Dynamic Sensor Networks Project Review of UCLA

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Dynamic Sensor Networks ProjectReview of UCLAs
Activities

• Mani Srivastava
• UCLA

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Two Separate Projects at UCLA
This Review

• DSN (Subcontract from USC/ISI)
• Sole PI Mani Srivastava
• Focus networking
• Low-power/low-latency link, MAC, and routing
• GPS-less discovery distribution of location
• Capability and attribute based addressing and
  connectivity
• Sensor network simulation and emulation
• Protocols for GPS-synchronized communication
  subsystem
• Sensorware (Subcontract from Rockwell Science
  Center)
• Two PIs Mani Srivastava, Miodrag Potkonjak
• Focus distributed middleware services
• Network coverage service for sensor networks
• Sensor control scripts light-weight, mobile,
  platform independent, secure
• Spatial addressing and communications, timing
  synchronization
• Implementation on Rockwells nodes

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This ReviewSelected Recent Activities

1. Update on Sensorsim
2. GPS-less ad hoc localization
3. Low-latency packet forwarding
4. Dynamic assignment of MAC addresses
5. Low-power multihop routing

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I. SensorSim Update

• Simulation framework for modeling sensor networks
  built on top of ns-2
• sensing channel and sensor models
• scenario generation tool (SensorViz)
• light weight protocol stacks
• hybrid simulation
• battery/power model (further model development
  under PAC/C)
• Alpha release at http//nesl.ee.ucla.edu/sensorsim
  /
• Selected features being migrated to official ns-2
  through Deborahs group
• In use by external groups such as U. Maryland

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SensorSim Architecture
Sensor Node
Functional Model
User Application
User Node
SensorWare
Network Stack
Power Model
Network Layer
Sensor App
Battery Model
MAC Layer
Sensor Stack3
Network Stack
Physical Layer
Sensor Stack2
Sensor Layer
Radio
Network Layer
Sensor Layer
Sensor Stack1
Wireless Channel
Physical Layer
CPU
Sensor Layer
MAC Layer
Physical Layer
Wireless Channel
Physical Layer
ADC (Sensor)
Physical Layer
Sensor Channel3
Wireless Channel
Sensor Channel2
Sensor Channel1
Sensor Channel
Target Node
Target Application
Target Node
Sensor Stack
Sensor Layer
Physical Layer
Sensor Channel
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Scenario Generation Visualization

• SensorViz Features
• Diverse scenario generation
• Node deployment patterns
• Target trajectories
• Sensor characteristics
• Node attributes
• Can be slaved to a running simulation (SensorSim)
• Monitor real sensor nodes
• Planned
• XML output
• Read in SITEX format scenarios

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II. Dynamic Location Discovery

• Discovery of absolute and relative location
  important
• location attribute based naming and addressing of
  nodes
• geographical routing
• tracking of moving phenomena (targets)
• GPS not enough
• not work everywhere due to requirement of LOS to
  satellites (trees, indoors)
• not on all nodes (costly, large, power-hungry)
• No infrastructure in sensor networks precludes
  solutions based on trilateration with special
  high power beacons
• also, susceptible to failure
• Problem given a network of sensor nodes where a
  few nodes know their location (e.g. through GPS)
  how do we calculate the location of the other
  nodes?

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Ad-Hoc Localization System(AHLoS)
Iterative Multilateration

• Every node contributes to process
• Small fraction of initial beacons
• Distributed
• Robust
• Energy Efficient
• Inter-node ranging uses
• RSSI
• Ultrasound
• Integrated with routing messages
• Location discovery almost free!
• Adapts to channel conditions via a joint
  estimation of location channel parameters

Collaborative Multilateration
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Centralized vs. Distributed Localization

• Distributed Pros
• More robust to node failure
• Less traffic gt less power
• Better handling of local environment variations
• Speed of ultrasound
• Radio path loss
• Rapid updates upon topology changes
• No time synch. required

• Centralized Cons
• A route to a central point
• Time synchronization is required
• High latencies for location updates
• Central node requires preplanning
• More traffic gt higher power consumption

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Basic Multilateration
Residual of measured and estimated distance
Linearize using Taylor Expansion
Linear form
MMSE Solution
Repeat until d becomes 0
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Iterative Multilateration

