PicoRadio Networks Opportunities and Challenges

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PicoRadio NetworksOpportunities and Challenges
Focus2000, June 26-27, Berkeley

• Jan M. Rabaey
• http//www.eecs.berkeley.edu/jan

In cooperation with the BWRC PicoRadio group.
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Wireless in the Home
Source IEEE Spectrum, December 99
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Only the Top of the IceBerg

• Technology advances enable realization of cheap,
  ultra-small, ultra-low energy information
  processing and communication nodes
• Opens the door for a whole world of unchartered
  applications that involve data sensing,
  monitoring and actuation
• The only limit is imagination

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The Post-PC Era The Distributed Approach to
Information Processing
The "last meter" problem" to information access
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The Smart Home and Network Appliances
The Obvious Choice
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Industrial Building Environment Management

• Task/ambient conditioning systems allow thermal
  condition in small, localized zones (e.g.
  work-stations) to be individually controlled by
  building occupants
• Requires dense network of sensor/monitor nodes
• Wireless infrastructure provides flexibility in
  composition and topology
• Joint research proposal CBE/BSAC/BWRC

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The Interactive Museum
Other applications toys, manufacturing,
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Smart Dust (K.Pister)

• Autonomous sensor node (mote) in 1 mm3
• MAV delivery
• Thousands of motes
• Many interrogators
• Demonstrate useful/complex integration in 1 mm3

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PicoRadio Challenge

• Develop meso-scale low-cost radios for
  ubiquitous wireless data acquisition that
  minimize power/energy dissipation
• Minimize energy (lt5 nJ/(correct) bit) for
  energy-limited source
• Minimize power (lt 100 mW) for power-limited
  source (enablingenergy scavenging)
• Target date 2004
• By using the following strategies
• self-configuring networks
• fluid trade-off between communication and
  computation
• aggressive low-energy architectures and circuits

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System Requirements and Constraints

• (from Exploratorium scenario)
• Large numbers of nodes between 0.05 and 1
  nodes/m2
• Cheap (lt0.5) and small ( lt 1 cm3)
• Limited operation range of network maximum
  50-100 m
• Low data rates per node 1-10 bits/sec average
• up to 10 kbit/sec in rare local connections to
  potentially support non-latency critical voice
  channel
• Crucial Design Parameter Spatial capacity (or
  density) 100-200 bits/sec/m2

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Some interesting numbers

• Energy cost of digital computation
• 1999 (0.25mm) 1pJ/op (custom) 1nJ/op (mproc)
• 2004 (0.1mm) 0.1pJ/op (custom) 100pJ/op
  (mproc)
• Factor 1.6 per year Factor 10 over 5 years
• Assuming reconfigurable implementation 1 pJ/op
• Energy cost of communication
• 1999 Bluetooth (2.4 GHz band, 10m distance)
• 1 nJ/bit transmission energy (thermal limit 30
  pJ/bit)
• Overall energy 170 nJ/bit reception / 150 nJ/bit
  transmission (!)
• Standby power 300 mW
• 2004 Radio (10 m)
• Only minor reduction in transmission energy
• Reduce transceiver energy with at least a factor
  10-50
• Trade-off
• _at_10m 5000 operations / transmitted bit
• _at_ 1m 0.5 operations / transmitted bit

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How to get there?
Network level
Constraints
Node level
Think Energy!
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Opportunities

• Exploit the application properties
• Sensor data is correlated in time and space
• Sensing without precise localization seldom makes
  sense
• Sensor networks rarely do need precise addressing
• Duty cycle of sensor nodes is very small
• And use node architectures that excel in the
  common case
• Stream-based data flow processing for baseband
• Concurrent Finite State Machines for protocol
  stack

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Power energy dissipationin ad-hoc wireless
networks

• a computation energy for transceiving a single
  bit
• b transmission cost factor for a single bit
• g path-loss exponent (2..4)
• e overhead (in extra bits needed for
  transmission of a single bit)
• Pstandby standby power (eg., due to need to
  keep receiver on)

