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An Application-Specific Protocol Architecture for Wireless Microsensor NetworksBy: W. Heinzelman, A. Chandrakasan & H. Balakrishnan
A review prepared for CEG 790By: Patrick Flaherty
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Presentation Outline
What is a Wireless Sensor Network?
Why are Protocols for Self-Organizing an issue?
The proposed “Low-energy adaptive clustering hierarchy” (LEACH) protocol
Concept Algorithms
Analysis and simulation of LEACH
Conclusions
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What Is A Wireless Sensor Network?
Sensor devices (source)
Base Station(sink)
Wireless communication
Network structure
Network Layer
Tasks (partial)
Application Source code
Transport Packets, congestion control
Network Routing
Data-Link MAC, error correction
Physical Wireless
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Wireless Sensor Networks
10’s to 1,000’s of wireless sensors placed into an environment
May be put into structures where truckloads of cabling would be required to connect to the data collection point (e.g., Golden Gate Bridge)
May be placed in a natural environment to monitor wildlife (e.g., study relationship between weather conditions and animal behavior)
May be used in hostile environments to detect movement of opponents
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Wireless Sensors
Devices that:
SensorsLightHeatVibration…
Measure some input variableProcessor
Process the measurement
Radio
Transmit data to higher level
Small & inexpensive (ideally)
Battery
Panasonic
CR2354 560 mAh
Powered by battery (typically)
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Energy Consumption is a Function of Device Activity
Telos is a recently released microsensor platform design
Source: “Telos Fourth Generation WSN Platform”Presented at: TinyOS Technology Exchange, Feb. 11, 2005
Radio power consumption dominates (even in receive mode)
Standby mode supports long life
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Wireless Transmission Issues
Line-of-sight (LOS) transmission attenuation
Power falls off as d 2 Tx Rx
d
Tx Rx
d
Tx Tx
Rx
Multiple paths lead to reflection and scattering
Power falls off as d 4 beyond a certain distance
Receiver may fail to discriminate valid signals due to
Interference from other Tx Noise (internal to the Rx)
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Characteristics That Affect Wireless Sensor Network Design
Characteristic ImplicationSevere power constraint Network protocols must take
power into consideration
Number of nodes could be in the thousands, scattered in any order
Network topology and routing not pre-engineered – protocols must handle establishing the network
Radio consumes large amounts of power when on
Keep radio off whenever feasible
Transmit power loss rises as d2 in close, as d4 further out
Minimize number (and duty cycle) of distant nodes transmitting to base station
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Authors’ Design Assumptions
All nodes can transmit with enough power to reach the base station if needed
Individual nodes can adjust the amount of transmit power
Each node has sufficient computational power to support different MAC protocols, perform signal processing, etc.
Nodes always have data to send Nodes that are sufficiently close have correlated
data
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Consider 3 Different Network Protocols To Clarify Concepts
“Simple” network -- every node talks directly to the base station
Minimum Transfer Energy (MTE) – nodes minimize transmit distance energy loss
Static Cluster – group nodes spatially, aggregate data, and assign one node to handle communications with the base station
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Network Scenario -- Simple
All nodes communicate directly with the base station – always on
Base Station
Problems: Who talks next?
Out of power fast! Especially distant nodes
Recall: Signal strength is inversely proportional to the square of the distance (best case)
Far away --> Hi-power --> Short life
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Network Scenario – MTE
Each node discovers the best hop-by-hop path to the base station during an initialization phase
Base Station
Data transmitted every tdelay seconds
Collision avoidance via CSMA protocol
As nodes run out of energy, routes are recomputed to maintain connection to base station
Problems: Problem: multiple hops --> latency
Problem: close-in nodes overused
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Network Scenario -- Static Clustering
Organize nodes into clusters
Base Station
Nodes send data to “Cluster Head”
Access via TDMA
Cluster head aggregates the data and sends results to base station
Problem: When cluster head’s energy depleted, no further data is sent from this cluster
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Summary of Previous Protocols
Issue Simple MTEStatic Cluster
Rate of energy Consumption
Very Rapid Rapid
Slower(but cluster head
failure is an issue)
Control of Access
Not addressed CSMA TDMA
Impact from Loss of 1 Node
One node lostOne node lost
(after rerouting)Entire cluster lost(if a cluster head)
Latency One hop Multiple hops Two hops
How to improve this idea?
