Transcript Sensor Network Simulation ( Sensim) Components Analysis

Sensor Network Simulation
( Sensim)
Components Analysis
Yang Liu
AICIP Research Group Presentation
Design Goal
•
Parallel discrete event sensor network
simulator
•
Scale to thousands of sensor nodes
•
Provide energy consumption computation
•
Integrate typical protocols of sensor
networks ( MAC, network, application ), adopt
an open architecture for future protocol
implementation
•
Easy to operate, easy to understand
•
ACA design, large dataset visualization
What to Simulate?
•
Metrics for sensor network
•
•
Three prospective
•
Individual
•
Group ( regional)
•
Network wide
Metrics
•
Throughput
•
Power consumption
•
Time ( Latency, lifetime, idle time, transmitting and receiving
time etc.)
•
QOS ( packet loss, Coverage, and sensor network failure)
How to Simulate?
•
A brief simulation architecture
Parallelization
•
Using Message Passing Interface (MPI)
Standard and PC cluster to realize
parallel simulation.
•
We choose MPICH-1.2.5.2 library.
•
PC cluster: peacock, P1, P2, and P3.
•
* How to decompose work ?
Parallel Algorithm Design
•
Typical techniques
•
•
•
•
•
•
•
•
Data parallel model:
• Identical operations applied concurrently on different data
Task-Graph Model
• Different processes are executing different tasks ( static
mapping)
Work-Pool Model
Dynamic mapping tasks to processes
Work-Manage Model
One process generates and distributes work to others
Producer-Consumer Model
Data is passed through several processes, each perform a
different task just like pipeline
Our Case
•
Scale to thousands of sensor nodes,
memory is a big issue.
•
The tasks are high correlated, therefore
it is hard to partition.
•
Data parallel model is desirable.
•
In our case, to partition sensor nodes based on
region
Work Decomposition
•
Region based partition ( histogram)
•
Maximize data locality
•
Minimize volume of data exchange
•
Minimize frequency of interactions
X axis
Partition
XY 2D
Partition
Y axis
Partition
Overlapping Communication
and computation
•
Initiate communication ( MPI_Isend/MPI_Irecv)
•
Calculate inner values
•
Finish communication ( MPI_Waitall)
•
Calculate boundary values
One ghost cell
N-ghost zone
Battery Model
•
Linear Model:
•
U: current capacity U’: previous capacity i(t): instantaneous current
t0 +t d
U = U' + ∫
t=t0
i(t) dt
Non-Linear Model: [2]
•
•
T: discharged time; K, h: constants depending on cell design and chemical
architecture of battery; Va: average value of the cell voltage during the
discharge; I: discharge current;
T =K I
-h
1 -h
E = Va • I • T = Va • K • I
Battery Model (cont.)
•
Pulsed Discharge Model [1]
•
Relaxation phenomena
If battery is allowed to relax, lost capacity can be recovered.
•
•
Binary Pulsed Discharge Model
•
•
- Based on Binary Markov Chain
Generalized Pulsed Discharge Model
Energy Consumption Model
•
Some experiment data
[3][4]
Energy Consumption Model (cont.)
•
Simple energy model
•
•
•
•
Energy spent in transmission = (edda + et )b
Energy spent in reception =erb
Energy spent sensing =esb
Energy spent in computation ( leakage current model)[6]
•
•
•
•
•
•
•
•
•
•
Depends on the total capacitance switched and the number of cycles the
program
takes
ed is the energy dissipated per bit per m2 (amplifier)
et is the energy spent by transmission circuitry per bit
er is the energy spent by reception circuitry per bit
es is the energy spent sensing per bit
b is number of bits to transmit or receive
t is the time
α is a constant 2(will use the common values of α=2 and α=4)
d is the transmitting distance
PHY Layer Abstraction
•
Radio Propagation Model: [5]
•
Outdoor propagation Model:
•
Free Space Model:
Pr (d) =
2
PfGfGT λ
2
2
( 4π) d L
•
Pf : transmitted signal power
•
Pr : receive signal power
•
Gt Gr : antenna gains of transmitter and receiver respectively
•
L is system loss ( L > 1)
•
λ : wavelength
•
d : distance from transmitter
PHY Layer Abstraction (cont.)
