Wireless Networked Sensors Routing Challenges

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Wireless Networked Sensors Routing Challenges

• Mikhail Nesterenko

• In this presentation I used the material from a
  presentation by
• David Culler, USB http//www.cs.berkeley.edu/cul
  ler/talks/mobihoc.ppt, http//www.cs.berkeley.e
  du/culler/cs294-f03/slides/awoo_oct_2nd_2003.ppt
  
• Kwong-Don Kang, SUNY, Binghamton
  www.cs.binghamton.edu/kang/teaching/cs580s/taming
  -bvr.ppt

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Reading List

• Deepak Ganesan, Bhaskar Krishnamachari, Alec Woo,
  David Culler, Deborah Estrin and Stephen Wicker,
  Complex Behavior at Scale An Experimental Study
  of Low-Power Wireless Sensor Networks , UCLA
  Computer Science Technical Report UCLA/CSD-TR
  02-0013
• A. Woo and D. Culler. Taming the Underlying
  Challenges of Reliable Multihop Routing in Sensor
  Networks. In Proc. of the 1st ACM Conf. on
  Embedded Networked Sensor Systems (SenSys), pp
  14--27. Los Angeles, Nov 5-7 2003

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Outline

• empirical measurements of low-power radio
  performance
• radio neighborhood
• link quality estimation
• issues with simple routing
• mintroute
• link quality estimation
• neighborhood selection
• routing metrics
• simulation results
• experimental results

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Radio Neighborhood

• radio neighborhood is notclearly defined
• reception is probabilistic
• not isotropic
• reception rate is low
• good link drops 1 outof 4 packets (cf.
  ethernetdrops 1 out of 10K)
• changes with time!

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Link Quality

• three regions based on reception
• nearly perfect
• unpredictable
• nearly none
• one the fringes some links are asymmetric
• more than 75 in one direction
• less than 25 in the other
• what to do with them?
• detect and ignore?
• embrace?

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Flood-Based Routing Issues

• simple flood-basedrouting is imperfect
• has
• stragglers
• backward links
• dense clusters

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Other Issues

• large variation in affinity
• asymmetric links
• long, stable high quality links
• short bad ones
• varies with traffic load
• collisions
• distant nodes raise noise floor
• reduce SNR for nearer ones
• many poor neighbors
• good ones mostly near, some far

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Outline

• empirical measurements of low-power radio
  performance
• radio neighborhood
• link quality estimation
• issues with simple routing
• mintroute
• link quality estimation
• neighborhood selection
• routing metrics
• simulation results
• experimental results

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Link Estimation

• Individual nodes estimate link quality by
  observing packet success and loss events
• Use the estimated link quality as the cost metric
  for routing
• Good estimator should
• React quickly to potentially large changes in
  link quality
• Stable
• Small memory footprint
• Simple, lightweight computation

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WMEWMA

• Snooping
• Track the sequence numbers of the packets from
  each source to infer losses
• Window mean with EWMA
• WMEWMA(t, a) (packets received in t) /
  max(packets expected in t, packets received in
  t)
• t, a tuning parameters
• t message opportunities
• Take average in a window
• Take EWMA of the average

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WMEWA (t 30, a 0.6)

• simulation of empirical trace in stable setting

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Neighborhood Management

• Neighborhood table
• Record information about nodes from which it
  receives packets
• MAC address, routing cost, parent address, child
  flag, reception (inbound) link quality, send
  (outbound) link quality, link estimator data
  structures
• Propagate back to the neighbors as the outbound
  rather than inbound link quality is needed for
  cost-based routing
• The receiving node may update its own table based
  on the received information possibly indicating
  topology changes ? Distance-vector based routing
• How does a node determine which nodes it should
  keep in the table?
• Keep a sufficient number of good neighbors in the
  table
• Similar to cache management

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Management Policies

• Insertion
• Heard from a non-resident source
• Adaptive down-sampling technique
• Probability of insertion N/T neighbor table
  size / distinct neighbors
• At most N messages can be inserted for every T
  messages
• Eviction
• FIFO, Least-Recently Heard, CLOCK, Frequency

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Good neighbors maintainable (table size 40)

• Frequency Algorithm
• Keep a frequency count for each entry in the
  table
• Reinforce a node by incrementing its count
• A new node will be inserted if there is an entry
  with a zero count
• Otherwise, decrement the count of all entries and
  drop the new candidate

good node 75 link accuracy
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Background Link-State and Distance-Vector Routing