Opportunities of optimization at application,
network, MAC, physical, and implementation
layers!
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Communicating over Long DistancesMulti-hop
Networks

• Example
• 1 hop over 50 m
• 1.25 nJ/bit
• 5 hops of 10 m each
• 5 ? 2 pJ/bit 10 pJ/bit
• Multi-hop reduces transmission energy by 125!
  (assuming path loss exponent of 4)

But network discovery and maintenance overhead
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Towards Novel Networking Schemes

• Traditional ad-hoc routing approaches occur huge
  overhead
• Proactive approach good for the discovery of many
  routes
• Reactive approach efficient for single route
• Exploitation of application properties leads to
  novel techniques
• Directed diffusion (Estrin)
• Geographic routing (Jain)
• Swarm intelligence
• Great challenge of distributed optimization
  problems

Example Pheromone based centroid location(Pister)
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Mostly-Sleepy MAC Layer Protocols

• Computational energy for receiving a bit is
  larger than the computational energy to transmit
  a bit (receiver has to discriminate and
  synchronize)
• Most MAC protocols assume that the receiver is
  always on and listening!
• Activity in sensor networks is low and random
• Careful scheduling of activity pays off big time,
  but has to be performed in distributed fashion
• Options
• Reactive MAC protocol, assumes that radio can be
  woken up! Requires extra radio channel
• Simulated reactive each nodes occasionally
  wakes up and announces its presence - inefficient
• Proactive or scheduled wake-up challenge in
  ad-hoc networks, has to be addressed in
  distributed fashion

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Synergy between networking and positioning
1 error

• Ubiquitous radio networks offer reasonable
  localization with minimal overhead
• Essential in sensor networks
• Use network state to prune and filter network
  updates
• Use positioning information todirect and dampen
  data traffic

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The search for the appropriate physical layer

• Issues
• standby power
• multi-access
• interference
• localization

Opportunity for innovative solutions
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PicoRadio Implementation Issues

• System-on-a-chip approach enables integration of
  heterogeneous and mixed mode modules on same die
• Dynamic nature of PicoRadio networks requires
  adaptive, flexible solution
• Traditional programmable platforms cannot meet
  the stringent low-power requirements
• 3 orders of magnitude in energy efficiency
  between custom and programmable solutions
• Configurable (parameterizable) architectures
  combine energy efficiency with limited
  flexibility
• Predicted improvements factor 10 each year!

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The Energy-Flexibility Gap
1000
Dedicated HW
100
Energy Efficiency MOPS/mW (or MIPS/mW)
10
1
0.1
Flexibility (Coverage)
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Envisioned PicoNode Platform

• Small footprint direct-downconversion R/F
  frontend
• Digital baseband processing implemented on
  combination of fixed and configurable datapath
  structures
• Protocol stack implemented on combination
  FPGA/reconfigurable state machines
• Embedded microprocessor running at absolute
  minimal rates

Embedded uP
Reconfigurable State Machines
FPGA
Dedicated DSP
Reconfigurable DataPath
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A need for fast prototyping PicoNode I

• Flexible platform for experimentation on
  networking and protocol strategies
• Size 3x4x2
• Power dissipation lt 1 W (peak)
• Multiple radio modules Bluetooth, Proxim,
• Collection of sensor and monitor cards

Motorola StarTac Cellular Battery (3.6V)
Serial Port Window
Casing Cover
Connectors for sensor boards
Pico Radio Test Bed
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The Holy Grail Energy Scavenging
SOURCE P. Wright S. Randy UC ME Dept.
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Example MEMS Variable Capacitor
Out of the plane, variable gap capacitor
Up to 10 mW of power demonstrated
Integrated Manufacturing Lab
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Summary A Multidisciplinary Challenge
PicoNode Architecture Design
Positioning Network Architecture
Performance Analysis
Energy Constraints
Analytical Analysis
Use Cases