Dynamic Sensor Networks ProjectReview of UCLA’s Activities

Mani Srivastava

UCLA

This Review

Two Separate Projects at UCLA

DSN (Subcontract from USC/ISI) Sole PI: Mani Srivastava Focus: networking

Low-power/low-latency link, MAC, and routing GPS-less discovery & distribution of location Capability and attribute based addressing and connectivity Sensor network simulation and emulation Protocols for GPS-synchronized communication subsystem

Sensorware (Subcontract from Rockwell Science Center) Two PIs: Mani Srivastava, Miodrag Potkonjak Focus: distributed middleware services

Network coverage service for sensor networks Sensor control scripts: light-weight, mobile, platform independent, secure Spatial addressing and communications, timing synchronization Implementation on Rockwell’s nodes

This Review:Selected Recent Activities

I. Update on Sensorsim

II. GPS-less ad hoc localization

III. Low-latency packet forwarding

IV. Dynamic assignment of MAC addresses

V. Low-power multihop routing

I. SensorSim Update

Simulation framework for modeling sensor networks built on top of ns-2 sensing channel and sensor models scenario generation tool (SensorViz) light weight protocol stacks hybrid simulation battery/power model (further model development under PAC/C)

Alpha release at http://nesl.ee.ucla.edu/sensorsim/

Selected features being migrated to official ns-2 through Deborah’s group

In use by external groups such as U. Maryland

SensorSim Architecture

Target Node

Sensor Layer

Physical Layer

Sensor Stack

Sensor Channel

Target Application

Wireless Channel

User ApplicationUser Node

Network Layer

Physical Layer

Network Stack

MAC Layer

Sensor Layer

Physical Layer

Sensor Stack3

Sensor Layer

Physical Layer

Sensor Stack2

Functional Model Sensor Node

SensorWarePower Model

Battery Model

Radio

CPU

ADC(Sensor)

Wireless Channel

Sensor Channel1

Network Layer

MAC Layer

Physical Layer

Network Stack

Sensor Layer

Physical Layer

Sensor Stack1

Sensor App

Sensor Channel2

Sensor Channel3Sensor Node

Sensor NodeSensor

Node

WirelessChannel

SensorChannel

UserNode

TargetNode

Scenario Generation & Visualization

SensorViz Features: Diverse scenario generation

Node deployment patterns Target trajectories Sensor characteristics Node attributes

Can be slaved to a running simulation (SensorSim)

Monitor real sensor nodes

Planned: XML output

Read in SITEX format scenarios

II. Dynamic Location Discovery

Discovery of absolute and relative location important location attribute based naming and addressing of nodes geographical routing tracking of moving phenomena (targets)

GPS not enough not work everywhere due to requirement of LOS to satellites (trees, indoors) not on all nodes (costly, large, power-hungry)

No infrastructure in sensor networks precludes solutions based on 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)

Every node contributes to process Small fraction of initial beacons Distributed

Robust Energy Efficient

Inter-node ranging uses RSSI Ultrasound

Integrated with routing messages Location discovery almost free!

Adapts to channel conditions via a joint estimation of location & channel parameters

Iterative Multilateration

Collaborative Multilateration

Centralized vs. Distributed Localization

Distributed Pros More robust to node failure

Less traffic => less power

Better handling of local environment variations Speed of ultrasound Radio path loss

Rapid updates upon topology changes

No time synch. required

Centralized Cons A route to a central point

Time synchronization is required

High latencies for location updates

Central node requires preplanning

More traffic => higher power consumption

Basic Multilateration

22 )()(),( aiaiiaaai yyxxDyxf

)( 2)0( OyxfeD yixiiiia ez

zAAA TT 1)(

Repeat until δ becomes 0

a

1

2

3

yaa

xaa

yy

xx

Linearize using Taylor Expansion

Residual of measured and estimated distance

Linear form

MMSE Solution

Iterative Multilateration

Basic multilateration can be applied iteratively across the network

Step 1

Step 2

Step 3

Node vs. Initial Beacon Densities

% Initial BeaconsTotal Nodes

% Resolved Nodes

Uniformly distributed deployment in a field 100x100. Node range = 10.

