Three Beacon Sensor Network Localization through Self Propagation

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Three Beacon Sensor Network Localization through
Self Propagation
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Mohit Choudhary Under Guidance of Dr. Bhaskaran
Raman
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Sensor Network Defined

• Collection of communicating sensing devices.
• The base station is a master node.
• Data sensed by the network nodes sent to the base
  station

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Localization in Sensor Networks

• Spatial localization - determining physical
  location of a sensor node in the network.
• Localization is an essential tool for
• The development of low-cost sensor networks for
  use in location-aware applications
• Geographic routing

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Aim of the Thesis

• Distributed algorithm for a sensor network
  localization in which nodes have the ability to
  assimilate and utilize the connectivity and
  negativity information provided by their nearby
  nodes.
• Presence of three beacon nodes that are aware of
  their position apriori.

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Problem Difficulty

• Often insufficient data to compute a unique
  position assignment for all nodes.
• Information itself may be erroneous
• due to presence of noise and gray areas
• Algorithm has to scale with the size of the
  network

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Contribution of the Thesis

• A novel distributed algorithm
• Evaluation using simulations for a wide range of
  scenarios to establish an optimal conditions
  required to achieve effective localization with
  the proposed scheme.
• Real life experiments with in-house developed
  sensor nodes (based on Atmel AT89C52
  microcontroller and radiometrix TX/RX)

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Related Work

• Use of GPS.
• Use of multiple static reference beacons.
• Use of signal-strength estimation.
• Use of acoustic ranging.
• Use of connectivity information.
• Use of Mobile Beacon.

• Expensive (cost, size, energy)
• Only works outdoors, on Earth
• Technology additional to the requirement of the
  application.
• Technology additional to the requirement of the
  application.
• Complex setup procedures
• Terrain uncertainties
• Expensive
• Not possible for ad hoc, sensor networks
• Additional Technology
• Terrain uncertainties

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Specific Related Work

• Doherty et al. - Centralized technique using
  convex optimization.
• Bulusu et al. - Distributed algorithm based on
  connectivity and multiple beacons.
• Savarese et al.- Two-phase approach, connectivity
  for initial position estimates and trilateration
  for position refinement. (multiple beacons but
  less in number.)
• Patwari et al. - One-hop multilateration from
  reference nodes received signal strength (RSS)
  and time of arrival (ToA)

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Preliminaries

• Connectivity based RF Localization

Bounding Box - A, B, C, D.
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Negativity based RF Localization
Bounding Box- A,B,C,G(K,J)
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Localization point and Localization Error

• Let Bounding Box- B1, B2, B3, B4 (derived
  points) then LP is
• If (segments B1B3 and B2B4 intersect) LP point
  of intersection.
• Else if (center points of both B1B3 and B2B4 lie
  inside the polygon) LP center point of B1B3 or
  B2B4 that lies closer to the centroid of the
  polygon represented by B1,B2,B3,B4(derivedpoint
  s).
• Else LP center point of B1B3 or B2B4 that lies
  inside the polygon represented by
  B1,B2,B3,B4(derivedpoints).
• The localization error, eA - Length of the line
  segment MLP .

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Proposed Algorithm- Terminology

• Beacons- Know their position apriori.
• Non-Beacon- Location unknown at start.
• Unique Node-ID NID
• Message ID - MID
• MID1 is used by beacon nodes.
• MID2 is used by pseudo-beacon nodes.
• MID3 is used by other nodes.
• Negativity constraint set (NCS) A set of
  negativity constraints, each consisting of a
  3-tuple (NID, a, b).
• Message transmitted - A 5-tuple (NID, a, b, MID,
  NCS).