• Link state routing algorithm (ex DSDV)
• assume knowledge of the network topology and all
  link costs
• apply Dijkstras algorithm to find the shortest
  path from one source to all the other nodes
• Implemented via link state broadcast
• memory intensive, has issues with information
  update
• Distance vector routing (ex AODV)
• each node propagates cumulative distance
  estimator (ex min hops) to all neighbors
• neighbors update their metric and propagate
  further
• has counting to infinity problem
• countered by poisoned reverse or split horizon

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Cost-based routing

• Key ideas
• Minimize the cost that is abstract measure of
  distance
• Could be hops, retransmissions, etc.
• Minimize retransmissions A longer path with
  fewer retransmission could be better than a
  shorter path with more retransmissions!
• Distance-vector based approach implemented by the
  parent selection component
• Periodically run parent selection to identify one
  of the neighbors for routing
• May also locally broadcast a route message
  including parent address, estimated routing cost
  to the sink, and a list of reception link
  estimations of neighbors
• A receiving node may update the neighbor table
  based on the received info or drop it
• Flag a child in the table to avoid a cycle
• When a cycle is detected trigger parent selection
  without the current parent

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Routing Framework
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Underlying Issues

• Parent selection
• If connectivity to the current parent is lost, a
  node disjoins from the tree, and sets its routing
  cost to infinity ? Reselect a parent
• Rate of parent change
• Periodic Parent selection for every route update
  msg from neighbors incurs a domino effect of
  route changes
• Parent snooping
• For example, quickly learn routing info
• Cycles
• Monitor forwarding traffic and snoop on the
  parent address in each neighbors msg -gt Identify
  child nodes and dont consider them as potential
  parents

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Underlying Issues

• Duplicate packet elimination
• Use sender address sequence number
• Queue management
• Give priority to originating traffic assuming
  originating data rate is lower than forwarding
  rate
• General fair queuing is not considered in this
  paper
• Relation to link estimation
• Link failure detection based on a fixed number of
  consecutive xmission failures can be ineffective
  over semi-lossy links
• Link quality estimation can be a better judgment
  of link failure
• Bidirectional link estimations can avoid routing
  over asymmetric links
• Stability and agility of link estimators can
  significantly affect routing
• Final tuning must be done while observing its
  effect on routing performance

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Cost metric

• MT (Minimum Transmission) metric
• Expected number of transmissions along the path
• For each link, MT cost is estimated by 1/(Forward
  link quality) 1/(Backward link quality)
• Inherently non-linear
• For MT, a substantial noise margin should be used
  in parent select to enhance stability
• Reliability
• Another cost metric
• Product of link qualities along the path
• Not explored in this paper

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Performance Evaluation Tested Routing Algorithms

• Minimum Transmission (MT)
• Use the expected transmissions as the cost
  metric
• Use a new path if the new cost is lesser by a
  noise margin
• MTTM
• Assume a neighbor table can maintain only 20
  entries
• Broadcast
• Root periodically floods the network
• A node chooses a parent that forwards the flooded
  msg to itself first in each epoch
• Use the reverse path to reach the root

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Performance Evaluation Tested Routing Algorithms

• Shortest Path
• Conventional distance-vector approach
• Each node picks a minimum hop-count neighbor as
  the parent and set its own hop-count to one
  greater than its parent
• Two variations for performance analysis
• SP A node is a neighbor if a packet is received
  from it
• SP(t) A node is a neighbor if its link quality
  exceeds the threshold t
• t 70 only consider the links in the effective
  region
• t 40 also consider good links in the
  transitional region

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Packet level simulations

• Built a discrete time, event-driven simulator in
  Matlab
• Network of 400 nodes 20 20 grid with 8 feet
  spacing
• Sink is placed at a corner to maximize the
  network depth

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Packet level simulation
Hop Distribution
Path reliability over distance
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Packet level simulation
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Empirical study of a sensor field

• Evaluate SP(40), SP(70), MT
• 50 Berkeley motes inside a building
• 5 10 grid w/ 8 foot spacing
• 90 link quality in 8 feet
• 3 inches above the ground
• sink in the middle of short edge of the grid
• measurements at night to avoid pedestrian traffic

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• Link Quality of MT
• Vary around 70
• SP(70) may suffer

• Hop Distribution
• SP(70) failed to
• construct a routing
• tree
• - MT congested Triple the data origination and
  route update rate

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E2E success rate
Stability
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Irregular Indoor Network

• 30 nodes scattered around an indoor office of
  1000ft2

Link Estimation of a node to its neighbors over
time
E2E Success Rate
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Conclusions

• Link quality estimation and neighborhood
  management are essential to reliable routing
• WMEWMA is a simple, memory efficient estimator
  that reacts quickly yet relatively stable
• MT (Minimum Transmissions) is an effective metric
  for cost-based routing
• The combinations of these techniques can yield
  high end-to-end success rates