FUTURE NETWORKING PARADIGMS
(Sensor Networks and
InterPlanetary Internet)
Ian F. Akyildiz
Broadband & Wireless Networking Laboratory
School of Electrical and Computer Engineering
Georgia Institute of Technology
Tel: 404-894-5141; Fax: 404-894-7883
Email: ian@ece.gatech.edu
Web: http://www.ece.gatech.edu/research/labs/bwn
SENSOR NETWORKS ARCHITECTURE
Internet,
Satellite,
etc
Sink
Sink
 Several thousand
nodes
 Nodes are tens of
feet of each other
 Densities as high as
20 nodes/m3
Task
Manager
I.F.Akyildiz, W.Su, Y. Sankarasubramaniam, E. Cayirci,
“Wireless Sensor Networks: A Survey”, Computer Networks (Elsevier) Journal, March 2002.
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SENSOR NODE HARDWARE
Location Finding System
SENSING UNIT
Mobilizer

PROCESSING UNIT


Processor
Transceiver
Sensor ADC
Memory




Power Unit
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Small
Low power
Low bit rate
High density
Low cost (dispensable)
Autonomous
Adaptive
Power Generator
3
MICA Motes
BWN Lab @ GaTech
Processor/Radio Board
MPR300CB
Speed
4 MHz
Flash
128K bytes
SRAM
4K bytes
EEPROM
4K bytes
Radio Frequency
916MHz or 433MHz
(ISM Bands)
Data Rate
40 Kbits/Sec (Max)
Power
0.75 mW
Radio Range
100 feet (prog.)
Power
2 x AA batteries
Processor and Radio platform (MPR300CB) is based on Atmel ATmega 128L low
power Microcontroller that runs TinyOs operating system from its internal flash
memory.
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Examples for Sensor Nodes
UCLA: WINS
UC Berkeley: COTS Dust
UC Berkeley:
Smart Dust
JPL: Sensor Webs
Rockwell: WINS
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Examples for Sensor Nodes
Rene Mote
Dot Mote
Mica node
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weC Mote
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SENSOR NETWORKS FEATURES
 APPLICATIONS:
Military, Environmental, Health, Home, Space Exploration,
Chemical Processing, Disaster Relief….
 SENSOR TYPES:
Seismic, Low Sampling Rate Magnetic, Thermal, Visual, Infrared,
Acoustic, Radar…
 SENSOR TASKS:
Temperature, Humidity, Vehicular, Movement, Lightning Condition,
Pressure, Soil Makeup, Noise Levels, Presence or Absence of Certain
Types of Objects, Mechanical Stress Levels on Attached Objects,
Current Characteristics (Speed, Direction, Size) of an Object ….
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Factors Influencing Sensor
Network Design
A. Fault Tolerance (Reliability)
B. Scalability
C. Production Costs
D. Hardware Constraints
E. Sensor Network Topology
F. Operating Environment
G. Transmission Media
H. Power Consumption
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Sensor Networks Communication
Architecture
Data Link Layer
Physical Layer
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Task Management Plane
Network Layer
Mobility Management Plane
Transport Layer
Power Management Plane
Application Layer
Used by sink and all sensor nodes
 Combines power and routing awareness
 Integrates data with networking protocols
 Communicates power efficiently through
wireless medium and
 Promotes cooperative efforts.

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WHY CAN’T AD-HOC NETWORK
PROTOCOLS BE USED HERE?
 Number of sensor nodes can be several orders of
magnitude higher
 Sensor nodes are densely deployed and are prone to
failures
 The topology of a sensor network changes very
frequently due to node mobility and node failure
 Sensor nodes are limited in power, computational
capacities, and memory
 May not have global ID like IP address.
 Need tight integration with sensing tasks.
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APPLICATON LAYER
SMP: Sensor Managament Protocol
System Administrators interact with Sensors using SMP.
TASKS:







Moving the sensor nodes
Turning sensors on and off
Querying the sensor network configuration and the status of
nodes and re-configuring the sensor network
Authentication, key distribution and security in data
communication
Time-synchronization of the sensor nodes
Exchanging data related to the location finding algorithms
Introducing the rules related to data aggregation,
attribute-based naming and clustering to the sensor nodes
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APPLICATON LAYER
(Query Processing)
Users can request data from the network-> Efficient Query Processing
User Query Types:
1. HISTORICAL QUERIES:
Used for analysis of historical data stored in a storage area (PC),
e.g., what was the temperature 2 hours back in the NW quadrant.
2. ONE TIME QUERIES:
Gives a snapshot of the network, e.g., what is the current
temperature in the NW quadrant.
3. PERSISTANT QUERIES:
Used to monitor the network over a time interval with respect to
some parameters, e.g., report the temperature for the next 2 hours.
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APPLICATON LAYER
Sensor Query and Tasking Language (SQTL):
(C-C Shen, et.al., “Sensor Information Networking Architecture and Applications”, IEEE
Personal Communications Magazine, pp. 52-59, August 2001.)


SQTL is a procedural scripting language.
It provides interfaces to access sensor hardware:
- getTemperature, turnOn
for location awareness:
- isNeighbor, getPosition
and for communication:
- tell, execute.
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APPLICATON LAYER
Sensor Query and Tasking Language (SQTL):

By using the upon command, a programmer can create
an event handling block for three types of events:
- Events generated when a message is received by a sensor node,
- Events triggered periodically,
- Events caused by the expiration of a timer.

These types of events are defined by SQTL keywords
receive, every and expire, respectively.
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Simple Abtract Querying Example
Select [ task, time, location, [distinct | all], amplitude,
[[avg | min |max | count | sum ] (amplitude)]]
from [any , every , aggregate m]
where [ power available [<|>] PA |
location [in | not in] RECT |
tmin < time < tmax |
task = t |
amplitude [<|==|>] a ]
group by task
based on [time limit = lt | packet limit = lp |
resolution = r | region = xy]
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Data Centric Query
 Attribute-based
naming architecture
 Data centric
protocol
 Observer sends a
query and gets the
response from valid
sensor node
 No global ID
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APPLICATON LAYER
Task Assignment and Data Advertisement Protocol

INTEREST DISSEMINATION
* Users send their interest to a sensor node,
a subset of the nodes or the entire network.
* This interest may be about a certain attribute
of the sensor field or a triggering event.

