CSCI 641 – EENG 641 1 CSCI-641/EENG-641 Computer Architecture Khurram Kazi.
Introduction to Physical Layer Properties, MAC and the IEEE 802.15.4 Lecture 6 September 26, 2006...
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Transcript of Introduction to Physical Layer Properties, MAC and the IEEE 802.15.4 Lecture 6 September 26, 2006...
Introduction to Physical Layer Properties, MAC and the IEEE 802.15.4
Lecture 6 September 26, 2006
EENG 460a / CPSC 436 / ENAS 960 Networked Embedded Systems &
Sensor Networks
Andreas [email protected]
Office: AKW 212Tel 432-1275
Course Websitehttp://www.eng.yale.edu/enalab/courses/2006f/eeng460a
Medium Access Control
Control access to the shared medium (radio channel) avoid interference between transmissions mitigate effects of collisions (retransmit)
Approaches contention-based: no coordination schedule-based: central authority (access point)
Collision-based MAC protocols
ALOHA : packet radio networks send when ready 18-35% channel utilization
CSMA (Carrier Sense Multiple Access): “listen before talk” 50-80% channel utilization
CSMA/CA: Collision Avoidance
MACA: Request To Send Clear To Send DATA
MACAW (Wireless) additional ACK
Tim
e
A B C
cs
DATA
RTSCTS
Blo
cked
ACK
Hidden terminal problem
Tim
e
A B C
cs
DATA
cs
DATA
Carrier sense at sender
may not preventcollision at receiver
Exposed terminal problem
A B C D
Tim
ecs
DATA
RTSCTSParallel CSMA transfers
are synchronized byCSMA/CA
Collision avoidance canbe too restrictive!
Blo
cked
ACK
IEEE 802.11
Operation infrastructure mode (access point) ad-hoc mode
Power save mechanism; not for multi-hop networks
Protocol carrier sense collision avoidance (optional)
IEEE 802.11
Network Allocation Vector (NAV) collision avoidance overhearing avoidance: other nodes may sleep
RTS DATA
Contention Window
Sender
Receiver
OthersNAV(RTS)NAV(CTS)
SIFS
SIFS
SIFS DIFS
DIFS
CTS ACK
Schedule-based MAC protocols
Communication is scheduled in advance no contention no overhearing support for delay-bound traffic (voice)
Time-Division Multiple Access time is divided into slotted frames access point broadcasts schedule coordination between cells required
TDMA
Typical WLAN setup no direct communication between nodes access point broadcast Traffic Control (TC) map (new) nodes signal needs in Contention Period (CP)
TC CP
Frame n Frame n+2Frame n+1
downlink uplink
Why is MAC critical to Wireless Sensor Networks
Power, power & powerHandle scarce resources CPU: 1 – 10 MHz memory: 2 – 4 KB RAM radio: ~100 Kbps energy: small batteries
Unattended operation plug & play, robustness long lifetime
Transceiver Processor SensorsLED
0
5
10
15
20
25
Energy consumption (mW)
Tra
nsm
it
Rec
eive
Sle
ep
5 M
Hz
1 M
Hz
Sta
ndby
LED
Com
pass
Acc
eler
omet
er
Ligh
t
[Hoesel:2004]
Friss Free Space Propagation Model22
44
d
cGG
dGG
P
PRTRT
T
R
er transmittandreceiver between distance -
light of speed -
metersin h wavelengt-
antenna receiving and ing transmittfor the gainspower theare and
(in watts) antennas ing transmittand receiving at the espower valu - and
d
c
GG
PP
RT
RT
Same formula in dB path loss form (with Gain constants filled in):
kmMHzB dfdBL 1010 log20log2044.32)( How much is the range for a 0dBm transmitter 2.4 GHz band transmitterand pathloss of 92dBm?
Friss Free Space Propagation Model22
44
d
cGG
dGG
P
PRTRT
T
R
er transmittandreceiver between distance -
light of speed -
metersin h wavelengt-
antenna receiving and ing transmittfor the gainspower theare and
(in watts) antennas ing transmittand receiving at the espower valu - and
d
c
GG
PP
RT
RT
Same formula in dB path loss form:
kmMHzB dfdBL 1010 log20log2044.32)( How much is the range for a 0dBm transmitter 2.4 GHz band transmitterand pathloss of 92dBm?