•
Ground Reflection (Two-Ray)
pt GtGrht2hr2
Pr (d) =
d4L
•
Faster power loss as distance increase
•
ht hr are the height of the transmit and receive antennas
respectively
•
Cross-over distance dc
dc = ( 4 πhthr ) / λ
•
d < dc free space use else two-ray model
PHY Layer Abstraction (cont.)
•
Indoor Propagation Model:
•
•
Shadowing model.
Underwater Acoustic Propagation Model:
Mac Layer Abstraction
•
Typical protocols
•
•
Contention-based protocols
•
IEEE 802.11
•
PAMAS
•
S-MAC
TDMA
MAC Layer Abstraction (cont.)
•
Take S – Mac as an example
•
Energy consumption abstraction
•
Three states: receive, transmit, sleep
•
Broadcast:
•
Cost = msend × size + bsend + ∑(mrec v × size + brec v)
n∈S
Point-to-point: RTS/CTS/ACK
•
Sender
•
receiver
Cost = bsendctl + brecvctl + msend × size + bsend + brecvctl
Cost = brecvctl + bsendctl + mrecv × size + brecv + bsendctl
Mac Layer Abstraction (cont.)
•
Latency computation
•
Carrier sense delay
•
Backoff delay
•
Transmission delay
•
Propagation delay
•
Sleep delay
•
Queuing delay
•
Processing delay
Queuing Model
•
Discrete time queuing models
•
Models
•
Depending on arrival and departure processes
•
Geo/Geo/1/ and Geo/Geo/1/N
•
M/M/1
Queuing Model (cont.)
•
•
•
•
Geo/Geo/1 and Geo/Geo/1/N queue model
Geo/Geo/1
Geo/Geo/1/N
M/M/1 queue model
M/M/1
Geo/Geo/1
Network Layer Abstraction
•
•
Protocols:
•
Flooding -SPIN
•
Gradient – Directed Diffusion
•
Clustering - LEACH
•
Geographic -GEAR
•
Energy aware - SPIN-EC, LEACH, GEAR
Highlight
•
Route setting up overhead
•
Communication Pattern (unicast, multicast,
broadcast)
•
Packet transmitted
Middleware Abstraction
•
Agent-based architecture
•
Client/Server based architecture
•
In-network processing scheme
•
Query system
APP Layer Abstraction
•
Traffic Generation Model
•
CBT
•
•
•
Constant bit rate transfer : Home surveillance,
parking
lot sensor network application
Target detection and tracking
•
Moving target Models:
•
1. Given start point, moving in straight line at
constant speed. When arriving the edge, change the
direction by “bouncing” off the virtual wall.
•
2. Moving target is given an initial location along
with a series of waypoints
Topology Generation Model
•
•
GT-ITM, Tiers Model
•
Concern with the hierarchical properties of
•
Internet
Inet, PLRG
Connectivity properties
•
•
BRITE
•
Hierarchical properties, degree
distributions,
•
Connectivity properties
Large Data Set Visualization
•
Large memory and high speed requirement for
sensor networks visualization
•
Hierarchy sensor data rendering
•
•
Higher level – region based information
•
Energy consumption map, Traffic distribution map
•
simulation data report based on the whole area etc.
Lower Level – portion of sensor nodes information
•
Detailed information of sensor nodes
•
Graph tools
References
•
[1] C.F.Chiasserini and R.R.Rao, “Pulsed Battery Discharge in Communication
Devices”, MobiCOM , 1999.
•
[2] H.D.Linden, “Handbook of Batteries”, 2nd ED. McGrawHill, 1995
•
[3] Vijay Raghunathan, Curt Schurgers, Sung Park, Mani B. Srivastava. “Energy –
aware wireless micro sensor networks." IEEE Signal Processing Magazine, Volume:
19 Issue: 2, Mar 2002.
•
[4] A. Mainwaring, D. Culler, J. Polastre, R. Szewczyk, J. Anderson. "Wireless
Sensor Networks for Habitat Monitoring," Proceedings of the First ACM
International Workshop on Wireless Sensor Networks and Applications (WSNA
'02), Georgia, September 2002.
•
[5] T.S.Rappaport, “Wireless communications principles and practice”, Prentice
Hall 2002
•
[6] A.sinha and A. Chandrakasan, “Energy Aware Software”, Proceedings of the
13th International Conference on VLSI Design, 2000