ADVERTISEMENT OF AVAILABLE DATA
* Sensor nodes advertise the available data to
the users and the users query the data which
they are interested in.
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APPLICATON LAYER
Sensor Query and Data Dissemination Protocol
Provides
user applicatons with interfaces to issue
queries, respond to queries and collect incoming
replies.
These
queries are not issued to particular nodes, instead
ATTRIBUTE
BASED NAMING (QUERY)
“The locations of the nodes that sense temperature
higher than 70F”
LOCATION BASED NAMING (QUERY)
“Temperatures read by the nodes in region A”
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Interest Dissemination


Interest dissemination is performed to assign the sensing tasks to the sensor nodes.
Either sinks broadcast the interest or sensor nodes broadcast an advertisement for
the available data and wait for a request from the sinks.
71
75
68
Sink
67
66
71
71
68
71
69
Query:
Sensor nodes that read >70oF temperature
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Data Aggregation (Data Fusion)

The sink asks the sensor nodes to report certain conditions.
Data coming from multiple sensor nodes are aggregated.
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75
68
Sink
66
67
71
71
68
71
69
Query:
Sensor nodes that read >70oF temperature
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Location Awareness
(Attribute Based Naming)

Query an Attribute
of the sensor field
Region A
71
75
68
Sink
67
66
71
71
Region C
Query:
Temperatures read by the nodes in
Region A
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68
69
Region B
Important
for broadcasting,
multicasting, geocasting and anycasting
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APPLICATON LAYER RESEARCH NEEDS




Sensor Network Management Protocol
Task Assignment and Data Advertisement Protocol
Sensor Query and Data Dissemination Protocol
Sophisticated GUI
(Graphical User Interface) Tool
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TRANSPORT LAYER
Reliable Multi-Segment Transport (RMST)
F. Stann and J. Heidemann, “RMST: Reliable Data Transport in Sensor
Networks,” In Proc. IEEE SNPA’03, May 2003, Anchorage, Alaska, USA



Sink

RMST Node
Source Node
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
RMST provides end-to-end data-packet
transfer reliability
Each RMST node caches the packets
When a packet is not received before the
so-called WATCHDOG timer expires, a
NAK is sent backward
The first RMST node that has the required
packet along the path retransmits the
packet
RMST relies on Directed Diffusion scheme
23
Transport Layer
PSFQ - Pump Slowly Fetch Quickly
C.
Y. Wan, A. T. Campbell and L. Krishnamurthy, “PSFQ: A Reliable Transport
Protocol for Wireless Sensor Networks,” In Proc. ACM WSNA’02, September
2002, Atlanta, GA
– Packets are injected slowly into the network
– Aggressive hop-by-hop recovery in case of packet losses
– “PUMP” performs controlled flooding and requires each intermediate
node to create and maintain a data cache to be used for local loss
recovery and in-sequence data delivery.
– Applicable only to strict sensor-sensor guaranteed delivery
– And for control and management of the end-to-end reliability for the
downlink from sink to sensors
– Does not address congestion control
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Related Work
 Wireless TCP variants are NOT suitable for sensor
networks
– Different notion of end-to-end reliability
– Huge buffering requirements
– ACKing is energy draining
 BOTTOMLINE: Traditional end-to-end guaranteed
reliability (TCP solutions) cannot be applied here.
 New Reliability Notion is required!!!
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Reliable EVENT Transport in WSN
 NEW NOTION: Reliably Detect/Estimate EVENT
features from COLLECTIVE information
 Challenges:
– Significant energy and processing constraints, multihop ad hoc communication
– Network congestion
Need to address Congestion Control
and Reliability in Sensor Networks !
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Event-to-Sink Reliability
O. B. Akan, I. F. Akyildiz, and Y. Sankarasubramaniam, “ESRT:Event-to-Sink Reliable
Transport in Wireless Sensor Networks,” in Proceedings of ACM MOBIHOC 2003, pp. 177-188,
Annapolis, Maryland, USA, June 2003.
Also to appear in IEEE/ACM Transactions on Networking, 2004.
Event Radius
Sink
Sensor nodes
 Sensor networks are event-driven
 Multiple correlated data flows from event to sink
 Goal is to reliably detect/estimate event features
from collective information
 Necessitates event-to-sink collective reliability notion
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Event-to-Sink Reliability
Event Radius
Sink
Sensor nodes
 Sink decides about event features every  time units
 Observed event reliability Di , the DISTORTION observed in
event estimation in the decision interval i at the sink
 Desired event reliability D* ,the desired event estimation
distortion level for reliable event detection
– Application specific, known a priori at the sink
 Normalized reliability  i = D*/ Di
 Reporting rate f packet transmissions rate at source nodes
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Network States
State
Description
Condition
(NC,LR)
(No Congestion, Low reliability)
f < fmax and  < 1 - 
(NC,HR)
(No Congestion, High reliability)
f  fmax and  > 1+ 
(C,HR)
(Congestion, High reliability)
f > fmax and  > 1
(C,LR)
(Congestion, Low reliability)
f > fmax and   1
Optimal Operating Region
f < fmax and   [1- , 1+ ]
OOR
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ESRT
Protocol Overview
 Determine reporting frequency f to achieve desired
reliability D* with minimum resource utilization
 Source (Sensor nodes):
f
– Send data with reporting frequency
f
– Monitor buffer level and notify congestion to the sink
 Sink:
– Measures the observed event reliability Di at the end of
decision interval i
– Performs congestion decision based on the feedback from
the sources nodes (to determine f >< fmax).
– Updates f based on i = D*/ Di and f >< fmax (congestion) to
achieve desired event reliability D*
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ESRT
Congestion Detection Mechanism
 ACK/NACK not suitable
 We use local buffer level monitoring in sensor
nodes
B
bk : Buffer fullness level at the
end of reporting interval k
Db : Buffer length increment
af
B : Buffer size
f
: reporting frequency
bk
bk-1
Db
 Mark CN field in packet if congested
Event
ID
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CN
(1 bit)
Time
Destination
Stamp
Payload
FEC
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ESRT Operation
Frequency Update
State
(NC,LR)
Frequency Update
fi+1 = fi / i
Comments
Multiplicative increase, achieve desired reliability
asap
fi+1 = fi (i + 1) / 2i
Conservative decrease, no compromise on
reliability
(C,HR)
fi+1 = fi / i
Aggressive decrease to state (NC,HR)
(C,LR)
fi+1 = fi i
(NC,HR)
OOR
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fi+1 = fi
Exponential decrease, relieve congestion asap
Unchanged
32
ESRT Performance
S0 = (NC,LR)
S0 = (NC,HR)
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S0 = (C,HR)
S0 = (C,LR)
NETWORK LAYER
(ROUTING BASIC KNOWLEDGE)
The constraints to calculate the routes:
1. Additive Metrics:
Delay, hop count, distance, assigned costs (sysadmin preference),
average queue length...
2. Bottleneck Metrics:
Bandwidth, residual capacity and other bandwidth related metrics.
REMARK:
All routing algorithms are based on the same principle used as in Dijkstra's,
which is used to find the minimum cost path from source to destination.
Dikstra and Bellman solve the SHORTEST PATH PROBLEM…
RIP (Distant Vector Algorithm) -> Bellman/Ford Algorithm
OSPF (Open Shortest Path Algorithm)  Dikstra Algorithm
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Routing Algorithms Constraints Regarding
Power Efficiency (Energy Efficient Routing)
E (PA=1)
F (PA=4)
 Maximum power available (PA) route
Minimum hop route
 Minimum energy route
D (PA=3)
T
Sink
 Maximum minimum PA node
route (Route along which the
minimum PA is larger than the
A (PA=2)
B (PA=2)
C (PA=2) minimum PAs of the other routes
is preferred, e.g., Route 3 is the
Route 1: Sink-A-B-T (PA=4)
most efficient; Route 1 is the
Route 2: Sink-A-B-C-T (PA=6)
second).
Route 3: Sink-D-T (PA=3)
Route 4: Sink-E-F-T (PA=5)

