Energy Management in
Wireless Sensor Networks
Mohamed Hauter
CMPE257
University of California, Santa Cruz
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Outline
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Wireless Sensor Networks
Energy and Wireless Sensor Networks
Paper1
Paper2
Paper3
Conclusion
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Wireless Sensor Network
• Consists of spatially
distributed autonomous
sensors.
• Monitors physical or
environmental conditions (i.e.
temperature, pressure, etc.)
• Cooperates to pass data
through network to main
location
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Energy and Wireless Sensor
Networks
 Usually deployed in remote
regions
 Energy consumption vs.
battery life
 Energy harvesting
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Energy aware efficient geographic routing in
lossy wireless sensor
networks with environmental energy supply
BY:
Kai Zeng
Kui Ren
Wenjing Lou
Patrick J. Moran
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Basic Idea!
Combine the efficiency of Geo-Aware routing and energy
harvesting techniques.
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Proposal
 Geographic Routing with Environmental Energy Supply
(GREES)
 Packets are delivered through low cost links
 Balances residual energy on nodes using environmental
energy supply
 Two protocols are proposed:
 GREES-L
 GREES-M
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Related Work
 Battery technology has been unchanged for many years
 Former energy aware routing protocols:
 Batteries have limited/fixed capacity
 Decisions are made based on energy consumption
 Energy scavengers:
 Harvests small amounts of energy from ambient sources
 Solar-aware routing protocols:
 Must have a global knowledge of the whole network
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Protocol Description
 Maintain one-hop neighbor’s information:
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Location
Residual energy
Energy harvesting rate
Energy consumption rate
Wireless link quality
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Protocol Description (Cont.)
 To balance the geographical advance efficiency per
packet transmission and the energy availability on
receiving nodes:
 GREES-L - uses linear combination
 GREES-M – uses multiplication
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GREES
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GREES (Cont.)
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GREES (Cont.)
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Simulation Results
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Simulation Results
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Conclusions
 Strengths:
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Maintains a higher mean residual energy on nodes
Achieves better load balancing
Small standard deviation of residual energy on nodes
Does not compromise the end-to-end throughput
performance
 Weaknesses:
 Exhibits graceful degradation on end-to-end delay
 What happens when energy harvesting fails?
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Minimum-Energy Asynchronous Dissemination
to Mobile Sinks in Wireless Sensor Networks
BY:
Hyung Seok Kim
Tarek F. Abdelzaher
Wook Hyun Kwon
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Basic Idea
 Achieve energy savings in wireless sensor networks by:
 Optimizing communications between sensor nodes and
sinks
 Tradeoff?
 Increase in path delay.
 Is the tradeoff a good one? We’ll see…
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Related Work
 Overlay Multicasting
 Uses sinks as intermediate nodes in the tree
 Uses flooding to disseminate information
 Flooding is energy-intensive
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Proposal
 SEAD – Scalable Energy-efficient Asynchronous
Dissemination protocol
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Stationary sensor node takes the mobile sink’s place
Build an optimal dissemination tree (d-tree)
Select dissemination paths to stationary sensor nodes
Stationary sensor nodes forward data
Minimize energy cost
As sink moves, forward delay increases (tradeoff)
Reconfigure d-tree when needed
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SEAD Tree Model in Wireless
Sensor Networks
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SEAD Sink Search
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SEAD Sink Search
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SEAD Sink Search
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SEAD Sink Search
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Results
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Results
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Results
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Results
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Results
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Conclusion
 Strengths:
 SEAD saves energy
 Strikes a balance between end-to-end delay and power
consumption
 Power savings are favored over delay minimization
 Weaknesses:
 Affects the lifetime of the access node
 Not robust in high density networks
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Meeting Lifetime Goals with
Energy Levels
BY:
Andreas Lachenmann
Pedro Jos´e Marr ´on
Daniel Minder
Kurt Rothermel
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Basic idea
 Levels : an abstraction for energy-aware programming
of wireless sensor networks.
 Goal is to meet the user-defined lifetime goals while
maximizing application quality
 Applied in applications with:
1. Known lifetime
2. No redundant nodes
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How does it work?
1. Define energy levels
2. Measure energy consumption of each level (using an
energy profiler)
3. Decide level of functionality to meet lifetime goal
4. Maximize performance within allowed energy level
5. Maintain network connectivity
6. Maintain optimal application quality
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Example
 ZebraNet monitoring system
 Gathers GPS traces
 If a node fails due to energy drought, what happens?
 Lost track of at least one animal
 Possible network disconnection
 Solution ???
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Solution
 A node can:
1. Stop forwarding data from other nodes
2. Decrease energy-intensive radio communications
3. Stop storing other nodes’ data (avoid flash memory
access)
4. Decrease queries of GPS position
5. …
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Benefits to developer
1. Eliminates low energy-levels issues
2. Ensures reaching targeted lifetime
3. Low overhead
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Design Considerations
 Single application running on each sensor
node
 Periodic behavior
 It is possible to simulate output behavior, thus
acquire energy consumption statistics
 Use voltage sensors
 Investing time to define energy levels
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Design Goals
 Provide a programming abstraction and
runtime support that helps to meet the
user’s lifetime goals by deactivating
parts of the application if necessary
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How to achieve
goals?
 Divide into sub goals:
1.
2.
3.
4.
5.
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Follow definition of optional functionality
Make it easy to use
Minimum overhead
Provide good application quality
Low runtime
Robust with inaccurate energy estimates
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Notice
Levels approach follows the
well-known model predictive
control (MPC) schemes
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Combining Energy Levels
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Code Example for Energy Levels
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Computing the Energy
Consumption of a Code
Block
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Special Cases
 Energy consumed by lower level
energy_level(1) = total_energy_consumed –
energy_estimated_all_other_levels
 Energy consumption that depends on some state of the
hardware of software
Example: attempting to turn on an active device. No energy
consumed, thus adjust estimates.
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Battery Discharge Characteristics
(from three experiments)
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Results
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Runtime Overhead
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Conclusion
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Helps meet user-defined lifetime goals
Requires small code modifications
Low overhead
Maximize performance within allowed
energy level
 Maintain network connectivity
 Maintain optimal application quality
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Questions ????
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