An Adaptive Link Layer for Range Diversity in Multi-radio Mobile Sensor Networks

UMass Computer Science Department

Jeremy GummesonDeepak GanesanMark D. Corner

Prashant Shenoy

An Adaptive Link Layer for Range Diversity in

Multi-radio Mobile Sensor Networks

2UMass Computer Science Department

Mobile Sensor Networks

Mobile entities equipped with sensors, radios

Exchange data with peer mobile nodes, infrastructure basestation

High-power long-range Radio maximizes communication opportunities, but expensive at short-range

Mobility Patterns difficult to predict

Tracking applications require small form factor


A Spectrum of Radio Choices

Radio Bandwidth Energy/bit Range

CC2420 250 kbps 208nJ/bit 80m

802.11b 11Mbps 120nJ/bit 100m

XE1205 38.1kbps 5276nJ/bit 800m

Xtend 9.6kbps 380.2uJ/bit 2-3km

Existing radios optimized for short or long range:• Long Range, Low bit rate, Low Energy Efficiency• Short Range, High bit rate, Higher Energy Efficiency

Designer chooses efficiency or range

Common Small Form Factor Radios


Approach

Design a node with heterogeneous radios to exploit short range efficiency and long range connectivity

Use unified link layer to manage radios and react to channel and mobility dynamics


Contributions

Our System makes the following contributions to Mobile Multi-Radio Sensors Research:

1. Arthropod: A low-power, multi-radio sensor platform2. A machine-learning Algorithm that uses link-layer

statistics to select between radio interfaces3. A multi-radio switching protocol that provides robust

transitions and manages radio state


Outline

Motivation System Design Implementation Results Conclusions


Arthropod: A Multi-Radio Sensor Platform Hardware platform consists of: MSP430 MCU, CC2420 radio, and

XE1205 radio:• Expansion board provides existing platform Tinynode with

CC2420 radio• Board connects CC2420 to unused SPI bus and GPIO pins.

Existing TinyOS-2.x drivers modified for use with new hardware

Hardware Prototype

Application

Unified Link Layer

CC2420 MAC XE1205 MAC

CC2420 Radio XE1205 Radio

System Block Diagram


Utilizing Multiple Radios

Problem: Need to determine energy-optimal radio at given time

Approach: Unified link-layer presents multiple radios as one entity

Two subcomponents:• Q-Learning Algorithm: Observe MAC retransmissions,

learn/choose optimal radio interface • Switching Protocol: Manage radio power states, coordinate

handoffs

Q-Learning Algorithm

CC2420 MAC

XE1205MAC

decision

SwitchingProtocol

CC2420 MAC

XE1205MAC

Send Receive


Q-Learning

Goal: Choose action a, arrive in state with maximal Q value In multi-radio context, Q represents learned energy needed to send packet

on given interface at particular power-level a represents decision to send packet using particular interface/power

combination After transmission, receive reward r, where i represents retransmissions:

r[i] = -(i*PacketSize*ByteTime*TxPower + AckTimeOut*RxPower) + RxPower*AckRTT + PacketSize*ByteTime*TxPower

r used to update Q using simple rule with fixed parameters Periodically explore alternate interface/power-levels by choosing random

action a; allows transitions when conditions improve


Multi-Radio Switching Protocol

Q-Learning finds optimal interface/power level, need handoff between radios non-trivial problem: radio transitions occur during periods of high loss Need to handle:

• State synchronization problems between sender and receiver• Graceful disconnections

Solution:• Embed control flags that negotiate handoffs• Handoff state temporarily powers both radio receivers; Minimize time spent

during handoff to minimize overhead


Switching Protocol Description

Sending node drives state transitions at receiver:1. Asserting EXPLORE flag in sent packet causes both radio interfaces to

become active until timeout2. Consecutive packets may be sent on either interface; continuously

asserting EXPLORE will keep both interfaces active3. Alternatively, the next packet may be sent with HIGH_ON or LOW_ON flag

asserted to commit receiver to one particular interface4. Two consecutive timeouts force receiver into Low Power Listen (LPL) on

long range interface; may proactively enter LPL by asserting END_BLOCK

Low On Handoff

Idle

High On

EXPLORE||

TimeoutEXPLORE

LOW_ON HIGH_ON ||

Timeout

END_BLOCK

|| Tim

eoutWakeupEND_BLOCK


Evaluation Methodology

1. Trace-driven simulations using real datasets:

2. Results from software implementation:• Show performance of link layer software implementation• Validate simulated link layer performance for indoor

continuous dataset

Environment Mobility Pattern Example Scenario

urban-indoor Continuous w/ obstruction

People moving in a building

urban-outdoor Continuous; Partial Line of Sight

Moving vehicle

urban-outdoor Nomadic Bus w/ stops

foliage Nomadic Animals visiting habitat


Trace Driven Simulation Results

Per Packet Energy Consumption

Fraction of Lost Packets

Multi-Radio approach improves per packet energy consumption while only marginally increasing packet loss


Multi-Radio Power Control Results

Additional simulation looks at power control across radios:• Data set uses max/min Tx power settings on each radio

