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Networking Algorithms. Mani Srivastava UCLA [Project: Dynamic Sensor Nets (ISI-East)]. Outline. Dynamic location discovery Topology management Dynamic MAC address assignment. Known Location. Unknown Location. I. Dynamic Location Discovery. - PowerPoint PPT Presentation
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Networking Algorithms
Mani Srivastava UCLA
[Project: Dynamic Sensor Nets (ISI-East)]
Outline
I. Dynamic location discovery
II. Topology management
III. Dynamic MAC address assignment
I. Dynamic Location Discovery
Discovery of absolute and relative location important Location-based naming and addressing, geographical routing, tracking
GPS not enough LOS-requirements, costly, large, power-hungry
Ad hoc precludes trilateration with special high power beacons also, susceptible to failure
Problem: given a network of sensor nodes where a few nodes know their location (e.g. through GPS) how do we calculate the location of the other nodes?
Known Location
Unknown Location
Ad-Hoc Localization System (AHLoS)
GOALS
Localization in a distributed fashion
Trade-offs Robustness Computation vs. communication
Ranging using Ultrasound
Integrated with routing messages Location discovery almost free
Implementation Ranging using radio-synchronized
ultrasound 3m range, noisy
Accuracy: Iterative: ~ 10 cm Collaborative: ~ 3 cm
Iterative Weighted Multilateration
Collaborative Multilateration
Iterative Multilateration
Atomic multilateration applied iteratively across the network may stall if network is sparse, % of beacons is low, terrain obstacles
Step 1
Step 2
Step 3
% Initial BeaconsTotal Nodes
% Resolved Nodes
Uniformly distributed deployment in a field 100x100. Node range = 10.
Iterative Multilateration Accuracy
00.020.040.060.08
0.10.120.140.160.18
12 13 16 18 20 21 24 26 28 31 34 36 38 40 42 44 47 49
Node Id
Err
or
Dis
tan
ce (
m)
Ranging Error Ranging + Beacon Error
50 Nodes 10% beacons 20mm white gaussian ranging error
Collaborative Multilateration
Step 1: form “Collaborative Subtrees” within their neighborhood an unknown node is collaborative if it has at least 3
participating neighbors a node is participating if it is either a beacon, or if it is
an unknown node that is also participating at each node at least one of its participating nodes are
new to the set at least one of the beacons used to determine the
position of a node should not be collinear with the other beacons used to determine the node position
Step II: obtain initial location estimate for subsequent computation use beacon locations & hop distances to obtain
approximate location bounds
Step III: perform computation Measurement Update part of Kalman Filter Centralized, at a leader elected in the subtree or, Fully Distributed
can start computing locations based on node connectivity and initial estimate
Uncertainty of estimatedlocation in first iteration
Uncertainty of estimatedlocation in second iteration
Example
Network of 30 nodes6 beacons, 24 unknowns
Ranging noise experimentally derived
0
5
10
15
20
25
30
35
7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Node Id
Err
or
Dis
tan
ce
(mm
)
Centralized Distributed
Computation & Communication Expense
0
10,000,000
20,000,000
30,000,000
40,000,000
50,000,000
60,000,000
70,000,000
6 9 14 19 24
No. of Unknown Nodes
Flo
ps
Centralized Distributed
Computation Expense Results using the FLOPS command in MATLAB
0
10
20
30
40
50
60
70
80
0.01 0.1 1 10 20 30 40 50 60 70 80 90 100
Convergence Criteria
Avg error distanceNo. of transmssion / unknown nodeNo. of KF-ops / transmission (computation)
Computation vs. Communication Tradeoff
Total number of transmissions: Centralized 70 packets Distributed 416 packets
Centralized approach has additional overhead for leader election
II. Topology Management
Two phases of sensor network operation Detect event Relay information to users
Energy consumption of radio dominates that of sensors & CPU perform event detection continuously
The only energy efficient mode of the radio is the sleep mode put radio to sleep as often as possible
Existing approaches: density-energy trade-off keep enough nodes awake to handle the data forwarding (forwarding state) but for substantial energy savings we need large densities
Observation most of the time, the network is only monitoring its environment, waiting