• Basic multilateration can be applied iteratively
  across the network

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Node vs. Initial Beacon Densities
Resolved Nodes
Total Nodes
Initial Beacons
Uniformly distributed deployment in a field
100x100. Node range 10.
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Challenges

• Iterative multilateration may stall if
• the network is very sparse
• the percentage of beacons is very low
• terrain obstacles
• If the network is large, error will accumulate
  from iterative multilateration

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Collaborative Multilateration
Uses location information over multiple
hops Linearize residuals over 2 types of edges
Both equations have the form Follow the same
solution procedure as basic multilateration
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Collaborative Multilateration (contd.)
Execute
Update
Until
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Collaborative Sub-trees

• Necessary conditions
• Each unknown node must have at least 3
  participating neighbors
• A participating node is either a beacon node or
  an unknown node connected to 3 participating nodes

18 equations 16 unknowns
Over-determined!
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Distributed Ad-Hoc Operation

• Location estimation takes place at the scope of a
  neighborhood
• Collaborative sub-trees can zoom in and out to
• Form a well-determined system
• Avoid degenerate cases
• Avoid obstacles
• Reduce Error Propagation
• Error can be further reduced if computation takes
  place at a central point.

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Platform Characterization
Ultrasound TDoA
RSSI in football field
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Iterative Multilateration Accuracy
50 Nodes 10 beacons 20mm white gaussian ranging
error
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Implementation Status

• Initial prototype competed Medusa
• Design of Medusa II(using non-SensIT resources)
• Longer range ultrasound (15-20m)
• Radio Power Control RSSI circuitry
• More computation (Atmel THUMB)
• Goal Hybrid Radio-acoustical localization
• use radio for long-range when ultrasound is
  unable to find a neighbor
• Medusa used standalone or as a location
  coprocessor to sensor nodes

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III. Low Latency Packet Forwarding

• Problem node often simply relays packets in
  multihop network
• NS-2 simulation 1000x1000 terrain, 30 nodes,
  DSR, CBR traffic from random SRC and DEST
• Traditional approach packets sent from radio to
  main CPU
• long latency (serial bus), power hungry (main CPU
  woken up)

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Our Packet Forwarding Architecture

• Our approach Embedded Packet Processor in the
  Radio
• exploit programmable microcontrollers in the
  radios to handle common cases of packet routing
• can also do operations such as combining of
  packets with redundant information
• Packets are redirected as low in the protocol
  stack as possible
• reduced latency (and, incidentally, also reduced
  power)
• Key challenge how to do it so that every new
  routing protocol will not require a new radio
  firmware?

MultihopPacket
CommunicationSubsystem
Rest of the Node
GPS
RadioModem
MicroController
CPU
Sensor
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Application-defined Routing Framework for Radio
Firmware
CommunicationSubsystem
Packet Classifier
GPS
Application-DefinedMatching Rules Actions
RadioModem
MicroController
Packet Modifier

• Packet-classifier and packet-modifier driven by
  application defined matching rules and actions
• Matching rules and/or expressions using , lt, gt,
  range operators on arbitrary packet fields
  (offset, length)
• Actions accept, forward, drop, field
  increment/decrement etc.
• Rules and actions operate on arbitrary packet
  fields (any layer)
• fields specified as (offset, length)
• For complex cases packet sent to the main
  processor
• only simple, common cases handled at the radio
• Expressiveness implemented the following as test
  cases
• Node ID-based addressing and routing (DSR-like)
• Geographical point-cast (send to a circular area
  specified as destination)

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Proof-of-concept Implementation

• Rockwell nodes with a prototype radio
• Prototype radio because Rockwells radio firmware
  is not open
• RFM radio with FPSLIC (microcontroller with FPGA)
• Mixed software/FPGA implementation
• FPGA used to accelerate packet matching/modificati
  on

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Performance Analysis

• Difference in packet DELAY between the
  traditional approach and our approach
• Serial port delay is the dominant factor

Packet Distribution
Serial port delay
Delay Overhead for FWD
Delay Overhead for ACCEPT
Measurements
Given the measurements the difference in delay is
When PrFW gt 3 PrAC the traditional approach
delay is more than our approach For our
simulation traffic data Ddiff 44ms
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IV. Dynamic MAC Address Allocation