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Why can’t we use conventional
routing algorithms here?
Global (Unique) addresses, local addresses.
Unique node addresses cannot be used in many sensor
networks
- sheer number of nodes
- energy constraints
- data centric approach
Node addressing is needed for
- node management
- sensor management
- querying
- data aggregation and fusion
- service discovery
- routing
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NETWORK LAYER
(ROUTING for SENSOR NETWORKS)
Important considerations:




Sensor networks are mostly data centric
An ideal sensor network has attribute based
addressing and location awareness
Data aggregation is useful unless it does not
hinder collaborative effort
Power efficiency is always a key factor
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Some Concepts
 Data-Centric
– Node doesn't need an identity
 What is the temp at node #27 ?
– Data is named by attributes
 Where are the nodes whose temp recently exceeded 30
degrees ?
 How many pedestrians do you observe in region X?
 Tell me in what direction that vehicle in region Y is
moving?
 Application-Specific
– Nodes can perform application specific data
aggregation, caching and forwarding
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Taxonomy of Routing Protocols
for Sensor Networks
Categorization of Routing Protocols for Wireless Sensor Networks:
(K. Akkaya, M. Younis, “A Survey on Routing Protocols for Wireless Sensor Networks,” Elsevier AdHoc Networks, 2004)
1. Data Centric Protocols
Flooding, Gossiping, SPIN, SAR (Sequential Assignment
Routing) , Directed Diffusion, Rumor Routing, Gradient Based
Routing, Constrained Anisotropic Diffused Routing, COUGAR,
ACQUIRE
2. Hierarchical
LEACH, TEEN (Threshold Sensitive Energy Efficient Sensor
APTEEN, PEGASIS, Energy Aware Scheme
Network Protocol),
3. Location Based
MECN, SMECN (Small Minimum Energy Com Netw), GAF
(Geographic Adaptive Fidelity), GEAR
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Conventional Approach
FLOODING
Broadcast data to all neighbor nodes
A
C
B
D
E
F
G
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ROUTING ALGORITHMS
Gossiping
GOSSIPING:
Sends data to one randomly selected neighbor.
Example:
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Problems of
Flooding and Gossiping
PROBLEMS:
Although these techniques are simple and reactive, they have
some disadvantages including:
* Implosion
(NOTE: Gossiping avoids this by selecting only one node; but this causes delays to
propagate the data through the network)
* Overlap
* Resource Blindness
* Power (Energy) Inefficient
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Problems
Implosion
(a)
A
B
(a)
(a)
C
D
Data Overlap
q
r
A
s
B
(a)
(q,r)
C
(r,s)
Resource Blindness
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No knowledge about the
available power of resources
43
Gossiping
 Uses randomization to save energy
Selects a single node at random and sends the data
to it
 Avoids implosions
 Distributes information slowly
 Energy dissipates slowly
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The Optimum Protocol
A
“Ideal”
–
–
–
–
Shortest-path routes
Avoids overlap
Minimum energy
Need global topology
information
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C
B
D
E
F
G
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SPIN: Sensor Protocol for
Information via Negotiation
(W.R. Heinzelman, J. Kulik, and H. Balakrishan, “Adaptive Protocols for
Information Dissemination in Wireless Sensor Networks”,
Proc. ACM MobiCom’99, pp. 174-185, 1999 )
Two basic ideas:
 Sensors communicate with each other
about the data that they already have and
the data they still need to obtain


to conserve energy and operate efficiently
exchanging data about sensor data may be cheap
 Sensors must monitor and adapt to changes
in their own energy resources
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SPIN
Good for disseminating information to all sensor nodes.
 SPIN is based on data-centric routing where the sensors broadcast an
advertisement for the available data and wait for a request from
interested sinks