for an event to happen (monitoring state)
Idea: put node radios to sleep and wake them up when they need to forward data low duty-cycle paging channel using a 2nd radio: trades off energy savings
for setup latency
STEM: High-level Operation
TxPow
erP
ower
Time
f1
f2Tx /RxSleep
Sleep
Rx
Pow
erP
ower
Time
f1
f2Tx /RxSleep
Initiator node
Target node
Wakeup plane
Data plane
Wakeup plane
Data plane
Detailed Operation
f1
f1
Target node
Initiator node
Train of beacon packets
TRx
B1B2 1. beacon received
2. beacon acknowledge
T
Latency – Energy Analysis
• Setup latency
• Energy savings
232
RxS
TTT
T
T
E
E Rx0
Appropriate choice of interval sizes
Mostly monitoring state: << 1 or >> 1
f1
f2
Wakeup plane
Data plane
Fraction of time in the forwarding state: 1
Forwarding state Monitoring state
Energy-Latency Trade-off
0E
E
Rx
S
T
T
= 101
• The tradeoff between energy and delay is manipulated by varying T
T E TS
• The energy savings increase as the monitoring state becomes more dominant,
= 102
= 103
= 104
TRx = 0.225 s
Topology Management in Forwarding State
• Conserve traffic forwarding capacity
• Divide network in virtual grids
• Each node in a grid is equivalent from a traffic forwarding perspective
• Keep 1 node awake in each grid at each time
GAF: Geographic Adaptive Fidelity [Ya2001]
M’ M 1.0 0 0
1.5 0.87 13.7
2.0 1.59 25.0
2.5 2.22 35.0
3.0 2.82 44.3
• GAF reduces the energy by a factor M’
• This factor is a function of the average number of nodes in a grid: M
Me
MM
1
Average num
ber of neighbors of a nodefor uniformly random
node deployment
Comparing STEM & GAF
Rx
S
T
T
STEM
GAF
Leverage latency
Leverage density
Curve of comparable energy savings
Combining STEM and GAF forJoint Energy-Latency-Density Trade-off
Rx
S
T
T
0E
E
GAF alone
STEM alone
= 200
= 100
= 60
= 30
= 10
As in GAF, 1 node is active in each grid the grid can be considered a virtual node
This virtual node runs the STEM protocol
III. Dynamic MAC Address Assignment
Wireless spectrum is broadcast medium MAC addresses are required
In wireless sensor networks, data size is small Unique MAC address present unneeded overhead
Employ spatial address reuse (similar to reuse in cellular systems) MAC address, link ids
Two aspects Dynamic assignment algorithm Address representation
40
1
1 2
2
3
5
Distributed Assignment Algorithm
0
0
1
2
0
13
2
0
1
0
0
1
2
4
1
3
1. Network is operational (nodes have valid address)
2. Listen to periodic broadcasts of neighboring nodes
3. In case of conflict, notify node (this node resends a broadcast)
4. Choose non-conflicting address and broadcast address in a periodic cycle. At this point the new node has joined the network.
Additive convergence: network remains operational during address selection
Mapping: unique ID to spatially reusable address
Algorithm also valid when unidirectional links
Encoded Address Representation
Address range 0-11 12-
1718-19 20-22 23 …
Codeword size (bits)
4 5 6 7 8 …
Encoded (bits/address) 1.7
Fixed size (bits/address) 2
Address
Frequency
Dav = 0.01 nodes/m2
0
1
2
3
0
10
110
1110 1 2 3
Fre
q. o
f oc
curr
ence
0.5
0.3
0.1
Size of the address field?
Non-uniform address frequency Huffman encoding Robust: can represent any address
Practical address selection All addresses with same codeword
size are equivalent Choose random address in that range
to reduce conflict messages
Non-uniform Network Density
X(m)
Nodes/m2
Y (m)X(m)
Average address size (bits)
Y (m)
Effect of Packet Losses ( = 10)
Address
Frequency
Pdrop
Convergence time (s)
Scalability
Address assignment Distributed algorithm with periodic localized communication
Address representation Encoded addresses depend only on distribution
Scales perfectly(neglecting edge effects)
Off-line Centralized Distributed
++ - +
Unique Fixedreusable
Encoded reusable
-- +
Rep
rese
nta
tion
Ass
ign
men
t
Average address size (bits)
=10
=5
=15
=20
Uni
que
addr
ess
Number of nodes
Simulation Results
Our schemes
SchemeAddress selection type
Av. size
(bits)
Address size
scalability
Globally unique Manufacturing
128 +
Network wide unique
Deployment 14
Centr. / Distr.
4.7
Encoded dynamic
Distributed 4.4 +
Fixed size dynamic
Dynamic Address Allocation: Summary
Spatial reuse of address
Dynamic assignment algorithm Localized: scalability Additive convergence: robustness
Encoded address representation Independent of network size: scalability Variable length addresses: robustness