Networking Sensors

1
Networking Sensors

• Ramesh Govindan
• USC/Information Sciences Institute
• Deborah Estrin, John Heidemann, Nirupama Bulusu,
  Alberto Cerpa, Jeremy Elson, Deepak Ganesan,
  Lewis Girod, Chalermek Intanagonwiwat, Jerry Zhao

2
The Goal

• Embed numerous devices to monitor and interact
  with physical world
• Network these devices so that they can coordinate
  to perform higher-level tasks
• Requires robust distributed systems of tens of
  thousands of devices

3
The Challenge Dynamics!

• The physical world is dynamic
• Dynamic operating conditions
• Dynamic availability of resources
• particularly energy!
• Dynamic tasks
• Devices must adapt automatically to the
  environment
• Too many devices for manual configuration
• Environmental conditions are unpredictable
• Unattended and un-tethered operation is key to
  many applications

4
Design Considerations

• Energy is the bottleneck resource
• Avoid communication over long distances
• Cannot assume global knowledge, cannot
  pre-configure networks
• Achieve desired global behavior through localized
  interactions
• Empirically adapt to observed environment
• Can leverage
• Small-form-factor nodes, densely distributed to
  achieve physical locality to sensed phenomena
• Application-specific, data-centric networks
• Data processing/aggregation inside the network

5
One Approach Directed Diffusion

• Application-aware communication primitives
• expressed in terms of named data (not in terms of
  the nodes generating or requesting data)
• Consumer of data initiates interest in data with
  certain attributes
• Nodes diffuse the interest towards producers via
  a sequence of local interactions
• This process sets up gradients in the network
  which channel the delivery of data
• Reinforcement and negative reinforcement used to
  converge to efficient distribution
• Intermediate nodes opportunistically fuse
  interests, aggregate, correlate or cache data

6
Illustrating Directed Diffusion
Setting up gradients
Source
Sink
Source
Sink
Recovering from node failure
7
Local Behavior Choices

• For propagating interests
• In our example, flood
• More sophisticated behaviors possible e.g. based
  on cached information, GPS
• For setting up gradients
• Highest gradient towards neighbor from whom we
  first heard interest
• Others possible towards neighbor with highest
  energy

• For data transmission
• Different local rules can result in single path
  delivery, striped multi-path delivery, single
  source to multiple sinks and so on.
• For reinforcement
• reinforce one path, or part thereof, based on
  observed losses, delay variances etc.
• other variants inhibit certain paths because
  resource levels are low

8
Initial simulation study of diffusion

• Compare diffusion to
• flooding
• centrally computed tree (omniscient multicast)
• Key metrics
• Average Dissipated Energy per event delivered
• indicates energy efficiency and network lifetime
• Delay
• indicates the temporal accuracy of information
• Event Delivery Ratio
• indicates the robustness

9
Diffusion Simulation Details

• Network Size 50-250 Nodes
• Constant Density 1.95e-3 nodes/m2
• To factor out the impact of increased
  connectivity
• Transmission Range 40m
• MAC IEEE802.11
• Not a completely satisfactory choice due to power
  consumption during idle intervals
• Energy Model Mimic the realistic sensor radio
  Pottie 2000
• Power consumption in transmission 660 mW
• Power consumption in reception 395 mW
• Power consumption during idle intervals 35 mW

10
Diffusion Simulation

• Surveillance application
• 5 sources are randomly selected within a 70m x
  70m corner in the field
• 5 sinks are randomly selected across the field
• High data rate is 2 events/sec
• Low data rate is 0.2 events/sec
• Event size 64 bytes
• Interest size 36 bytes
• All sources send the same location estimate for
  base experiments

11
Average Dissipated Energy
0.018
0.016
Flooding
0.014
0.012
0.01
0.008
(Joules/Node/Received Event)
Omniscient Multicast
Average Dissipated Energy
0.006
Diffusion
0.004
0.002
0
0
50
100
150
200
250
300
Network Size
12
Delay
0.35
0.3
0.25
Flooding
0.2
Delay (secs)
0.15
0.1
0.05
Diffusion
Omniscient Multicast
0
0
50
100
150
200
250
300
Network Size
13
Impact of Negative Reinforcement
0.012
0.01
Without Negative Reinforcement
0.008
Average Dissipated Energy
(Joules/Node/Received Event)
0.006
With Negative Reinforcement
0.004
0.002
0
0
50
100
150
200
250
300
Network Size
14
Impact of Duplicate Suppression
0.025
0.02
Without Suppression
0.015
(Joules/Node/Received Event)
Average Dissipated Energy
0.01
With Suppression
0.005
0
0
50
100
150
200
250
300
Network Size
15
Domination of Idle Energy
0.14
Diffusion
0.12
Flooding
Omniscient Multicast
0.1
0.08
Average Dissipated Energy
(Joules/Node/Received Event)
0.06
0.04
0.02
0
0
50
100
150
200
250
300
Network Size
16
Summary of Diffusion Results

• Under the investigated scenarios, diffusion
  outperformed omniscient multicast and flooding
• All layers have to be carefully designed
• Not only network layer but also MAC and
  application level
• Application-level data dissemination has the
  potential to improve energy efficiency
  significantly
• Duplicate suppression is only one simple example
  out of many possible ways.
• Aggregation

17
Sensor Network Tomography

• Continuously updated indication of sensor network
  health
• Useful for
• performance tuning
• adjusting sensing thresholds
• incremental deployment
• refurbishing sections of sensor field with
  additional resources
• self testing
• validating sensor field response to known input

Tomogram indicating connection quality
18
Sensor Network Tomography Key Ideas and
Challenges

• Kinds of tomograms
• network health
• resource-level indicators
• responses to external stimuli
• Can exchange resource health
• during low-level housekeeping functions
• such as radio synchronization
• Key challenge energy-efficiency
• need to aggregate local representations
• algorithms must auto-scale
• outlier indicators are different