College of Engineering Grid-based Coordinated Routing in Wireless Sensor Networks Uttara Sawant Major Advisor : Dr. Robert Akl Department of Computer Science

College of Engineering

Grid-based Coordinated Routing in

Wireless Sensor Networks

Grid-based Coordinated Routing in

Wireless Sensor Networks

Uttara Sawant

Major Advisor : Dr. Robert Akl

Department of Computer Science and Engineering

Uttara Sawant

Major Advisor : Dr. Robert Akl

Department of Computer Science and Engineering

04/20/23

OutlineOutline

• Wireless Sensor Networks Overview

• Grid-based Coordinated Routing

• Simulation Results

• Future Work

• Wireless Sensor Networks Overview

• Grid-based Coordinated Routing

• Simulation Results

• Future Work

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Wireless Sensor Networks Overview

Wireless Sensor Networks Overview

• Introduction to Sensor Networks

• Sensor Routing Protocols

• Motivation

• Objectives



• Motivation

• Objectives

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Wireless Sensor Networks OverviewWireless Sensor Networks Overview


• Distributed networks

• Sensing, communication, computation


• Distributed networks

• Sensing, communication, computation

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FeaturesFeatures

• Ad hoc networks

• Low-power and battery-operated

• Sensors and radio

• Self-organizing

• Harsh environmental conditions

• Node mobility

• Node failure

• Dynamic topology

• Node heterogeneity

• Unattended operation

• Large scale deployment

• Ad hoc networks

• Low-power and battery-operated

• Sensors and radio

• Self-organizing

• Harsh environmental conditions

• Node mobility

• Node failure

• Dynamic topology

• Node heterogeneity

• Unattended operation

• Large scale deployment

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ApplicationsApplications

• Video surveillance

• Traffic monitoring

• Environmental monitoring

• Structure and system health monitoring in buildings and aircraft interiors

• Video surveillance

• Traffic monitoring

• Environmental monitoring

• Structure and system health monitoring in buildings and aircraft interiors

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• Hardware

• Crossbow Motes – MICA2, MICA2DOT, MICAz, Cricket

• Intel Motes with Bluetooth support

• Software

• TinyOS – a component-based operating system for Motes

• EmStar – software system for Linux-based platforms

• nesC – programming Motes

• Middleware

• TinyDB – sensor database system

• Hardware

• Crossbow Motes – MICA2, MICA2DOT, MICAz, Cricket

• Intel Motes with Bluetooth support

• Software

• TinyOS – a component-based operating system for Motes

• EmStar – software system for Linux-based platforms

• nesC – programming Motes

• Middleware

• TinyDB – sensor database system

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• Routing Protocols – data-centric, hierarchical, location-based, network flow approach

• Flooding

• Sending data to all neighbors

• Duplication of packets, packet congestion, more energy

• Sending identical information of overlapped regions

• LEACH – cluster-based

• PEGASIS

• Hierarchical-PEGASIS


• Routing Protocols – data-centric, hierarchical, location-based, network flow approach

• Flooding

• Sending data to all neighbors

• Duplication of packets, packet congestion, more energy

• Sending identical information of overlapped regions

• LEACH – cluster-based

• PEGASIS

• Hierarchical-PEGASIS

C1 C2 C3 C4 C5

C1 C2 C3 C4 C5

C3 C5

C3

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• Location-based protocols

• MECN and SMECN

• AFECA, GAF, Span

• Ascent, GEAR

• Location-based protocols

• MECN and SMECN

• AFECA, GAF, Span

• Ascent, GEAR

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Wireless Sensor Networks Overview Wireless Sensor Networks Overview

• Data-centric protocols

• SPIN – one of the most dominant data-centric routing protocol for sensor networks

• Directed diffusion

• Data-centric

• Named data

• Selecting paths, caching and managing data in-network

• Rumor routing, gradient-based routing

• Data-centric protocols

• SPIN – one of the most dominant data-centric routing protocol for sensor networks