1.
2.
1. ADV
2. REQ
3. DATA
3.
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SPIN
ADV
REQ
DATA
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ADV
DATA
REQ
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ROUTING ALGORITHM
(DIRECTED DIFFUSION)
(C. Intanagonwiwat, R. Gowindan and D. Estrin, “Directed Diffusion: A Scalable and Robust
Communication Paradigm for Sensor Networks”, Proc. ACM MobiCom’00, pp. 56-67, 2000.)
This is a DATA CENTRIC ROUTING scheme!!!!
- The idea aims at diffusing data through sensor nodes by using
a naming scheme for the data.
- The main reason behind this is to get rid off unnecessary
operation of routing schemes to save Energy.
Also Robustness and Scaling requirements need to be considered.
-
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Directed Diffusion
Source
Sink
Data
Delivery
Gradient
Setup
Interest
Propagation
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Directed Diffusion
Features
Sink sends interest, i.e., task descriptor, to all sensor nodes.
 Interest is named by assigning attribute-value pairs.

source
source
sink
Interest Propagation
source
sink
Gradient Setup
sink
Data Delivery
Drawbacks
Cannot change interest unless a new interest is broadcast.
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LEACH
Low Energy Adaptive Clustering Hierarchy (LEACH)
(W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-Efficient Communication
Protocol for Wireless Microsensor Networks,'' IEEE Proceedings of the Hawaii International
Conference on System Sciences, pp. 1-10, January, 2000.)
-
* LEACH is a clustering based protocol which minimizes energy dissipation
in sensor networks.
Idea:
* Randomly select sensor nodes as cluster heads, so the high energy
dissipation in communicating with the base station is spread to all sensor
nodes in the sensor network.
* Forming clusters is based on the received signal strength.
* Cluster heads can then be used kind of routers (relays) to the sink.
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LEACH
 Optimum Number of Clusters ---????????
- too few: nodes far from cluster heads
– too many: many nodes send data to SINK.
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Other Protocols
1. Energy Aware Routing
R. Shah, J. Rabaey, “Energy Aware Routing for Low Energy Ad Hoc Sensor
Networks,” IEEE WCNC’02, Orlando, March 2002.
2. Rumor Routing
D. Braginsky, D. Estrin, “Rumor Routing Algorithm for Sensor Networks,”
ACM WSNA’02, Atlanta, October 2002.
3. Threshold sensitive Energy Efficient sensor Network (TEEN)
A. Manjeshwar, D.P. Agrawal, “TEEN: A Protocol for Enhanced Efficiency in
Wireless Sensor Networks,” IEEE WCNC’02, Orlando, March 2002.
4. Constrained Anisotropic Diffusion Routing (CADR)
M. Chu, H.Hausecker, F. Zhao, “Scalable Information-Driven Sensor Querying
and Routing for Ad Hoc Heterogeneous Sensor Networks,” International Journal
of High Performance Computing Applications, Vol. 16, No. 3, August 2002.
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Other Protocols
5. Power Efficient Gathering in Sensor Information Systems
(PEGASIS)
S. Lindsey, C.S. Raghavendra, “PEGASIS: Power Efficient Gathering in Sensor
Information Systems,” IEEE Aerospace Conference, Montana, March 2002.
6. Self Organizing Protocol
L. Subramanian, R.H. Katz, “An Architecture for Building Self Configurable
Systems,” IEEE/ACM Workshop on Mobile Ad Hoc Networking and
Computing, Boston, August 2000.
7. Geographic Adaptive Fidelity (GAF)
Y. Yu, J. Heideman, D. Estrin, “Geography-informed Energy Conservation for
Ad Hoc Routing,” ACM MobiCom’01, Rome, July 2001.
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Open Research Issues
• Store and Forward Technique
that combines data fusion and aggregation.
• Routing for Mobile Sensors
Investigate multi-hop routing techniques for
high mobility environments.
• Priority Routing
Design routing techniques that allow different priority
of data to be aggregated, fused, and relayed.
• 3D Routing
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Medium Access Control (MAC) in WSN
 IEEE 802.11 [1]
– Originally developed for WLANs
– Per-node fairness
– High energy consumption due to idle listening
 S-MAC [2]
– Aims to decrease energy consumption by sleep
schedules with virtual clustering
– Redundant data are still sent with increased
latency due to sleep schedules
[1] IEEE 802.11, “Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY)
Specifications,” 1999
[2] W. Ye, J. Heidemann and D. Estrin, “An Energy Efficient MAC Protocol for Wireless
Sensor Networks,” In Proc. ACM MOBICOM ’01, pp.221 –235, Rome, Italy 2001
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Related Work
 TRAMA[3]
– Based on time-slotted structure
– Information about every two-hop neighbor is used
for slot selection
– High signaling overhead for high density networks
– High latency due to time-slotted structure
[3] V. Rajendran, K. Obraczka, and J. J. Garcia-Luna-Aceves,
“Energy-Efficient, Collision-Free Medium Access Control for Wireless
Sensor Networks,” in Proc. ACM SenSys 2003, Los Angeles, California,
November 2003.
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MAC for Sensor Networks
 WSN are characterized by dense deployment
of sensor nodes
 MAC Layer Challenges
– Limited power resources
– Need for a self-configurable, distributed
protocol
– Data centric approach rather than per-node
fairness
Exploit spatial correlation to reduce
transmissions in MAC layer !
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Collaborative MAC (CMAC) Protocol
M.C. Vuran, and I. F. Akyildiz, “Spatial Correlation-based
Collaborative Medium Access Control in Wireless Sensor Networks,”
submitted for publication, Nov. 2003.
 If a node transmits data then all its
correlation neighbors have redundant
information
 Route-thru data has higher priority over
generated data
Filter out transmission of redundant data
and prioritize filtered data through the
network!
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Collaborative MAC (CMAC) Protocol
 Source function:
Transmit event
information
 Router function: Forward
packets from other
nodes in the multi-hop
path to the sink
 Two components
– Event MAC (E-MAC)
– Network MAC
(N-MAC)
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Node Selections
 Choose representative nodes such that
– They are located as close to the event source as
possible
– They are located as farther apart from each
other as possible.
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CMAC Performance
Medium Access Delay
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Packet Drop Rate
63
CMAC Performance
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Avg. Energy Consumption
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Conclusions
 Spatial correlation in sensor networks is
exploited on both Transport and MAC layers
 Redundant transmissions are suppressed
 Number of transmissions are reduced instead of
number of transmitted bits
 Both protocols achieve low energy consumption
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Research Needs for Sensor Networks
• An Analytical Framework for Sensor Networks
 Find a Basic Generic Architecture and Protocol
development which can be tailored to specific
applications.
• More theoretical investigations of the Architecture and
Protocol developments
• Follow the TCP/IP Stack, i.e., keep the Strict Layer
Approach ???
• Cross Layer Optimization
• Explore both Spatial-Temporal Correlations for
Protocol development
IFA’2004
66
FURTHER OPEN RESEARCH ISSUES
 Research to integrate WSN domain into NGWI
(Next Generation Wireless Internet)
e.g., interactions of Sensor and AdHoc Networks
or Sensor and Satellite or any other
combinations…
 Explore the SENSOR/ACTOR NETWORKS
 Explore the SENSOR-ANTISENSOR NETWORKS
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Need for Realistic Applications
• Clear Demonstration of Testbeds and Realistic Applications
• Not only data or audio but also video 
Overall I  Integrated Traffic.
SOME OF OUR EFFORTS IN BWN LAB @ GaTech
•
•
•
•
•
MAN  for Meteorological Observations
SpINet  for Mars Surface
Airport Security  Sensors/Actors
Sensor Wars
Wide Area Multi-Campus Sensor Network
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MEDIUM ACCESS CONTROL (MAC)
FURTHER RESEARCH NEEDS
 MAC for sensor networks must have inbuilt power



