Post on 04-Jan-2016
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
• Introduction to Sensor Networks
• Sensor Routing Protocols
• Motivation
• Objectives
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Wireless Sensor Networks OverviewWireless Sensor Networks Overview
• Introduction to Sensor Networks
• Distributed networks
• Sensing, communication, computation
• Introduction to Sensor Networks
• 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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Wireless Sensor Networks OverviewWireless Sensor Networks Overview
• 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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Wireless Sensor Networks OverviewWireless Sensor Networks Overview
• Sensor Routing Protocols
• 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
• Sensor Routing Protocols
• 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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Wireless Sensor Networks OverviewWireless Sensor Networks Overview
• 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
• Coordinator election
• Grid size estimation
• Load balancing
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Grid-based Coordinated RoutingGrid-based Coordinated Routing
• 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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Grid-based Coordinated RoutingGrid-based Coordinated Routing
• 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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Grid-based Coordinated RoutingGrid-based Coordinated Routing
• 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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Grid-based Coordinated RoutingGrid-based Coordinated Routing
• Coordinator election
• Random node ID
• Coordinator = maximum node ID• Grid size estimation
• Coordinator election
• Random node ID
• Coordinator = maximum node ID• Grid size estimation
5nr R
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Grid-based Coordinated RoutingGrid-based Coordinated Routing
• 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
• 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 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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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
Simulation varying the transmit power
• Transmit power = -2 dBm, 1 dBm, 4 dBm
• Sensitivity = -90 dBm
• Node energy = 250 units
• Transmit power = -2 dBm, 1 dBm, 4 dBm
• Sensitivity = -90 dBm
• Node energy = 250 units
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Simulation Resultsvarying the transmit power
Simulation Resultsvarying the transmit power
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Simulation varying the transmit power
Simulation varying the transmit power
• Transmit power = -2 dBm, 1 dBm, 4 dBm
• Sensitivity = -90 dBm
• Node energy = 100 units
• Transmit power = -2 dBm, 1 dBm, 4 dBm
• Sensitivity = -90 dBm
• Node energy = 100 units
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Simulation Resultsvarying the transmit power
Simulation Resultsvarying the transmit power
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Simulation varying the transmit power
Simulation varying the transmit power
• Transmit power = -2 dBm, 1 dBm, 4 dBm
• Sensitivity = -90 dBm
• Node energy = 500 units
• Transmit power = -2 dBm, 1 dBm, 4 dBm
• Sensitivity = -90 dBm
• Node energy = 500 units
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Simulation Resultsvarying the transmit power
Simulation Resultsvarying the transmit power
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Simulation Resultsvarying the transmit power
Simulation Resultsvarying the transmit power
• 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
• A grid width of 200 m provides longest network partition
• All the grid width networks perform better than traditional flooding
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Simulation varying the sensitivity
Simulation varying the sensitivity
• Transmit power = 1 dBm
• Sensitivity = -87 dBm, -90 dBm, -93 dBm
• Node energy = 250 units
• Transmit power = 1 dBm
• Sensitivity = -87 dBm, -90 dBm, -93 dBm
• Node energy = 250 units
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Simulation varying the sensitivity
Simulation varying the sensitivity
• Transmit power = 1 dBm
• Sensitivity = -87 dBm, -90 dBm, -93 dBm
• Node energy = 100 units
• Transmit power = 1 dBm
• Sensitivity = -87 dBm, -90 dBm, -93 dBm
• Node energy = 100 units
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Simulation varying the sensitivity
Simulation varying the sensitivity
• Transmit power = 1 dBm
• Sensitivity = -87 dBm, -90 dBm, -93 dBm
• Node energy = 500 units
• Transmit power = 1 dBm
• Sensitivity = -87 dBm, -90 dBm, -93 dBm
• Node energy = 500 units
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Simulation Resultsvarying the sensitivity
Simulation Resultsvarying the sensitivity
• 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
• A grid width of 200 m provides longest network partition
• 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
• A grid width of 200 m provides longest network partition
• All grid widths perform better than traditional flooding
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Simulation Resultsscalability
Simulation Resultsscalability
• Parameters:
• Number of nodes = 100, 250, 500, 750, 1000, 1250, 1500
• Sensor field = 1000 m X 1000 m
• Battery life per node = 250 units
• Transmit power = 1 dBm
• Sensitivity = -90 dBm
• Transition region width = 60 m
• Path loss exponent = 3.5
• Grid width = 200 m
• Parameters:
• Number of nodes = 100, 250, 500, 750, 1000, 1250, 1500
• Sensor field = 1000 m X 1000 m
• Battery life per node = 250 units
• Transmit power = 1 dBm
• Sensitivity = -90 dBm
• Transition region width = 60 m
• Path loss exponent = 3.5
• Grid width = 200 m 42
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Simulation Resultsscalability
Simulation Resultsscalability
• Node redundancy increases, partition is extended
• Partition for 1500 nodes is extended by a factor of 2 compared to 1000 nodes
• Partition for 1500 nodes is extended by a factor of 17 compared to 100 nodes
• Linear increase in network partition
• Node redundancy increases, partition is extended
• Partition for 1500 nodes is extended by a factor of 2 compared to 1000 nodes
• Partition for 1500 nodes is extended by a factor of 17 compared to 100 nodes
• 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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