Highly idealized model. It assumes:• Free space, Isotropic antennas• Perfect power match & no interference• Represent the theoretical max transmission range
A more realistic model: Log-Normal Shadowing Model
XdnfndBL kmMHzB 1010 log10log1044.32)(
• Model typically derived from measurements
dB)(in deviation
standard with dB)(in r.vGaussian mean -zero is
X
• Statistically describes random shadowing effects• values of n and σ are computed from measured data using linear regression
• Log normal model found to be valid in indoor environments!!!
IEEE 802.15.4 Radio Characteristics
Power output• The standard does not specify a power output limit.
• Devices should be able to transmit -3dBmo In US 1Watt limit in Europe 10mW for 2.4GHz band
Receiver should be able to decode a packet with receive power of• -85dBm in 2.4GHz and -92dBm in the lower frequency
bands
What does that mean in terms of range?
Going from Watts to dBm
1mW
mW)P(in 10logdBm)P(in
+20dBm=100mW
+10dBm=10mW
+7dBm=5mW
+6dBm = 4mW
+4dBm=2.5mW
+3dBm=2mW
0dBm=1mW
-3dBm=.5mW
-10dBm=.1mW
Frequency Bands and Data Rates
In 2.4GHz band 62.5 ksymbols/second• 1 symbol is 4 bits• 1 symbol is encoded into a 32-bit pseudorandom sequence the chip
chip rate = 62.5 x 32 = 2000 kchips/sRaw data rate = Symbol rate * chips per symbol = 62.5 * 4 = 250kb/s• In 868/915 MHz bands
1 bit symbol (0 or 1) is represented by a 15-chip sequence
Physical Layer Transmission Process
Binary Data fromPPDU
Bit to Symbol Conversion
O-QPSKModulator
Symbol to Chip Conversion
RF Signal
Propagation Mechanisms in Space with Objects
Reflection • Radio wave impinges on an object >> λ (30 cm @1 GHz)• Earth surface, walls, buildings, atmospheric layers
Diffraction• Radio path is obstructed by an impenetrable surface with sharp
irregularities (edges)• Secondary waves “bend” arounf the obstacle• Explains how RF energy can travel without LOS
Scattering• When medium has large number of objects < λ (30cm @1 GHz)• Similar principles as diffraction, energy reradiated in many directions• Rough surfaces, small objects (e.g foliage, lamp posts, street signs)
Other: Fading and multipath
An Experiment at Yale
XYZ sensor node designed at Yale (http://www.eng.yale.edu/enalab/XYZ)
CC2420 wireless radio from Chipcon
2.4 GHz IEEE 802.5.14/Zigbee-ready RF transceiver
DSSS modem with 9 dB spreading gain
Effective data rate: 250 Kbps
8 discrete power levels: 0, -1, -3, -5 , -7, -10, -15 and -25 dBm
Power consumption: 29mW – 52mW
Monopole antenna with length equal to 1.1inch.