management, mobility management and failure recovery
strategies
Need for a self-configurable, distributed protocols
Data centric approach rather than per-node fairness
Develop MACs which differentiate Multimedia Traffic
 Exploit Spatial & Temporal Correlation
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Error Control
 Some sensor network applications like mobile tracking
require high data precision


Coding gain is generally expressed in terms of the additional
transmit power needed to obtain the same BER without coding
FEC is preferred over ARQ

Since power consumption is crucial, we must take into
account encoding and decoding energy expenditures

Coding is profitable only if the encoding and decoding
power consumption is less than the coding gain.
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ERROR CONTROL
RESEARCH NEEDS

Design of suitable FEC codes with minimal encoding
and relatively higher decoding complexities

Feasibility of ARQ schemes in multihop sensor networks
(hop by hop ARQ instead of end-to-end). This is needed for
reliable communications (data critical)

Adaptive/Hybrid FEC/ARQ schemes

Extension to Rayleigh/Rician fading conditions with mobile
nodes
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Optimal Packet Size for Wireless Sensor
Networks
Y. Sankarasubramaniam, I. F. Akyildiz, S. McLaughlin, ”Optimal Packet Size
for Wireless Sensor Networks”, IEEE SNPA, May 2003.


Determining the optimal packet size for sensor networks is
necessary to operate at high energy efficiencies.
The multihop wireless channel and energy consumption
characteristics are the two most important factors that
influence choice of packet size.
Header (2) Payload (<=73)
IFA’2004
Trailer (FEC) (>=3)
72
PHYSICAL LAYER

New Channel Models (I/O/Underwater/Deep Space)

Explore Antennae Techniques
(e.g., Smart Antennaes)

Software Radios??

