Switchr reducing system power consumption in a multi client multi radio environment
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SwitchR: Reducing System Power Consumption in a Multi-Client Multi-Radio Environment . Yuvraj Agarwal (University of California, San Diego) Trevor Pering, Roy Want (Intel Research), Rajesh Gupta (UC San Diego). Wearable and Mobile Devices: . Increasing Functionality

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Switchr reducing system power consumption in a multi client multi radio environment l.jpg

SwitchR: Reducing System Power Consumption in a Multi-Client Multi-Radio Environment

Yuvraj Agarwal (University of California, San Diego)Trevor Pering, Roy Want (Intel Research),Rajesh Gupta (UC San Diego)

Wearable and mobile devices l.jpg
Wearable and Mobile Devices:

  • Increasing Functionality

    • Faster processors, more memory

  • Applications are increasingly communication intensive

    • Streaming video, VoIP, Downloading files

  • Multiple wirelessradios often integrated on single device

    • (Bluetooth for PANs, WiFi for high-bandwidth data access)

  • Wearable/Mobile Computers  Power Consumption is very important!

    • Limited by battery lifetime

    • Communication over WiFi reduces battery lifetime even further….

      • In some cases up to 50% of total energy drain!

Reducing the energy for communication l.jpg
Reducing the energy for communication

  • Opportunity: Availability of multiple radio interfaces …

    • Can all be used for data transfer

    • Different characteristics : bandwidth, range, power consumption

  • Typically function as isolated systems,

    • Can we coordinate usage to provide a unified network connection ?

      • Seamlessly switch between radios

    • Primary Goal: Save energy



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Radio Characteristics

Higher throughput radios have a lower energy/bit value … have a higher idle power consumption

…and they have different rangecharacteristics

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Multi-Radio Switching

  • CoolSpots [Mobisys ‘06]:

    • Multi-Radio switching for a single-clientscenario

    • Specialized access point (Bluetooth + WiFi)

    • Switching decisions – Local to client

  • SwitchR:

    • Leverage existing WiFi APs : Incrementally deployable

    • Considers traffic imposed by other devices in a multi-clientscenario

    • Switching decision – global since it affect other clients

    • Evaluate energy savings on a distributed testbed

Problem Statement: Reduce energy consumption by choosing appropriate radio interface, while taking into consideration other clients.

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SwitchR Architecture

Infrastructure Network

BTG (Bluetooth Gateway)

Bluetooth Link


WiFi Link

Ethernet Link






MD = Mobile Devices


  • Switching Policy:

  • Hybrid Approach

  • Application requirements at nodes (local)

  • Channel quality and bandwidth (global)

  • Switching Mechanism:

  • Network Level Reconfigurations

  • ARPs and Routing updates

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Multi-Client Switching Policy

  • Hybrid approach to make switching decisions

    • Local knowledge (node level)

    • Global (channel utilization by other nodes)

  • Switching up (Bluetooth  WiFi)

    • ICMP response time and radio RSSI values

    • Capture application needs and channel characteristics

  • Switching-down (WiFi  Bluetooth)

    • Measure application bandwidth requirements

    • Periodically query BTG for residual capacity

    • Measure channel/link quality (local)

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Evaluation: Testbed

BTG (Bluetooth Gateway)

Infrastructure Network

Bluetooth (Always Connected)


WiFi (Dynamically Switched)

Static Wired Connection




Wi-Fi AP


Mobile Device (MD)


Stargate2 node

  • Stargate2 research platform

    • WiFi + Bluetooth + Integrating power and data monitoring

  • Benchmark applications are striped across devices

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Evaluation: Benchmarks

  • Baselines:

  • Idle: connected, but no data transfer

  • Transfer: bulk TCP data transfer

  • Streaming:

  • Media: 128k, 156k and g711 VoIP codec

  • Various QoS requirements

  • Web:

  • Combination of idle and data transfer

  • Idle: “think time”

  • Small transfer: basic web-pages

  • Bulk transfer: documents or media

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Evaluation: Switching Policies

  • Baselines policies

    • “Wifi-CAM” (Awake Mode)

    • “Wifi-PSM” (Power Save Mode)

  • Single-Client based “cap-dynamic” switching policy

  • SwitchR: “multi-client” switching policy

    • Combines both local (per client) and global knowledge

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Results: Baselines

Switching policies perform better that WiFi policies for “idle” benchmark, similar for “transfer”

Results l.jpg

multi-client policy saves up to 62% over single-client cap-dynamic policy

VoIP and streaming benchmarks benefit most since streams can use BT channel

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  • SwitchR: Multi-radio switching architecture

    • Incrementally deployable

    • Energy Savings (72% over WiFi-PSM)

    • Can increase battery lifetime substantially

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Thank You!

Website : http://mesl.ucsd.edu/yuvraj

Email : [email protected]

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Results: VoIP traffic

Although, bandwidth requirements less than bluetooth channel capacity

Web benchmark causes VoIP streams to switch to WiFi

multi-client policy saves upto 65% over cap-dynamic, allows VoIP streams to switch back