Less Energy Consumption
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Low-Energy Adaptive Clustering Hierarchy (LEACH) Protocol
Structure of rounds occurring over time Nodes organize themselves into local clusters One node acts as the cluster head Member nodes transmit data to the cluster head
during the timeslot allocated by a TDMA algorithm Cluster head aggregates the data from the member
nodes (e.g., computes mean value) Cluster head transmits aggregated data to base
station Repeat until time to begin a new round
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Cluster Behavior During a Round
Organize new cluster
Round
Base Station
Each member node (in turn) transmits their data to the cluster head during the assigned timeslot
Cluster head processes the data
Cluster head transmits to base station
Time
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Clusters Reform Periodically
Each round consists of a setup period and some number of frames
Round 1 Round 2 Round n……
Frame
Round 1
Round 2
Cluster Heads
Each round establishes a new structure of clusters
Repeat rounds until the network fails (due to energy depletion)
Each cluster has a new cluster head
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Now For Some Details
Cluster head selection algorithms
Cluster formation algorithm
Steady-state phase
Alternate scheme LAECH-C
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Cluster Head Selection Algorithms
Need: distributed Algorithms Desired results
Specified number of cluster heads formed for each round Cluster head duties rotated among nodes so as to evenly
draw power from the nodes over time (no overly-utilized nodes)
Case 1: Nodes begin with equal energy All nodes transmit data during each frame
Case 2: Nodes begin with unequal energy, and/or Nodes transmit “upon event”
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Cluster Head Selection – Case 1
Each node elects itself to be a cluster head with probability Pi(t) such that for N total network nodes
Where: k = # cluster heads
To ensure that each node becomes a cluster head only once in each of N/k rounds, assign Ci(t) = 0 if the node has already been a cluster head in the current round and Ci(t) = 1 otherwise.
Each individual node chooses to become a cluster head in round r with probability
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Cluster Head Selection – Case 1 (continued)
Value of N - k*(r mod N/k) represents the number of unpicked nodes
Use of r mod N/k ensures restarting after all nodes have been picked
End of Round
(r)
Un-picked Nodes
Pi(t) of remaining nodes
0 9 .22
1 7 .29
2 5 .40
3 3 .67
4 1 1.00
5 8 .25
N = 9k = 2
Base Station
Example
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Cluster Head Selection – Case 2
Nodes with more energy should have a higher probability of being chosen than nodes with less energy
Thus, the probability that a given node will be chosen is determined by that node’s share of the total remaining energy
Where Ei(t) is the energy of the ith node and
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Cluster Head Selection – Case 2 (continued)
Notice that this algorithm requires each node to know (or have an estimate for) the value of Etotal(t)
To know the exact value would take time and consume energy
As an estimate we could compute the average energy of each node in a given cluster and multiply by N
Nodes report current energy to cluster head
Cluster head computes estimated Etotal(t) and returns the value to all nodes in the cluster
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Distributed Cluster Formation
Cluster heads broadcasts “advertisement” message (ADV) using CSMA MAC protocol
Non-cluster head nodes measure received strength of ADV and select strongest sender as their cluster head
Nodes notify cluster head of their selection with a “Join-REQ” message
Cluster head creates TDMA schedule for nodes in its cluster
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Steady State Phase
Recall: Rounds are divided into frames Each node sends data once per frame TDMA requires accurate synchronization Possible method base station sends synchronization
signals Energy saved at non-cluster head nodes since
Power is reduced to only that required to reach local cluster head
Radio turned off except for short period provided by TDMA
Cluster head steady state tasks: Listen to non-cluster head nodes Aggregate the data Transmit the data to the base station
Round 1
Frame 1 Frame 2
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Steady State Phase (continued)
Transmissions must succeed even though other nodes and cluster heads are broadcasting
LEACH uses Direct-Sequence Spread Spectrum (DSSS)
Each cluster uses a unique spreading code Chosen from a pre-defined list With enough spreading, potentially interfering signals
can be filtered out during de-correlation Easier to implement than dynamically assigned
frequency bands Difficulty is need for tight timing synchronization
How does DSSS work? (Not addressed in this paper)
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DSSS Key Ideas
A wireless transmission technology that enables multiple users to share the same bandwidth
SourceRF
Modulator
CodeGenerator
X
Code Bits (Code)
Digital Signal (Data)
Tx
Frequency Spectrum “Spread” FrequencySpectrum
f1 Mhz 11 Mhz
f
The resulting “Spread Spectrum” is less susceptible to interference
Receiver must have the same code to recover the original data
Spreading: Data signal is multiplied by a unique, high rate code which spreads the bandwidth before sending (1 data bit now represented by many bits)
Wireless Technologies
Spread SpectrumNarrow Band
FrequencyHopping
Direct Sequence
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LEACH-C -- A Variation of LEACH
Idea: using a central control algorithm may produce better clusterings
Each node sends location information and energy level to the base station
Base station: Eliminates low energy nodes from consideration Finds k optimal clusters (since this is NP-hard, uses the
“Simulated annealing algorithm” ) Goal is to minimize the total sum of squared distances
between non-cluster heads and the nearest cluster head
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Performance Analysis
Simulate the performance of four protocols (Static Clustering, MTE, LEACH, & LEACH-C)
How?
