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Energy M anagement in Wireless Sensor Networks. Mohamed Hauter CMPE257 University of California, Santa Cruz. Outline. Wireless S ensor Networks Energy and Wireless Sensor Networks Paper1 Paper2 Paper3 Conclusion. Wireless Sensor Network.

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energy m anagement in wireless sensor networks

Energy Management in Wireless Sensor Networks

Mohamed Hauter


University of California, Santa Cruz


Wireless Sensor Networks

Energy and Wireless Sensor Networks





wireless sensor network
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

energy and wireless sensor networks
Energy and Wireless Sensor Networks

Usually deployed in remote regions

Energy consumption vs. battery life

Energy harvesting

Energy aware efficient geographic routing in lossy wireless sensornetworks with environmental energy supply

BY:Kai Zeng

Kui Ren

Wenjing Lou

Patrick J. Moran

basic idea
Basic Idea!

Combine the efficiency of Geo-Aware routing and energy harvesting techniques.

  • 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
related work
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
protocol description
Protocol Description
  • Maintain one-hop neighbor’s information:
    • Location
    • Residual energy
    • Energy harvesting rate
    • Energy consumption rate
    • Wireless link quality
protocol description cont
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
  • Strengths:
    • 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?
minimum energy asynchronous dissemination to mobile sinks in wireless sensor networks
Minimum-Energy Asynchronous Dissemination to Mobile Sinks in Wireless Sensor Networks


Tarek F. Abdelzaher

Wook Hyun Kwon

basic idea18
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…
related work19
Related Work
  • Overlay Multicasting
    • Uses sinks as intermediate nodes in the tree
    • Uses flooding to disseminate information
    • Flooding is energy-intensive
  • SEAD – Scalable Energy-efficient Asynchronous Dissemination protocol
    • 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
  • 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
Meeting Lifetime Goals with Energy LevelsBY:Andreas LachenmannPedro Jos´e Marr ´onDaniel MinderKurt Rothermel
basic idea33
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:
    • Known lifetime
    • No redundant nodes
how does it work
How does it work?

Define energy levels

Measure energy consumption of each level (using an energy profiler)

Decide level of functionality to meet lifetime goal

Maximize performance within allowed energy level

Maintain network connectivity

Maintain optimal application quality

  • 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 ???
  • A node can:
    • Stop forwarding data from other nodes
    • Decrease energy-intensive radio communications
    • Stop storing other nodes’ data (avoid flash memory access)
    • Decrease queries of GPS position
benefits to developer
Benefits to developer

Eliminates low energy-levels issues

Ensures reaching targeted lifetime

Low overhead

design considerations
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

design goals
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

how to achieve goals
How to achieve goals?
  • Divide into sub goals:
    • Follow definition of optional functionality
    • Make it easy to use
    • Minimum overhead
    • Provide good application quality
    • Low runtime
    • Robust with inaccurate energy estimates

Levels approach follows the well-known model predictive control (MPC) schemes

special cases
Special Cases

Energy consumed by lower level energy_level(1) = total_energy_consumed –


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.


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