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

1. An Adaptive Link Layer for Range Diversity inMulti-radio Mobile Sensor Networks Jeremy Gummeson Deepak Ganesan Mark D. Corner Prashant Shenoy

2. 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

3. A Spectrum of Radio Choices • 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

4. 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

5. Contributions Our System makes the following contributions to Mobile Multi-Radio Sensors Research: • Arthropod: A low-power, multi-radio sensor platform • A machine-learning Algorithm that uses link-layer statistics to select between radio interfaces • A multi-radio switching protocol that provides robust transitions and manages radio state

6. Outline • Motivation • System Design • Implementation • Results • Conclusions

7. 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 Application Unified Link Layer CC2420 MAC XE1205 MAC CC2420 Radio XE1205 Radio Hardware Prototype System Block Diagram

8. Send Receive Switching Protocol Q-Learning Algorithm decision CC2420 MAC XE1205MAC CC2420 MAC XE1205MAC 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

9. 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

10. 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

11. Switching Protocol Description • Sending node drives state transitions at receiver: • Asserting EXPLORE flag in sent packet causes both radio interfaces to become active until timeout • Consecutive packets may be sent on either interface; continuously asserting EXPLORE will keep both interfaces active • Alternatively, the next packet may be sent with HIGH_ON or LOW_ON flag asserted to commit receiver to one particular interface • Two consecutive timeouts force receiver into Low Power Listen (LPL) on long range interface; may proactively enter LPL by asserting END_BLOCK EXPLORE|| Timeout EXPLORE Low On Handoff High On LOW_ON HIGH_ON || Timeout Wakeup END_BLOCK END_BLOCK|| Timeout Idle

12. Evaluation Methodology • Trace-driven simulations using real datasets: • Results from software implementation: • Show performance of link layer software implementation • Validate simulated link layer performance for indoor continuous dataset

13. Trace Driven Simulation Results Fraction of Lost Packets Per Packet Energy Consumption • Multi-Radio approach improves per packet energy consumption while only marginally increasing packet loss

14. Multi-Radio Power Control Results • Additional simulation looks at power control across radios: • Data set uses max/min Tx power settings on each radio Summary of results for each power level Cumulative Energy Consumption for Single and Multi-power level strategies • Unified Link Layer successfully tracks energy-minimal radio/power setting

15. 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 Summary of Implementation Results • Multi-Radio implementation loses more packets, consumes substantially less energy

16. 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

17. 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

18. 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)

19. Thank You Questions?

20. 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

21. 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

22. 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

23. Current Strategies Communication is Expensive! • Use communication resources intelligently: • Minimize radio time spent in active mode • Send data when channel conditions are “good”

24. 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 αand γ • The agent will also periodically take a random action, ε, which allows unexplored state to be reached Formal definition of Q-Learning

25. 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. αand 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

26. 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