EWSN 2006 February 15th Dimitrios Lymberopoulos
Received Signal Strength Indicator (RSSI)
P = RSSI + RSSIOFFSET [dBm]
The power P at the input RF pins can be obtained directly from RSSI:
RSSI is an 8-bit value computed by the radio over 8 symbols (128μs)
RSSIOFFSET is determined experimentally based on the front-end gain. It is equal to -45dbm for the CC2420 radio
Sources of RSSI Variability
Intrinsic
Radio transmitter and receiver calibration
Extrinsic
Antenna orientation
Multipath, Fading, Shadowing
EWSN 2006 February 15th Dimitrios Lymberopoulos
Path Loss Prediction Model
Log-normal shadowing signal propagation model:
RSSI(d) = PT – PL(d0) – 10ηlog10(d/d0) + Xσ
0 5 10 15 20 25-45
-40
-35
-30
-25
-20
Distance(feet)
RS
SI
(db
m)
Averaged RSSI valueslog-fit
RSSI(d) is the RSSI value recorded at distance d
PT is the transmission power
PL(d0) is the path loss for a reference distance d0
η is the path loss exponent
Xσ is a gaussian random variable with zero mean and σ2 variance
Model verification using data from a basketball court
EWSN 2006 February 15th Dimitrios Lymberopoulos
Radio Calibration
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
Receiver
1.31ftTransmitter
For each location and orientation 20 packets were sent @ -15dBm
EWSN 2006 February 15th Dimitrios Lymberopoulos
Tra
nsm
itter
Radio Calibration
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
Receiver
1.31ft
For each location and orientation 20 packets were sent @ -15dBm
EWSN 2006 February 15th Dimitrios Lymberopoulos
Radio Calibration
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
Receiver
1.31ft
Transmitter
For each location and orientation 20 packets were sent @ -15dBm
EWSN 2006 February 15th Dimitrios Lymberopoulos
Radio Calibration
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
Receiver
1.31ft
Tra
nsm
itter
For each location and orientation 20 packets were sent @ -15dBm
EWSN 2006 February 15th Dimitrios Lymberopoulos
Radio Calibration
Experiment in an empty room
TX calibration: 9 different transmitters
RX calibration: 6 different receivers
1 2 3 4 5 6 7 8 90
5
10
15
20
25
30
350 Degrees
Transmitter ID
RSSI(
dbm
)
1 2 3 4 5 6 7 8 90
5
10
15
20
25
30
3590 Degrees
Transmitter ID
RSSI(
dbm
)
1 2 3 4 5 6 7 8 90
5
10
15
20
25
30
35180 Degrees
Transmitter ID
RSSI(
dbm
)
1 2 3 4 5 6 7 8 90
5
10
15
20
25
30
35270 Degrees
Transmitter ID
RSSI(
dbm
)
1 2 3 4 50
5
10
15
20
25
300 Degrees
Receiver ID
RSSI(
dbm
)
1 2 3 4 50
5
10
15
20
25
3090 Degrees
Receiver ID
RSSI(
dbm
)
1 2 3 4 50
5
10
15
20
25
30180 Degrees
Receiver ID
RSSI(
dbm
)
1 2 3 4 50
5
10
15
20
25
30270 Degrees
Receiver ID
RSSI(
dbm
)
TX Standard Deviation: 2.24dBm RX Standard Deviation: 1.86dBm
EWSN 2006 February 15th Dimitrios Lymberopoulos
Antenna Characterization
Side View
8ft6.5ft
3.5ft1.25ft
Top View
2ft
2ft
2ft
: measurement point
EWSN 2006 February 15th Dimitrios Lymberopoulos
Experiment took place in a basketball court
Minimize multipath effect
At each measurement point 20 packets @ -15dBm were received
Antenna Characterization
0 2 4 6 8 10 12 14 16-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
Distance (ft)
RS
SI
(db
m)
Optimal AntennaSuboptimal Antenna
Optimal antenna length-1.1inch
Random RSSI values due to multipath
Large communication range
Suboptimal antenna with 2.9inch length
EWSN 2006 February 15th Dimitrios Lymberopoulos
Antenna Characterization
5 10 15 20 25 30-48
-46
-44
-42
-40
-38
-36
-34
Distance(feet)
RS
SI
(db
m)
04590135180225270315
0 5 10 15 20 25 30-48
-46
-44
-42
-40
-38
-36
-34
-32
-30
Distance(feet)
RS
SI
(db
m)
04590135180225270315
0 5 10 15 20 25-45
-40
-35
-30
-25
-20
Distance(feet)
RS
SI
(db
m)
04590135180225270315
Similar distances (<1ft difference) can produce very different RSSI values (even up to 11dBm)
Very different distances ( even >18ft) can produce the same RSSI values
EWSN 2006 February 15th Dimitrios Lymberopoulos
1.25ft 3.5ft 6.5ft
Antenna Characterization
0 5 10 15 20 25-45
-40
-35
-30
-25
-20
Distance(feet)
RS
SI
(db
m)
6.5ft3.5ft1.5ft
0 5 10 15 20 25-50
-45
-40
-35
-30
-25
Distance(feet)
RS
SI
(db
m)
6.5ft3.5ft1.5ft
Best antenna orientation Worst antenna orientation
EWSN 2006 February 15th Dimitrios Lymberopoulos
Antenna orientation effect
For a given height of the receiver very different RSSI values are recorded for different antenna orientations
Antenna Radiation Pattern
Side View Top View
Communication rangeSymmetric Region Antenna orientation
independent regions
Communication range
Antenna Effects in Indoor Environments
The basketball court experiment was performed inside our lab
We focused on the best antenna orientation
0 2 4 6 8 10 12 14 16-50
-45
-40
-35
-30
-25
-20
Distance (ft)
RS
SI
(db
m)
6.17ft 5.65ft 4.6ft 1.25ft
EWSN 2006 February 15th Dimitrios Lymberopoulos
Large Scale Indoors Experiment
40 nodes were placed on the testbed (15ft (W) x 20ft(L) x 10ft(H))
Each node transmitted 10 packets at each one of the 8 power levels. The recorded RSSI values were transmitted to a base station for logging.
