A Distributed Framework for Correlated Data Gathering in Sensor Networks Kevin Yuen, Ben Liang, Baochun Li IEEE Transactions on Vehicular Technology 2008 1
Dec 30, 2015
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A Distributed Framework for Correlated Data Gathering in Sensor
Networks
Kevin Yuen, Ben Liang, Baochun Li
IEEE Transactions on Vehicular Technology 2008
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Outline
IntroductionProblem FormulationLocalized Slepian-Wolf CodingDistributed Solution: A Price-Based FrameworkImplementation IssuesPerformance Evaluation
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Introduction
Recent technological advances have enabled the production of low-cost sensors.
Usually sensors are densely deployed in sensor networks. (Overlapping sensing ranges)
Find a transmission structure to minimize total energy
This framework should be compatiblee.g. multi-sink, distributed solution, asynchronous network settings, sink mobility, duty schedules
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Problem Formulation
Model the WSN as a directed graph G=(V,E)V = Assign every node i with rate Transmission range and exists if Each link(i,j) has a weight represents the flow rate of link(i,j)
We can minimize the optimization objective by adjusting and
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Problem Formulation
Use rate distortion theory to analyze the problem
Let S be a spatially correlated random Gaussian vector
Σ𝑖𝑗=𝑊𝑑𝑖𝑗
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Problem Formulation
Goal : Minimize transmission energy
ConstraintsFlow Conservation
Channel Contention
Rate Admissibility
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Problem Formulation
The constraints and the correlated data-gathering problem can be modeled as an exponential-constraint linear programming formulation
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Localized Splepian-Wolf Coding
Disadvantages of the optimization formulationsDifficult to solveRequire global knowledge of the correlation structure
Use Slepian-Wolf coding to relax the rate admissibility constraints such that only local correlation information is required.Each sensor node i should encode its data at a rate equal to
the conditioned entropyConsider the data correlation with one-hop neighbors in
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Localized Splepian-Wolf Coding
Supports multiple sinks:a subset of sensors within the neighborhood of
sensor that are closer to sensor ’s sink
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Distributed Solution:A Price-Based
Framework
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Lagrangian Dualization(1/2)
Goal: allocate the limited capacity of the wireless shared medium
Price-based resource allocationEach wireless link is a basic resource unitA price can reflect the relation between the traffic load of
the link and its bandwidth capacity
Relax the channel contention constraints with Lagrangian dualization
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Lagrangian Dualization(2/2)
The weight of each link is equal to the sum of its energy and capacity cost.
energy capacity cost
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Subgradient Algorithm
An efficient iterative algorithm to solve the Lagrangian dual problem.
Solve the Lagrangian sub problem by finding the shortest path from each sensor node to its nearest sink node with current Lagrangian multiplier during each iteration
Update the Lagrangian multiplier
𝛽𝑖𝑗 [𝑘+1 ]
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Distributed Algorithm(1/2)
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Distributed Algorithm(2/2)
The algorithm requires 3 control packetsFlow rates of all links within the clusterPrices for all clusters that are inherent to itThe identities of other sensor nodes in its neighborhood
and their distance to destination sink node
90sensors,10sinks,Transmission rage=30m
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Asynchronous Network Model
Synchronous network modelEvery node simultaneously execute at every time instanceIt is expensive to synchronize local clocks across the
entire networkPartial-asynchronous network model
The time between consecutive updates is bounded by BAt time t, instead of the most recent information, a node
may receive a sequence of recent updatesCompute the average of the sequence of updates from
time to
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Implementation Issues
Primal RecoveryGuarantee to generate feasible primal solutionThe network must remain static
: step size : the weights of convex combination
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Implementation Issues
Capacity ReservationThe rate allocation generated by subgradient algorithm
often violate the channel contention constraintsGenerate feasible solutions by reserving a suitable
amount of capacity (e.g. 10%)
Handling Network DynamicsNodes retrieve up-to-date topology in their neighborhood
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Performance Evaluation
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Simulation Environments
Implement with C++Experiments are performed on the random
topology with 90 sensor nodes and 10 sink nodesTransmission range & interference range are 30mThe capacity of wireless shared medium is 150
bitsCorrelation parameter Per node distortion
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Converge Speed
Chose 10% as sink nodesThe algorithm is executed in synchronous
environment with 500 iterations
Primal Sub gradient
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Impact of Asynchronous Network Settings
• Run 500 iterations with different time bounds B = 1,5,10,25
• The convergence speed is associated with the time bound B.
Primal
Sub gradient
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Effect of Data Correlation
Compare the effect of data correlation between synchronous and independent environment.
D = 0.001, 0.01 and 0.1W = 0.9 to 0.9999
Implementation I : localImplementation II: global
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Adapta
tion to
Sin
k M
obility
Adaptation to Duty Schedules
Model duty schedules as a 2-state Markov chain and are state transition probabilitiesSet the simulation environment for 300s
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𝜶𝜷
=𝟓 𝜶+𝜷=𝟎 .𝟎𝟏