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Proposed Algorithm-Example Run
Y
E
Step-1 Beacon nodes transmit their location. We
assume that there is at least one other node
which can hear all three beacons. A node which
hears all three beacon nodes is termed as a
pseudo-beacon node.
Step-2 In the second stage, the pseudo-beacon
localizes itself, and transmits its location to
its neighbours. This transmission includes the
negativity constraint of all the three beacon
nodes. For this reason, this transmission is very
expressive.
Step-2 (contd.) Nodes which hear a transmission
from a pseudo-beacon thus immediately localize
themselves.
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Valid Set of Messages

• A set of threshold number of messages of MID3,
  with the same x and y values, but different NIDs
  constitutes a valid set. These different messages
  would be sent by different non-beacon nodes which
  localize themselves to the same point.
• Also, a single message with MID1 constitutes a
  valid set.

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Example Run Contd.
Y
F
X4
A
X1
X3
E
X2
X
Step-3 In the third stage, the algorithm
proceeds in a distributed fashion. Nodes which
have localized themselves transmit their location
to their neighbours, along with any negativity
constraints as appropriate. This in turn helps in
the localization of further nodes.
Let us assume X1, X2, X3 and X4 are the nodes in
region E
Threshold 1
Threshold 2
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Tuning

• In our simulations, we have found that the use of
  threshold2 usually works well.
• A node which does not receive two valid sets of
  messages, but only one valid set, temporarily
  localizes itself with just this information and
  sends a message. On receiving the second set, it
  further refines its position, and sends a second
  message announcing this information.

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Sources of Error Possible

• There is error inherent in the connectivity-based
  and negativity-based constraints.
• A negative constraint may be false, due to
  non-receipt of a valid message
• The presence of gray areas and non-uniform radio
  connectivity can cause further errors in
  calculations.
• Some nodes which are "far away" from other nodes
  may not receive enough information to localize
  themselves.

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Simulation Setup

• Platform- Visual Sense that builds on and
  leverages Ptolemy II.
• Two sets of simulations for various region of
  operation.
• TX radius of 30mts/ node
• TX radius of 50mts/ node
• Study the effect of varying the value of
  threshold(1,2 or 3) in each case.
• Nodes within a localization error of 20 of
  transmission range turn green, those within 10
  turn red.

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Example Simulation Run
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Simulation Results
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Simulation Results
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Simulation Results
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Factors

• Node Density (Number of Nodes deployed / Region
  of Operation)
• Node TX Range
• Value of Threshold
• We define
• Coverage(Node Density) x (Node Tx Range)

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Compiled Simulation Results
Node TX Radius Threshold Region of Operation Coverage of Nodes localized Avg. Localization Error
30 1 0,120x0,120 9.8125 72 6.0
30 2 0,120x0,120 9.8125 82 5.6
30 3 0,120x0,120 9.8125 100 3.9
30 1 0,180x0,180 4.36 82 5.022
30 2 0,180x0,180 4.36 98 3.97
30 3 0,180x0,180 4.36 74 3.81
30 1 0,240x0,240 2.452 54 5.206
30 2 0,240x0,240 2.452 32 5.17
50 1 0,200x0,200 9.8125 82 8.44
50 2 0,200x0,200 9.8125 86 7.06
50 3 0,200x0,200 9.8125 100 5.437
50 1 0,300x0,300 4.358 86 7.306
50 2 0,300x0,300 4.358 98 5.469
50 3 0,300x0,300 4.358 68 4.64
50 1 0,400x0,400 2.452 58 7.096
50 2 0,400x0,400 2.452 38 5.952
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Analysis

• Coverage 4.36
• Value of threshold2 is good both in terms of
  percentage of nodes localized and the Avg.
  Localization error.
• We now put forward the following questions that
  we aim to answer
• How well does the algorithm scale with increased
  area of operation?
• Can the algorithm be used in harsh environments
  such as indoors considering the multipath effect
  and gray areas produced in RF communication?
• What is the effect of unreliable communication on
  the proposed scheme and what are the measures
  needed to ensure reliability in communication?