• Wireless spectrum is broadcast medium
• MAC addresses are required
• In wireless sensor networks, data size is small
• Unique MAC address would present too much
  overhead
• Employ spatial address reuse (similar to reuse in
  cellular systems)
• Two aspects
• Dynamic assignment algorithm
• Address representation

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Distributed Assignment Algorithm

• Network is operational (nodes have valid address)
• Listen to periodic broadcasts of neighboring
  nodes
• In case of conflict, notify node
• (this node resends a broadcast)
• Choose non-conflicting address and broadcast
  address in a periodic cycle. At this point the
  new node has joined the network.
• Additive convergence network remains operational
  during address selection
• Mapping unique ID to spatially reusable address
• Algorithm also valid when unidirectional links

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Encoded Address Representation
Address range 0-11 12-17 18-19 20-22 23
Codeword size (bits) 4 5 6 7 8
Encoded (bits/address) 1.7
Fixed size (bits/address) 2

• Size of the address field?
• Non-uniform address frequency
• Huffman encoding
• Robust can represent any address
• Practical address selection
• All addresses with same codeword size are
  equivalent
• Choose random address in that range to reduce
  conflict messages

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Network Density Parameter

• Taking only bulk nodes eliminates edge effects
• Virtually extends network size to infinity (so
  independent of L)
• Suggests that only close proximity is critical
• Characterization of node density
• Connectivity is key
• Average degree

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Non-uniform Network Density
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Effect of Packet Losses (? 10)
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Scalability

• Address assignment
• Distributed algorithm with periodic localized
  communication
• Address representation
• Encoded addresses depend only on distribution

Scales perfectly (neglecting edge effects)
Off-line Centralized Distributed
-

Unique Fixedreusable Encoded reusable
-- ?
Assignment
Representation
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Simulation Results
Fixed size dynamic
Our schemes
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Implementation Issues

• Functionality
• Dynamic address assignment
• Address resolution (mapping)
• Address Resolution Assignment Protocol (ARAP)
• Unique receiver ID is mapped into MAC address
  without being included into the packet
• The own MAC address is modified by the ARAP

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Dynamic Address Allocation Summary

• Spatial reuse of address
• Dynamic assignment algorithm
• Localized scalability
• Additive convergence robustness
• Encoded address representation
• Independent of network size scalability
• Variable length addresses robustness

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V. Low-power Multihop Routing

• ATHENA Adaptive Transmission-power Heuristic and
  Energy-optimizing ad-hoc Network routing
  Algorithm
• adapts transmission power to find power-optimal
  multi-hop paths.
• uses alternate routes to maximizes lifetime of
  the network.
• Recent work from Maryland
• offers the same benefit combine alternate routes
  with tx power control
• but is not easy to implement (cost of algorithm
  vs. convergence)

Principle in adapting tx power
a
b
E(x) energy to send a packet over distance
x E(a) E(b) lt E(c)
c
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Constant Tx Power Case

• Constant power case (for comparison)
• On-demand algorithm
• using request and reply messages
• resemble DSR, AODV source path carried to avoid
  loops
• siblings not visited

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Adaptable Tx Case

• Increased of requests and replies
• if destination reached, algorithm not over
• siblings have to be asked
• Three main rules to prevent explosion of requests
• one of them produces suboptimal routes, but
  simulations show the cost savings are worth it

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Example
10 tx levels 10m - 250m packet
125bytes signal attenuation 1/d3
Constant tx power case (level 8) 6 req, 8
replies 2.4810-4Joules/packet
Adaptablet tx power case 8 req, 30
replies 6.63810-5Joules/packet
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25-node Network
replies
requests
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25-node Network
replies
requests
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Average Gains
25-node network
signal attenuation 1/d3 1/d4
50-node network
signal attenuation 1/d3 1/d4
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Using Alternate Paths

• Self_Energy, Next-Hop_Energy should affect path
  cost.
• Each time the nodes energy changes 10
• notify neighbors
• recalculate best paths
• Heuristic used Remaining_Energyself-x1
  Power_Costnext_hop Remaining_Energynext_hop-x1
  Power_Costdestination
• Simulations show best x1 2
• 30 more packets routed than vanilla ATHENA
• 96 of the packets routed in the optimal case.

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Recent Accomplishment Summary

1. Sensorsim
2. GPS-less ad hoc localization
3. Low-latency packet forwarding
4. Dynamic assignment of MAC addresses
5. Low-power multihop routing