01
23
45
6
0
1
2
3
4
50
0.5
1
1.5
2
2.5
7
8
9
6
27
10
5
28
12
X coordinate
42
33
4
30
35
29
11
13
14
3
34
32
37
36
17
31
Connectivity at Power Level 7
41
25
15
1
2
3839
18
26
24
16
Y coordinate
40
19
22
23
20
21
Z c
oo
rdin
ate
01
23
45
6
0
1
2
3
4
50
0.5
1
1.5
2
2.5
7
8
9
6
27
10
5
28
12
X coordinate
42
33
4
30
35
29
11
13
14
3
34
32
37
36
17
31
Connectivity at Power Level 4
41
25
15
1
2
38
39
18
26
24
16
Y coordinate
40
19
22
23
20
21
Z c
oo
rdin
ate
Placement and Connectivity
EWSN 2006 February 15th Dimitrios Lymberopoulos
Large Scale Indoors Experiment
RSSI does not change linearly with the log of the distance
Multipath
3-D antenna orientation
EWSN 2006 February 15th Dimitrios Lymberopoulos
Maximum (0dBm) Medium (-5dBm) Low (-15dbm)
Link Asymmetry
Asymmetric link between nodes A and B
RSSI(A) ≠ RSSI(B)
1 2 3 4 5 6 7 820
22
24
26
28
30
32
34
36
Power Level (1= Maximum)
Per
cen
tag
e o
f O
ne-
Way
Lin
ks
One way Links
1 2 3 4 5 6 7 820
25
30
35
40
45
50
55
Power Level (1 = Maximum)
Per
cen
tag
e o
f as
sym
etri
c li
nks
>=2 >=3 >=4 >=5 >=6
One way links Asymmetric links
EWSN 2006 February 15th Dimitrios Lymberopoulos
What else can we do?
More than 30% of the links are affected by human presence or motion
Detection of:
Human presence
Human motion
EWSN 2006 February 15th Dimitrios Lymberopoulos
Experiment Lessons
3-D space is very different than 2-D space
Antenna orientation effects are dominant in 3-D deployments
3-D deployments are a more realistic for evaluating RSSI localization methods
RSSI distance prediction in 3-D deployments is almost impossible
Ordering of the RSSI values is not helpful
Even if antenna orientation is known!
Probabilistic approaches
A probabilistic model of RSSI exists for the symmetric region of the antenna
Generalizing this model to 3-D deployments is extremely difficult if not impossible.