Set up the simulation
Find the optimal number of clusters
Compare the protocols’ energy consumption performance
Conclusions
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Simulation of Protocol Performance
Analytical model of even moderately-sized realistic networks is “difficult”
Authors used the network simulator “ns“ Comparison of performance over four metrics
System lifetime Energy dissipation Amount of data transferred Latency
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Experiment - Setup
100 nodes randomly distributed over a 100 X 100 grid: (0, 0) to (100, 100)
Base station placed outside the grid: (50, 175)
Channel bandwidth = 1 Mb/s
Packets have 25 byte header and 500 byte data size
Power loss determined by distance d
Base Station d
d < d o
d o
If d < do, loss d 2
If d >= do, loss d 4
d
d > d o
d o
Free spacemodel
Multi-pathmodel
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Experiment - Setup (continued)
Radio energy dissipation model lEelec: energy consumed by the
electronics to process l bits l fs: energy consumed by the
amplifier to transmit l bits over distance d where d < do
l mp: energy consumed by the amplifier to transmit l bits over distance d where d >= do
Then total energy consumed by the transmitter:
Total energy consumed by the receiver:
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Experiment - Setup (continued)
Energy parameters used: Eelec = 50 nJ/bit fs = 10 pJ/bit/m2
mp = 0.0013pJ/bit/m4
Energy for Data Aggregation: EDA = 5 nJ/bit/signal
Question: How many clusters should be used?
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How Will The Number Of Clusters Affect Results?
Case 1: Baseline (BL) non-cluster head energy = ENCHBL
Cluster head energy = ECHBL
Base Station
Case 1
Base Station
Case 2
Base Station
Case 3
Case 2: Fewer Clusters (FC) ENCHFC > ENCHBL
ECHFC < ECHBL
Does Case 2 use less energy than case 1?
Case 3: More Clusters (MC) ENCHMC < ENCHBL
ECHMC > ECHBL
Does Case 3 use less energy than case 1?
Is there an optimal number of clusters?
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Step 1: Develop expressions for node energy use Cluster heads (always on): (assumes dtoBS > do)
Non-cluster heads: (assumes dtoCH < do)
Optimal Number Of Clusters
With a given spatial distribution of nodes and known energy consumption parameters, we can compute an optimal number of cluster heads (k)
Listening Aggregating Transmitting
Step 2: Develop an expression for the expected squared distance from the nodes in a cluster to the cluster head
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Optimum Number Of Clusters (continued)
Step 2 (continued): Assumptions In an M x M grid, each cluster occupies an area of M2/k Clusters have a node distribution of p(x, y) The cluster head is at the center of mass of the cluster
(in Cartesian coordinates)
(in polar coordinates)
Then the expected d2 from the nodes to the cluster head is
Further assume the area is a circle radius R = (M/(k)1/2) And p(r, ) constant for r and , then
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Step 3: Combine energy and distance expression for non-
cluster heads:
Optimum Number Of Clusters (continued)
Step 2 (continued): Assumptions Node density is uniform across all clusters p = (1/( M2/k)) Then simplifies to
Then for the entire cluster: (During a single frame)
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Optimum Number Of Clusters (continued)
Step 3 (continued): Total energy for a frame:
Simulation results agree with analytical prediction
Step 4: Set derivative of Etotal with respect to k to zero
Results for this case (100 nodes, etc.): Analytical method predicts 1 < kopt < 6
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Comparison of Algorithms
Each node was given 2 Joules of energy (def: J = W·s)
This is equivalent to a 5 volt device @ 20 mA for 20 s Parameters tracked during simulations
Rate at which data packets were transferred to the BS Energy required to get the data to the BS
What is not in the simulation No static energy loss (e.g., RTC energy use) Energy for CSMA is ignored ( CSMA energy use in
MTE is understated) Energy expended during cluster organization (not
mentioned in the paper)
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Simulation Results – Data Received
LEACH-C and LEACH deliver far more data than MTE and Static Clustering (SC) and they are far more energy efficient (as measured by signals per Joule)
SC fails when all cluster heads die, even with most energy still unused MTE slow to deliver data due to multi-hops LEACH-C is the best performer due to optimal cluster design
~40% more data for the
same energy as LEACH
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Simulation Results – Nodes Alive
LEACH-C and LEACH maintain full network availability far longer than MTE and SC MTE lasts the longest, but at the price of very limited effective data delivery due
to Lack of data aggregation Energy wasted in CSMA collisions
LEACH-C is again the best performer
LEACH-C delivers more data due to higher data
rate/J
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Conclusions
Wireless Sensor Networks which meet the original assumptions will benefit from: Rotating the cluster head position among all nodes Adapting cluster organization to new cluster heads Aggregating data
Disadvantages LEACH & LEACH-C are very dependent on nodes
having correlated data Both require tight time synchronization LEACH-C requires location information
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Future Work
If nodes send data “on condition” They can be on standby for longer periods than TDMA
permits Efficient bandwidth use will require a different
communication protocol If nodes are beyond max possible communication
range Multi-hop protocols may be required “Super cluster heads” may prove a better solution
If the original cluster is kept and the nodes within the existing clusters just rotate the cluster head job
No setup overhead is used after round one Downside nodes may expend more energy
communicating since current cluster head may be far away
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