and supervision.

Algorithms (DV-Hop, DV-Dist, RSSI) execution integrated in LND communication protocol.

Intermediate data & location results analysis: MatLab scripts.


Phase 1a. Internodal ranging (DIN, RSSI1, RSSI1)
DIN noticeably more accurate (>50%) and precise than RSSI-based methods.
Average range errors: DIN (|Et|=0.887-1.1338m ≈33%xR), RSSI-based (|Et|>2.14m).
Slightly better results of DIN in:
Isotropic configurations (2-15cm poorer in horseshoe).
High node densities (N=100).

Figure 17. Comparison of internodal range methods in horseshoe
configuration using 8 anchors and 100 unknowns.


DIN: experimental vs simulation  performance degradation (≈0.5m).
Causes  undesireable propagation effects of wireless medium
Reflections, refractions, scattering
Selective fading
Link asymmetries

Figure 18. Detected link asymmetries during Neighbour Discovery.


Bias analysis
DIN: almost symmetric error distribution around 0, left slope extends to -5m (slight undershooting).
RSSI-based: clear negative bias (RSSI2 higher undershooting than RSSI1).

a) DIN b) RSSI1

c) RSSI2
Figure 19. Range error histogram in uniform configuration using 8 anchors and 100 unknowns.


Error spatial distribution: greater at the edges of coverage area.
Why? Proximity to potentially distorting elements (furniture, metallic doors, blackboards)

a) DIN b) RSSI1

Figure 20. Absolute range error tridimensional representation in uniform configuration
using 8 anchors and 50 unknowns.


Phase 1b-c. Node-to-anchor ranges
(DV-Hop, DV-Dist, Sum-dist/FCH, RSSI1, RSSI1)
RSSI-based ✗
Usually poorest performers. RSSI1 (2.37-2.79m), RSSI2 (2.32-2.66m).
Undershooting tendency  relative error < -0.3184 x dr.

Hop-by-hop alternatives  >0.5m more accurate, ±20-30cm more precise.

DV-Hop
Worst non RSSI-based alternative. Inaccuracy 0.2-0.5m higher than DV-Dist or FCH.
Overshooting effect  relative error ≥ 0.0184 x dr. Cause: short routes (diameter 4-5 hops).

DV-Dist ✓✓
Usually best performer despite lack of correction stage.
Accuracy: 1.46-2.05m.
Overestimation 0.35-0.5 x dr.

Sum-dist/FCH (LND algorithm) ✓
Second best behind simplest range-based alternative DV-Dist.
Generally fails to reduce initial overshooting  degradation.


Phase 1b-c. NTA range error per node

a) DV-Hop b) DV-Dist

c) FCH d) RSSI1

Figure 21. Relative node-to-anchor distance error in uniform configuration using 4 anchors and 50 unknowns.

Phase 1b-c. Range error distribution


c) FCH d) RSSI1

Figure 22. Spatial distribution of node-to-anchor distance error in uniform configuration using 8 anchors and 100 unknowns.

Phase 2. Initial node positions
Hop-by-hop algorithms
DV-Hop ✗
Poorest performer. Highest misplacement 2.41-3.52m and imprecision ±1.04-1.57m.

DV-Dist ✓✓
Usually best performer despite being cheapest/simplest alternative.

Sum-dist/FCH (LND algorithm) ✓
Second best in most scenarios.
Benefit of running FCH stage questionable!

RSSI-based
Comparable accuracies to hop-by-hop techniques: RSSI1 (2.37-2.79m), RSSI2 (2.24-2.63m).
Better precision! ≤ ±0.98m (vs hop-by-hop ≤ ±1.55m).

General trends
Anisotropic topologies  slight performance degradation.
Anchor fraction(A), node density(N)  inconclusive results.


Phase 2. Simulation vs Experimental
Uniform: pronounced performance gap (1-3m).
Horseshoe: nodes at edges benefit from transmission irregularities in real environments.


Figure 23. Comparison of position errors per node in simulation and testbed environment in horseshoe
configuration using 4 anchors and 100 unknowns.


Phase 2. Correlation NTA inaccuracy – node
misplacement

a) DV-Hop b) Sum-dist/FCH

Figure 24. Comparison of NTA distance error vs node position errors in uniform configuration using 4
anchors and 50 unknowns.


Phase 2. Position error spatial distribution


c) FCH d) RSSI1

Figure 25. Spatial distribution of position error in uniform configuration using 4 anchors and 100 unknowns.

Phase 3. PIV iterative position improvement
Algorithms: DV-Hop, DV-Dist, LND.
30 iterations.
2 evaluation scenarios:
High node density (N=50, 100).
Low node density (N=9).