New Modulation Schemes

SYNCH Schemes

FEC Schemes on the Bit Level

New Data Encryption

Investigate UWB
IFA’2004
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FINAL REMARKS
IFA’2004
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Basic Research Needs
•
An Analytical Framework for Sensor Networks
 Find a Basic Generic Architecture and Protocol
Development which can be tailored to specific
applications.
• More theoretical investigations of the
Architecture and Protocol
developments
• Network Configuration and Planning Schemes
IFA’2004
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FURTHER OPEN RESEARCH ISSUES
 Research to integrate WSN domain into NGWI (Next
Generation Wireless Internet)
e.g., interactions of Sensor and AdHoc Networks or Sensor
and Satellite or any other combinations…
 Explore the SENSOR/ACTOR NETWORKS
 Explore the SENSOR-ANTISENSOR NETWORKS
 SECURITY ISSUES
IFA’2004
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Some Applications
• Clear Demonstration of Testbeds and Realistic Applications
• Not only data or audio but also video as well as integrated
traffic.
SOME OF OUR EFFORTS IN BWN LAB @ GaTech
•
•
•
•
•
MAN  for Meteorological Observations
SpINet  for Mars Surface
Airport Security  Sensors/Actors
Sensor Wars
Wide Area Multi-campus Sensor Network
IFA’2004
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FURTHER CHALLENGES
Protocol Stack
• Follow the TCP/IP Stack, i.e., keep the
Strict Layer Approach ???
• Or Interleave the Layer functionalities???
• Cross Layer Optimization
• Standardization???
IFA’2004
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Commercial Viability
of WSN Applications
 Within the next few years, distributed sensing and
computing will be everywhere, i.e., homes, offices,
factories, automobiles, shopping centers, supermarkets, farms, forests, rivers and lakes.
 Some of the immediate commercial applications of
wireless sensor networks are
–
–
–
–
–
–
–
–
Industrial automation (process control)
Defense (unattended sensors, real-time monitoring)
Utilities (automated meter reading),
Weather prediction
Security (environment, building etc.)
Building automation (HVAC controllers).
Disaster relief operations
Medical and health monitoring and instrumentation
IFA’2004
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Commercial Viability
of WSN Applications
 XSILOGY Solutions is a company which provides wireless sensor
network solutions for various commercial applications such as
tank inventory management, stream distribution systems,
commercial buildings, environmental monitoring, homeland
defense etc.
http://www.xsilogy.com/home/main/index.html
 In-Q-Tel provides distributed data collection solutions with
sensor network deployment.
http://www.in-q-tel.com/tech/dd.html
 ENSCO Inc. invests in wireless sensor networks for
meteorological applications.
http://www.ensco.com/products/homeland/msis/msis_rnd.htm
 EMBER provides wireless sensor network solutions for
industrial automation, defense, and building automation.
IFA’2004
http://www.ember.com
80
Commercial Viability
of WSN Applications
 H900 Wireless SensorNet System(TM), the first commercially
available end-to-end, low-power, bi-directional, wireless mesh
networking system for commercial sensors and controls is
developed by the company called Sensicast Systems. The
company targets wide range of commercial applications from
energy to homeland security.
http://www.sensicast.com
 The Sensor-based Perimeter Security product is introduced by a
company called SOFLINX Corp. (a wireless sensor network
software company)
http://www.soflinx.com
 XYZ On A Chip: Integrated Wireless Sensor Networks for the
Control of the Indoor Environment In Buildings is another
commercial application project currently performed by Berkeley.
http://www.cbe.berkeley.edu/research/briefs-wirelessxyz.htm
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Commercial Viability
of WSN Applications
 The Crossbow wireless sensor products and its environmental
monitoring and other related industrial applications of such as
surveillance, bridges, structures, air quality/food quality,
industrial automation, process control are introduced.
http://www.xbow.com
 Japan's Omron Corp has two wireless sensor projects in the US
that it hopes to commercialize in the near future. Omron's
Hagoromo Wireless Web Sensor project consists of wireless
nodes equipped with various sensing abilities for providing
security for major cargo-shipping ports around the world.
http://www.omron.com
 Possible business opportunity with a big home improvement
store chain, Home Depot, with Intel and Berkeley using wireless
sensor networks
http://www.svbizink.com/otherfeatures/spotlight.asp?iid=314
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Commercial Viability
of WSN Applications
 Millennial Net builds wireless networks combining sensor
interface endpoints and routers with gateways for industrial
and building automation, security, and telemetry
http://www.millennial.net
 CSEM provides sensing and actuation solutions
http://www.csem.ch/fs/acuating.htm
 Dust Inc. develops the next-generation hardware and
software for wireless sensor networks
http://www.dust-inc.com
 Integration Associates designs sensors used in medical,
automotive, industrial, and military applications to costeffective designs for handheld consumer appliances, barcode
readers, and wireless computer input devices
http://www.integration.com
IFA’2004
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Commercial Viability
of WSN Applications
 Melexis produces advanced integrated semiconductors, sensor
ICs, and programmable sensor IC systems.
http://www.melexis.com
 ZMD designs, manufactures and markets high performance,
low power mixed signal ASIC and ASSP solutions for wireless
and sensor integrated circuits.
http://www.zmd.biz
 Chipcon produces low-cost and low-power single-chip 2.4 GHz
ISM band transceiver design for sensors.
http://www.chipcon.com
 ZigBee Alliance develops a standard for wireless low-power,
low-rate devices.
http://www.zigbee.com
IFA’2004
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InterPlanetary Internet
(Deep Space Network):
State-of-the-Art and Research Challenges*
* I.F. Akyildiz, O. Akan, C.Chen, J. Fang, W. Su, “InterPlanetary Internet:
State-of-the-Art and Research Challenges”, Computer Networks Journal, Oct. 2003.
IFA’2004
85
InterPlaNetary Internet
Architecture
 InterPlaNetary Backbone Network
 InterPlaNetary External Network
 PlaNetary Network
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86
PlaNetary Network
Architecture
 PlaNetary Satellite Network
 PlaNetary Surface Network
IFA’2004
87
CHALLENGES
–
–
–
–
–
–
Extremely long and variable propagation delays
Asymmetrical forward and reverse link capacities
Extremely high link error rates
Intermittent link connectivity, e.g., Blackouts
Lack of fixed communication infrastructure
Effects of planetary distances on the signal strength and
the protocol design
– Power, mass, size, and cost constraints for
communication hardware and protocol design
– Backward compatibility requirement due to high cost
involved in deployment and launching processes
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Planned InterPlaNetary
Internet Missions
Mission Name
Schedule
Description/Objective
Galaxy Evolution Explorer
2003
To measure star formation 11 billion years ago with UV wavelengths.
Rosetta
February 2004
Comet orbiter and lander to gather scientific data.
Messenger
March 2004
To study the characteristics of Mercury, and to search for water ice
and other frozen volatiles.
Deep Impact
December 2004
To investigate the interior of the comet, the crater formation process,
the resulting crater, and any outgassing from the nucleus.
Mars Reconnaissance
Orbiter
July 2005
To study Mars from orbit, perform high-resolution measurements and
serve as communications relay for later Mars landers until 2010.
Venus Express
November 2005
To study the atmosphere and plasma environment of Venus.
New Horizons
January 2006
To fly by Pluto and its moon Charon and return scientific data/images.
Dawn
May 2006
To study two of the largest asteroids, Ceres and Vesta.
Kepler
October 2006
Search for terrestrial planets, i.e., similar to Earth.
Europa Orbiter
2008
To study the Jupiter’s Moon Europa’s icy surface.
LISA
2007
To probe the gravity waves emitted by dwarf stars and other objects
sucked into black holes.
Mars 2007
Late 2007
To launch a remote sensing orbiter and four small Netlanders to Mars.
Mars 2009
Late 2009
Smart Lander, Long Range Rover and Communication Satellite.
BepiColombo
January 2011
To study Mercury’s form, interior structure, geology, composition, etc.
IFA’2004
89
Proposed Consultative Committee for Space
Data Systems (CCSDS) Protocol Stack
for Mars
IFA’2004
Exploration Mission Communications
90
Proposed Delay Tolerant Networking
(DTN) Protocol Stack
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Transport Layer Issues
– Extremely High Propagation Delays
– High Link Error Rates
– Asymmetrical Bandwidth
– Blackouts
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Extremely Long Propagation Delays
IFA’2004
Planet
RTTmin
RTTmax
Mercury
Venus
Mars
1.1
5.6
9
30.2
35.8
55
Jupiter
Saturn
Uranus
81.6
165.3
356.9
133.3
228.4
435.6
Neptune
594.9
646.7
Pluto
593.3
1044.4
93
Performance of Existing TCP Protocols
 Window-Based TCP’s are not suitable!!!
For RTT = 40 min  20B/s throughput on 1Mb/s link !!
O. B. Akan, J. Fang, I. F. Akyildiz, “Performance of TCP Protocols in Deep
Space Communication Networks”,
IFA’2004
94
IEEE Communications Letters, Vol. 6, No. 11, pp. 478-480, November 2002.
Space Communications Protocol Standards –
Transport Protocol (SCPS-TP)
 Addresses link errors, asymmetry, and outages
 SCPS-TP: Combination of existing TCP protocols:
–
–
–
–
Window-based
Slow Start
Retransmission timeout
TCP-Vegas congestion control scheme – variation of
the RTT value as an indication of congestion
 SCPS-TP Rate-Based:
– Does not perform congestion control
– Uses fixed transmission rate
New Transport Protocols are needed !!!
* Space Communications Protocol Specification-Transport Protocol (SCPS-TP)", Recommendation for Space
Data Systems Standards, CCSDS 714.0-B-1, May 1999.
IFA’2004
95
TP-Planet
*O. B. Akan, J. Fang and I.F. Akyildiz, “TP-Planet: A Reliable Transport Protocol
for InterPlaNetary Internet”, to appear in IEEE Journal of Selected Areas in
Communications (JSAC), early 2004.
Initial State
Steady State
t=2*RTT
Hold
t=RTT
Immediate
Start
Blackout
FollowUP
Follow
Up
Decrease




