energy-conserving data placement and asynchronous multicast in wireless sensor networks sagnik...
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Energy-Conserving Data Placement and Asynchronous Multicast in Wireless Sensor Networks
Sagnik Bhattacharya, Hyung Kim, Shashi Prabh, Tarek Abdelzaher
Department of Computer Science University of Virginia
ACM Mobisys’03
speaker : Jenchi
Outline
Introduction Related work Service Model Data placement Evaluation Conclusion
Introduction
The primary function of sensor networks is the collection and delivery of sensory data
Power is one of the most expensive resources In this paper
develop a distributed framework that improves power conservation by application-layer sensor data caching and asynchronous update multicast
The goal of the framework is to reduce the total power expended on the primary network function
Communication is a prime candidate for optimization
Related Work
The approach differs from traditional multicast routing Updates are propagated asynchronously in a lazy
manner in accordance with consistency constraints The depth of the tree is determined by the update and
the request rates, and it adapts itself to minimize the communication
The work in an overlay multicast algorithm that works on top of the network layer, rather than traditional multicast routing that takes place at the network layer
Related Work (cont.)
Data placement is also similar to some of the ideas used in the placement of web server replicas Data placement furthers this idea by using the
property of location-awareness of the sensor nodes
Service Model
A dense ad hoc wireless sensor network with multiple observers, spread over a large monitored area
The observers’ attention is directed to a relatively limited number of key locales in the network Focus locales : important events or activities
are taking place Sensor nodes at each focus locale elect a loc
al representative for communication with the rest of the world
Service Model (cont.)
Our service adopts a publish-scribe model Each representative publishes sensory data about its
focus locale to observers who subscribe to a corresponding multicast group to receive such data
Update traffic is multicast from focus locales to receivers in an asynchronous manner
Data caches are created at the nodes of the multicast tree
Different observers may specify different period requirements for the same measurement
Service Model (cont.)
Our middleware achieves four main functions It determines the number of data caches for
each focus locale It chooses the best location for each cache
such that communication energy is minimized It maintains each cache consistent with its
data source at the corresponding focus locale It feeds data to observers from the most
suitable cache instead of the original sources
Service Model (cont.)
Key differences between this problem and the problem of caching in an internet context Internet
The topology restricts the choice of cache locations Sensor network
Is dense enough such that a data cache can be placed at any arbitrary physical location
Internet The number of Internet proxy caches is typically much smaller
than the number of different web sites Sensor network
The middleware caching service runs on every sensor node The number of sensor nodes is larger than the number of focus
locales
Problem formulation
Focus locale (X, Y) sensor updates at (X, Y) occur at an average rate Rupdate
BS={BS1, BS2,…, BSM} is a set of M observers that request data from that locale with rates Rreq={R1,R2,…,RM}
Sensor : (X,Y)BS1
BS2
BS3
Problem formulation (cont.)
The cost of message transfer between two nodes in the tree the power expended on a packet’s transfer on the
shortest route multiplied by the packet rate
1
2
3
Mi
iiupdatesens RnRnT1
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Niii
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Niii
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The center of gravity of the N input points
Problem formulation (cont.)
The problem is that of constructing a minimum-cost weighted Steiner tree, which connects the sensor node to the observers
Data placement
Is a distributed physical systems
Each step of the algorithm reduces a measure of total energy until a minimum energy tree is found
Use a distributed greedy heuristic that iteratively places each node at the center of gravity of its neighbors
Data placement (cont.)