Challenges

Iterative multilateration may stall if the network is very sparse the percentage of beacons is very low terrain obstacles

If the network is large, error will accumulate from iterative multilateration

Collaborative Multilateration

a b

1

2

3

4

)( 2)0( OyyxxyyxxfeD bbbbbaaaiikik

)( 2)0( OyxfeD yixiiiia

Uses location information over multiple hopsLinearize residuals over 2 types of edges:

Both equations have the formFollow the same solution procedure as basic multilateration

ez

Collaborative Multilateration (contd.)

b

b

a

a

y

x

y

x

ababbaba

bb

bb

aa

aa

yyxx

yx

yx

yx

yx

A

44

33

22

11

00

00

00

00

)0(

)0(44

)0(33

)0(22

)0(11

abab

bb

bb

aa

aa

fD

fD

fD

fD

fD

z

zAAA TT 1)( Execute

bbaa yxyx ,,,Update

Until 0

Collaborative Sub-trees

Necessary conditions:

Each unknown node must have at least 3 participating neighbors

A participating node is either a beacon node or an unknown node connected to 3 participating nodes

18 equations16 unknowns

Over-determined!

Distributed Ad-Hoc Operation

Location estimation takes place at the scope of a neighborhood

Collaborative sub-trees can zoom in and out to Form a well-determined system Avoid degenerate cases Avoid obstacles Reduce Error Propagation

Error can be further reduced if computation takes place at a central point.

Platform Characterization

Ultrasound TDoA RSSI in football field

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

Implementation Status

Initial prototype competed: Medusa

Design of Medusa II(using non-SensIT resources) Longer range ultrasound (15-20m) Radio Power Control & RSSI circuitry More computation (Atmel THUMB)

Goal: Hybrid Radio-acoustical localization use radio for long-range when ultrasound is unable to find a neighbor Medusa used standalone or as a location coprocessor to sensor nodes

AtmelAVR

RFMRadio

UltrasoundReceiver

Ultrasound Transmitter

INT

III. Low Latency Packet Forwarding

Problem: node often simply relays packets in multihop network NS-2 simulation: 1000x1000 terrain, 30 nodes, DSR, CBR traffic from

random SRC and DEST

Traditional approach: packets sent from radio to main CPU long latency (serial bus), power hungry (main CPU woken up)

Action % of received packets

ACCEPT 34.300

FORWARD 65.567

DROP 0.133

CommunicationSubsystem

RadioModem

GPS

MicroController

Rest of the Node

CPU Sensor

MultihopPacket

Traditional Approach

Our Packet Forwarding Architecture

Our approach: Embedded Packet Processor in the Radio exploit programmable microcontrollers in the radios to handle common cases of packet

routing can also do operations such as combining of packets with redundant information

Packets are redirected as low in the protocol stack as possible reduced latency (and, incidentally, also reduced power…)

Key challenge: how to do it so that every new routing protocol will not require a new radio firmware?

CommunicationSubsystem

RadioModem

GPS

MicroController

Rest of the Node

CPU Sensor

MultihopPacket

Application-defined Routing Framework for Radio Firmware

Packet-classifier and packet-modifier driven by application defined matching rules and actions Matching rules: and/or expressions using =, <, >, range operators on arbitrary packet

fields (offset, length) Actions: accept, forward, drop, field increment/decrement etc.

Rules and actions operate on arbitrary packet fields (any layer) fields specified as (offset, length)

For complex cases packet sent to the main processor only simple, common cases handled at the radio

Expressiveness: implemented the following as test cases Node ID-based addressing and routing (DSR-like) Geographical point-cast (send to a circular area specified as destination)

CommunicationSubsystem

RadioModem

GPS

MicroController

Packet Classifier

Packet Modifier

Application-DefinedMatching Rules

& Actions

Proof-of-concept Implementation

Rockwell nodes with a prototype radio

Prototype radio because Rockwell’s radio firmware is not open

RFM radio with FPSLIC (microcontroller with FPGA)

Mixed software/FPGA implementation

FPGA used to accelerate packet matching/modification

Performance Analysis

Difference in packet DELAY between the traditional approach and our approach:

Serial port delay is the dominant factor

FWMCUFWCPUFWSRACMCUACCPUACFNFdiff DDDDDDDD Pr2Pr

Packet Distribution

Delay Overhead for ACCEPT Delay Overhead for FWDSerial port delay

Measurements

FWACdiffD Pr281.68Pr0285.2 Given the measurements the difference in delay is:

When PrFW > 3% PrAC the traditional approach

delay is more than our approachFor our simulation traffic data Ddiff= 44ms

Parameter Value (ms)

DMCUAC 4.182

DMCUFW 4.894

DSR 36.532

DCPUAC 0.111

DCPUFW 0.125

IV. Dynamic MAC Address Allocation

Wireless spectrum is broadcast medium MAC addresses are required

In wireless sensor networks, data size is small Unique MAC address would present too much overhead

