Sensor

Network Si

mulation

( Sensi

m)

Components

Analysis

Sensor

Network Si

mulation

( Sensi

m)

Components

Analysis

AICIP Research Group PresentationAICIP Research Group Presentation

Yang Liu Yang Liu

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

Y axis Partition

XY 2D 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

• 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

t=t

0

0

dt i(t) + U' = Udt

-hIK = T h-1aa I•K•V=T•I•V = E

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:

• 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

Ld)π4(

λGGP=)d(P 22

2Tff

r

PHY Layer Abstraction (cont.)

• Ground Reflection (Two-Ray)

• Faster power loss as distance increase

• ht hr are the height of the transmit and receive antennas respectively

• Cross-over distance dc

• d < dc free space use else two-ray model

Ld

hhGGp=)d(P 4

2r

2trtt

r

λ/)hhπ4(=d rtc

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:

• Point-to-point: RTS/CTS/ACK• Sender

• receiver

)b+size×m(+b+size×m=Cost recvS∈n

recvsendsend ∑

recvctlsendsendrecvctlsendctl b+b+size×m+b+b=Cost

sendctlrecvrecvsendctlrecvctl b+b+size×m+b+b=Cost

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 trackingMoving 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