Radio calibration has minimal effect on localization
EWSN 2006 February 15th Dimitrios Lymberopoulos
The IEEE 802.15.4 MAC Protocol
Based on an IEEE standard for WPAN• Goal: Ultra-low cost, low power radios• Support multiple configurations (e.g point-to-point, groups,
ad-hoc etc)• CSMA-CA based protocol
o Each packet can be individually acknowledged
Key features• Three types of node functionalities
o PAN Coordinator, Coordinator and Device
• Two device types o FFD – Full Function Deviceo RFD – Reduced Function Device
Frequencies and Data Rates
BAND COVERAGE DATA RATE # OF CHANNEL(S)
2.4 GHz ISM Worldwide 250 kbps 16
868 MHz Europe 20 kbps 1
915 MHz ISM Americas 40 kbps 10
Now Back to IEEE 802.15.4 MAC
MAC supports 2 topology setups: star and peer-to-peer Star topology supports beacon and no-beacon structure
• All communication done through PAN coordinator
Start Topology: The PAN Coordinator
Any FFD may establish its own network by becoming the PAN coordinator
After formation, STAR networks operate independently from neighboring networks
PAN coordinate starts sending beacons• Other devices can associate with the network
by sending an association request
Peer-to-Peer Topology
Any FFD can communicate with any other FFD, can use multihop communication• i.e this is ad-hoc networking
RFDs can participate only as peripherals• Do not have the capabilities of forwarding packets
Each device responsible for proactively searching for other devices• Once a device is found, then they can exchange
information about what devices form
Star: Optional Beacon Structure
Beacon packet transmitted by PAN Coordinator to help Synchronization of network devices. It includes:Network identifier, beacon periodicity and superframe structure
Generic Superframe Structure
GTS: Guaranteed timeSlots assigned by PANcoordinator
Star Network: Communicating from a Coordinator
Beacon packet indicates that thereis data pending for a network device
Device sends request on a data slot
Network device has to ask coordinator if there is data pending.If there is no data pending the Coordinator will respond with a zeroLength data packet
Peer-to-Peer Data Transfer
Peer-to-peer data transfer governed by the network layer – not specified by the standard
Four types of frames the standard can use• Beacon frame – only needed by a coordinator
• Data frame – used for all data transfers
• ACK frame – confirm successful frame reception
• A MAC Command Frame – MAC peer entity controltransfers
Radio Energy Model: the Deeper Story….
Wireless communication subsystem consists of three components with substantially different characteristics
Their relative importance depends on the transmission range of the radio
Tx: Sender Rx: Receiver
ChannelIncominginformation
Outgoinginformation
TxelecE Rx
elecERFETransmit
electronicsReceive
electronicsPower
amplifier
Energy Implication
Active transceiver power consumption more related to symbol rate rather than raw data rate
To minimize power consumption:• Minimize Ton - maximize data rate• Also minimize Ion by minimizing symbol rate
Conclusion: Multilevel or M-ary signalling should be employed in the physical layer of sensor networks• i.e need to send more than 1-bit per symbol
Energy-efficient MAC protocols
WSN-specific protocols starting from 2000 (1 paper) exponential growth (2004, 16+ papers)
Classification (up to May 2004, 20 papers) the number of channels used the degree of organization between nodes the way in which a node is notified of an incoming msg
Protocol classification
Protocol Channels Organization Notification
2000
SMACS [34] FDMA frames schedule
2001
PACT [28] single frames schedule
PicoRadio [10] CDMA+tone random wakeup
2002
STEM [33] data+ctrl random wakeup
Preamble sampling [6] single random listening
Arisha [2] single frames schedule
S-MAC [36] single slots listening
PCM [18] single random listening
Low Power Listening [13] single random listening
Protocol classification
2003
Sift [17] single random listening
EMACs [15] single frames schedule
T-MAC [5] single slots listening
TRAMA [30] single frames schedule
WiseMAC [7] single random listening
2004
BMA [24] single frames schedule
Miller [27] data+tone random wakeup+list
DMAC [26] single slots listening
SS-TDMA [23] single frames schedule
LMAC [14] single frames listening
B-MAC [29] single random listening
Case Study: S-MAC
S-MAC• Ye, Heidemann and Estrin, Infocom 2002
Tradeoffs Major components in S-MAC
• Periodic listen and sleep• Collision avoidance• Overhearing avoidance• Massage passing
Latency
FairnessEnergy
Coordinated Sleeping
Problem: Idle listening consumes significant energy
Solution: Periodic listen and sleep
