energy-efficient distributed algorithms for ad hoc wireless networks gopal pandurangan department of...
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Energy-Efficient Distributed Algorithms for Ad hoc Wireless Networks
Gopal Pandurangan
Department of Computer SciencePurdue University
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Energy-Efficient Distributed Algorithms
Ad hoc wireless sensor networks operate under severe
energy constraints. Energy-Efficient distributed algorithms are critical. Low energy algorithms even possibly at the cost of
reduced quality of solution : Distributed approximation algorithms.
Algorithms use only “local” knowledge: Localized algorithms
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Distributed Algorithms
Traditionally complexity measures: messages, time.
Much of theory assumes point-to-point network communication model.
Wireless needs new models for designing distributed algorithms.
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Traffic Monitoring with Sensors
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Data Aggregation - Low Cost Tree
Data aggregation
Aggregate data on a tree Use a low cost tree
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Desirable Features
Simple and local
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Desirable Features
Simple and local
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Desirable Features
Simple and local Dynamic- handle node failures
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Desirable Features
Simple and local Dynamic- handle node failures Distributed Low energy Low synchronization Small number of messages Low degree
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Problem
Network Model: Weighted unit disk graph (UDG)
Find a Minimum Spanning Tree (MST) rooted at a given
node
MST is a difficult problem
Can we construct an approximately good spanning tree?
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Nearest Neighbor Tree (NNT) SchemeKhan and Pandurangan. DISC, 2006, Best Student Paper
Award.
Given: A (connected) undirected weighted graph G.
Each node chooses a unique rank.
Each node connects to its nearest node (via a shortest path) of higher rank.
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NNT Construction
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Output is a spanning tree called NNT.
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NNT Theorem
(Khan, Pandurangan, and Kumar. Theoretical Computer Science, 2007
Theorem 1:
On any graph G, NNT scheme
produces a spanning tree that has a
cost of at most O(log n) times the
(optimal) MST.
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Distributed NNT AlgorithmEach node executes the same algorithm
simultaneously:
Rank selection.
Finding the nearest node of higher rank.
Connecting to the nearest node of higher rank.
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u s
Rank Selection
Root s selects a number p(s) from [b-1, b] s sends ID(s) and p(s) to all of its neighbor in one time step. Any other node u after receiving the first message with ID(v)
and p(v) from a neighbor v: Selects a number p(u) from [p(v)-1, p(v)) Sends ID(u) and p(u) to all of its neighbors
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Defining Rank
For any u and v, r(u) < r(v) iff p(u) < p(v) or p(u) = p(v) and ID(u) < ID(v)
A node with lower random number p() has lower rank. Ties are broken using ID()
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Tree construction
Each node knows the rank of all of its neighbors. The leader s has the highest rank among all nodes
in the graph. For every node (except s), there is a neighbor with
higher rank. It connects to that node.
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NNT algorithm
Very localized. O(|E|) messages. O(Diameter) time. Low energy complexity.
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Energy complexity of a distributed algorithm Energy complexity is a measure of the
energy needed by the distributed algorithm. Various factors affect energy complexity
Time needed. Number of messages exchanged. Radiation energy needed to transmit a message through a certain distance --- typically assumed proportional to
some power of the distance. Energy overheads of the hardware (startup energy,
receiver energy etc.) ….
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Energy Complexity
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W r
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A Comparison of Algorithms
Algorithm Energy Complexity MST Quality
GHS (log^2 n) optimal
KPK (TPDS 08) O(log n) on average O(log n)approximation
CKKP (SPAA 08) O(log n) on average optimal
CKKP (SPAA 08) O(1) on average O(1)-approximation
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Questions
Good energy model of hardware? Distributed network computing model for
wireless ? How to design energy-efficient distributed
algorithms? Approximation algorithms? How do cross layer issues affect design? A new theory needed.