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L 3 (Live & Let Live)- Increasing Longevity in Sensor Networks EE 228A Professor Walrand Contributors: Tanya Roosta Anshuman Sharma. Outline. Introduction Problem Definition Existing Approaches Our Approach Future Work Conclusion Q&A. Outline. Introduction Problem Definition - PowerPoint PPT Presentation
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LL3 3 (Live & Let Live)-(Live & Let Live)- Increasing Increasing Longevity in Sensor NetworksLongevity in Sensor Networks
EE 228A
Professor Walrand
Contributors:
Tanya Roosta
Anshuman Sharma
Introduction
Problem Definition
Existing Approaches
Our Approach
Future Work
Conclusion
Q&A
Outline
IntroductionIntroduction
Problem Definition
Existing Approaches
Our Approach
Future Work
Conclusion
Q&A
OutlineOutline
IntroductionIntroduction
What are sensor networks Networks comprised of hundreds to thousand
nodes, where each node is a sensor Examples of use include guidance and control,
data collection and aggregation Sensor nodes are designed to be
– Low cost– Non obtrusive– Dynamically reprogrammable
Introduction
Problem DefinitionProblem Definition
Existing Approaches
Our Approach
Future Work
Conclusion
Q&A
OutlineOutline
Problem DefinitionProblem Definition
Sensors must be lightweight and compact Limited Power Supply Replenishing power is not an option Important to minimize power consumption of each
node to maximize battery life and lifetime of entire network
Existing network protocols stress on QoS (high throughput and low delay) and high bandwidth efficiency
Problem Definition (cont…)Problem Definition (cont…)
Energy Consumption Energy consumption occurs in three domains: sensing,
data processing and communication. In a wireless sensor network, communication is the
major consumer of energy Example
For ground to ground transmission, it costs 3J to transmit 1 Kb over a distance of 100m. However, a general-purpose processor with 100 MIPS processing capability executes 300 million instructions for the same amount of energy
Problem Definition (cont…)Problem Definition (cont…)
Design ChallengesThree main classes
– Hardware– Wireless Networking– Application
Problem Definition (cont…)Problem Definition (cont…)
Routing in Wireless Networks: Revisited Direct Communication Protocol:
Each sensor sends its data directly to the base station
Multi-hop routing protocol (MTE)Nodes route data destined to the base station through intermediate nodes
At first look it seems that a multi-hop approach would be able conserve more power
Problem Definition (cont…)Problem Definition (cont…)
Multi-hop Routing Protocols Table-driven (proactive)
– Destination-Sequenced Distance-Vector Routing– Cluster Gateway Switch Routing– Wireless Routing Protocol
Source-initiated (Reactive)– Ad Hoc On-Demand Distance Vector Routing– Dynamic Source Routing– Temporally-Based Routing– Signal Stability Routing
Introduction
Problem Definition
Existing ApproachesExisting Approaches
Our Approach
Future Work
Conclusion
Q&A
OutlineOutline
Existing ApproachesExisting Approaches
Power-Aware Routing: Metrics Minimize energy consumed/packet: Minimizes the
total energy consumed over n nodes Maximize Time to Network Partition: A load
balancing problem so that the response time is minimized
Minimize Cost/Packet: Assigns a cost function to each node and minimizes the total cost of routing a packet from that node
Existing Approaches (cont…)Existing Approaches (cont…)
Routing in Clustered Multi-hop Networks Aggregate nodes into clusters controlled by a
cluster-head Clustering on the basis of either lowest-ID
distributed clustering algorithm or highest-connectivity algorithm
Within a cluster, a cluster-head controlled token protocol used to allocate channel.
Cluster Routing Protocol
Total system energy dissipated for the 100-node random network
Existing Approaches (cont…)Existing Approaches (cont…)
Adaptive Energy-Conserving Routing BECA
– Turn of radio power– Involvement of application layer information– Can increase latency and packet loss
AFECA– All the nodes do not need be involved– Exploiting node density– Can interchange nodes for routing purposes
Existing Approaches (cont…)Existing Approaches (cont…)
Adaptive Energy-Conserving Routing (cont…)
BECA– Nodes are in three possible states:
sleeping, listening, active.– Start in sleeping state. Radio is off.– After a certain time, transition to
listening state– If a node has data to transmit it
transitions to active state
Existing Approaches (cont…)Existing Approaches (cont…)
Adaptive Energy-Conserving Routing (cont…)
AFECA– Used in densely-populated networks– Each node estimates its neighborhood– Each node increases its sleeping time proportional
to the number of nodes in its neighborhood
BECA versus AODV for different values of sleeping time
The latency for unmodified AODV is fixed The latency grows roughly linearly The growth is slightly lower at higher traffic rates
Percentage of energy saved is (Er - Es) / Er
Less saving for higher traffic rates since more nodes in active mode High values of sleeping time give no energy improvement
PE is the loss rate PE=P/E where P is the size of data delivered and E is the total energy
consumed by all nodes We can use PE to determine an optimal value for the sleeping time
Assumption: Unlimited amount of energy in the nodes
As expected AFECA and BECA do worse in terms of latency and packet loss than unmodified AODV
AFECA has a better energy consumption than BECA as expected
AFECA aggressive power savings result in the consistently highest efficiency
BECA protocol is about 20% longer and AFECA is about 55% longer than unmodified AODV when the energy in the nodes is limited.