Cumulative Energy Consumption for Single and Multi-power level strategies

Radio/power level

% packets lost

Energy Consumed

XE1205@0dBm

4.24 .659mJ/Tx Success

XE1205@15dBm

0 .925 mJ/Tx Success

CC2420@-25dBm

37.01 1.1mJ/Tx Success

CC2420 @0dBm

35.45 1.2 mJ/Tx Success

Q-Learning 3.53 .430 mJ/Tx Success

Summary of results for each power level

Unified Link Layer successfully tracks energy-minimal radio/power setting


Implementation Loss Rates and Energy/Packet

TinyOS-2.x software implementation for Arthropod shows algorithm running online; measures performance of radio switching protocol

Recreate mobility pattern of indoor continuous trace; implementation results compared to single radio performance from indoor continuous

Trace Type XE1205Utilization

CC2420 Utilization

Packets Lost Energy Consumed

Multi-RadioImplementation

28.97% 71.03% 3.95% .48mJ / Tx Success

XE1205@15dBm 100% 0 .6% 1.0mJ / Tx Success

CC2420@0dBm O% 100% 43.0% 1.6mJ / Tx Success

Summary of Implementation Results

Multi-Radio implementation loses more packets, consumes substantially less energy


Breakdown of Receiver Energy Costs

Energy Spent during different Rx States

Multi-Radio approach uses significantly less power than an XE1205 only implementation; Loss rate comparable to the CC2420


Conclusions

Showed hardware implementation of multi-radio sensor node Arthropod

Designed and tested a unified link layer for multi-radio hardware:• Uses learning algorithm and MAC statistics to select radio interace• Implemented switching protocol to handoff between radios

Evaluated link-layer via trace driven simulation and algorithm running online:• Considerably more energy efficient for different mobility patterns,

while only marginally increasing losses


Related Work

Existing Multi-Radio Systems: Separate Radio Roles:• Wake-On-Wireless: low-power, low-bandwidth radio wakes up high-power,

high-band-width radio (Agarwal, 2007)• DieselNet Throwboxes: Long-range radio maximizes utility of short-range,

high-bandwidth radio in a mobile scenario (Banerjee, 2007)

Dynamic Radio Selection:• Mobile Access Router: Use heterogeneous radios to maximize bandwidth

and minimize stalled transfers; neglects energy (Rodriguez, 2004)• Coolspots: Use Bluetooth for communication when available, otherwise uses

802.11 (Pering, 2006)

Mesh Networking:• MR-LQSR: Use Multiple Radios per mesh node, makes channel assignment

more effective (Draves, 2004)


Thank You

Questions?


Sender State Machine States represent sender’s view of the receiver Intermediate handoff state used to activate alt. radio Transition out of IDLE requires wakeup packet Receiver -> Both radios active during handoff


Receiver State Machine

Used to manage radio receiver power states Flags used to coordinate handoff between radios Two Consecutive Timeouts result in transition to IDLE state May proactively switch to IDLE state at end of block

transfer


Research Contributions

A prototype low-power multi-radio hardware system

Develop low-overhead techniques for dynamically switching between radio interfaces

Evaluation methodologies for showing energy performance benefits of multi-radio systems


Current Strategies

Communication is Expensive!

Use communication resources intelligently:• Minimize radio time spent in active mode• Send data when channel conditions are “good”

Active Power Consumption (mW)

Sleep Mode Power Consumption (mW)

CC2420 Radio 59.1 (Rx) .02

XE1205 Radio 65.4 (Rx) .03

MSP430 MCU 3 .015


Q-Learning

Q-Learning is a reinforcement-learning technique used for decision-making by agents in an unknown environment:• A Matrix Q contains the accumulated reward by an agent in a given

state• The agent has several choices of action and chooses the action a such

that the Q-value of the arrival state is maximized.• After Taking action a, the agent receives a reward r and adjusts Q with

an update rule defined by parameters α and γ• The agent will also periodically take a random action, ε, which allows

unexplored state to be reached

Formal definition of Q-Learning


More Q-Learning

In the context of a multi-radio system:• Each state S is an individual radio/power-level combination• An action a corresponds to sending a packet over a given radio

interface.• Reward r corresponds to the negative energy used for sending the

packet. The amount of energy used is defined by a combination of radio hardware characteristics and channel dynamics.

• Q represents cumulative energy consumption across multiple transmission attempts. α and γ are used to control how quickly Q is updated as well as limiting the reward value r for staying in a given state.

• ε defines when the alternate radio interface should be explored. In a multi-radio scenario, it does not make sense to take a random action


Defining reward value r

The success of the Q-Learning algorithm depends heavily on r:• r is defined as energy required to send a packet. Energy is calculated

via MAC layer statistics• The following equation shows how a reward is calculated, where i is

the number of packet retransmissions:r[i] = -(i*PacketSize*ByteTime*TxPower + AckTimeOut*RxPower) +

RxPower*AckRTT + PacketSize*ByteTime*TxPower• A radio-agnostic quantity, energy, allows head-to-head comparison of

performance across radios. Maximizing Q is synonymous with minimizing energy

• Congestion backoffs also contribute to power consumption, but not in practice

An Adaptive Link Layer for Range Diversity in Multi-radio Mobile Sensor Networks

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