• Directed diffusion

• Data-centric

• Named data

• Selecting paths, caching and managing data in-network

• Rumor routing, gradient-based routing8

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MotivationMotivation

• Energy consumption in sensor networks

• Network connectivity

• Network partition - define

• Energy consumption in sensor networks

• Network connectivity

• Network partition - define

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ObjectivesObjectives

• Design grid-based coordinated routing protocol

• Extend network lifetime, prolong partition

• Maintain connectivity

• Compare with traditional flooding algorithm

• Design grid-based coordinated routing protocol

• Extend network lifetime, prolong partition

• Maintain connectivity

• Compare with traditional flooding algorithm

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Grid-based Coordinated RoutingGrid-based Coordinated Routing

• Flooding

• Grid-based coordinated routing

• Link model

• Coordinator election

• Grid size estimation

• Load balancing

• Flooding

• Grid-based coordinated routing

• Link model


• Grid size estimation

• Load balancing

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• Flooding

• Every node rebroadcasts packets after receive

• Information is disseminated across entire network

• Duplicate packets, infinite loops

• Results in tree structure rooted at the source

• Flooding

• Every node rebroadcasts packets after receive

• Information is disseminated across entire network

• Duplicate packets, infinite loops

• Results in tree structure rooted at the source

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• Based on flooding

• Randomly placed sensor nodes – limited energy

• Fixed source and sink – infinite energy

• Square-shaped grids of specific width

• One coordinator per grid square

• Based on flooding

• Randomly placed sensor nodes – limited energy

• Fixed source and sink – infinite energy

• Square-shaped grids of specific width

• One coordinator per grid square14

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• Link model

• Dynamic and lossy wireless links

• Deterministic link model:

• Link model

• Dynamic and lossy wireless links

• Deterministic link model:

/ nr tP P d

If Pr >= S, reception success

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Link ModelLink Model

• Probabilistic Link Model

• Probabilistic Link Model

/ nr tP P d R

nA tR A S P

1 1A AR R R rand

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Link ModelLink Model

• Log Normal Shadowing Model

• Variations in environmental clutter

• Link model with log normal distribution

• Log Normal Shadowing Model

• Variations in environmental clutter

• Link model with log normal distribution

1010Xnr tP P d

XZero mean Gaussian distributed random variable with std. dev. σ

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• Random node ID

• Coordinator = maximum node ID• Grid size estimation


• Random node ID

• Coordinator = maximum node ID• Grid size estimation

5nr R

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• Load balancing

• Nodes are ranked based on energy available

• Coordinator nodes with energy greater than 25 % of battery – rank + 1

• Coordinator nodes with energy less than 25 % of battery – rank + 2

• Current coordinators are replaced by lower ranked nodes in respective grid squares

• Equal distribution of routing load

• Load balancing

• Nodes are ranked based on energy available

• Coordinator nodes with energy greater than 25 % of battery – rank + 1

• Coordinator nodes with energy less than 25 % of battery – rank + 2

• Current coordinators are replaced by lower ranked nodes in respective grid squares

• Equal distribution of routing load

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SimulationSimulation

• Assumptions

• Source and sink nodes have infinite energy

• Sensor nodes have limited energy

• Sensor field = 1000 m X 1000 m

• Number of nodes = 1000

• Transmit power = -2 dBm, 1 dBm, 4 dBm

• Sensitivity = -87 dBm, -90 dBm, -93 dBm

• Node energy = 100 units, 250 units, 500 units

• Path loss exponent = 3.5

• Transition region width = 60 m

• Grid width = 50 m, 100 m, 150 m, 200 m, 250 m

• Assumptions

• Source and sink nodes have infinite energy

• Sensor nodes have limited energy


• Number of nodes = 1000



• Node energy = 100 units, 250 units, 500 units



• Grid width = 50 m, 100 m, 150 m, 200 m, 250 m 22

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Energy Consumption ModelEnergy Consumption Model