The algorithm each node on the multicast tree rooted at the sensor
maintains a location pointer to its parent as well as a location pointer to each of its children
Each child node maintains the maximum propagation rate, which is the maximum of all requested update rates of all observers served by that child
Flurries of environmental updates that exceed some receivers’ requested rates are not propagated unnecessarily to those receivers
Data placement—Joining the Multicast Tree
k
New Node (observer)
Join Request
1.The location of the observer2.Its desired update rate Rk
Data placement—Joining the Multicast Tree
k
New Node (observer)
Join Request
Data placement—Joining the Multicast Tree
k
New Node (observer)
Join Request
Data placement—Joining the Multicast Tree
k
New Node (observer)
Join Request
Data placement—Joining the Multicast Tree
k
New Node (observer)
Join Request
Data placement—Joining the Multicast Tree
k
New Node (observer)
Join Request
No children that are closer to the observer
Nearest neighbor
New link
Data placement—Copy Creation and Migration
N
k
New Node (observer)
Nearest neighbor
Computes the center of gravity of itself and all its neighbors
Node N computes the savings, if any, resulting from creating a new copy at that center of gravity
If the savings from creating the copy exceed a threshold, the option of creating this copy is deemed viable
Data placement—Copy Creation and Migration
Nearest neighbor creates downstream copy If N is the origin sensor
k
N
Nearest Neighbor
(Origin sensor)
Computes the center of gravity of itself and all its neighbors
Prospective Copy New Node
Data placement—Copy Creation and Migration
Nearest neighbor creates downstream copy If N is the origin sensor
k
N
Nearest Neighbor
(Origin sensor)
Computes the center of gravity of itself and all its neighbors
Prospective Copy New Node
Data placement—Copy Creation and Migration
Nearest neighbor creates upstream copy If N is not the origin sensor
kNNearest Neighbor
New Node
Computes the center of gravity of itself and all its neighbors
Prospective Copy
Data placement—Copy Creation and Migration
Nearest neighbor moves If N is not a fixed copy
k
NNearest Neighbor
New Node
Computes the center of gravity of all its neighbors
Prospective Move
Data placement—Copy Creation and Migration
Nearest neighbor moves If N is not a fixed copy
k
NNearest Neighbor
New Node
Prospective Move
Data placement—Copy Creation and Migration
Nearest neighbor moves If N is not a fixed copy
k
Nearest Neighbor
New Node
Prospective MoveN
Data placement—Copy Creation and Migration
At most one copy is created for every newly joined member
The algorithm creates at most m-2 copies where m is the total number of observers
Data placement—Leaving the Multicast Tree
Observer K sends a leave() message to its parent N Node N stops forwarding messages to the departed
observer N resets the maximum forwarding rate If N is a migratory mode, it computes the center of
gravity of all remaining neighbors, and moves there if the savings exceed a threshold If there is only one child left for the migratory node, the
node is deleted and its parent takes over its child
Data placement (cont.)
Sampling Rupdate
To take the inverse of the average of the last five inter-arrival times
Evaluation Use Berkeley motes as the underlying distributed platform
Each node has up to three sensors Runs on an 8-bit 4MHz micro-controller and has 128kb of program memory and
4kb of data memory NS-2 simulator
network : 200m×200m nodes : 200 N 500≦ ≦ each node have a radio range of 20m Packet sizes : 30 bytes Base station : roughly 5% the number of nodes in the network Each node knows it own location Focus locale : is generated at random The request rates are generated at random with a specific average throughout th
e experiment Energy consumption is measured in terms of Joules per node per flow Transmitting a single bit consumes 1 μJ and receiving consumes 0.5 μJ Use Geographic forwarding as a routing algorithm
Evaluation—Simulation Results
To compare the performance of the data placement middleware against four baselines A simple unicast-based query-response model Update multicast (synchronous push model) Directed diffusion Update flooding
Evaluation—Simulation Results
Comparing the energy consumption of the four baselines for different node densities
Regular multicast is better than data placement!Because the overhead of data placement is offset by considerable savings when the average update rate increases beyond the request rate
Evaluation—Simulation Results
The average energy consumption in the steady state after all observers have joined the tree
Data placement is better!Because it does not send unnecessary updates
Evaluation—Simulation Results
To measure energy consumed when a new observer joins the tree
Evaluation—Simulation Results
Lifetime of nodes in a sensor network using data placement
Conclusion
Data placement reduces energy consumption and increase the lifetime of a sensor network
The algorithm places copies of the requested data and updates them so as to minimize the communication overhead and power consumption of data transfer
The algorithm is completely distributed and requires very little local processing
Data placement is a new approach for energy conservation in wireless sensor networks