Dissemination
protocols for large
sensor networks
Fan Ye, Haiyun Luo, Songwu Lu and Lixia Zhang
Department of Computer Science
UCLA
Chien Kang Wu
Outline
Introduction
 Current Dissemination strategy

 Reverse
path forwarding
 Cost field based forwarding
 Routing with virtual hierarchy
 Additional approaches

Future work
Introduction(1/2)

Goal : Communicate organized data using
data-centric paradigm
 Scalable
and distributed solution
 Energy efficiency
 Robustness

Definition
 Data
source : sensor node which generate data
 Sink : user collect data from sensor networks
Introduction(2/2)

Assumption
 Application
semantics
 Location awareness
 Stationary nodes
 Dense deployment

Solution roadmap
 Query-Reply
process
 Install dissemination states in intermediate
nodes
Outline
Introduction
 Current Dissemination strategy

 Reverse
path forwarding
 Cost field based forwarding
 Routing with virtual hierarchy
 Additional approaches

Future work
Reverse path forwarding
Sensor broadcast data to neighbors,
and neighbor recursively forward it .

Including:
 Declarative




routing protocol (DRP)
Directed Diffusion (DD)
Sink sends out a query using flooding strategy
through the network
Data flow in the reverse direction of query
Set up forwarding state in the form of vector
Declarative routing protocol
Reachability ,remaining energy
directionality, link quality



Establish a routing tree for every sink
Use factors to select which node of the next
Neighborhood the query should be sent
Using cashed data to improve efficiency
Declarative routing protocol

Base on :
 Location
awareness
 Node with small buffer

Faced challenges:
 Buffer
management
 Route inconsistency
 Data aggregation strategy
Directed diffusion
Similar to DRP, but focus more on scaling to
multiple sinks 
Node do not keep per sink state
 Each node has a cache to detect loop and
drop redundant packets
 sink can use reinforcement mechanism to
help neighbors select the best quality path
 To handle network dynamics , source need
to maintain alternative paths

Directed diffusion

Base on:
 Location
awareness
 Node with small buffer

Faced challenges:
 Maintain
alternative paths to handle nodes failure
 Quick Path repairing methods
 In-network processing
Problems in reversed path
Overhead has to be paid to maintain
vector states
 Sink mobility problems

Outline
Introduction
 Current Dissemination strategy

 Reverse
path forwarding
 Cost field based forwarding
 Routing with virtual hierarchy
 Additional approaches

Future work
Cost-field based forwarding
Hop count, energy consumption
physical distance
Each node store forwarding state named
scalar denoting the node’s distance to sink
 Scalar of all nodes forms a cost-field
 Cost-field is per-sink based, a node keep
cost for each sink
 Data report flow from higher cost to
smaller cost

Cost-field based forwarding
If query-packet(ADV) flow from node i to node j ,
C(i new) = C(j) + C(i , j) (set 0 at sink)
set C(i) to C(i new) , if C(i) > C(i new)
 There is no loop in cost-field
 Two-ways to make
forwarding decision

 Receiver-decided
 Sender-appointed
Receiver-decided
Data sender includes its cost in a report
and broadcast to every neighbor
 Let receiver decides forwarding or not

 Only
receiver with cost less than sender may
forward the report

Robust data forwarding:
 Typically
, several neighbors with smaller cost
than the sender
 Need strategy to prevent data redundancy
problem
Sender-appointed
Single path forwarding, sender choose
only one neighbor for each report
 Can not ensure robustness, need to
maintain states regarding which neighbors
are still alive
 Statistically distribute strategy ,save more
energy than receiver-based

Problems in Cost-field based
Mobile sinks problems
 Does not scale well with numerous sinks,
each sink needs a separate cost at every
node

Outline
Introduction
 Current Dissemination strategy

 Reverse
path forwarding
 Cost field based forwarding
 Routing with virtual hierarchy
 Additional approaches

Future work
Virtual hierarchy
Sensor nodes have different functionality,
network hierarchy is formed during data
dissemination
 Sink mobility support
 Including:

 Two-tier
data dissemination
 Low-energy adaptive clustering
Two-tier data dissemination


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Data source construct virtual grid infrastructure for
query and data forwarding, each grid is an α*α
square
Data Source propagate data announcement to reach
all other crossing of grid, called dissemination point
Sink forward query to its upstream dissemination
node  dissemination node further forward the
query toward data source
Grid infrastructure
Two-tier query-reply process
Low-energy adaptive clustering


Designed towards energy-optimal network
organization
Assumption :
 Sensor
can adjust power consumption, adapt network
topology
 Sensor’s max power is able to communicate with the sink

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Data message from sensor first transmitted to local
cluster head and then forward to base station
System using distributed algorithm to elect a
number of cluster heads
Virtual hierarchy summary

Advantage:
 Structure
suitable for Data aggregation
 Mobile sink is feasible

Faced challenges:
 Structure
maintain problem
 Cluster head election problem
Outline
Introduction
 Current Dissemination strategy

 Reverse
path forwarding
 Cost field based forwarding
 Routing with virtual hierarchy
 Additional approaches

Future work
Additional approaches

Real-time delivery
 Define:
distance d ,packet lifetime t
desired velocity v = d / t
 Required velocity is updated at each hop
 A node can use multiple FIFO queue with
different priority to handle packets
 Choose neighbor with high velocity to forward
the report
Outline
Introduction
 Current Dissemination strategy

 Reverse
path forwarding
 Cost field based forwarding
 Routing with virtual hierarchy
 Additional approaches

Future work
Future work

Network topology:
 Location
awareness
 Hierarchy structure maintain
 Path maintain issue
 Alternative paths reinforcement

General issues:
 Buffer
management and data aggregation
 Data redundancy problem