2. SENSOR NETWORKS ARCHITECTURE
Internet, Satellite, etc
Sink
Task
Manager
Several thousand nodes
Nodes are tens of feet of each other
Densities as high as 20 nodes/m3
I.F.Akyildiz, W.Su, Y. Sankarasubramaniam, E. Cayirci,
“Wireless Sensor Networks: A Survey”, Computer Networks (Elsevier) Journal, March 2002.

3. Location Finding System
SENSOR NODE HARDWARE
Power Unit
Power Generator
Sensor ADC
Processor
Memory
Transceiver
Location Finding System
Mobilizer
Small
Low power
Low bit rate
High density
Low cost (dispensable)
Autonomous
Adaptive
SENSING UNIT
PROCESSING UNIT

4. MICA Motes BWN Lab @ GaTech
Processor/Radio Board
MPR300CB
Speed
4 MHz
Flash
128K bytes
SRAM
4K bytes
EEPROM
Radio Frequency
916MHz or 433MHz (ISM Bands)
Data Rate
40 Kbits/Sec (Max)
Power
0.75 mW
Radio Range
100 feet (prog.)
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.

5. Examples for Sensor Nodes
UC Berkeley: COTS Dust
UC Berkeley:
Smart Dust
UCLA: WINS
JPL: Sensor Webs
Rockwell: WINS

6. Examples for Sensor Nodes
Dot Mote
Rene Mote
weC Mote
Mica node

7. 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 ….

8. 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

9. Sensor Networks Communication Architecture
Application Layer
Transport Layer
Network Layer
Data Link Layer
Physical Layer
Power Management Plane
Mobility Management Plane
Task Management Plane
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.

10. 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.

11. 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

12. 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.

13. APPLICATON LAYER Sensor Query and Tasking Language (SQTL):
(C-C Shen, et.al., “Sensor Information Networking Architecture and Applications”, IEEE Personal Communications Magazine, pp , 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.

14. 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.

15. 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]

16. 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

17. APPLICATON LAYER INTEREST DISSEMINATION
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.

18. APPLICATON LAYER Provides user applicatons with interfaces to issue
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”

19. 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 68 69 75 67 66
Sink
Query:
Sensor nodes that read >70oF temperature

20. Data Aggregation (Data Fusion)
The sink asks the sensor nodes to report certain conditions.
Data coming from multiple sensor nodes are aggregated. 71 75
Sink 68 69 71 67 66
Query:
Sensor nodes that read >70oF temperature

21. Location Awareness (Attribute Based Naming) Query an Attribute
of the sensor field
Region A 71 68 69 75 67 66
Sink
Region C
Region B
Query:
Temperatures read by the nodes in Region A
Important for broadcasting,
multicasting, geocasting and anycasting

22. 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

23. 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
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
Sink
RMST Node
Source Node

24. Transport Layer PSFQ - Pump Slowly Fetch QuicklyC. Y. Wan, A. T
Transport Layer PSFQ - Pump Slowly Fetch QuicklyC. 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

25. Related Work  New Reliability Notion is required!!!
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!!!

26. Reliable EVENT Transport in WSN
NEW NOTION: Reliably Detect/Estimate EVENT features from COLLECTIVE information
Challenges:
Significant energy and processing constraints, multi-hop ad hoc communication
Network congestion
Need to address Congestion Control
and Reliability in Sensor Networks !

27. 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 , 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

28. 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

29. 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
OOR
Optimal Operating Region
f < fmax and   [1- , 1+ ]

30. ESRT Protocol Overview
Determine reporting frequency f to achieve desired reliability D* with minimum resource utilization
Source (Sensor nodes):
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*

31. ESRT Congestion Detection Mechanism
ACK/NACK not suitable
We use local buffer level monitoring in sensor nodes bk
bk-1 Db B
a f
bk : Buffer fullness level at the end of reporting interval k
Db : Buffer length increment
B : Buffer size
f : reporting frequency
Mark CN field in packet if congested
Event ID CN
(1 bit)
Destination
Time
Stamp
Payload
FEC

32. ESRT Operation Frequency Update
State
Frequency Update
Comments
(NC,LR)
fi+1 = fi / i
Multiplicative increase, achieve desired reliability asap
(NC,HR)
fi+1 = fi (i + 1) / 2i
Conservative decrease, no compromise on reliability
(C,HR)
Aggressive decrease to state (NC,HR)
(C,LR)
fi+1 = fi i
Exponential decrease, relieve congestion asap
OOR
fi+1 = fi
Unchanged

