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Grafting Energy-Harvesting Leaves onto the Sensornet Tree

Grafting Energy-Harvesting Leaves onto the Sensornet Tree. AUTHORS: Lohit Yervay , Bradford Campbelly , Apoorva Bansaly , Thomas Schmidz , Prabal Duttay Presenting by : Phanindar Reddy Tati. Contents:. Abstract Introduction System overview Low-Power leaf node design Evaluation

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Grafting Energy-Harvesting Leaves onto the Sensornet Tree

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  1. Grafting Energy-Harvesting Leavesonto the Sensornet Tree AUTHORS: LohitYervay, Bradford Campbelly, ApoorvaBansaly, Thomas Schmidz, PrabalDuttay Presenting by: Phanindar Reddy Tati

  2. Contents: Abstract Introduction System overview Low-Power leaf node design Evaluation Related work Conclusion

  3. Abstract: We study the problem of augmenting battery-powered sensornet trees with energy-harvesting leaf nodes. Our results show that leaf nodes that are smaller in size than today’s typical battery-powered sensors can harvest enough energy from ambient sources to acquire and transmit sensor readings every minute, even under poor lighting conditions. However, achieving this functionality, especially as leaf nodes scale in size, requires new platforms, protocols, and programming. Platforms must be designed around low-leakage operation, offer a richer power supply control interface for system software, and employ an unconventional energy storage hierarchy. Protocols must not only be low-power, but they must also become low-energy, which affects initial and ongoing synchronization, and periodic communications. Systems programming, and especially bootup and communications, must become low-latency, by eliminating conservative timeouts and startup dependencies, and embracing high-concurrency. Applying these principles, we show that robust, indoor, perpetual sensing is viable using off-the-shelf technology.

  4. Continued… ABSTRACT • Problem: Augmenting battery-powered sensornet trees with energy- harvesting leaf nodes • Results shows leaf nodes smaller in size works fine. • Need new platforms, protocols and programming • Platforms: • Low leakage operation • Offer richer power supply • Employs energy storage hierarchy • Protocols: • Low-power and low-energy protocols • Programming: • Fast Boot-up • Low-latency

  5. Introduction: ABSTRACT INTRODUCTION • Energy Harvesting operation • Approaches: • EnOcean • ZigBee Green Power • Energy harvesting and Mesh Networking are not exclusive , can exist in a unified network architecture. • New technologies coupled with Star topology addresses challenges in energy harvesting operation. • Existing technologies can be combined in new ways with simple protocols to achieve energy harvesting operation.

  6. Continued… ABSTRACT INTRODUCTION • Adding stable clock and minor software improvements to existing battery powered mesh nodes prepares them to interact with energy harvesting leaf nodes. • Leaf nodes: • Similar to Branch nodes • No batteries • Solar cells • Design Constraints: • Low-leakage • Low-power operation

  7. Continued… ABSTRACT INTRODUCTION • Networking problems in augmentation: • Initial Synchronization • Ongoing Synchronization • Bi-directional communications • Challenge: Achieve low-energy operation • Positive side: • low communication is possible • low-energy neighbor discovery protocols available • Optimizations are required • Goal: Understanding design space of low-maintenance, high-density sensor networks

  8. Continued… ABSTRACT INTRODUCTION • Authors Showed, • With available parts, we can build solar-powered node with ultra low leakage currents • Node works in very low indoor lighting conditions • Delivers data every minute • Adapted existing protocols to meet challenges of the problem

  9. System Overview: INTRODUCTION SYSTEM OVERVIEW • Elements: • Wall-powered trunk nodes • Battery powered Branch nodes • Energy harvesting leaf nodes • Leaf node: • Integrates sensor node like ‘Epic mote’ • Energy harvesting power supply • Accurate time keeping

  10. Continued… INTRODUCTION SYSTEM OVERVIEW

  11. Continued… INTRODUCTION SYSTEM OVERVIEW • Five design principles in leaf nodes:. • Minimize power transfer inefficiencies • Minimize power conversion inefficiencies • Minimize leakages • Improve energy consumption efficiency • Minimize communication cost

  12. Continued… INTRODUCTION SYSTEM OVERVIEW • Branch Nodes: • Battery and Regulator • Clock • Identical to sensors ‘Telos’ • Leaf to Branch Communications: • Wakes up on a fixed period • Takes a sensor reading • Transmits packet • Listens inbound traffic • What if a leaf node does not have power? • Low duty cycle neighbor discovery protocol

  13. Low-power leaf node design: SYSTEM OVERVIEW LOW-POWER LEAF NODE DESIGN • Leaf Node: • Processor • Radio • Real-time Clock • Energy Harvesting power supply

