Network architecture and processing for positioning and distributed estimation

1
Network architecture and processing for
positioning and distributed estimation

• Stefano Tennina, Fortunato Santucci

HYCON WP4d meeting KTH - March 14th, 2008
2
Outline

• Background and motivation
• Communication architectures
• Latency and energy models
• Distributed estimation
• Advanced Services
• Positioning service
• Voice over WSN
• Long term platform
• Remarks and further issues

3
Background and motivation

• Existing mining ventilation controls are poor
• Continuous monitoring of air quality absent
• Air pumped in the rooms manually controlled with
  huge safety margins
• Communication capability simply by voice over
  walkie talkie.

• Automatic control solutions for safety and energy
  optimization.
• Low Latency and High Reliability of measurements

4
Reminder

• Background and motivation
• Communication architectures
• Latency and energy models
• Distributed estimation
• Advanced Services
• Remarks and further issues

5
Communication architectures

• Networked sensors in the vertical ventilation
  shaft, in access tunnels and extraction rooms
• Sensor nodes in extraction rooms are wireless due
  to blasting activities
• Basic configuration
• Uniform radio network where nodes in tunnels and
  extraction room are wireless (e.g. IEEE 802.15.4)

6
Communication architectures

• Hybrid wired-wireless configuration
• Presence of cabling leads to use of power line
  communication (PLC) devices or simply 802.3
• Gateway GW1 PLC/IEEE 802.15.4 at tunnel entrance
• Nodes N1 monitor the shaft and are powered by the
  line
• Static ad hoc network and multi-hop routing
• Nodes N2 in tunnel and extraction rooms powered
  by batteries
• Mainly static network and multi-hop routing
• Low power strategies, especially for nodes close
  to GW.

7
Communication architectures

• With some mobile nodes
• Nodes N3 are mobile (on trucks or hand-held)
  power supply is not an issue
• Cluster tree topology with dynamic backbone
• Nodes N3 act as cluster heads to save nodes N2
  energy
• Nodes N2 dynamically associate with Nodes N3 or
  acts as cluster heads forming a static backbone.

8
Reminder

• Background and motivation
• Communication architectures
• Latency and energy models
• Distributed estimation
• Advanced Services
• Remarks and further issues

9
Latency and Energy models

• Three classical methodologies of latency analysis
  are currently under investigation
• Analytical high level end-to-end delay by
  resorting to an abstraction of the communication
  protocol
• Analytical Network Calculus which iteratively
  sums per-hop delays ()
• Simulations with discrete event network
  simulators for validation and synthesis.

() Source A. Koubaa, M. Alves, E. Tovar,
"Modelling and Worst-Case Dimensioning of
Cluster-Tree Wireless Sensor Networks",
technical report, Polytechnic Institute of Porto
(ISEP-IPP), http//www.hurray.isep.ipp.pt
10
Latency and Energy models

• Energy constraints on battery powered sensor
  nodes involves proper communication protocols and
  algorithm design at all layers
• Distributed Source Coding techniques for
  compressing data, exploiting their redundancy
• Energy-Aware Routing protocols for taking into
  account energy costs in the path selection
  metric
• Joint Routing MAC designs like the SERAN
  clustered protocol, which supports
• Data aggregation mechanisms
• Robustness against node failures and clock drifts

11
Latency and Energy models
Sleep 0.020mAh
Simulated energy depletion over a tunnel with an
EAR algorithm
Idle 0.426mAh
RX / Carrier Sense 19.7mAh
TX 17.4mAh
CC2420 radio states on CrossBows MICAz node
12
Network Lifetime example
Once assumed a measurements reporting period
(e.g. 60s) and a battery discharge model
Network Lifetime
Per node estimated Battery Consumptions
13
Reminder

• Background and motivation
• Communication architectures
• Latency and energy models
• Distributed estimation
• Advanced Services
• Remarks and further issues

14
Distributed estimation

• Gas concentration dynamics
• incoming airflow from tube
• gas emission-consumption of trucks
• outgoing airflow through room exit.
• Reliability of measurements implies the need for
  a method to reconstruct an expected profile from
  distributed sensors

15
Distributed estimation

• Assumptions
• Gas stratification in a room is a function of the
  height of the room
• Gas concentration is constant over the plane at
  each fixed height
• No fixed sensor in the room due to blasting
  activities.
• Goal
• Reconstruct the profile with a grid of H x W
  fixed points at each room entrance, minimizing
  the number of points of H and W.
• Note
• The profile is obtained by averaging the W noisy
  sensor measurements for each point of H.

16
Distributed estimation

• A practical example
• Measure a known profile (of light) by means of a
  very dense network of MICAz nodes
• Downsample the measurement points while taking
  into account the performance of reconstruction.

17
Distributed estimation
18
Distributed estimation

• Parameters
• h, w integer decimation factors
• D interpolation factor
• M (h,w,i) linear interpolation of the measured
  under-sampled curve, i1,,N
• Mref (i) the reference curve profile.

• Performance indexes
• Goodness Of Fit (GOF)
• Error Root Mean Square (RMS)
• Area Ratio
• Normalized Error.