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Large Area of Operation
94 nodes localize
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Gray areas and multipath effect

• Zhao J et al. Understanding Packet Delivery
  Performance in Dense Wireless Sensor Networks.
• Extent of gray areas is less for lower
  transmission ranges.
• Extent of gray areas varies greatly with the
  environment (indoor, out door, habitat) even for
  the same transmission range.
• For a given transmission range the extent of gray
  area is the maximum for indoor.
• the extent
• of gray areas in indoor environment measures up
  to 1/3rd of the transmission range.

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Gray areas and Multipath effect
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Effect of unreliable communication
Number of Retransmissions/ node 1
Probability of transmission error 0.2
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Experiment Setup

• Area of operation of 0,100 X 0,100
• Sensor nodes are made up of onboard
• Atmel AT89C52 microcontroller.
• A radiometrix BiM-433-F radio TX/RX. operating
  at 433 MHz frequency.
• A voltage stabilization circuit .
• A 9V battery.

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Block Diagram
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• Features
• Maximum Data Rate 40 kbps.
• Frequency 433 MHz.
• Output power 6 dBm, Receiver sensitivity
  -107dBm.
• Rapid RX power up ( lt1ms )
• Simple UART interface

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• Features
• 8K Bytes of In-System Reprogrammable Flash Memory
• Endurance 1,000 Write/Erase Cycles
• 256 x 8-bit Internal RAM
• 32 Programmable I/O Lines
• Three 16-bit Timer/Counters
• Eight Interrupt Sources
• Programmable Serial Channel
• Low-power Idle and Power-down Modes
• One Serial Interface

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Circuit Diagram
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Communication Protocol Details

• Microcontrollers configured to communicate with
  the radio in UART mode 1 at 9600 Bauds.
• Microcontroller used to select the radio to
  transmit or receive.
• CD (carrier detect) of the radio is connected to
  INT0 to decide changeover.

Byte Transfer UART Mode 1
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Packet Structure

• Preamble 3ms Preamble mandatory for the receiver
  in the radiometrix chip to stabilize.
• Control We used a unique Byte pattern (0xAA) to
  indicate the start of message
• Data The data as used in the actual
  implementation is as shown in Figure6.4.
• CRC 16 Bit Checksum of control - data fields has
  been used by the decoder to verify the integrity
  of the packet.

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Spatial Profile
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Experiment Setup

• The experiment setup has been considered keeping
  in mind the results obtained after simulation
  (Coverage 4.358) , Threshold2
• Coverage (Node density) X (TX Area / Node)
• For the transmission radius of 30 meters
  following node density is desired
• Node Density 4.358/ (3.14x30x30)
• Node Density 0.00154
• Thus, for area of 10,000 sq meters (100x100), the
  number of nodes required would be
• Number of nodes required (Node density) x
  (Area)
• Number of nodes 0.00154 x 10000
• Number of nodes 15.4

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Experiment Details

• Four sensor nodes available.
• Requirement of 15 nodes and three beacons.
• Technique of rotating the nodes that had once
  achieved localization.

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Experiment Results
Total nodes 15
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Conclusions

• Proposed method of localization is a complete and
  reliable process in itself.
• Applications based on sensor networks requiring
  reasonable localization accuracy can effectively
  utilize the proposed method to cut down on
  additional cost of localization otherwise
  incurred.
• As a few numbers of nodes in each region localize
  to a common point by the proposed method, this
  information can be further utilized by the
  applications to either perform power management
  by utilizing a very small number of such nodes at
  one time or perform easy clustering of all such
  nodes.

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Future Work

• Analyzing the effect of using other radio models
  such as a octagon instead of a square on the
  proposed scheme.
• Attempt to extend the methodology to 3D networks.
  
• On the hardware level as a first step attempt to
  integrate the developed node with temperature
  sensors in form of DS 1631 (Dallas semiconductor
  digital thermometer chip).
• As a second step attempt to integrate RF-id
  reader to the node as onboard sensor.

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• Thank you