  14. Continued… SYSTEM OVERVIEW LOW-POWER LEAF NODE DESIGN • Processor and Radio: • Epic core mote • MSP430F1611 microcontroller • CC2420 Radio • Real time Clock: • To investigate leaf activity • NXP PCF2127A RTC • Excellent time keeping stability • Low current draw • Flexible triggering options

  15. Continued… SYSTEM OVERVIEW LOW-POWER LEAF NODE DESIGN • Hardware Operation: • Charge • Startup • Active • Software Operation: • Shutdown • Oscillator Fast Start • Optimized startup • Concurrent initializations

  16. Evaluation : LOW-POWER LEAF NODE DESIGN EVALUATION • Evaluates the viability of energy harvesting operation, characterizes typical indoor lighting conditions, demonstrates initial and ongoing synchronization, and shows that leaf and branch nodes can communicate successfully. • Energy Harvesting Operation: • Demonstrates relation between irradiance and leaf node activity • Two Leaf nodes with Amorphous Solar cell | Two Leaf nodes with Crystalline Solar cell • Work: Transmit a packet and disconnect processor and radio from power supply • Exposed to varying irradiance levels from 4 indoor light sources • Question: Given certain level of radiance, what is the transmission rate of leaf node?

  17. Continued… LOW-POWER LEAF NODE DESIGN EVALUATION

  18. Continued… LOW-POWER LEAF NODE DESIGN EVALUATION

  19. Continued… LOW-POWER LEAF NODE DESIGN EVALUATION • Initial Synchronization: • Two techniques to synchronize leaf nodes and branch nodes • Asynchronous neighbor discovery • Synchronous event triggered • Asynchronous neighbor discovery: • Disco neighbor discovery protocol • Leaf nodes transmits beacons, branch nodes listen • Worst case discovery latency = 50mins • Discovery burden is small for both nodes

  20. Continued… LOW-POWER LEAF NODE DESIGN EVALUATION • Leaf Nodes: • Transmits beacons in a 5ms window, every 60s • This allows a branch node to both employ a compatible neighbor discovery schedule and predict future transmission times • Branch Nodes: • listens for beacons in 5ms window, every 245ms

  21. LOW-POWER LEAF NODE DESIGN EVALUATION • Synchronous event triggered: • Designed a simple, zero-power, light activated trigger switch • Leaf node transmits first and then branch node responds • Transmission is bidirectional

  22. Continued… LOW-POWER LEAF NODE DESIGN EVALUATION • Ongoing Synchronization: • More burden on Branch node • Leaf node simply transmits beacons every multiples of 60s • Branch node keeps track of leaf nodes transmission times • Example

  23. Continued… LOW-POWER LEAF NODE DESIGN EVALUATION Ongoing Synchronization:

  24. Continued… LOW-POWER LEAF NODE DESIGN EVALUATION • Leaf to Branch Communication: • Relatively straight forward • Transmits on multiples of 60s • If it does not have power, skips that activity cycle • Branch to Leaf Communication: • Like sending ACK to leaf node • To enable this, we modify the branch to pipeline payload reception with transmit FIFO loading. This allows the branch node to reply with a full packet with a 0.67ms turnaround time.

  25. Related Work: EVALUATION RELATED WORK • Architectures: • The canonical sensornet is the Great Duck Island deployment. • The battery-powered nodes generated a message every 5 minutes. In this work, authors show that purely energy-harvesting indoor nodes can send a message about every minute during daylight hours. • Authors extended this architecture one level further, into energy harvesting leaves, and describe how these leaves can interoperate with the existing architectures.

  26. Continued… EVALUATION RELATED WORK • Indoor Photo-Voltaic Systems: • TwinStar is a mixed indoor-outdoor solar energy harvesting system that explores a capacitor-only energy storage design. • The idea behind TwinStar is to use energy when it is available, and thus reduce energy leakage. • Their design is practical with batteries, however, our work explores the scenario in which complete energy loss is possible.

  27. Conclusion: RELATED WORK CONCLUSION Batteries have a finite lifetime, they incur replacement costs, and their average power delivery scales poorly compared with indoor photovoltaic. Today many believe that energy-harvesting holds the key to long-term, cost-effective, and sustainable sensing. This paper shows that it is possible to augment battery powered mesh networks with energy-harvesting leaf nodes. Authors created a new tier of sensor nodes that are free from the constraints of battery power, but still retain the many benefits of interoperating with contemporary wireless multihop mesh networks. This work paves the way for a new tier of perpetual computing systems, shows the viability of the architectural approach, and demonstrates interoperability with existing sensor network nodes.

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