Highly satisfactory performance. Most experiments:
∆DIN ≥ 10%. a) Uniform – 8A 50N
Absolute accuracy improvement 0.3-1.2m.

Improvement not ensured  Horseshoe 4A-100N DV-Dist (-5.45%).

Variability in convergence ratio between methods (2-8%).

Anchor fraction positive impact in PIV performance:
↑A  ↑↑ ∆DIN
b) Horseshoe – 4A 100N

Figure 26. PIV absolute accuracy improv./it.


Phase 3. PIV iterative position improvement
Comparable improvements (%) in algorithms accross experiments.
Determinant factor: initial position error.

DV-Dist outperforms FCH (2-20cm better)  correction benefit questionable!

DV-Dist: best final results. Accuracy 1.37-3.53m. ✓ ✓

DV-Hop: Worst performer. Lowest accuracy 1.58-3.78m and precision ±0.88-1.99m. ✗

a) Uniform – 4A 100N b) Horseshoe – 8A 50N
Figure 27. PIV average position error/it.


Phase 3. PIV improvement per node


c) FCH
Figure 28. Absolute position improvement per node in horseshoe configuration using 8 anchors and 100 unknowns.

Phase 3. PIV improvement spatial distribution

a) DV-Hop – Initial pos. error b) DV-Hop – PIV pos. improv.

c) FCH – Initial pos. error d) FCH – PIV pos. improv.
Figure 29. Spatial distribution of initial pos. error vs PIV pos. Improv. in uniform configuration using 8 anchors and 50 unknowns.


Table of Contents
6. Simulation

8. Conclusions
9. Future Work


Conclusions
Simulation:
No best performer in all scenarios: selection dependent on network conditions
(communication range, anchor fraction, topology, node density).
LND algorithm: positive results for low transmission radios R=0.3-0.4L. Absolute position
error ≤ 3.943m, standard deviation ≤ ±1.71m.

Experimental study:
First step to bridge gap between simulations and real-world positioning systems.
Internodal ranging: DIN >50% more accurate than RSSI-based methods (≤33%R).
Range-based hop-by-hop methods outperform range-free counterpart (DV-Hop).
RSSI-based alternatives comparable initial positions despite signal strength variability.
Benefit of running additional FCH correction stage questionable.
PIV highly satisfactory performance for low and medium-high node densities
(∆DIN ≥ 10%, Absolute improvement 0.3-1.2m).
LND algorithm: competitive final position errors for 8 anchors 1.37-2.07m.


Future Work
Extensive simulation over ns-2 or OMNet++ discrete event platforms.

Determine optimal context factors for FCH corrective procedure.

Formal analysis of PIV robustness: study network constraints to guarantee
convergence to more accurate position estimates.

Enhancements to original PIV implementation:
Filter out adjacent nodes based on consistency indicator (e.g., nº hops to anchors).
Reformulation as weighted least-squares problem, associate confidence to nodes:
Check convex constraints
Anchor nodes are assigned maximum confidence.

More and larger testbeds over extended deployment areas (multiple rooms).


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Thank you for your attention.


Questions.


Additional supporting slides.


ScatterWeb Modular Sensor Board

Table 1. Key features of the ScatterWeb Modular Sensor Board (MSB-430).


Empirical analysis of FCH effectivity

Figure 30. Analysis of FCH correction procedure effectivity . Horseshoe configuration using 8 anchors and 50 unknowns.


Analysis of DV-Hop effectivity

Figure 31. Analysis of DV-Hop calibration effectivity . Uniform configuration using 8 anchors and 100 unknowns.


Extended ranges – Hop-by-hop methods

a) Absolute error b) Relative error

Figure 32. Comparison of NTA distance error per anchor in horseshoe configuration using 8 anchors and 100 unknowns.


LND algorithm power cost
Estimates dependent on:
Network connectivity c (avg. Neighbours/node).
Nº deployed anchors a.
Nº iterations executed in PIV algorithm it.
Nº iterations executed for square root calculation n (Babylonian numerical method).
Power cost of single transmission(Ctx) or reception(Crx) of broadcast packet (transceiver-specific).
Power cost of single execution flop F (microcontroller specific).
Nº dimensions of coordinates systems Dim.

Table 2. Communication costs of the LND localization algorithm.



Table 3. Computational costs of the LND localization algorithm.

Table 4. Computational costs of the LND localization algorithm in bidimensional space (Dim = 2).


CC1020 current consumption (868MHz transmit/receive mode)
Single broadcast packet transmission P=0x01 (-5dBm) Ctx = 17.0mA
Single broadcast packet reception Crx = 19.9mA


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