Employ spatial address reuse (similar to reuse in cellular systems)

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

Network Density Parameter

1. Taking only bulk nodes eliminates edge effects Virtually extends network size to infinity (so independent of L) Suggests that only close proximity is critical

2. Characterization of node density Connectivity is key Average degree

2RDav

Address

Frequency

All nodes Bulk nodes

= 10Dav = 0.01 nodes/m2

N = 125 N = 250 N = 1000

2RBulk nodes

L

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

Implementation Issues

Functionality Dynamic address assignment Address resolution (mapping)

Address Resolution & Assignment Protocol (ARAP)

Unique receiver ID is mapped into MAC address without being included into the packet

The own MAC address is modified by the ARAP

Link Layer

MAC

PHY

APP/NETW

ARAP(~ARP)

Rx-ID

Rx-Addr

Own Address

APP DataDestRx-AddrTx-Addr

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

V. Low-power Multihop Routing

ATHENA: Adaptive Transmission-power Heuristic and Energy-optimizing ad-hoc Network routing Algorithm adapts transmission power to find power-optimal multi-hop paths. uses alternate routes to maximizes lifetime of the network.

Recent work from Maryland offers the same benefit: combine alternate routes with tx power control but is not easy to implement (cost of algorithm vs. convergence)

E(x) =energy to send a packet over distance xE(a) + E(b) < E(c)

ab

c

Principle in adapting tx power

Constant Tx Power Case

Constant power case (for comparison)

On-demand algorithm using request and reply messages resemble DSR, AODV: source path carried to avoid loops siblings not visited

210

3

45

Source path for nodes 3, 4

Parent child

Propagation of request

Nodes 3, 4 are "siblings"

Adaptable Tx Case

Increased # of requests and replies if destination reached, algorithm not over siblings have to be asked

Three main rules to prevent explosion of requests one of them produces suboptimal routes, but simulations show the cost

savings are worth it

21

3

45

Find route from 1 to 5

Node 2 can reachnodes 3,4,and 5

Propagation of requestOptimal path

Node 3 has to send the request to its sibling,node 4.

Example

0 100 200 300 400 5000

100

200

300

400

500

12 3 4 5 6 7 8 9

10

0 100 200 300 400 5000

100

200

300

400

500

12 3 4 5 6 7 8 9

10

Constant tx power case (level 8):6 req, 8 replies2.48*10-4Joules/packet

Adaptablet tx power case:8 req, 30 replies6.638*10-5Joules/packet

10 tx levels [10m - 250m] packet = 125bytessignal attenuation ~ 1/d3

25-node Network

0 100 200 300 400 500 600 7000

100

200

300

400

500

600

700

12

3

4

5

6

7

8

9

1011

12

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18

19

20

21

22

23

24

25

0 50 100 150 200 250 3000

500

1000

1500

2000

2500

3000

3500

4000

# of source-dest pairs

# of

requ

ests

and

repli

es

requests

replies

25-node Network

0 50 100 150 200 250 3000

500

1000

1500

2000

2500

3000

# of source-dest pairs

# of

requ

ests

and

repl

ies

0 100 200 300 400 500 600 7000

100

200

300

400

500

600

700

12

3

4

5

6

7

8

9

1011

12

13

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15

16

17

18

19

20

21

22

23

24

25

requests

replies

Average Gains

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

aver

age

path

pow

er

constant txpower

adaptable txpower

0

0.5

1

1.5

2

2.5

aver

age

path

pow

er

0

0.02

0.04

0.06

0.08

0.1

0.12

aver

age

path

pow

er

constant txpower

adaptable txpower

0

0.5

1

1.5

2

2.5

3

aver

age

path

pow

er

signal attenuation: ~ 1/d3 ~ 1/d4

25-node network

50-node network

signal attenuation: ~ 1/d3 ~ 1/d4

Using Alternate Paths

Self_Energy, Next-Hop_Energy should affect path cost.

Each time the node’s energy changes 10%: notify neighbors recalculate best paths

Heuristic used: Remaining_Energyself-x1 Power_Costnext_hop +

Remaining_Energynext_hop-x1 Power_Costdestination

Simulations show best x1 = 2

30% more packets routed than vanilla ATHENA

96% of the packets routed in the optimal case.

Recent Accomplishment Summary

I. Sensorsim

II. GPS-less ad hoc localization

III. Low-latency packet forwarding

IV. Dynamic assignment of MAC addresses

V. Low-power multihop routing