• Turn off radio when sleeping• Reduce duty cycle to ~ 10% (120ms
on/1.2s off)
sleeplisten listen sleep
Latency Energy
Coordinated Sleeping
Schedules can differ
• Prefer neighboring nodes have same schedule— easy broadcast & low control overhead
Border nodes: two schedules
or broadcast twice
Node 1
Node 2
sleeplisten listen sleep
sleeplisten listen sleep
Schedule 2
Schedule 1
Coordinated Sleeping
Schedule Synchronization • New node tries to follow an existing schedule
• Remember neighbors’ schedules — to know when to send to them
• Each node broadcasts its schedule every few periods of sleeping and listening
• Re-sync when receiving a schedule update
Periodic neighbor discovery• Keep awake in a full sync interval over long periods
Coordinated Sleeping
Adaptive listening• Reduce multi-hop latency due to periodic sleep• Wake up for a short period of time at end of each
transmission
41 2 3
CTS
RTS
CTS
Reduce latency by at least half
listen listenlisten
t1 t2
Collision Avoidance
S-MAC is based on contention Similar to IEEE 802.11 ad hoc mode (DCF)
• Physical and virtual carrier sense• Randomized backoff time• RTS/CTS for hidden terminal problem• RTS/CTS/DATA/ACK sequence
Overhearing Avoidance
Problem: Receive packets destined to others Solution: Sleep when neighbors talk
• Basic idea from PAMAS (Singh, Raghavendra 1998)• But we only use in-channel signaling
Who should sleep?• All immediate neighbors of sender and receiver
How long to sleep?• The duration field in each packet informs other
nodes the sleep interval
Message Passing
Problem: Sensor net in-network processing requires entire message
Solution: Don’t interleave different messages• Long message is fragmented & sent in burst• RTS/CTS reserve medium for entire message• Fragment-level error recovery — ACK
— extend Tx time and re-transmit immediately Other nodes sleep for whole message time
FairnessEnergy
Msg-level latency
Implementation on Testbed Nodes
Platform• Mica Motes (UC Berkeley)
o 8-bit CPU at 4MHz,o 128KB flash, 4KB RAMo 20Kbps radio at 433MHz
• TinyOS: event-driven Configurable S-MAC options
• Low duty cycle with adaptive listen• Low duty cycle without adaptive listen• Fully active mode (no periodic sleeping)
Experiments: two-hop network Topology and measured energy consumption on
source nodes
Source 1
Source 2
Sink 1
Sink 2
• S-MAC consumes much less energy than 802.11-like protocol w/o sleeping
• At heavy load, overhearing avoidance is the major factor in energy savings
• At light load, periodic sleeping plays the key role 0 2 4 6 8 10
200
400
600
800
1000
1200
1400
1600
1800Average energy consumption in the source nodes
Message inter-arrival period (second)
Ene
rgy
cons
umpt
ion
(mJ)
802.11-like protocolwithout sleep
Overhearing avoidance
S-MAC w/o adaptive listen
0 2 4 6 8 100
5
10
15
20
25
30
Message inter-arrival period (S)
En
erg
y co
nsu
mp
tion
(J)
10% duty cycle without adaptive listen
No sleep cycles
10% duty cycle with adaptive listen
Energy consumption at different traffic load
Energy Consumption over Multi-Hops
Ten-hop linear network at different traffic load
3 configurations of S-MAC
At light traffic load, periodic sleeping has significant energy savings over fully active mode
Adaptive listen saves more at heavy load by reducing latency
Latency as Hops Increase Adaptive listen significantly reduces latency
causes by periodic sleeping
0 2 4 6 8 100
2
4
6
8
10
12Latency under highest traffic load
Number of hops
Ave
rag
e m
essa
ge la
tenc
y (S
)
10% duty cycle withoutadaptive listen
10% duty cycle with adaptive listen
No sleep cycles
0 2 4 6 8 100
2
4
6
8
10
12Latency under lowest traffic load
Number of hops
Ave
rag
e m
essa
ge la
tenc
y (S
)
10% duty cycle withoutadaptive listen
10% duty cycle withadaptive listen
No sleep cycles
Throughput as Hops Increase Adaptive listen significantly increases
throughput
0 2 4 6 8 100
20
40
60
80
100
120
140
160
180
200
220Effective data throughput under highest traffic load
Number of hops
Eff
ectiv
e da
ta t
hrou
ghp
ut (
Byt
e/S
)
No sleep cycles
10% duty cycle with adaptive listen
10% duty cycle without adaptive listen
• Using less time to pass the same amount of data
Combined Energy and Throughput
Periodic sleeping provides excellent performance at light traffic load
With adaptive listening, S-MAC achieves about the same performance as no-sleep mode at heavy load 0 2 4 6 8 10
0
0.5
1
1.5
2
2.5
3
Message inter-arrival period (S)
Ene
rgy-
time
prod
uct
per
byte
(J*
S/b
yte)
Energy-time cost on passing 1-byte data from source to sink
No sleep cycles
10% duty cycle withoutadaptive listen
10% duty cycle with adaptive listen