Increase
Objective: To address challenges of InterPlaNetary Internet
A New Initial State Algorithm
A New Congestion Detection Algorithm in Steady State
A New Rate-Based scheme instead of Window-Based
IFA’2004
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Multimedia Transport in
InterPlaNetary Internet
Additional Challenges
*
*
*
*
IFA’2004
Bounded Jitter
Minimum Bandwidth
Smoothness
Error Control
97
RCP-Planet: Overview
J. Fang and I.F. Akyildiz, “RCP Planet: A Rate Control Scheme
for Multimedia Traffic in InterPlaNetary Internet”, July 2003.
t=RTT
OPERATIONAL State
Increase
BEGIN State
Decrease
Blackout
 Objective: To Address the Challenges
 Framework:
* A New Packet Level FEC
* A New Rate-Based Approach
* A New BEGIN State Algorithm
* A New Rate Control Algorithm in OPERATIONAL State
IFA’2004
98
Transport Layer
Open Research Issues
 End-to-End Transport:
– Feasibility of the end-to-end transport should be
investigated and new end-to-end transport protocols
should be devised accordingly.
 Extreme PlaNetary Distances:
– Deep Space links with extreme delays such as Jupiter,
Pluto have intermittent connectivity even within an RTT.
 Cross-layer Optimization:
– The interactions between the transport layer and
lower/higher layers should be maximized to increase
network efficiency for scarce space link resources.
IFA’2004
99
Network Layer Issues

