1 Caching/storage problems and solutions in wireless sensor network Bin Tang CSE 658 Seminar on...
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Transcript of 1 Caching/storage problems and solutions in wireless sensor network Bin Tang CSE 658 Seminar on...
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Caching/storage problems and solutions in wireless sensor network
Bin Tang
CSE 658 Seminar on Wireless and Mobile Networking
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Outline Introduction Web caching and
replication/placement Caching/storage in sensor network (based on Mobisys’ 03 paper) Our approach and preliminary
simulation results
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Motivation of data placement/ caching in sensor network
Collection and delivery sensory data
Power conservation of each node is important
Communication cost is dominant among sensing, processing and communication cost.
Save communication cost - caching
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Problem and objective Given network topology and user
access pattern, decide the number and location of web content replicas
Objective function can be minimized
clients’ latency, bandwidth, or overall cost function
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General Approach
Map the placement problem onto a graph optimization problem Facility location problem Minimum K-median problem
Solve graph optimization problem Using various approximation algorithms
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Minimum K-Median Problem
Given a complete graph G=(V,E), d(j), c(i,j)
d(j): # demands c(i,j): distance
between node i and j
Find a subset V’ V with |V’| = K s.t. it minimizes vV minwV’ d(v)c(v,w)
Facility Location problem Given a complete graph
G=(V,E), f(i), d(j), c(i,j) f(i): # cost of building i d(j): # demands c(i,j): distance between
node i and j Find a subset V’ V s.t. it
minimizes f(i) + vV minwV’
d(v)c(v,w)
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Algorithms Tree based algorithm
underlying topologies are trees, and model it as a dynamic programming problem
O(N3M2) for choosing M replicas among N potential places
Random Pick the best among several random
assignments Hot spot
Place replicas near the clients that generate the largest load
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Greedy algorithm Calculate costs of assigning clients to replicas Select replica with lowest cost
Super-Optimal algorithm Lagrangian relaxation + subgradient method
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Data Storage/placement in sensor network What’s the difference with web caching?
High density makes topology more flexible Caching service runs on every sensor node
Approaches: Data-centric
Attribute-based naming Data stored by name
Web caching approach (heuristics) “Energy-conserving Data Placement and
Asynchronous Multicast in Wireless Sensor Networks” by Sagnik Bhattacharya, et al. (Mobisys’03)
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Model Multiple observers(sinks) Focus locales, representatives Publish-subscribe model
Problem formulation – construction of a minimum-cost Steiner tree, which connects sensor node to observers
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MS(teiner)T, MS(spanning)T, MK-Median problem.
Data placement/tree construction Join the multicast tree Copy creation and migration Leave the multicast tree
Simulation – comparison with unicast model,
synchronous multicast, directed diffusion
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Our approach Minimum Steiner tree:
N={1, 2, …, n}, S is source node with D dist(i,j), hop numbers b/t i and j U: update frequency of D a(i): access frequency of node i to D Find a set of intermediate nodes M N, to
minimize: U*(optimal cost of a Steiner tree covering S UM) +iN minjM a(i)dist(i,j)
O(N6M2) for choosing M replicas among N potential places in tree topology.
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Simplified model Steiner tree cost is replaced by
individual unicast cost
Update cost is considered as constraint, objective function is
t(N, M) = iN minjM a(i)dist(i,j)
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Greedy algorithm Benefit of A: B(A, M)=t(N,M)-t(N,M U A) Greedy algorithm: In each iteration,
select the node with maximum B until the update constraint is reached.
Optimal bound for both single and multiple documents storage of each node: 1-1/e
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Simulations
Simulation model: A network of (200 <= N <= 1000)
sensor nodes 100m x 100m area Transmission radius (12m <=R <=
25m)
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Update cost constraint
Total enegy dissipated vs. maximum update cost
0
250
500
750
1000
0 50 100 150 200
Maximum update cost (num of hops)
En
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y d
issi
pat
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(nu
mb
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f h
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Energy saved from greedy algorithm
Engergy saved vs. number of nodes
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0 250 500 750 1000
Number of nodes
En
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(n
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The effect of update cost
Total energy dissipated plotted against sensor update reate
0
200
400
0 1 2 3 4 5 6 7
Average update rate/average request rate
En
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The role of transmission rate
Total Energy dissipated vs. transmission radius
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0 5 10 15 20 25 30
Transmission radius
En
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sip
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(n
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References “Energy-conserving Data Placement and
Asynchronous Multicast in Wireless Sensor Networks” (Mobisys’03)
Sagnik Bhattacharya et al. On the placement of Web Server Repilcas”
(INFOCOM’01) Lili Qiu et al.
“Data-Centric Storage in Sensornets” (WSNA’02) Deborah Estrin et al.