33. ESRT Performance
S0 = (NC,LR)
S0 = (NC,HR)
S0 = (C,HR)
S0 = (C,LR)

34. (ROUTING BASIC KNOWLEDGE)
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 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

35. 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
Maximum minimum PA node
route (Route along which the
minimum PA is larger than the
minimum PAs of the other routes
is preferred, e.g., Route 3 is the
most efficient; Route 1 is the
second).
D (PA=3) T
Sink
A (PA=2)
B (PA=2)
C (PA=2)
Route 1: Sink-A-B-T (PA=4)
Route 2: Sink-A-B-C-T (PA=6)
Route 3: Sink-D-T (PA=3)
Route 4: Sink-E-F-T (PA=5)

36. 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

37. (ROUTING for SENSOR NETWORKS)
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

38. Some Concepts Data-Centric Application-Specific
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

39. 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 Network Protocol),
APTEEN, PEGASIS, Energy Aware Scheme
3. Location Based
MECN, SMECN (Small Minimum Energy Com Netw), GAF
(Geographic Adaptive Fidelity), GEAR

40. Conventional Approach FLOODING
Broadcast data to all neighbor nodes B D E F G C A

41. Gossiping ROUTING ALGORITHMS GOSSIPING:
Sends data to one randomly selected neighbor.
Example:

42. Flooding and Gossiping
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

43. Problems Data Overlap Implosion Resource Blindness
(r,s)
(q,r) q s r
Resource Blindness
No knowledge about the available power of resources

44. Gossiping Uses randomization to save energy Avoids implosions
Selects a single node at random and sends the data to it
Avoids implosions
Distributes information slowly
Energy dissipates slowly

45. The Optimum Protocol “Ideal” Shortest-path routes Avoids overlapB D E F G C A
“Ideal”
Shortest-path routes
Avoids overlap
Minimum energy
Need global topology information

46. SPIN: Sensor Protocol for Information via Negotiation (W. R
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 , 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

47. 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.
1. ADV
2. REQ
3. DATA 2. 3.

48. SPIN
ADV
ADV
DATA
DATA
REQ
REQ

49. 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 , 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.

50. Directed Diffusion Interest Propagation Data Delivery Gradient Setup
Source
Sink
Data Delivery

51. Directed Diffusion Features
Sink sends interest, i.e., task descriptor, to all sensor nodes.
Interest is named by assigning attribute-value pairs.
sink
source
source
source
sink
sink
Interest Propagation
Gradient Setup
Data Delivery
Drawbacks
Cannot change interest unless a new interest is broadcast.

52. 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.

53. LEACH - too few: nodes far from cluster heads
Optimum Number of Clusters ---????????
- too few: nodes far from cluster heads
too many: many nodes send data to SINK.

54. Other Protocols 1. Energy Aware Routing 2. Rumor 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.

55. 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.

56. 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

57. Medium Access Control (MAC) in WSN
IEEE [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 , “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

58. 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.

59. 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 !

60. 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
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!

61. 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)

62. 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.

63. CMAC Performance
Medium Access Delay
Packet Drop Rate

64. Avg. Energy Consumption
CMAC Performance
Avg. Energy Consumption

65. 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

66. 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

67. 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

68. 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 GaTech
MAN  for Meteorological Observations
SpINet  for Mars Surface
Airport Security  Sensors/Actors
Sensor Wars
Wide Area Multi-Campus Sensor Network

69. 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

70. 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.

71. 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

72. 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.
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.
Header (2)
Payload (<=73)
Trailer (FEC) (>=3)

73. 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

74. FINAL REMARKS

75. Basic Research Needs  Find a Basic Generic Architecture and Protocol
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

76. 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

77. 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 GaTech
MAN  for Meteorological Observations
SpINet  for Mars Surface
Airport Security  Sensors/Actors
Sensor Wars
Wide Area Multi-campus Sensor Network

78. 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???

79. 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, super-markets, 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

80. 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.
In-Q-Tel provides distributed data collection solutions with sensor network deployment.
ENSCO Inc. invests in wireless sensor networks for meteorological applications.
EMBER provides wireless sensor network solutions for industrial automation, defense, and building automation.

81. 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.
The Sensor-based Perimeter Security product is introduced by a company called SOFLINX Corp. (a wireless sensor network software company)
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.