Naming and Addressing
in the InterPlaNetary Internet

Routing
in the InterPlaNetary Backbone Network

Routing
in PlaNetary Networks
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Naming and Addressing
 Purpose: To provide inter-operability between
different elements in the architecture
 Influencing Factors:
– What objects are named?
(Typically nodes or data objects)
– Whether a name can be directly used by a
data router in order to determine the
delivery path?
– The method by which name/object binding
is managed?
IFA’2004
101
Domain Name System
(DNS) Approach in Internet
If an application on a remote planet needs to
resolve an Earth based name to an address:
 Problems:
– If query an Earth-resident name server:
A significant delay of a round-trip time in the
commencement of communication
– If maintain a secondary name server locally:
State updates would dominate
communication channel utilization
– If maintain a static list of host names and
addresses:
Not scale well with system’s growth
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102
Tiered Naming and Addressing
 Name Tuple = {region ID, entity ID}
– Region ID identifies the entity’s region and is known by
all regions in the InterPlaNetary Internet
– Entity ID is a name local to its entity’s local region and
treated as opaque data outside this region
 The opacity of entity names outside their local region
enforces Late Binding: the entity ID of a tuple is not
interpreted outside its local region
which avoids a universal name-to-address binding
space and preserves a significant amount of autonomy
within each region.
IFA’2004
103
An InterPlaNetary Internet:
Example and Host Name Tuples
Earth’s Internet
SRC
The “Backbone”
GW1
IPN region: earth.sol
Mars’ Internet
DST
GW2
IPN region: ipn.sol
IPN region: mars.sol
Host
IPN
regions
Host name tuples
SRC
earth.sol
{earth.sol, src.jpl.nasa.gov:6769}
GW1
earth.sol
ipn.sol
{earth.sol, ipngw1.jpl.nasa.gov:6769}
{ipn.sol, ipngw1.jpl.nasa.gov:6769}
GW2
ipn.sol
mars.sol
{ipn.sol, ipngw2.jpl.nasa.gov:6769}
{mars.sol, ipngw2.jpl.nasa.gov:6769}
DST
mars.sol
{mars.sol, dst.jpl.nasa.gov:6769}
IFA’2004
104
Challenges
Network Layer
 Long and Variable Delays
– Without timely distribution of topology information,
routing computations fail to converge to a common
solution, resulting in route inconsistency or oscillation
– The node movement adds to the variability of delays
 Intermittent Connectivity
– Determine the predicted time and duration of intermittent
links and the degree of uncertainity
– Obtain knowledge of the state of pending messages
– Schedule transmission of the pending messages when links
become available
SCPS-NP  possible solution???
IFA’2004
105
Open Research Issues
Network Layer
 Distribution of Topology Information
– Combination of link state and distance vector information
exchange
– Distribution of trajectory and velocity information
 Path Calculation
– Hop-by-hop routing is expected using incomplete topology
information and probabilistic estimation
– Adaptive algorithms are needed for rerouting and caching
decisions
 Interaction with Transport Layer Protocols
IFA’2004
106
Challenges
Network Layer (Planet)
 Extreme Power Constraints
– Space elements mainly depend on rechargeable battery using
solar energy
 Frequent Network Partitioning
– The network can be partitioned due to harsh environmental
factors
 Adaptive Routing through Heterogeneous Networks
– Fixed elements (e.g., landers)
– Satellites with scheduled movement
– Mobile elements with slow movement (e.g., rovers)
– Mobile elements with fast movement (e.g., spacecraft)
– Low-power sensor nodes in clusters
IFA’2004
107
Medium Access Control
InterPlaNetary Backbone Network
Challenges:
– Very Long Propagation Delays
– Physical Design Change Constraints
– Topological Changes
– Power Constraints
IFA’2004
108
Medium Access Control
InterPlaNetary Backbone Network
 Vastly unexplored research field
 The suitability and performance evaluation of
fundamental MAC schemes, i.e., TDMA, CDMA,
and FDMA, should be investigated
 Thus far, Packet Telecommand, and Packet
Telemetry standards developed by CCSDS are
used to address deep space link layer issues
(Virtual Channelization method!!!)
IFA’2004
109
Error Control
InterPlaNetary Backbone Network
 Deep space channel is generally modelled as
Additive White Gaussian Noise (AWGN) channel
 Scientific space missions require bit-error rate of 10-5
or better after physical link layer coding
 Error control at link layer is necessary
IFA’2004
110
Error Control
InterPlaNetary Backbone Network
 CCSDS Telemetry Standard: (Telemetry Channel Coding):
– For Gaussian Channels 
½ Rate Convolutional Code
– For Bandwidth-Constrained Channels 
Punctured Convolutional Codes
– For Further Constrained Channels 
Concatenated Codes (i.e.,Convolutional code as the
inner code and the RS code as the outer code)
Own Experience  TORNADO CODES!!!
IFA’2004
111
Error Control
InterPlaNetary Backbone Network
 Advance Orbiting Systems Rec. by CCSDS 
Space Link (ARQ) Protocol (SLAP)
 Packet Telecommand Standard of CCSDS 
Command Operation Procedure (COP) (GoBack N)
IFA’2004
112
Open Research Issues
Link Layer
 MAC for InterPlaNetary Backbone Network
 MAC for PlaNetary Networks
 Error Coding Schemes
 Cross-layer Optimization
 Optimum Packet Sizes
IFA’2004
113
Physical Layer Issues
InterPlaNetary Backbone Network
 Possible approach is to increase radiated RF signal energy:
– Use of high power amplifiers such as travelling wave tubes (TWT) or
klystrons which can produce output powers up to several thousand
watts
– This comes with an expense of increased antenna size, cost and also
power problems at remote nodes
 Current NASA DSN has several 70m antennas for deep space missions
 DSN operates in S-Band and X-Band (2GHz and 8GHz, respectively) for
spacecraft telemetry, tracking and command
– Not adequate to reach high data rates aimed for InterPlaNetary
Internet
 New 34m antennas are being developed to operate in Ka-Band (32
GHz) which will significantly improve data rates
IFA’2004
114
Open Research Issues
PHYSICAL LAYER
 Signal Power Loss:
– Powerful and size-, mass-, and cost-efficient antennas and power
amplifiers need to be developed
 Channel Coding:
– Efficient and powerful channel coding schemes should be investigated
to achieve reliable and very high rate bit delivery over the long delay
InterPlaNetary Backbone links
 Optical Communications:
– Optical communication technologies should be investigated for
possible deployment in InterPlaNetary Backbone links
 Hardware Design:
– Low-power low-cost transceiver and antennas should be developed
 Modulation Schemes:
– Simple and low-power modulation schemes should be developed for
PlaNetary Surface Network nodes. Ultra-wide Band (UWB) could be
explored for this purpose
IFA’2004
115
Challenges in Deep Space Time
Synchronization

Variable and long transmission delays
– The long and variable delays may cause a fluctuating
offset to the clock

Variable transmission speed
– It may produce a fluctuating offset problem

Variable temperature
– It may cause the clock to drift in different rate

Variable electromagnetic interference
– This may cause the clock to drift or even permanent
damage to the crystal if the equipment is not properly
shielded
IFA’2004
116
Challenges in Deep Space Time
Synchronization (cont’d)

Intermittent connectivity
– The situation may cause the clock offset to fluctuate and
jump

Impractical transmissions
– A time synchronization protocol can not depend on message
retransmissions to synchronize the clocks, because the
distance between deep space equipments are simply too
large

Distributed time servers
– Deep space equipments may require to synchronize to their
local time servers, and the time servers have to synchronize
among themselves
IFA’2004
117
Related Work

Network Time Protocol
–
–

DSN Frequency and Time Subsystems
–

Can not handle mobile servers and clients (variable range and
range rate with intermittent connectivity)
Has time offset wiggles of few milliseconds of amplitude
Uses several atomic frequency standards to synchronize the
devices and provide references for the three DSN sites, i.e.,
Goldstone, USA; Madrid, Spain; Canberra, Australia
Recommendation for proximity-1 space link protocol
–
Finds the correlation between the clocks of proximity nodes. The
correlation data and UTC time are used to correct the past and
project the future UTC values
IFA’2004
118
Conclusions
 InterPlaNetary Internet will be the Internet of
next generation deep space networks.
 There exist many significant challenges for the
realization of InterPlaNetary Internet.
 Many researchers are currently engaged in
developing the required technologies for this
objective.
IFA’2004
119
FiNAL WORDS
NASA’s VISION:
to improve life here, to extend life to there, to find
life beyond...
NASA’s MISSION:
to understand and protect our home planet, to explore
the Universe and search for life, to inspire
the next generation of explorers…
OUR AIM:
to point out the research problems and inspire the
researchers worldwide to realize these objectives!!!!!!!!! 120
IFA’2004