• RangeLAN2 7401/02 PC card

• 300 mA – transmit

• 150 mA – receive

• Average 5 mA - doze mode

• RangeLAN2 7401/02 PC card

• 300 mA – transmit

• 150 mA – receive

• Average 5 mA - doze mode

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Energy ConsumptionEnergy Consumption

• Assumptions:

• A node spends 0.5, 1.0, and 2.0 units of battery energy for transmission when transmit power of -2 dBm, 1 dBm, and 4 dBm resp.

• A node spends 0.5 unit of battery energy for reception

• An active coordinator spends 0.5 unit of battery energy if it is within the radio range of transmitting coordinator

• Assumptions:

• A node spends 0.5, 1.0, and 2.0 units of battery energy for transmission when transmit power of -2 dBm, 1 dBm, and 4 dBm resp.

• A node spends 0.5 unit of battery energy for reception

• An active coordinator spends 0.5 unit of battery energy if it is within the radio range of transmitting coordinator

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SimulationTopology

SimulationTopology

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Simulation Parameter GUI

Simulation Parameter GUI

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Simulationmetrics

Simulationmetrics

• Metrics and terms:

• Normalized energy

• Event

• Network partition

• Metrics and terms:

• Normalized energy

• Event

• Network partition

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Simulation varying the transmit power



• Sensitivity = -90 dBm

• Node energy = 250 units




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Simulation Resultsvarying the transmit power


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• Network partition is extended with increase in transmit power

• A grid width of 200 m provides longest network partition

• All the grid width networks perform better than traditional flooding

• Network partition is extended with increase in transmit power


• All the grid width networks perform better than traditional flooding

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Simulation varying the sensitivity


• Transmit power = 1 dBm






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Simulation Resultsvarying the sensitivity


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• Network partition is extended as sensitivity is increased

• Network partition is extended by a factor of 4 when S=-93 dBm and by a factor of 3 when S=-90 dBm compared to when S=-87 dBm


• All grid widths perform better than traditional flooding

• Network partition is extended as sensitivity is increased

• Network partition is extended by a factor of 4 when S=-93 dBm and by a factor of 3 when S=-90 dBm compared to when S=-87 dBm


• All grid widths perform better than traditional flooding

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Simulation Resultsscalability


• Parameters:

• Number of nodes = 100, 250, 500, 750, 1000, 1250, 1500


• Battery life per node = 250 units





• Grid width = 200 m

• Parameters:

• Number of nodes = 100, 250, 500, 750, 1000, 1250, 1500


• Battery life per node = 250 units





• Grid width = 200 m 42

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• Node redundancy increases, partition is extended

• Partition for 1500 nodes is extended by a factor of 2 compared to 1000 nodes


• Linear increase in network partition

• Node redundancy increases, partition is extended



• Linear increase in network partition

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ConclusionsConclusions

• Network partition is prolonged as transmit power increases

• Network partition is prolonged as sensitivity increases

• Grid width of 200 m show consistently better performance in extending network partition

• Network partition for 1500 nodes is extended by a factor of 17 compared to 100 nodes

• Comparison with traditional flooding algorithm

• Network partition is prolonged as transmit power increases

• Network partition is prolonged as sensitivity increases

• Grid width of 200 m show consistently better performance in extending network partition

• Network partition for 1500 nodes is extended by a factor of 17 compared to 100 nodes

• Comparison with traditional flooding algorithm 45

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Future workFuture work

• Physical implementation

• Localized reflooding

• Node mobility

• Physical implementation

• Localized reflooding

• Node mobility

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Thank YouThank You

Questions?Questions?

Documents

College of Engineering Grid-based Coordinated Routing in Wireless Sensor Networks Uttara Sawant Major Advisor : Dr. Robert Akl Department of Computer Science