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LPT for Data Aggregation in Wireless Sensor networksMarc Lee and Vincent W.S Wong
Department of Electrical and Computer Engineering, University of British Columbia, Canada
GLOBECOM 2005
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Outline
Introduction Related Works Problem Formulation Centralized LPT Construction Distributed LPT Construction Experiments Conclusion
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Introduction (1/2)
Why data aggregation? A event can trigger many nearby sensor nodes in
sensor networks.
Transmitting consumes much higher energy than other actions.
Nearby sensor nodes aggregate data and remove any redundancy can reduce communication cost.
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Introduction (2/2)
The paper proposes the lifetime-preserving tree (LPT) for data aggregation.
LPT aims to prolong lifetime of sources, the node with higher energy are chosen as the aggregation parent.
When a node is no longer functional or a link is broken, the LPT will be re-constructed.
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Related Works (1/2) Distributed aggregation algorithms
Energy-aware data aggregation tree (EADAT) algorithm [4] : Tree-based solution: Choose a sink as root, each node has expiration time inversely proportional to energy. Use the time to control node select parent.
Energy-aware spanning tree construction (E-Span) algorithm [10] : Tree-based solution: Choose the highest-energy node as root,
and other nodes choose aggregation parent by shortest path to root.
Hybrid energy-efficient distributed clustering (HEED) approach [8] : Cluster-based solution: nodes with higher energy has higher probability to become cluster head
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Related Works (2/2)
Related Researches Dynamic convoy tree-based collaboration (DCTC)
framework for tracking a mobile target [5] : A dynamic tree is created by adding or pruning nodes near the target to track the moving target.
Energy-efficient area monitoring for sensor networks [6] : Periodically searching the smallest subset of nodes that
cover the monitoring area.
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Problem Formulation (2/3)
LPT approach intend to extend the refresh time of a tree to reduce the cost of maintenance. Assign nodes with higher energy to be the parents
Energy definition
branch : route from a root to a leaf node set of nodes along a branch rooted at node “x” set of nodes in a tree rooted at node “y”
:Ix
:Jy
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Problem Formulation (3/3)
LPT construction problem :
: set of possible routes form “s” to “r” : energy of a tree rooted at “z” : energy of a branch “h” with the leaf node “f” and
root “g”
rsP ,
ztreeE
hgfbrE ,,
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Centralized LPT Algorithm (1/3) Centralized LPT construction
1. Arrange nodes in ascending energy levels2. Start from the least-energy node3. Remove all the links to the node except from its highest-
energy neighbor4. Check If the removal disconnects the existing graph
true current selected node is the bottleneck node, restore the removed links and selected one of
the remained nodes as root, run shortest path algorithm and return.
false go to the next least-energy node and jump to step3.
5. If finally it comes to the last node, there is no bottleneck in the graph, select the last node as root, then run shortest path algorithm and return.
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Centralized LPT Algorithm (2/3)
: the highest-energy neighbor of node n : the link between n and j : the highest-energy node of the N sources
max,nnode
jnlink ,
Nnode
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Distributed LPT Construction (1/5) Step1 - Exploring the highest-energy branch from
every source to any root (any node):
1. Each source node initiate a message containing its energy information and broadcast it.
2. When another source receives this message, it appends its energy information and broadcast only if : It has not received this message from a new initiating node Or it has forwarded the message having a lower energy
Eventually, the message with highest branch energy will arrive at the root.
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Distributed LPT Construction (2/5)
: energy of node n : pair of energy and ID information of node n : branch energy from initial node i to node j through route k : branch list from initial node i to node j through route k, the
format is
neid
kjibrList ,,
kjibrE ,,
ne
jyxi eideideideid ...
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Distributed LPT Construction (3/5) Step2 - Constructing a tree spanning for every source
:
1. Each source node has an initial tree structure that only comprises of itself.
2. Each source node incrementally updates its tree : On receiving any message with an unknown initiating node When receiving node identifies a message with higher branch
energy.
Each source node avoids creating loops during updates: Reject a new-arrived branch if each parent on the new-arrived
branch does not match the route on the already-stored branch.
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Distributed LPT Construction (5/5) Step3 - Searching a lifetime-preserving tree :
1. Each source node initially selected stored tree as its LPT
2. Each source node broadcast its tree structure
3. Each source node update the selected LPT and forward it when receiving a tree with higher energy
3. Finally, every source get highest-energy tree as its LPT
: LPT of node n : energy of LPT n
nlpt
nlptE
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Experiments (1/7)
Simulation Model Nodes number : M = 50, 100, 150, 200, 250 Nodes density : D = 50/1602 (nodes/meter2) Source nodes number : N = 0.1M Sinks number : 5 Radio range : 40 meter Implement on Directed Diffusion in ns-2 simulator Each source generates random data reports (fixed 136
byte) in constant interval 1 packet/sec Nodes energy : source 10 ~ 15J, others much higher Energy consumption : idle 35mW, receive 395mW,
transmit 660mW, data processing and aggregation cost ignore