Post on 11-Jan-2016
Sprinkler: A Reliable and Energy Efficient Data
Dissemination Service in Extreme Scale Wireless
Networks of Embedded Devices
Vinayak NaikVinayak Naik, Anish Arora, , Anish Arora,
Prasun Sinha, and Hongwei ZhangPrasun Sinha, and Hongwei Zhang
Dependable Distributed and Networked Systems
December 7, 2005December 7, 2005
Vinayak NaikVinayak Naik, Anish Arora, , Anish Arora,
Prasun Sinha, and Hongwei ZhangPrasun Sinha, and Hongwei Zhang
Dependable Distributed and Networked Systems
December 7, 2005December 7, 2005
7
New Model due to Extreme Scale Wireless Embedded Devices
• Embedded devices are constrained in following resources: CPU Memory Power
• Characteristics of wireless medium Spatial and Temporal variation in link quality Hidden terminal effect
• Extreme scale demands sub-linear time complexity O(n) isn’t good enough for resource constrained devices
• Different model as compared to that of the Internet Existing network services may not work
8
Outline
a. Motivation and Requirements
b. Insight behind Solution
c. Formal Problem Statement and Algorithms
d. Analysis and Comparison
e. Conclusion
10
Irrigating ExScal
• Motivation behind data dissemination service
1. Reprogramming in the field (hundreds of packets)
2. System reconfiguration (tens of packets)
3. Health monitoring (< ten packets)
• Problem of bulk data dissemination service
1. 100% Reliability
2. Energy efficiency
3. Low latency
11
Outline
a. Motivation and Requirements
b. Insight behind Solution
c. Formal Problem Statement and Algorithms
d. Analysis and Comparison
e. Conclusion
12
Energy Saved is Energy Generated
Operation Current Draw
Mote Stargate
Microprocessor and Idle Radio 8 mA 330 mA
Packet Reception 16 mA 280 mA
Packet Transmission 24 mA 650 mA
• Load shedding
1. Packet Transmissions
2. Microprocessor and Idle Radio (Not covered in this talk)
13
Unit Disk Model
R = Transmission Radius
R
R A
B
14
Connected Dominating Set
Fewer number of senders
CDS
15
Hidden Terminal Effect
Collision!
Lost packet
A
BC
16
Time Division Multiple Access
Schedule transmissions
R R
A
C BD
17
Outline
a. Motivation and Requirements
b. Insight behind Solution
c. Formal Problem Statement and Algorithms
d. Analysis and Comparison
e. Conclusion
18
Formal Problem Statement
Divide-n-Conquer
1. An algorithm to compute a CDS, of size O(1) times the
minimum, in O(1) time
2. An algorithm to compute a distance-2 vertex coloring, with
O(1) times the minimum # of colors, in O(1) time
3. A reliable data dissemination protocol that utilizes a CDS
and a corresponding distance-2 vertex coloring
Assumptions
1. Minimum density: ≥ 1 node per square of length
2. Location information
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Algorithm to Compute CDS
Division of network into disjoint square-shaped clusters,each of
length
Election of a cluster-head in each cluster
Decision whether a cluster-head belongs to CDS or not
Variables:
1. r be the total number of cluster-heads in X axis
2. c be the total number of cluster-heads in Y axis
3. u(i,j) be any cluster-head and (i,j) be its (X,Y) coordinates
1. Program: A node u(i,j) ∈ M, where 0 ≤ i ≤ r−1 and 0 ≤ j ≤ c−1, if
• r mod 3 ≡ 0 : [i mod 3 ≡ 1] ∨ [(i mod 3 ≡ 1) ∧ (0 < i < r−1) ∧ (j = 0)]
• r mod 3 ≡ 1 : [i mod 3 ≡ 0] ∨ [(i mod 3 ≡ 0) ∧ (j = 0)]
• r mod 3 ≡ 2 : [i mod 3 ≡ 1] ∨ [(i mod 3 ≡ 1) ∧ (i ≡ 0) ∧ (j = 0)]
O(1)
20
CDS Computation
Selecting cluster-heads, Computing CDS
R
Clustering,
Performance Ratio =
21
D-2 Vertex Coloring
R
8 9 10 11 1213
14
14
15
15
0
0
1
1
2
2
3
3
4
4 5
5
6
6
6 7
7
7
Numbers indicate colors.
R
< 2R
Performance Ratio =
22
Data Dissemination Protocol
• Streaming phase
Only CDS nodes transmit
Transmissions in TDMA slots
Results in reliable data dissemination to all CDS nodes
• Recovery phase
Any node can transmit
Unscheduled transmissions
Results in reliable data dissemination to all the nodes
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Streaming Phase
R 2
A B C D
0 0 01 201
Lost!
1
Empty Slot
2
Empty Slot
12,1
Recovery Req
2,1
Recovery Req
3 1
Recovery Packet
14 1E
25
Models for Real Radio
• Radio models in real environment are more complex than unit disk model
• Packet delivery rate for XSS in an outdoor environment
• Similarly, for indoor testbeds
27
Adapting Sprinkler to Real Radio Models
• Input parameter
Transmission radius ( )
• Procedure
Initialize = , where is the reliable communication
range (100% packet delivery)
Keep incrementing till the number of transmissions for the
test broadcast are reducing
• Density assumption still holds
Since every square of length contains at least one node,
every square of length also contains at least one node
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Outline
a. Motivation and Requirements
b. Insight behind Solution
c. Formal Problem Statement and Algorithms
d. Analysis and Comparison
e. Conclusion
31
Anatomy of XSS
• XSS: Extreme Scaling Stargate Stargate
SMC 2532W-B High Power IEEE 802.11b PCMCIA card
BU-303 GPS mouse via USB
External antenna connection
33
Kansei [The 2nd TinyOS Technology Exchange at Berkeley, 2005]
• A testbed containing 200 pairs of XSSs and XSMs
• A multi-hop IEEE 802.11 network Using attenuators and S/W Tx
power control
• Applications Debugging Measuring performances of
protocols
• Web interface for experimentations http://exscal.nullcode.org/kansei
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Scalability of Sprinkler
Hops Density
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Comparison
• Existing reliable bulk data dissemination services Deluge Infuse MNP PSFQ
• Deluge protocol Doesn’t uses CDS and TDMA Uses sender suppression technique to reduce number of
packet transmissions Commonly used service for mote reprogramming
• Simulation and experiment setup A 7x7 network with a base station at a corner Payload of 240 packets
37
Performance: # Packet Transmissions
Deluge Sprinkler
Source Source
38
Performance: Latency
Deluge Sprinkler
Source Source
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Outline
a. Motivation and Requirements
b. Insight behind Solution
c. Formal Problem Statement and Algorithms
d. Analysis and Comparison
e. Conclusion
40
Conclusion
• Sprinkler: Reliable and energy efficient data dissemination
service [The 26th IEEE Real-Time Systems Symposium at Miami, 2005]
1. Energy efficient
– Reduces # packet transmissions
2. Scalable
– Constant time algorithms
3. Low latency
– Pipelines transmissions in space
• Future work
• Use of hexagon-shaped clusters instead of square-shaped clusters
• CDS and D-2 vertex coloring in the presence of holes of bounded
size and regular shape