19
Distributed estimation
20
Reminder

• Background and motivation
• Communication architectures
• Latency and energy models
• Distributed estimation
• Advanced Services
• Positioning service
• Voice over WSN
• Long term platform
• Remarks and further issues

21
Positioning in mines
Gold mine "CANMET" (Canadian Center for Minerals
and Energy Technology) Val dOr, 700km north of
Montreal (Quebec), Canada.
Source C. Nerguizian, C. Despins and S. Affès,
Geolocation in Mines With an Impulse Response
Fingerprinting Technique and Neural Networks",
IEEE Transactions on Wireless Communications, VOL
. 5, NO. 3, MARCH 2006
Source A. Chehri, P. Fortier, P.-M. Tardif,
Frequency Domain Analysis of UWB Channel
Propagation in Underground Mines, Vehicular
Technology Conference, 2006. VTC-2006 Fall. 2006
IEEE 64th Sept. 2006 Page(s)1 - 5
22
Positioning

• In a 2D scenario a node can estimate its position
  if its distance from at least three reference
  nodes is available, along with the position of
  these nodes in a Common Reference System

R2
BS
BS
MS
R1
R3
BS
23
Position RefinementAlgorithm
Steepest descent algorithm
Error function
Update algorithm for a node i Require
, where al AccuracyLevel Ensure a new
estimation of 1 for each node i do 2 3
4 , rp RangingPenalty 5 Update
estimation and accuracy level
24
MICAz RSSI Calibration

• Received Signal Strength Indicator (RSSI) imposes
  a characterization of the propagation
  environment, by means of the estimation of two
  parameters
• Parameter A defined as the absolute value of the
  average power in dBm received at a close-in
  reference distance of one meter from the
  transmitter, assuming an omni-directional
  radiation pattern
• Parameter n defined as the path loss exponent
  that describes the rate at which the signal power
  decays with increasing distance from the
  transmitter. This decay is proportional to d(n),
  being d the distance between transmitter and
  receiver.

25
MICAz RSSI Calibration

• The parameters A and n can be estimated
  empirically by collecting RSSI data (thus path
  loss data) for which the distances between the
  transmitting and receiving devices are known.
• A least-squares best-fit line is used to glean
  the specific values of A and n for the
  environment in which the data were measured
• A is the y-intercept of the line, and
• n is the slope of that line.

For that environment - A 59.6dBm - n 1.84
26
Geometric Dilution Of Precision

• Mean and Standard Deviation of positioning error
  decrease as the angle under which a node sees
  references increases
• This results to a constraint on anchors positions
  for obtaining an acceptable level of the
  positioning error.

27
Reminder

• Background and motivation
• Communication architectures
• Latency and energy models
• Distributed estimation
• Advanced Services
• Positioning service
• Voice over WSN
• Long term platform
• Remarks and further issues

28
Voice over WSN

• Advantages
• More pervasiveness
• Reduced power consumption
• Capability to perform local signal processing
• Improve communication quality exploiting
  distributed source coding.
• Disadvantage
• Limited bandwidth (802.15.4 250Kbps).
• Cooperation between nodes distributed in the
  environment.

29
Voice over WSN
FireFly node
A network of 42 nodes in the National Institute
for Occupational Safety and Health (NIOSH)
experimental coal mine in Pennsylvania Appropriat
e choices on time synchronization, MAC and
Routing allow to carry a streaming audio signal
from a mobile node to an external gateway over a
multihop path
Source R. Mangharam, A. Rowe and R.
Rajkumar, "Voice over Sensor Networks, 27th IEEE
Real-Time Systems Symposium (RTSS), Rio de
Janeiro, Brazil, December 2006.
30
Voice over WSN

• Our prototype
• A MICAz node samples an audio signal with 8,6KHz
  frequency
• It applies an Adaptive Differential Pulse Code
  Modulation (ADPCM) compression and send packets
  over the radio
• A Stargate gateway receives packets and decodes
  audio signal
• Audio can be reproduced on a PC wired (via e.g.
  LAN) or wireless (via e.g. WLAN) linked to
  gateway.

31
Reminder

• Background and motivation
• Communication architectures
• Latency and energy models
• Distributed estimation
• Advanced Services
• Positioning service
• Voice over WSN
• Long term platform
• Remarks and further issues

32
Advanced services and Long term platform
Ventilation Control
Security
Video
Phone Calls
Applications
Safety
Middleware of Services on the mobile gateways
Data Fusion
Protocol Adaptation
Positioning
IEEE 802.15.4
IEEE 802.11
IEEE 802.3
Power Line
Heterogeneous Networks
33
Reminder

• Background and motivation
• Communication architectures
• Latency and energy models
• Distributed estimation
• Advanced Services
• Remarks and further issues

34
Remarks and further issues

• Need for characterization of propagation channel
  a ray tracing simulation tool for tunnels is
  available at UAQ, along with a kit for UWB
  channel measurements and a coverage planning tool
• Mobile gateways are under development at UAQ by
  resorting to Software Defined Radio (SDR)
  technologies
• A biometric badge for physical and logical access
  control has been developed at UAQ
• A hybrid (symmetric/asymmetric) key management
  scheme has been proposed and is under development
  in cooperation with UCB, along with an Intrusion
  Detection System (IDS) that can be deployed also
  over tiny nodes.