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.
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
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
13
14
15
16
17
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
14
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.