82. 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.
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.
Possible business opportunity with a big home improvement store chain, Home Depot, with Intel and Berkeley using wireless sensor networks

83. 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
CSEM provides sensing and actuation solutions
Dust Inc. develops the next-generation hardware and software for wireless sensor networks
Integration Associates designs sensors used in medical, automotive, industrial, and military applications to cost-effective designs for handheld consumer appliances, barcode readers, and wireless computer input devices

84. Commercial Viability of WSN Applications
Melexis produces advanced integrated semiconductors, sensor ICs, and programmable sensor IC systems.
ZMD designs, manufactures and markets high performance, low power mixed signal ASIC and ASSP solutions for wireless and sensor integrated circuits.
Chipcon produces low-cost and low-power single-chip 2.4 GHz ISM band transceiver design for sensors.
ZigBee Alliance develops a standard for wireless low-power, low-rate devices.

85. 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

86. InterPlaNetary Internet Architecture
InterPlaNetary Backbone Network
InterPlaNetary External Network
PlaNetary Network

87. PlaNetary Network Architecture
PlaNetary Satellite Network
PlaNetary Surface Network

88. 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

89. 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
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.

90. Proposed Consultative Committee for Space Data Systems (CCSDS) Protocol Stack
for Mars Exploration Mission Communications

91. Proposed Delay Tolerant Networking (DTN) Protocol Stack

92. Transport Layer Issues
Extremely High Propagation Delays
High Link Error Rates
Asymmetrical Bandwidth
Blackouts

93. Extremely Long Propagation Delays
Planet
RTTmin
RTTmax
Mercury
1.1
30.2
Venus
5.6
35.8
Mars 9 55
Jupiter
81.6
133.3
Saturn
165.3
228.4
Uranus
356.9
435.6
Neptune
594.9
646.7
Pluto
593.3
1044.4

94. 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”,
IEEE Communications Letters, Vol. 6, No. 11, pp , November 2002.

95. 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 B-1, May 1999.

96. TP-Planet. O. B. Akan, J. Fang and I. F
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.
Steady State
Initial State
t=2*RTT
Hold
Blackout
Decrease
Increase
t=RTT
Immediate
Start
Follow Up
FollowUP
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

97. Multimedia Transport in InterPlaNetary Internet
Additional Challenges
* Bounded Jitter
* Minimum Bandwidth
* Smoothness
* Error Control

98. RCP-Planet: Overview J. Fang and I. F
RCP-Planet: Overview J. Fang and I.F. Akyildiz, “RCP Planet: A Rate Control Scheme for Multimedia Traffic in InterPlaNetary Internet”, July 2003.
BEGIN State
t=RTT
Increase
Decrease
Blackout
OPERATIONAL State
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
RCP-Planet deploys a newly developed end-to-end rate control scheme based on rate probing.
Two novel algorithms, i.e., Initial State and Steady State, constitute the structure of the RCP-Planet protocol.
Initial State algorithm is used to send multimedia data in a controlled and conservative manner because no network condition is available in initial state.
In Steady State, a new rate control scheme is deployed based on a rate probing scheme. In order to reduce the effects of blackout conditions on the throughput performance, RCP-Planet incorporates Blackout State procedure into the protocol operation. Bandwidth asymmetry problem is addressed by block level ACKs.

99. 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.

100. Network Layer Issues Naming and Addressing
in the InterPlaNetary Internet
Routing
in the InterPlaNetary Backbone Network
in PlaNetary Networks

101. 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?

102. 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

103. 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.

104. An InterPlaNetary Internet: Example and Host Name Tuples
Earth’s Internet
IPN region: earth.sol
The “Backbone”
IPN region: ipn.sol
Mars’ Internet
IPN region: mars.sol
SRC
GW1
GW2
DST
Host
IPN regions
Host name tuples
SRC
earth.sol
{earth.sol, src.jpl.nasa.gov:6769}
GW1
ipn.sol
{earth.sol, ipngw1.jpl.nasa.gov:6769}
{ipn.sol, ipngw1.jpl.nasa.gov:6769}
GW2
mars.sol
{ipn.sol, ipngw2.jpl.nasa.gov:6769}
{mars.sol, ipngw2.jpl.nasa.gov:6769}
DST
{mars.sol, dst.jpl.nasa.gov:6769}

105. 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???

106. 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

107. 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

108. Medium Access Control InterPlaNetary Backbone Network
Challenges:
Very Long Propagation Delays
Physical Design Change Constraints
Topological Changes
Power Constraints

109. 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!!!)

110. 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

111. 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!!!

112. 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)

113. Open Research Issues Link Layer
MAC for InterPlaNetary Backbone Network
MAC for PlaNetary Networks
Error Coding Schemes
Cross-layer Optimization
Optimum Packet Sizes

114. 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

115. 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

116. 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

117. 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

118. 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
DSN Frequency and Time Subsystems
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

119. 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.

120. 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!!!!!!!!!