Assumption: The nodes have limited amount of power
OutlineOutline
IntroductionProblem DefinitionExisting ApproachesOur ApproachOur ApproachFuture WorkConclusionQ&A
Our ApproachOur Approach
Insight Computation is much cheaper than communication Use of distributed approach to reduce
– Total number of transmissions– Energy dissipated in the network
Application-level/ higher layer feedback is important
Establish trade-offs (complexity vs. performance improvement, etc)
Our Approach (cont…)Our Approach (cont…)
Radio Model (First Order)
ETx(k,d) =ETx-elec(k) + ETx-amp(k,d)
=Eelec*k + amp*k*d2
ERx(k) =ERx-elec(k)
=Eelec*k
ETx-elec = ERx-elec = Eelec (Energy dissipated to run Rx/Tx)
amp (Energy dissipated for amplifying to get good gain)Source: Energy-Efficient Communication Protocol for Wireless Microsensor Networks: MIT
Our Approach (cont…)Our Approach (cont…)
Additions to Radio Model Does not consider energy consumption while
radios are idle Inclusion of idle time based on experiments with
WaveLAN radios Most of the time the radio is idle, hence idle time
dominates energy consumption Add term idle (idle energy expended per unit
time)
Our Approach (cont…)Our Approach (cont…)
Important to determine critical transmission range Let there be
– n total nodes– k cliques that we intend to form
Use modified Prim algorithm to form cliques of at least 3 nodes
Why the magic number 3?
Our Approach (cont…)Our Approach (cont…)
Pick k nodes at random (for each of the k cliques) k nodes are temporary cluster-heads Start with some minimum radius of discovery - Goal is to discover a minimum of 3 nodes for each
clique Increments of , if cannot find any node in the
periphery After the first node is discovered it tries to look
for another node, incrementing by each time
Our Approach (cont…)Our Approach (cont…)
All three nodes then adjust their transmission power to reach other
This results in a Hamiltonian Cycle If more than 3 nodes are possible without
increasing power then OK to have > 3 nodes in clique
After forming cliques, use TDMA to allocate time-slots for nodes to be cluster-head.
The nodes also use TDMA to schedule updates to cluster-head (intra-clique communication).
Our Approach (cont…)Our Approach (cont…)
The other nodes are put to sleep (turn-off radios) when not communicating, similar to PAMAS
A cluster head is responsible for discovering other cliques and sharing information within the clique.
Possibility of adding multiple hierarchies depending upon the trade-off between complexity and advantages
Our Approach (cont…)Our Approach (cont…)
Considerations GPS is available but might not be viable Next generation design of Low power ICs can
make adjusting duty cycle easy Exploring node density as a measure of reducing
computation and communication overhead CDMA codes allow efficient use of the channel
bandwidth
OutlineOutline
IntroductionProblem DefinitionExisting ApproachesOur ApproachFuture WorkFuture WorkConclusionQ&A
Future WorkFuture Work
Evaluating model through simulations Tuning density to trade operational quality against
lifetime Using multiple sensor modalities to obtain robust
measurements Exploiting fixed environmental characteristics Using a more comprehensive radio model that
takes into account time to wake up from sleep cycles
Exploring of various benchmarks for “lifetime” of a network
OutlineOutline
IntroductionProblem DefinitionExisting ApproachesOur ApproachFuture WorkConclusionConclusionQ&A
ConclusionConclusion
Our model is based on work that has already been done
We exploit characteristics of proven approaches Simulations would provide a measure of
advantages incurred by using our approach
OutlineOutline
IntroductionProblem DefinitionExisting ApproachesOur ApproachFuture WorkConclusionQ&AQ&A
Q&AQ&A
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