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DIRECTED DIFFUSION. Directed Diffusion. Data centric A node request data by sending interest for named data Data matching interest is drawn toward that node Intermediate nodes can cache or transform data directly Attribute-naming based Data aggregation

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directed diffusion1
Directed Diffusion
  • Data centric
    • A node request data by sending interest for named data
    • Data matching interest is drawn toward that node
    • Intermediate nodes can cache or transform data directly
  • Attribute-naming based
  • Data aggregation
  • Interest, data aggregation and data propogation are determined by localized interactions.
  • Trades off some energy efficiency for increased robustness
directed diffusion2
Directed Diffusion
  • Consists of elements: Interests, data messages, gradients and reinforcements.
  • Interest: a query or an interrogation which specifies what a user wants.
  • Data: collected or processed information
  • Gradient: direction state created in each node that receives interest.
    • Gradient direction is toward the neighboring node which the interest is received
    • Events start flowing from originators of interests along multiple gradient paths.
    • Task descriptions are named by a list of attribute value pairs that describe a task
    • eg:
      • type=wheeled vehicle // detect vehicle location

interval=20ms // send events every 20 ms

duration=10s // for the next 10s

rect=[-100,100,200,400] // from sensors within rectangle

  • Interests and Gradients
    • Interest is usually injected to the network from sink
    • For each active task, sink periodically broadcasts an interest message to each of its neighbors
    • Initial interest contains the specified rect and duration attributes but larger interval attribute
    • Interests tries to determine if there are any sensor nodes that detect the wheeled vehicle(exploratory).
  • Soft state, periodically refreshed by the sink
  • Sink sends the same interest in monotonically increasing timestamp attribute.
    • Because interests are not reliably transmitted through the network.
  • Refresh rate increase robustness to loss interests with the trade off overhead
  • Every node has an interest cache storing each distinct interest.
  • Interest entries do not contain information about the sink, but just about immediately previous hop.
  • Two interests overlapping rect attributes aggregated to a single interest entry.
  • eg:
    • Type=wheeled vehicle
    • Interval=1s
    • Rect=[-100,200, 200,400]
    • Timestamp=01:20:40
    • Expires at=01:30:40
  • When a node receives an interest, it checks to see if the interest exists in the cache
    • If no matching, node creates an entry(gradient and data rate)
    • If interest exists but no gradient, adds a gradient and updates the timestamp and duration fields.
    • If interest exists and have gradient, just update the timastamp and duration
    • When gradient expires, it is removed from the interest entry.
interests diffusion
Interests (diffusion)
  • After receiving an interest, a node may decide to resend the interest to subset of its neighbors.
    • To its neighbors, it apeears that it is originating from the sending node, although it is coming from distant sink(local interaction).
    • Not all received interest are resent
    • If a node recently resent matching interest, it may suppress the received interest
gradient establishment
Gradient Establishment
  • Every node establishes a gradient towards each other
  • This two way gradient can cause low data rate because it would receive one copy from each node.
  • Reinforcement is a solution for this problem
  • Gradient includes data rate and direction in which to send events.
data propogation
Data Propogation
  • When a sensor node receives a data message, it searches its interest cache for a matching interest entry.
    • If matching, checks data cache(keep track of recently seen data items)
      • Advantage of data cache: loop prevention
      • By examining the data cache, data rate can be determined
      • If exists in data cache, silently drop data message
      • If not, added to the data cache and resent to the neighbors
        • To resend a received data message, examine gradient list
          • If all gradient have data rate greater than or equal to the rate of incoming events(means more interest), resend data to neighbors.
          • If some gradients have lower data rates, node may donwconvert to appropriate gradients.
    • If no match, the data message is silently dropped
reinforcement for path establishment
Reinforcement for Path Establishment
  • Sink periodically diffuses interest for a low-rate event (exploratory events)
  • Once source detects a matching target, it sends exploratory events toward sink(multiple paths)
  • After sink starts receiving these, it reinforces one particular neighbor in order to draw down real data.
positive reinforcement
Positive Reinforcement
  • Local rule – selects an epmirically low-delay path
  • Reinforce any neighbor from which node receives a previously unseen event
    • To reinforce this neighbor, the sink resends the original interest message with a smaller interval(higher data rate)
      • Type=wheeled vehicle
      • Interval=10ms
      • Rect=[-100,200, 200,400]
      • Timestamp=01:22:35
      • Expires at=01:30:40
    • When the neighboring node receives this interest, it notices that it already has a gradient toward this node(it notice the interval is small)
    • If this new data rate is also higher than the existing gradient (outflow from this node has increased), the node must reinforce at least one more neighbor.
    • We do not need to reinforce neighbors that are already sending data at higher rate.
local repair for failed paths
Local Repair for Failed Paths
  • Intermediate nodes on a previously reinforced path can apply reinforcement rules(useful for failed or degraded paths)
  • C detects degradation
    • By noticing that the event reporting rate from its upstream neighbor(source) is now lower
    • By realizing that other neighbors have been transmitting previously unseen location estimates.
  • And apply reinforcement rules
  • Problem: wasted resources
  • Avoid this is interpolate location estimates from the events
negative reinforcement
Negative Reinforcement
  • If sink reinforces A, but then receives a new event from B, it will reinforce path through B
  • If path through B is better, negatively reinforce path through A
  • Two mechanisms
    • Time out all data gradients in the network unless they are explicitly reinforced
      • Sink periodically reinforces B, stop reinforcing A
    • Explicitly degrade the path through A by sending a negative reinforcement(interest with lower data rate)
      • When A receives this, it degrades its gradients toward the sink
  • Cost: decreased resource utilization
  • negatively reinforce which neighbor?
    • From which no new events have been received within a window of N evets or time T
self organization
Self Organization
  • Zero knowledge of identity or topology
  • Each node knows its own identity
  • The base directly connected to the host PC
  • Base, periodically broadcast out its identity and that it is connected to the PC.
  • Devices at one-hop distance receive the info and use to update routing information
  • Rebroadcast a new routing update to everyone that there is a path to the sink through them.
  • In order to prevent cycles, time is divided into eras and route updates are broadcast once per era.
tiny diffusion
Tiny Diffusion
  • Tiny Diffusion
    • Application Programmer’s Interface(API)
    • Tiny Diffusion is based on the concept of data-centric or subject-based routing as is the SCADDS datadiffusion implementation.
    • Provide aninterface to access sensor data by naming attributes.